Algorithms | An Open Access Journal from MDPI

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Management (JRFM) </option> <option value="jsan" > Journal of Sensor and Actuator Networks (JSAN) </option> <option value="joma" > Journal of the Oman Medical Association (JOMA) </option> <option value="jtaer" > Journal of Theoretical and Applied Electronic Commerce Research (JTAER) </option> <option value="jvd" > Journal of Vascular Diseases (JVD) </option> <option value="jox" > Journal of Xenobiotics (JoX) </option> <option value="jzbg" > Journal of Zoological and Botanical Gardens (JZBG) </option> <option value="journalmedia" > Journalism and Media </option> <option value="kidneydial" > Kidney and Dialysis </option> <option value="kinasesphosphatases" > Kinases and Phosphatases </option> <option value="knowledge" > Knowledge </option> <option value="labmed" > LabMed </option> <option value="laboratories" > Laboratories </option> <option value="land" > Land </option> <option value="languages" > Languages </option> <option value="laws" > Laws </option> <option value="life" > Life 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Mining </option> <option value="modelling" > Modelling </option> <option value="mmphys" > Modern Mathematical Physics </option> <option value="molbank" > Molbank </option> <option value="molecules" > Molecules </option> <option value="mti" > Multimodal Technologies and Interaction (MTI) </option> <option value="muscles" > Muscles </option> <option value="nanoenergyadv" > Nanoenergy Advances </option> <option value="nanomanufacturing" > Nanomanufacturing </option> <option value="nanomaterials" > Nanomaterials </option> <option value="ndt" > NDT </option> <option value="network" > Network </option> <option value="neuroglia" > Neuroglia </option> <option value="neurolint" > Neurology International </option> <option value="neurosci" > NeuroSci </option> <option value="nitrogen" > Nitrogen </option> <option value="ncrna" > Non-Coding RNA (ncRNA) </option> <option value="nursrep" > Nursing Reports </option> <option value="nutraceuticals" > Nutraceuticals </option> <option value="nutrients" > 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<option value="toxins" > Toxins </option> <option value="transplantology" > Transplantology </option> <option value="traumacare" > Trauma Care </option> <option value="higheredu" > Trends in Higher Education </option> <option value="tropicalmed" > Tropical Medicine and Infectious Disease (TropicalMed) </option> <option value="universe" > Universe </option> <option value="urbansci" > Urban Science </option> <option value="uro" > Uro </option> <option value="vaccines" > Vaccines </option> <option value="vehicles" > Vehicles </option> <option value="venereology" > Venereology </option> <option value="vetsci" > Veterinary Sciences </option> <option value="vibration" > Vibration </option> <option value="virtualworlds" > Virtual Worlds </option> <option value="viruses" > Viruses </option> <option value="vision" > Vision </option> <option value="waste" > Waste </option> <option value="water" > Water </option> <option value="wild" > Wild </option> <option value="wind" > Wind </option> <option 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class="orbit-caption"> Augmented Dataset for Vision-Based Analysis of Railroad Ballast via Multi-Dimensional Data Synthesis </div> </a> </li> <li class="hidden"> <a href="/1999-4893/17/8/357"> <img src="https://pub.mdpi-res.com/title_story/title_story_17310399234311.jpg?1733905199" alt="A System Design Perspective for Business Growth in a Crowdsourced Data Labeling Practice" /> <div class="orbit-caption"> A System Design Perspective for Business Growth in a Crowdsourced Data Labeling Practice </div> </a> </li> <li class="hidden"> <a href="/1999-4893/17/8/356"> <img src="https://pub.mdpi-res.com/title_story/title_story_17310398740094.jpg?1733905199" alt="A Virtual Machine Platform Providing Machine Learning as a Programmable and Distributed Service for IoT and Edge On-Device Computing: Architecture, Transformation, and Evaluation of Integer Discretization" /> <div class="orbit-caption"> A Virtual Machine Platform Providing Machine Learning as a Programmable and Distributed Service for IoT and Edge On-Device Computing: Architecture, Transformation, and Evaluation of Integer Discretization </div> </a> </li> <li class="hidden"> <a href="/1999-4893/17/8/350"> <img src="https://pub.mdpi-res.com/title_story/title_story_17310398121113.jpg?1733905199" alt="A Non-Smooth Numerical Optimization Approach to the Three-Point Dubins Problem (3PDP)" /> <div class="orbit-caption"> A Non-Smooth Numerical Optimization Approach to the Three-Point Dubins Problem (3PDP) </div> </a> </li> </ul> </div> </div> <div class="content__container"> <div class="custom-accordion-for-small-screen-link show-for-small-only"> <h2 class="no-padding-left no-margin">Journal Description</h2> </div> <div class="custom-accordion-for-small-screen-content show-for-medium-up"> <div class="journal__description"> <h1> <em>Algorithms</em> </h1> <div class="journal__description__content"> <em>Algorithms</em> is a <a href="https://www.mdpi.com/editorial_process">peer-reviewed</a>, open access journal which provides an advanced forum for studies related to algorithms and their applications. <em>Algorithms</em> is published monthly online by MDPI. The <a href="https://eusflat.org/" target="_blank" rel="noopener noreferrer">European Society for Fuzzy Logic and Technology (EUSFLAT)</a> is affiliated with <em>Algorithms</em> and their members receive discounts on the article processing charges.<br /> <ul> <li><strong><span class="label openaccess"><a title="Open Access" href="https://www.mdpi.com/openaccess">Open Access</a> </span></strong>— free for readers, with <a href="https://www.mdpi.com/journal/algorithms/apc">article processing charges (APC)</a> paid by authors or their institutions.</li> <li><strong>High Visibility:</strong> indexed within <a href="https://www.scopus.com/sourceid/21100199795">Scopus</a>, <a href="https://mjl.clarivate.com/search-results?issn=1999-4893&hide_exact_match_fl=true&utm_source=mjl&utm_medium=share-by-link&utm_campaign=search-results-share-this-journal">ESCI (Web of Science)</a>, <a href="http://www.engineeringvillage.com/" target="_blank" rel="noopener noreferrer">Ei Compendex</a>, and <a href="https://www.mdpi.com/journal/algorithms/indexing">other databases</a>.</li> <li><strong><strong>Journal Rank:</strong></strong> JCR - Q2 (Computer Science, Theory and Methods) / CiteScore - Q1 (Numerical Analysis)</li> <li><strong>Rapid Publication:</strong> manuscripts are peer-reviewed and a first decision is provided to authors approximately 15 days after submission; acceptance to publication is undertaken in 2.9 days (median values for papers published in this journal in the first half of 2024).</li> <li><strong>Testimonials:</strong> <a href="https://www.mdpi.com/testimonials?type=all&journal_id=13&page_count=20">See what our editors and authors say about <em>Algorithms</em></a>.</li> <li><strong>Recognition of Reviewers:</strong> reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.</li> </ul> </div> <div style="margin-bottom: 15px;"> <strong>Impact Factor:</strong> 1.8 (2023); 5-Year Impact Factor: 1.9 (2023) </div> <div> <a href="/journal/algorithms/imprint" class="UI_JournalImprintsInfoButton"> <i class="material-icons spaced-link">subject</i> Imprint Information </a>    <a href="/journal/algorithms/algorithms_flyer.pdf" class="UD_JournalFlyer"> <i class="material-icons spaced-link">get_app</i> Journal Flyer </a>     <a class="oa-link" href="https://www.mdpi.com/about/openaccess"> <i class="material icons spaced-link"></i> Open Access </a>     <strong> ISSN: 1999-4893 </strong> </div> <div style="clear: both;"></div> </div> </div> </div> <div class="content__container content__container--overflow-initial"> <div class="custom-accordion-for-small-screen-link active"> <h2 class="no-padding-left">Latest Articles</h2> </div> <div class="custom-accordion-for-small-screen-content"> <div class="expanding-div collapsed"> <div class="generic-item article-item no-border"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 25 pages, 2552 KiB   </span> <a href="/1999-4893/17/12/567/pdf?version=1733896016" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Primary Methods and Algorithms in Artificial-Intelligence-Based Dental Image Analysis: A Systematic Review" data-journal="algorithms"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Systematic Review</span></div> <a class="title-link" href="/1999-4893/17/12/567">Primary Methods and Algorithms in Artificial-Intelligence-Based Dental Image Analysis: A Systematic Review</a> <div class="authors"> by <span class="inlineblock "><strong>Talal Bonny</strong>, </span><span class="inlineblock "><strong>Wafaa Al Nassan</strong>, </span><span class="inlineblock "><strong>Khaled Obaideen</strong>, </span><span class="inlineblock "><strong>Tamer Rabie</strong>, </span><span class="inlineblock "><strong>Maryam Nooman AlMallahi</strong> and </span><span class="inlineblock "><strong>Swati Gupta</strong></span> </div> <div class="color-grey-dark"> <em>Algorithms</em> <b>2024</b>, <em>17</em>(12), 567; <a href="https://doi.org/10.3390/a17120567">https://doi.org/10.3390/a17120567</a> - 11 Dec 2024 </div> <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> Artificial intelligence (AI) has garnered significant attention in recent years for its potential to revolutionize healthcare, including dentistry. However, despite the growing body of literature on AI-based dental image analysis, challenges such as the integration of AI into clinical workflows, variability in dataset <a href="#" data-counterslink = "https://www.mdpi.com/1999-4893/17/12/567/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> Artificial intelligence (AI) has garnered significant attention in recent years for its potential to revolutionize healthcare, including dentistry. However, despite the growing body of literature on AI-based dental image analysis, challenges such as the integration of AI into clinical workflows, variability in dataset quality, and the lack of standardized evaluation metrics remain largely underexplored. This systematic review aims to address these gaps by assessing the extent to which AI technologies have been integrated into dental specialties, with a specific focus on their applications in dental imaging. A comprehensive review of the literature was conducted, selecting relevant studies through electronic searches from Scopus, Google Scholar, and PubMed databases, covering publications from 2018 to 2023. A total of 52 articles were systematically analyzed to evaluate the diverse approaches of machine learning (ML) and deep learning (DL) in dental imaging. This review reveals that AI has become increasingly prevalent, with researchers predominantly employing convolutional neural networks (CNNs) for detection and diagnosis tasks. Pretrained networks demonstrate strong performance in many scenarios, while ML techniques have shown growing utility in estimation and classification. Key challenges identified include the need for larger, annotated datasets and the translation of research outcomes into clinical practice. The findings underscore AI’s potential to significantly advance diagnostic support, particularly for non-specialist dentists, improving patient care and clinical efficiency. AI-driven software can enhance diagnostic accuracy, facilitate data sharing, and support collaboration among dental professionals. Future developments are anticipated to enable patient-specific optimization of restoration designs and implant placements, leveraging personalized data such as dental history, tissue type, and bone thickness to achieve better outcomes. <a href="/1999-4893/17/12/567">Full article</a> </div> </div> <div class="belongsTo" style="margin-bottom: 10px;"> (This article belongs to the Special Issue <a href=" /journal/algorithms/special_issues/TW19FK1CGT ">Machine Learning in Medical Signal and Image Processing (2nd Edition)</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/1999-4893/17/12/567/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1540913"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1540913"><i class="fa fa-caret-right"></i></div><div class="absgraph cycle-slideshow manual" data-cycle-fx="scrollHorz" data-cycle-timeout="0" data-cycle-next="#next1540913" data-cycle-prev="#prev1540913" data-cycle-progressive="#images1540913" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1540913-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/algorithms/algorithms-17-00567/article_deploy/html/images/algorithms-17-00567-g001-550.jpg?1733896102" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1540913" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1540913-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00567/article_deploy/html/images/algorithms-17-00567-g002-550.jpg?1733896105'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1540913-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00567/article_deploy/html/images/algorithms-17-00567-g003-550.jpg?1733896106'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-1540913-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00567/article_deploy/html/images/algorithms-17-00567-g004-550.jpg?1733896108'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-1540913-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00567/article_deploy/html/images/algorithms-17-00567-g005-550.jpg?1733896109'><p>Figure 5</p></div> --- <div class='openpopupgallery' data-imgindex='5' data-target='article-1540913-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00567/article_deploy/html/images/algorithms-17-00567-g006-550.jpg?1733896111'><p>Figure 6</p></div></script></div></div><div id="article-1540913-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00567/article_deploy/html/images/algorithms-17-00567-g001-550.jpg?1733896102" title=" <strong>Figure 1</strong><br/> <p>The hierarchy of machine learning and deep learning.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/567'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00567/article_deploy/html/images/algorithms-17-00567-g002-550.jpg?1733896105" title=" <strong>Figure 2</strong><br/> <p>Comparison of deep learning and machine learning based on several factors.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/567'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00567/article_deploy/html/images/algorithms-17-00567-g003-550.jpg?1733896106" title=" <strong>Figure 3</strong><br/> <p>Machine learning approach and deep learning approach.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/567'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00567/article_deploy/html/images/algorithms-17-00567-g004-550.jpg?1733896108" title=" <strong>Figure 4</strong><br/> <p>Flowchart of the research criteria.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/567'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00567/article_deploy/html/images/algorithms-17-00567-g005-550.jpg?1733896109" title=" <strong>Figure 5</strong><br/> <p>The application of DL and ML in dental images.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/567'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00567/article_deploy/html/images/algorithms-17-00567-g006-550.jpg?1733896111" title=" <strong>Figure 6</strong><br/> <p>Thematic clusters of the top keywords, self-extract from VOSviewer.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/567'>Full article</a></strong> "></a></div> </div> </div> </div> <div class="extending-content content-ready"> <div class="expanding-div collapsed"> <div class="generic-item article-item"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 15 pages, 647 KiB   </span> <a href="/1999-4893/17/12/566/pdf?version=1733894689" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Anchor-Based Method for Inter-Domain Mobility Management in Software-Defined Networking" data-journal="algorithms"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Article</span></div> <a class="title-link" href="/1999-4893/17/12/566">Anchor-Based Method for Inter-Domain Mobility Management in Software-Defined Networking</a> <div class="authors"> by <span class="inlineblock "><strong>Akichy Adon Jean Rodrigue Kanda</strong>, </span><span class="inlineblock "><strong>Amanvon Ferdinand Atta</strong>, </span><span class="inlineblock "><strong>Zacrada Françoise Odile Trey</strong>, </span><span class="inlineblock "><strong>Michel Babri</strong> and </span><span class="inlineblock "><strong>Ahmed Dooguy Kora</strong></span> </div> <div class="color-grey-dark"> <em>Algorithms</em> <b>2024</b>, <em>17</em>(12), 566; <a href="https://doi.org/10.3390/a17120566">https://doi.org/10.3390/a17120566</a> - 11 Dec 2024 </div> <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> Recently, there has been an explosive growth in wireless devices capable of connecting to the Internet and utilizing various services anytime, anywhere, often while on the move. In the realm of the Internet, such devices are called mobile nodes. When these devices are <a href="#" data-counterslink = "https://www.mdpi.com/1999-4893/17/12/566/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> Recently, there has been an explosive growth in wireless devices capable of connecting to the Internet and utilizing various services anytime, anywhere, often while on the move. In the realm of the Internet, such devices are called mobile nodes. When these devices are in motion or traverse different domains while communicating, effective mobility management becomes essential to ensure the continuity of their services. Software-defined networking (SDN), a new paradigm in networking, offers numerous possibilities for addressing the challenges of mobility management. By decoupling the control and data planes, SDN enables greater flexibility and adaptability, making them a powerful framework for solving mobility-related issues. However, communication can still be momentarily disrupted due to frequent changes in IP addresses, a drop in radio signals, or configuration issues associated with gateways. Therefore, this paper introduces Routage Inter-domains in SDN (RI-SDN), a novel anchor-based routing method designed for inter-domain mobility in SDN architectures. The method identifies a suitable anchor domain, a critical intermediary domain that contributes to reducing delays during data transfer because it is the closest domain (i.e., node) to the destination. Once the anchor domain is identified, the best routing path is determined as the route with the smallest metric, incorporating elements such as bandwidth, flow operations, and the number of domain hops. Simulation results demonstrate significant improvements in data transfer delay and handover latency compared to existing methods. By leveraging SDN’s potential, RI-SDN presents a robust and innovative solution for real-world scenarios requiring reliable mobility management. <a href="/1999-4893/17/12/566">Full article</a> </div> </div> <div class="belongsTo" style="margin-bottom: 10px;"> (This article belongs to the Section <a href="/journal/algorithms/sections/Algorithms_for_Multidisciplinary_Applications">Algorithms for Multidisciplinary Applications</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/1999-4893/17/12/566/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1540903"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1540903"><i class="fa fa-caret-right"></i></div><div class="absgraph cycle-slideshow manual" data-cycle-fx="scrollHorz" data-cycle-timeout="0" data-cycle-next="#next1540903" data-cycle-prev="#prev1540903" data-cycle-progressive="#images1540903" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1540903-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/algorithms/algorithms-17-00566/article_deploy/html/images/algorithms-17-00566-g001-550.jpg?1733894780" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1540903" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1540903-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00566/article_deploy/html/images/algorithms-17-00566-g002-550.jpg?1733894783'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1540903-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00566/article_deploy/html/images/algorithms-17-00566-g003-550.jpg?1733894783'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-1540903-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00566/article_deploy/html/images/algorithms-17-00566-g004-550.jpg?1733894783'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-1540903-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00566/article_deploy/html/images/algorithms-17-00566-g005-550.jpg?1733894784'><p>Figure 5</p></div> --- <div class='openpopupgallery' data-imgindex='5' data-target='article-1540903-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00566/article_deploy/html/images/algorithms-17-00566-g006-550.jpg?1733894784'><p>Figure 6</p></div> --- <div class='openpopupgallery' data-imgindex='6' data-target='article-1540903-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00566/article_deploy/html/images/algorithms-17-00566-g007-550.jpg?1733894785'><p>Figure 7</p></div> --- <div class='openpopupgallery' data-imgindex='7' data-target='article-1540903-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00566/article_deploy/html/images/algorithms-17-00566-g008-550.jpg?1733894785'><p>Figure 8</p></div> --- <div class='openpopupgallery' data-imgindex='8' data-target='article-1540903-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00566/article_deploy/html/images/algorithms-17-00566-g009-550.jpg?1733894786'><p>Figure 9</p></div> --- <div class='openpopupgallery' data-imgindex='9' data-target='article-1540903-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00566/article_deploy/html/images/algorithms-17-00566-g010-550.jpg?1733894786'><p>Figure 10</p></div> --- <div class='openpopupgallery' data-imgindex='10' data-target='article-1540903-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00566/article_deploy/html/images/algorithms-17-00566-g011-550.jpg?1733894787'><p>Figure 11</p></div> --- <div class='openpopupgallery' data-imgindex='11' data-target='article-1540903-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00566/article_deploy/html/images/algorithms-17-00566-g012-550.jpg?1733894787'><p>Figure 12</p></div></script></div></div><div id="article-1540903-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00566/article_deploy/html/images/algorithms-17-00566-g001-550.jpg?1733894780" title=" <strong>Figure 1</strong><br/> <p>Algorithm for anchor domain selection.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/566'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00566/article_deploy/html/images/algorithms-17-00566-g002-550.jpg?1733894783" title=" <strong>Figure 2</strong><br/> <p>Algorithm for route selection.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/566'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00566/article_deploy/html/images/algorithms-17-00566-g003-550.jpg?1733894783" title=" <strong>Figure 3</strong><br/> <p>Basic architecture, from [<a href="#B19-algorithms-17-00566" class="html-bibr">19</a>].</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/566'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00566/article_deploy/html/images/algorithms-17-00566-g004-550.jpg?1733894783" title=" <strong>Figure 4</strong><br/> <p>Basic simplified architecture.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/566'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00566/article_deploy/html/images/algorithms-17-00566-g005-550.jpg?1733894784" title=" <strong>Figure 5</strong><br/> <p>Adjacency matrix.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/566'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00566/article_deploy/html/images/algorithms-17-00566-g006-550.jpg?1733894784" title=" <strong>Figure 6</strong><br/> <p>Architecture with routes.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/566'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00566/article_deploy/html/images/algorithms-17-00566-g007-550.jpg?1733894785" title=" <strong>Figure 7</strong><br/> <p>Architecture 2 × 3.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/566'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00566/article_deploy/html/images/algorithms-17-00566-g008-550.jpg?1733894785" title=" <strong>Figure 8</strong><br/> <p>Architecture 3 × 3.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/566'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00566/article_deploy/html/images/algorithms-17-00566-g009-550.jpg?1733894786" title=" <strong>Figure 9</strong><br/> <p>Architecture 3 × 4.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/566'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00566/article_deploy/html/images/algorithms-17-00566-g010-550.jpg?1733894786" title=" <strong>Figure 10</strong><br/> <p>Architecture 3 × 5.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/566'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00566/article_deploy/html/images/algorithms-17-00566-g011-550.jpg?1733894787" title=" <strong>Figure 11</strong><br/> <p>Data transfer delay.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/566'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00566/article_deploy/html/images/algorithms-17-00566-g012-550.jpg?1733894787" title=" <strong>Figure 12</strong><br/> <p>Handover latency.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/566'>Full article</a></strong> "></a></div> </div> </div> </div> <div class="expanding-div collapsed"> <div class="generic-item article-item"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 21 pages, 2687 KiB   </span> <a href="/1999-4893/17/12/565/pdf?version=1733827445" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="A Random PRIM Based Algorithm for Interpretable Classification and Advanced Subgroup Discovery" data-journal="algorithms"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Article</span></div> <a class="title-link" href="/1999-4893/17/12/565">A Random PRIM Based Algorithm for Interpretable Classification and Advanced Subgroup Discovery</a> <div class="authors"> by <span class="inlineblock "><strong>Rym Nassih</strong> and </span><span class="inlineblock "><strong>Abdelaziz Berrado</strong></span> </div> <div class="color-grey-dark"> <em>Algorithms</em> <b>2024</b>, <em>17</em>(12), 565; <a href="https://doi.org/10.3390/a17120565">https://doi.org/10.3390/a17120565</a> - 10 Dec 2024 </div> <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> Machine-learning algorithms have made significant strides, achieving high accuracy in many applications. However, traditional models often need large datasets, as they typically peel substantial portions of the data in each iteration, complicating the development of a classifier without sufficient data. In critical fields <a href="#" data-counterslink = "https://www.mdpi.com/1999-4893/17/12/565/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> Machine-learning algorithms have made significant strides, achieving high accuracy in many applications. However, traditional models often need large datasets, as they typically peel substantial portions of the data in each iteration, complicating the development of a classifier without sufficient data. In critical fields like healthcare, there is a growing need to identify and analyze small yet significant subgroups within data. To address these challenges, we introduce a novel classifier based on the patient rule-induction method (PRIM), a subgroup-discovery algorithm. PRIM finds rules by peeling minimal data at each iteration, enabling the discovery of highly relevant regions. Unlike traditional classifiers, PRIM requires experts to select input spaces manually. Our innovation transforms PRIM into an interpretable classifier by starting with random input space selections for each class, then pruning rules using metarules, and finally selecting definitive rules for the classifier. Tested against popular algorithms such as random forest, logistic regression, and XG-Boost, our random PRIM-based classifier (R-PRIM-Cl) demonstrates comparable robustness, superior interpretability, and the ability to handle categorical and numeric variables. It discovers more rules in certain datasets, making it especially valuable in fields where understanding the model’s decision-making process is as important as its predictive accuracy. <a href="/1999-4893/17/12/565">Full article</a> </div> </div> <div class="belongsTo" style="margin-bottom: 10px;"> (This article belongs to the Special Issue <a href=" /journal/algorithms/special_issues/FZTNMW3F08 ">Algorithms in Data Classification (2nd Edition)</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/1999-4893/17/12/565/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1540462"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1540462"><i class="fa fa-caret-right"></i></div><div class="absgraph cycle-slideshow manual" data-cycle-fx="scrollHorz" data-cycle-timeout="0" data-cycle-next="#next1540462" data-cycle-prev="#prev1540462" data-cycle-progressive="#images1540462" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1540462-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/algorithms/algorithms-17-00565/article_deploy/html/images/algorithms-17-00565-g001-550.jpg?1733827598" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1540462" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1540462-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00565/article_deploy/html/images/algorithms-17-00565-g002-550.jpg?1733827598'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1540462-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00565/article_deploy/html/images/algorithms-17-00565-g003-550.jpg?1733827599'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-1540462-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00565/article_deploy/html/images/algorithms-17-00565-g004-550.jpg?1733827599'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-1540462-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00565/article_deploy/html/images/algorithms-17-00565-g005-550.jpg?1733827600'><p>Figure 5</p></div> --- <div class='openpopupgallery' data-imgindex='5' data-target='article-1540462-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00565/article_deploy/html/images/algorithms-17-00565-g006-550.jpg?1733827601'><p>Figure 6</p></div> --- <div class='openpopupgallery' data-imgindex='6' data-target='article-1540462-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00565/article_deploy/html/images/algorithms-17-00565-g007-550.jpg?1733827604'><p>Figure 7</p></div></script></div></div><div id="article-1540462-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00565/article_deploy/html/images/algorithms-17-00565-g001-550.jpg?1733827598" title=" <strong>Figure 1</strong><br/> <p>Illustration of the top-down peeling phase to find one box.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/565'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00565/article_deploy/html/images/algorithms-17-00565-g002-550.jpg?1733827598" title=" <strong>Figure 2</strong><br/> <p>Example of a numeric box.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/565'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00565/article_deploy/html/images/algorithms-17-00565-g003-550.jpg?1733827599" title=" <strong>Figure 3</strong><br/> <p>Example of a categorical box.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/565'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00565/article_deploy/html/images/algorithms-17-00565-g004-550.jpg?1733827599" title=" <strong>Figure 4</strong><br/> <p>An illustration of the organization of rules in the ruleset using metarules. (<b>a</b>) Display of the ruleset without the organization. (<b>b</b>) Illustration of the new organized ruleset with association between R6–R7, R1–R4, and R5–R3. The one-way relationship between R4 and R1 shows that R4 is more specific than R1.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/565'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00565/article_deploy/html/images/algorithms-17-00565-g005-550.jpg?1733827600" title=" <strong>Figure 5</strong><br/> <p>Illustration of the metarules creation.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/565'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00565/article_deploy/html/images/algorithms-17-00565-g006-550.jpg?1733827601" title=" <strong>Figure 6</strong><br/> <p>Illustration of the construction and the validation of the classifier after the obtention of all the rules.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/565'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00565/article_deploy/html/images/algorithms-17-00565-g007-550.jpg?1733827604" title=" <strong>Figure 7</strong><br/> <p>Visualization of the four measures obtained in the experiment: (<b>a</b>) accuracy of each model, (<b>b</b>) recall of each model, (<b>c</b>) precision of each model, and (<b>d</b>) F1-score of each model.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/565'>Full article</a></strong> "></a></div> </div> </div> </div> <div class="expanding-div collapsed"> <div class="generic-item article-item"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 39 pages, 25059 KiB   </span> <a href="/1999-4893/17/12/564/pdf?version=1733825187" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Exploratory Study of a Green Function Based Solver for Nonlinear Partial Differential Equations" data-journal="algorithms"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Article</span></div> <a class="title-link" href="/1999-4893/17/12/564">Exploratory Study of a Green Function Based Solver for Nonlinear Partial Differential Equations</a> <div class="authors"> by <span class="inlineblock "><strong>Pablo Solano-López</strong>, </span><span class="inlineblock "><strong>Jorge Saavedra</strong> and </span><span class="inlineblock "><strong>Raúl Molina</strong></span> </div> <div class="color-grey-dark"> <em>Algorithms</em> <b>2024</b>, <em>17</em>(12), 564; <a href="https://doi.org/10.3390/a17120564">https://doi.org/10.3390/a17120564</a> - 10 Dec 2024 </div> <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> This work explores the numerical translation of the weak or integral solution of nonlinear partial differential equations into a numerically efficient, time-evolving scheme. Specifically, we focus on partial differential equations separable into a quasilinear term and a nonlinear one, with the former defining <a href="#" data-counterslink = "https://www.mdpi.com/1999-4893/17/12/564/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> This work explores the numerical translation of the weak or integral solution of nonlinear partial differential equations into a numerically efficient, time-evolving scheme. Specifically, we focus on partial differential equations separable into a quasilinear term and a nonlinear one, with the former defining the Green function of the problem. Utilizing the Green function under a short-time approximation, it becomes possible to derive the integral solution of the problem by breaking it into three integral terms: the propagation of initial conditions and the contributions of the nonlinear and boundary terms. Accordingly, we follow this division to describe and separately analyze the resulting algorithm. To ensure low interpolation error and accurate numerical Green functions, we adapt a piecewise interpolation collocation method to the integral scheme, optimizing the positioning of grid points near the boundary region. At the same time, we employ a second-order quadrature method in time to efficiently implement the nonlinear terms. Validation of both adapted methodologies is conducted by applying them to problems with known analytical solution, as well as to more challenging, norm-preserving problems such as the Burgers equation and the soliton solution of the nonlinear Schrödinger equation. Finally, the boundary term is derived and validated using a series of test cases that cover the range of possible scenarios for boundary problems within the introduced methodology. <a href="/1999-4893/17/12/564">Full article</a> </div> </div> <div class="belongsTo" style="margin-bottom: 10px;"> (This article belongs to the Section <a href="/journal/algorithms/sections/algorithms_analysis_complexity_theory">Analysis of Algorithms and Complexity Theory</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/1999-4893/17/12/564/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1540399"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1540399"><i class="fa fa-caret-right"></i></div><div class="absgraph cycle-slideshow manual" data-cycle-fx="scrollHorz" data-cycle-timeout="0" data-cycle-next="#next1540399" data-cycle-prev="#prev1540399" data-cycle-progressive="#images1540399" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1540399-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/algorithms/algorithms-17-00564/article_deploy/html/images/algorithms-17-00564-g001-550.jpg?1733825262" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1540399" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1540399-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00564/article_deploy/html/images/algorithms-17-00564-g002-550.jpg?1733825264'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1540399-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00564/article_deploy/html/images/algorithms-17-00564-g003-550.jpg?1733825267'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-1540399-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00564/article_deploy/html/images/algorithms-17-00564-g004-550.jpg?1733825269'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-1540399-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00564/article_deploy/html/images/algorithms-17-00564-g005-550.jpg?1733825271'><p>Figure 5</p></div> --- <div class='openpopupgallery' data-imgindex='5' data-target='article-1540399-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00564/article_deploy/html/images/algorithms-17-00564-g006-550.jpg?1733825273'><p>Figure 6</p></div> --- <div class='openpopupgallery' data-imgindex='6' data-target='article-1540399-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00564/article_deploy/html/images/algorithms-17-00564-g007-550.jpg?1733825276'><p>Figure 7</p></div> --- <div class='openpopupgallery' data-imgindex='7' data-target='article-1540399-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00564/article_deploy/html/images/algorithms-17-00564-g008-550.jpg?1733825277'><p>Figure 8</p></div> --- <div class='openpopupgallery' data-imgindex='8' data-target='article-1540399-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00564/article_deploy/html/images/algorithms-17-00564-g009-550.jpg?1733825279'><p>Figure 9</p></div> --- <div class='openpopupgallery' data-imgindex='9' data-target='article-1540399-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00564/article_deploy/html/images/algorithms-17-00564-g010-550.jpg?1733825283'><p>Figure 10</p></div> --- <div class='openpopupgallery' data-imgindex='10' data-target='article-1540399-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00564/article_deploy/html/images/algorithms-17-00564-g011-550.jpg?1733825286'><p>Figure 11</p></div> --- <div class='openpopupgallery' data-imgindex='11' data-target='article-1540399-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00564/article_deploy/html/images/algorithms-17-00564-g012-550.jpg?1733825287'><p>Figure 12</p></div> --- <div class='openpopupgallery' data-imgindex='12' data-target='article-1540399-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00564/article_deploy/html/images/algorithms-17-00564-g013-550.jpg?1733825288'><p>Figure 13</p></div> --- <div class='openpopupgallery' data-imgindex='13' data-target='article-1540399-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00564/article_deploy/html/images/algorithms-17-00564-g014-550.jpg?1733825290'><p>Figure 14</p></div></script></div></div><div id="article-1540399-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00564/article_deploy/html/images/algorithms-17-00564-g001-550.jpg?1733825262" title=" <strong>Figure 1</strong><br/> <p>Example of the boundary behavior of the first-order Green function.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/564'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00564/article_deploy/html/images/algorithms-17-00564-g002-550.jpg?1733825264" title=" <strong>Figure 2</strong><br/> <p>Second-order, Neumann semi-open domain Green function, explained using the method of images.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/564'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00564/article_deploy/html/images/algorithms-17-00564-g003-550.jpg?1733825267" title=" <strong>Figure 3</strong><br/> <p>Visual representation of the Hermanns Navarro Algorithm for optimizing Piecewise interpolation through the Chebyshev–Gauss–Lobatto collocation methodology.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/564'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00564/article_deploy/html/images/algorithms-17-00564-g004-550.jpg?1733825269" title=" <strong>Figure 4</strong><br/> <p>Tendency of the difference between the 2 time-evolving weights as a function of the CFL number for diffusive problems, the ones obtained exactly over a subdomain of validity and the ones obtained through the Taylor Series Expansion.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/564'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00564/article_deploy/html/images/algorithms-17-00564-g005-550.jpg?1733825271" title=" <strong>Figure 5</strong><br/> <p>Error between the exact and the numerical solution for the advective test case (first-order operator), with the initial condition presented in Equation (<a href="#FD48-algorithms-17-00564" class="html-disp-formula">48</a>), for 3 different CFL numbers (points, line, dashed) and 4 different values of the stencil, <math display="inline"><semantics> <mrow> <mn>2</mn> <mi>q</mi> <mo>+</mo> <mn>1</mn> </mrow> </semantics></math> (color map).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/564'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00564/article_deploy/html/images/algorithms-17-00564-g006-550.jpg?1733825273" title=" <strong>Figure 6</strong><br/> <p>Error between the exact and the numerical solution for the diffusive test case (second-order operator), with the initial condition presented in Equation (<a href="#FD48-algorithms-17-00564" class="html-disp-formula">48</a>), for 3 different CFL numbers (points, line, dashed) and 5 different values of the stencil, <math display="inline"><semantics> <mrow> <mn>2</mn> <mi>q</mi> <mo>+</mo> <mn>1</mn> </mrow> </semantics></math> (color map).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/564'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00564/article_deploy/html/images/algorithms-17-00564-g007-550.jpg?1733825276" title=" <strong>Figure 7</strong><br/> <p>Temporal evolution (<b>left</b>) for the advective test case under a non-constant transport coefficient over a homogeneous grid of <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>=</mo> <mn>500</mn> </mrow> </semantics></math>, a stencil with <math display="inline"><semantics> <mrow> <mi>q</mi> <mo>=</mo> <mn>9</mn> </mrow> </semantics></math>, a timestep <math display="inline"><semantics> <mrow> <mi>τ</mi> <mo>=</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>3</mn> </mrow> </msup> </mrow> </semantics></math> and a characteristic advection of <math display="inline"><semantics> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>2</mn> </mrow> </msup> </semantics></math> and the corresponding propagated corrections for this coefficient (<b>right</b>), <math display="inline"><semantics> <msub> <mi>u</mi> <mrow> <mi>c</mi> <mi>o</mi> <mi>r</mi> <mi>r</mi> </mrow> </msub> </semantics></math> as defined in Equation (<a href="#FD54-algorithms-17-00564" class="html-disp-formula">54</a>).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/564'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00564/article_deploy/html/images/algorithms-17-00564-g008-550.jpg?1733825277" title=" <strong>Figure 8</strong><br/> <p>Temporal evolution (<b>left</b>) for the diffusive test case under a non-constant transport coefficient over a homogeneous grid of <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>=</mo> <mn>500</mn> </mrow> </semantics></math>, a stencil with <math display="inline"><semantics> <mrow> <mi>q</mi> <mo>=</mo> <mn>9</mn> </mrow> </semantics></math>, a timestep <math display="inline"><semantics> <mrow> <mi>τ</mi> <mo>=</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>3</mn> </mrow> </msup> </mrow> </semantics></math> and a characteristic diffusion of <math display="inline"><semantics> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>3</mn> </mrow> </msup> </semantics></math> and the corresponding propagated corrections for this coefficient (<b>right</b>), <math display="inline"><semantics> <msub> <mi>u</mi> <mrow> <mi>c</mi> <mi>o</mi> <mi>r</mi> <mi>r</mi> </mrow> </msub> </semantics></math> as defined in Equation (<a href="#FD51-algorithms-17-00564" class="html-disp-formula">51</a>).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/564'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00564/article_deploy/html/images/algorithms-17-00564-g009-550.jpg?1733825279" title=" <strong>Figure 9</strong><br/> <p>Temporal evolution (<b>left</b>) for the Burgers viscid equation test case under a non-constant transport coefficient over a homogeneous grid of <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>=</mo> <mn>100</mn> </mrow> </semantics></math>, a stencil with <math display="inline"><semantics> <mrow> <mi>q</mi> <mo>=</mo> <mn>9</mn> </mrow> </semantics></math>, a timestep <math display="inline"><semantics> <mrow> <mi>τ</mi> <mo>=</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>4</mn> </mrow> </msup> </mrow> </semantics></math> and a characteristic diffusion of <math display="inline"><semantics> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>5</mn> </mrow> </msup> </semantics></math> and a comparison (<b>right</b>) between the direct error of the numerical norms of the evolution, following the definition from (<a href="#FD59-algorithms-17-00564" class="html-disp-formula">59</a>).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/564'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00564/article_deploy/html/images/algorithms-17-00564-g010-550.jpg?1733825283" title=" <strong>Figure 10</strong><br/> <p>Temporal evolution of the soliton for the nonlinear Scrödinger equation over a homogeneous grid of <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>=</mo> <msup> <mn>10</mn> <mn>4</mn> </msup> </mrow> </semantics></math>, a stencil with <math display="inline"><semantics> <mrow> <mi>q</mi> <mo>=</mo> <mn>9</mn> </mrow> </semantics></math>, a time-step <math display="inline"><semantics> <mrow> <mi>τ</mi> <mo>=</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>4</mn> </mrow> </msup> </mrow> </semantics></math> and a characteristic diffusion of <math display="inline"><semantics> <msup> <mn>10</mn> <mn>1</mn> </msup> </semantics></math>. We plot the three functions of the solution: the imaginary part of the wave function (<b>top left</b>), the real part of the wave function (<b>bottom left</b>) and the shape of the time evolving soliton (<b>top right</b>).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/564'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00564/article_deploy/html/images/algorithms-17-00564-g011-550.jpg?1733825286" title=" <strong>Figure 11</strong><br/> <p>Evolution of the relative error of the conserved magnitudes, norm and energy, of the soliton solution of the nonlinear Schödinger equation. Only the solution with the RK scheme is presented as for this set of parameters, not using it imply errors of order one in the norm.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/564'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00564/article_deploy/html/images/algorithms-17-00564-g012-550.jpg?1733825287" title=" <strong>Figure 12</strong><br/> <p>Temporal evolution of a constant coefficient diffusive problem (<b>left</b>) and its first-order spatial derivative (<b>right</b>) for an implementation of two homogeneous boundaries at the boundaries of the domain: Neumann at <math display="inline"><semantics> <mrow> <mi>x</mi> <mo>=</mo> <mo>−</mo> <mn>1</mn> </mrow> </semantics></math> and Dirichlet at <math display="inline"><semantics> <mrow> <mi>x</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math>. The implementation does not require for any extra boundary term as the Green function codifies the discretization scheme in order to satisfy automatically the boundary conditions.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/564'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00564/article_deploy/html/images/algorithms-17-00564-g013-550.jpg?1733825288" title=" <strong>Figure 13</strong><br/> <p>Temporal evolution of diffusive problem (<b>left</b>) and its first-order spatial derivative (<b>right</b>) for an implementation of two homogeneous boundaries at the boundaries of the domain: Neumann at <math display="inline"><semantics> <mrow> <mi>x</mi> <mo>=</mo> <mo>−</mo> <mn>1</mn> </mrow> </semantics></math> and Dirichlet at <math display="inline"><semantics> <mrow> <mi>x</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math>. The diffusion coefficient presents a spatio-temporal variation, modifying the temporal evolution of the problem and introducing the corrections terms at the Green function.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/564'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00564/article_deploy/html/images/algorithms-17-00564-g014-550.jpg?1733825290" title=" <strong>Figure 14</strong><br/> <p>Temporal evolution of diffusive problem (<b>left</b>) and its first-order spatial derivative (<b>right</b>) for an implementation of two Dirichlet boundary conditions, one non-homogeneous following the temporal evolution law, Equation (<a href="#FD67-algorithms-17-00564" class="html-disp-formula">67</a>), and another one, homogeneous. The diffusion coefficient presents a spatial variation, in order to include all the possible boundary modifications to the basic time evolving scheme at once.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/564'>Full article</a></strong> "></a></div> </div> </div> </div> <div class="expanding-div collapsed"> <div class="generic-item article-item"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 21 pages, 391 KiB   </span> <a href="/1999-4893/17/12/563/pdf?version=1733739452" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Issues on a 2–Dimensional Quadratic Sub–Problem and Its Applications in Nonlinear Programming: Trust–Region Methods (TRMs) and Linesearch Based Methods (LBMs)" data-journal="algorithms"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Article</span></div> <a class="title-link" href="/1999-4893/17/12/563">Issues on a 2–Dimensional Quadratic Sub–Problem and Its Applications in Nonlinear Programming: Trust–Region Methods (TRMs) and Linesearch Based Methods (LBMs)</a> <div class="authors"> by <span class="inlineblock "><strong>Giovanni Fasano</strong>, </span><span class="inlineblock "><strong>Christian Piermarini</strong> and </span><span class="inlineblock "><strong>Massimo Roma</strong></span> </div> <div class="color-grey-dark"> <em>Algorithms</em> <b>2024</b>, <em>17</em>(12), 563; <a href="https://doi.org/10.3390/a17120563">https://doi.org/10.3390/a17120563</a> - 9 Dec 2024 </div> <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> This paper analyses the solution of a specific quadratic sub-problem, along with its possible applications, within both constrained and unconstrained Nonlinear Programming frameworks. We give evidence that this sub–problem may appear in a number of Linesearch Based Methods (LBM) schemes, and to some <a href="#" data-counterslink = "https://www.mdpi.com/1999-4893/17/12/563/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> This paper analyses the solution of a specific quadratic sub-problem, along with its possible applications, within both constrained and unconstrained Nonlinear Programming frameworks. We give evidence that this sub–problem may appear in a number of Linesearch Based Methods (LBM) schemes, and to some extent it reveals a close analogy with the solution of trust–region sub–problems. Namely, we refer to a two-dimensional structured quadratic problem, where five linear inequality constraints are included. Finally, we detail how to compute an exact global solution of our two-dimensional quadratic sub-problem, exploiting first order Karush-Khun-Tucker (KKT) conditions. <a href="/1999-4893/17/12/563">Full article</a> </div> </div> <div class="belongsTo" style="margin-bottom: 10px;"> (This article belongs to the Special Issue <a href=" /journal/algorithms/special_issues/1XMDC2A8II ">Numerical Optimization and Algorithms: 2nd Edition</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/1999-4893/17/12/563/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1539659"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1539659"><i class="fa fa-caret-right"></i></div><div class="absgraph cycle-slideshow manual" data-cycle-fx="scrollHorz" data-cycle-timeout="0" data-cycle-next="#next1539659" data-cycle-prev="#prev1539659" data-cycle-progressive="#images1539659" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1539659-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/algorithms/algorithms-17-00563/article_deploy/html/images/algorithms-17-00563-g001-550.jpg?1733739515" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1539659" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1539659-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00563/article_deploy/html/images/algorithms-17-00563-g002-550.jpg?1733739515'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1539659-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00563/article_deploy/html/images/algorithms-17-00563-g003-550.jpg?1733739517'><p>Figure 3</p></div></script></div></div><div id="article-1539659-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00563/article_deploy/html/images/algorithms-17-00563-g001-550.jpg?1733739515" title=" <strong>Figure 1</strong><br/> <p>A graphical representation of the feasible set in (<a href="#FD2-algorithms-17-00563" class="html-disp-formula">2</a>). The dashed-dotted lines represent all the extreme choices for the last inequality constraint in (<a href="#FD2-algorithms-17-00563" class="html-disp-formula">2</a>).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/563'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00563/article_deploy/html/images/algorithms-17-00563-g002-550.jpg?1733739515" title=" <strong>Figure 2</strong><br/> <p>Examples where the structure of the feasible set in (<a href="#FD2-algorithms-17-00563" class="html-disp-formula">2</a>) is helpful: case (<b>a</b>) is treated in <a href="#sec4dot1-algorithms-17-00563" class="html-sec">Section 4.1</a>, case (<b>b</b>) is treated in <a href="#sec4dot2-algorithms-17-00563" class="html-sec">Section 4.2</a>, case (<b>c</b>) is treated in <a href="#sec4dot3-algorithms-17-00563" class="html-sec">Section 4.3</a>, case (<b>d</b>) is treated in <a href="#sec4dot4-algorithms-17-00563" class="html-sec">Section 4.4</a> and case (<b>e</b>) is treated in <a href="#sec4dot5-algorithms-17-00563" class="html-sec">Section 4.5</a>.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/563'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00563/article_deploy/html/images/algorithms-17-00563-g003-550.jpg?1733739517" title=" <strong>Figure 3</strong><br/> <p>Overview of possible solutions (I)–(XII) for KKT conditions in (<a href="#FD15-algorithms-17-00563" class="html-disp-formula">15</a>).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/563'>Full article</a></strong> "></a></div> </div> </div> </div> <div class="expanding-div collapsed"> <div class="generic-item article-item"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 26 pages, 5833 KiB   </span> <a href="/1999-4893/17/12/562/pdf?version=1733649214" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Unlocking the Potential of Remanufacturing Through Machine Learning and Data-Driven Models—A Survey" data-journal="algorithms"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Review</span></div> <a class="title-link" href="/1999-4893/17/12/562">Unlocking the Potential of Remanufacturing Through Machine Learning and Data-Driven Models—A Survey</a> <div class="authors"> by <span class="inlineblock "><strong>Yong Han Kim</strong>, </span><span class="inlineblock "><strong>Wei Ye</strong>, </span><span class="inlineblock "><strong>Ritbik Kumar</strong>, </span><span class="inlineblock "><strong>Finn Bail</strong>, </span><span class="inlineblock "><strong>Julia Dvorak</strong>, </span><span class="inlineblock "><strong>Yanchao Tan</strong>, </span><span class="inlineblock "><strong>Marvin Carl May</strong>, </span><span class="inlineblock "><strong>Qing Chang</strong>, </span><span class="inlineblock "><strong>Ragu Athinarayanan</strong>, </span><span class="inlineblock "><strong>Gisela Lanza</strong>, </span><span class="inlineblock "><strong>John W. Sutherland</strong>, </span><span class="inlineblock "><strong>Xingyu Li</strong> and </span><span class="inlineblock "><strong>Chandra Nath</strong></span> </div> <div class="color-grey-dark"> <em>Algorithms</em> <b>2024</b>, <em>17</em>(12), 562; <a href="https://doi.org/10.3390/a17120562">https://doi.org/10.3390/a17120562</a> - 8 Dec 2024 </div> <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> As a key strategy for achieving a circular economy, remanufacturing involves bringing end-of-use (EoU) products or cores back to a ‘like new’ condition, providing more affordable and sustainable alternatives to new products. Despite the potential for substantial resources and energy savings, the industry <a href="#" data-counterslink = "https://www.mdpi.com/1999-4893/17/12/562/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> As a key strategy for achieving a circular economy, remanufacturing involves bringing end-of-use (EoU) products or cores back to a ‘like new’ condition, providing more affordable and sustainable alternatives to new products. Despite the potential for substantial resources and energy savings, the industry faces operational challenges. These challenges arise from uncertainties surrounding core quality and functionality, return times, process variation required to meet product specifications, and the end-of-use (EoU) product values, as well as their new life expectancy after extended use as a ‘market product’. While remanufacturing holds immense promise, its full potential can only be realized through concerted efforts towards resolving the inherent complexities and obstacles that impede its operations. Machine learning (ML) and data-driven models emerge as transformative tools to mitigate numerous challenges encountered by manufacturing industry. Recently, the integration of cutting-edge technologies, such as sensor-based product data acquisition and storage, data analytics, machine health management, artificial intelligence (AI)-driven scheduling, and human–robot collaboration (HRC), in remanufacturing procedures has received significant attention from remanufacturers and the circular economy community. These advanced computational technologies help remanufacturers to implement flexible operation scheduling, enhance quality control, and streamline workflows for EoU products. This study embarks on a comprehensive review and in-depth analysis of state-of-the-art algorithms across various facets of remanufacturing processes and operations. Additionally, it identifies key challenges to advancing remanufacturing practices through data-driven and ML methods and uncovers research opportunities in synergy with smart manufacturing techniques. The study aims to offer guidelines for stakeholders and to reinforce the industry’s pivotal role in circular economy initiatives. <a href="/1999-4893/17/12/562">Full article</a> </div> </div> <div class="belongsTo" style="margin-bottom: 10px;"> (This article belongs to the Special Issue <a href=" /journal/algorithms/special_issues/7C92M715OY ">Scheduling Theory and Algorithms for Sustainable Manufacturing</a>)<br/> </div> </div> </div> </div> <div class="expanding-div collapsed"> <div class="generic-item article-item"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 15 pages, 418 KiB   </span> <a href="/1999-4893/17/12/561/pdf?version=1733718467" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Variational Autoencoders-Based Algorithm for Multi-Criteria Recommendation Systems" data-journal="algorithms"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Article</span></div> <a class="title-link" href="/1999-4893/17/12/561">Variational Autoencoders-Based Algorithm for Multi-Criteria Recommendation Systems</a> <div class="authors"> by <span class="inlineblock "><strong>Salam Fraihat</strong>, </span><span class="inlineblock "><strong>Qusai Shambour</strong>, </span><span class="inlineblock "><strong>Mohammed Azmi Al-Betar</strong> and </span><span class="inlineblock "><strong>Sharif Naser Makhadmeh</strong></span> </div> <div class="color-grey-dark"> <em>Algorithms</em> <b>2024</b>, <em>17</em>(12), 561; <a href="https://doi.org/10.3390/a17120561">https://doi.org/10.3390/a17120561</a> - 8 Dec 2024 </div> <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> In recent years, recommender systems have become a crucial tool, assisting users in discovering and engaging with valuable information and services. Multi-criteria recommender systems have demonstrated significant value in assisting users to identify the most relevant items by considering various aspects of user <a href="#" data-counterslink = "https://www.mdpi.com/1999-4893/17/12/561/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> In recent years, recommender systems have become a crucial tool, assisting users in discovering and engaging with valuable information and services. Multi-criteria recommender systems have demonstrated significant value in assisting users to identify the most relevant items by considering various aspects of user experiences. Deep learning (DL) models demonstrated outstanding performance across different domains: computer vision, natural language processing, image analysis, pattern recognition, and recommender systems. In this study, we introduce a deep learning model using VAE to improve multi-criteria recommendation systems. Specifically, we propose a variational autoencoder-based model for multi-criteria recommendation systems (VAE-MCRS). The VAE-MCRS model is sequentially trained across multiple criteria to uncover patterns that allow for better representation of user–item interactions. The VAE-MCRS model utilizes the latent features generated by the VAE in conjunction with user–item interactions to enhance recommendation accuracy and predict ratings for unrated items. Experiments carried out using the Yahoo! Movies multi-criteria dataset demonstrate that the proposed model surpasses other state-of-the-art recommendation algorithms, achieving a Mean Absolute Error (MAE) of 0.6038 and a Root Mean Squared Error (RMSE) of 0.7085, demonstrating its superior performance in providing more precise recommendations for multi-criteria recommendation tasks. <a href="/1999-4893/17/12/561">Full article</a> </div> </div> <div class="belongsTo" style="margin-bottom: 10px;"> (This article belongs to the Special Issue <a href=" /journal/algorithms/special_issues/92B5GZY9QT ">Algorithms for Complex Problems</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/1999-4893/17/12/561/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1539212"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1539212"><i class="fa fa-caret-right"></i></div><div class="absgraph cycle-slideshow manual" data-cycle-fx="scrollHorz" data-cycle-timeout="0" data-cycle-next="#next1539212" data-cycle-prev="#prev1539212" data-cycle-progressive="#images1539212" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1539212-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/algorithms/algorithms-17-00561/article_deploy/html/images/algorithms-17-00561-g001-550.jpg?1733718637" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1539212" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1539212-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00561/article_deploy/html/images/algorithms-17-00561-g002-550.jpg?1733718638'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1539212-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00561/article_deploy/html/images/algorithms-17-00561-g003-550.jpg?1733718638'><p>Figure 3</p></div></script></div></div><div id="article-1539212-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00561/article_deploy/html/images/algorithms-17-00561-g001-550.jpg?1733718637" title=" <strong>Figure 1</strong><br/> <p>Architecture of variational autoencoder model.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/561'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00561/article_deploy/html/images/algorithms-17-00561-g002-550.jpg?1733718638" title=" <strong>Figure 2</strong><br/> <p>The proposed VAE-MCRS model architecture. R: Rating, C: Criterion, m: Movie, i: User.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/561'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00561/article_deploy/html/images/algorithms-17-00561-g003-550.jpg?1733718638" title=" <strong>Figure 3</strong><br/> <p>Yahoo dataset sample.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/561'>Full article</a></strong> "></a></div> </div> </div> </div> <div class="expanding-div collapsed"> <div class="generic-item article-item"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 29 pages, 4333 KiB   </span> <a href="/1999-4893/17/12/560/pdf?version=1733888186" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Sensors, Techniques, and Future Trends of Human-Engagement-Enabled Applications: A Review" data-journal="algorithms"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Review</span></div> <a class="title-link" href="/1999-4893/17/12/560">Sensors, Techniques, and Future Trends of Human-Engagement-Enabled Applications: A Review</a> <div class="authors"> by <span class="inlineblock "><strong>Zhuangzhuang Dai</strong>, </span><span class="inlineblock "><strong>Vincent Gbouna Zakka</strong>, </span><span class="inlineblock "><strong>Luis J. Manso</strong>, </span><span class="inlineblock "><strong>Martin Rudorfer</strong>, </span><span class="inlineblock "><strong>Ulysses Bernardet</strong>, </span><span class="inlineblock "><strong>Johanna Zumer</strong> and </span><span class="inlineblock "><strong>Manolya Kavakli-Thorne</strong></span> </div> <div class="color-grey-dark"> <em>Algorithms</em> <b>2024</b>, <em>17</em>(12), 560; <a href="https://doi.org/10.3390/a17120560">https://doi.org/10.3390/a17120560</a> - 6 Dec 2024 </div> <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> Human engagement is a vital test research area actively explored in cognitive science and user experience studies. The rise of big data and digital technologies brings new opportunities into this field, especially in autonomous systems and smart applications. This article reviews the latest <a href="#" data-counterslink = "https://www.mdpi.com/1999-4893/17/12/560/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> Human engagement is a vital test research area actively explored in cognitive science and user experience studies. The rise of big data and digital technologies brings new opportunities into this field, especially in autonomous systems and smart applications. This article reviews the latest sensors, current advances of estimation methods, and existing domains of application to guide researchers and practitioners to deploy engagement estimators in various use cases from driver drowsiness detection to human–robot interaction (HRI). Over one hundred references were selected, examined, and contrasted in this review. Specifically, this review focuses on accuracy and practicality of use in different scenarios regarding each sensor modality, as well as current opportunities that greater automatic human engagement estimation could unlock. It is highlighted that multimodal sensor fusion and data-driven methods have shown significant promise in enhancing the accuracy and reliability of engagement estimation. Upon compiling the existing literature, this article addresses future research directions, including the need for developing more efficient algorithms for real-time processing, generalization of data-driven approaches, creating adaptive and responsive systems that better cater to individual needs, and promoting user acceptance. <a href="/1999-4893/17/12/560">Full article</a> </div> </div> <div class="belongsTo" style="margin-bottom: 10px;"> (This article belongs to the Special Issue <a href=" /journal/algorithms/special_issues/33CQTM57VM ">AI Algorithms for Positive Change in Digital Futures</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/1999-4893/17/12/560/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1538452"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1538452"><i class="fa fa-caret-right"></i></div><div class="absgraph cycle-slideshow manual" data-cycle-fx="scrollHorz" data-cycle-timeout="0" data-cycle-next="#next1538452" data-cycle-prev="#prev1538452" data-cycle-progressive="#images1538452" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1538452-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/algorithms/algorithms-17-00560/article_deploy/html/images/algorithms-17-00560-g001-550.jpg?1733888353" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1538452" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1538452-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00560/article_deploy/html/images/algorithms-17-00560-g002-550.jpg?1733888355'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1538452-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00560/article_deploy/html/images/algorithms-17-00560-g003-550.jpg?1733888356'><p>Figure 3</p></div></script></div></div><div id="article-1538452-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00560/article_deploy/html/images/algorithms-17-00560-g001-550.jpg?1733888353" title=" <strong>Figure 1</strong><br/> <p>An illustration of the sub-processes of engagement. The human appraisal system, cognitive system, motivation system, and motor system all reveal important information about engagement. Emotions are part of the appraisal process of the behaviour of the interaction partner. A certain level of cognitive load will be associated with engagement. Motor responses such as gross motor, gaze, and facial expression can be captured through observation. Many techniques can measure engagement sub-processes even while not directly measuring engagement as whole.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/560'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00560/article_deploy/html/images/algorithms-17-00560-g002-550.jpg?1733888355" title=" <strong>Figure 2</strong><br/> <p>Three typical DNN architectures for human engagement classification or regression. <b>Left</b> takes as input a video containing multiple frames and applies CNN+LSTM in conjunction with fully connected layers for engagement output, such as [<a href="#B14-algorithms-17-00560" class="html-bibr">14</a>,<a href="#B57-algorithms-17-00560" class="html-bibr">57</a>]. <b>Top right</b> evaluates engagement from a single frame with CNN+MLP as is tested in [<a href="#B131-algorithms-17-00560" class="html-bibr">131</a>,<a href="#B138-algorithms-17-00560" class="html-bibr">138</a>]. <b>Bottom right</b> curates a feature vector concatenating sole or multimodal features before learning hidden states with Recurrent Neural Networks (RNNs), such as GRU and LSTM in [<a href="#B13-algorithms-17-00560" class="html-bibr">13</a>].</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/560'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00560/article_deploy/html/images/algorithms-17-00560-g003-550.jpg?1733888356" title=" <strong>Figure 3</strong><br/> <p>Example applications enabled by engagement estimation. (<b>A</b>) E-learning can benefit from automatic learner engagement evaluation. (<b>B</b>) Driver drowsiness detection is critical for safety and reducing road accidents (data from [<a href="#B68-algorithms-17-00560" class="html-bibr">68</a>]). (<b>C</b>) Engagement estimation plays a key role in designing human–computer interfaces, social robots, and autonomous systems for HRI.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/560'>Full article</a></strong> "></a></div> </div> </div> </div> <div class="expanding-div collapsed"> <div class="generic-item article-item"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 22 pages, 1599 KiB   </span> <a href="/1999-4893/17/12/559/pdf?version=1733744408" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Single-Stage Entity–Relation Joint Extraction of Pesticide Registration Information Based on HT-BES Multi-Dimensional Labeling Strategy" data-journal="algorithms"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Article</span></div> <a class="title-link" href="/1999-4893/17/12/559">Single-Stage Entity–Relation Joint Extraction of Pesticide Registration Information Based on HT-BES Multi-Dimensional Labeling Strategy</a> <div class="authors"> by <span class="inlineblock "><strong>Chenyang Dong</strong>, </span><span class="inlineblock "><strong>Shiyu Xi</strong>, </span><span class="inlineblock "><strong>Yinchao Che</strong>, </span><span class="inlineblock "><strong>Shufeng Xiong</strong>, </span><span class="inlineblock "><strong>Xinming Ma</strong>, </span><span class="inlineblock "><strong>Lei Xi</strong> and </span><span class="inlineblock "><strong>Shuping Xiong</strong></span> </div> <div class="color-grey-dark"> <em>Algorithms</em> <b>2024</b>, <em>17</em>(12), 559; <a href="https://doi.org/10.3390/a17120559">https://doi.org/10.3390/a17120559</a> - 6 Dec 2024 </div> <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> Pesticide registration information is an essential part of the pesticide knowledge base. However, the large amount of unstructured text data that it contains pose significant challenges for knowledge storage, retrieval, and utilization. To address the characteristics of pesticide registration text such as high <a href="#" data-counterslink = "https://www.mdpi.com/1999-4893/17/12/559/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> Pesticide registration information is an essential part of the pesticide knowledge base. However, the large amount of unstructured text data that it contains pose significant challenges for knowledge storage, retrieval, and utilization. To address the characteristics of pesticide registration text such as high information density, complex logical structures, large spans between entities, and heterogeneous entity lengths, as well as to overcome the challenges faced when using traditional joint extraction methods, including triplet overlap, exposure bias, and redundant computation, we propose a single-stage entity–relation joint extraction model based on HT-BES multi-dimensional labeling (MD-SERel). First, in the encoding layer, to address the complex structural characteristics of pesticide registration texts, we employ RoBERTa combined with a multi-head self-attention mechanism to capture the deep semantic features of the text. Simultaneously, syntactic features are extracted using a syntactic dependency tree and graph neural networks to enhance the model’s understanding of text structure. Subsequently, we integrate semantic and syntactic features, enriching the character vector representations and thus improving the model’s ability to represent complex textual data. Secondly, in the multi-dimensional labeling framework layer, we use HT-BES multi-dimensional labeling, where the model assigns multiple labels to each character. These labels include entity boundaries, positions, and head–tail entity association information, which naturally resolves overlapping triplets. Through utilizing a parallel scoring function and fine-grained classification components, the joint extraction of entities and relations is transformed into a multi-label sequence labeling task based on relation dimensions. This process does not involve interdependent steps, thus enabling single-stage parallel labeling, preventing exposure bias and reducing computational redundancy. Finally, in the decoding layer, entity–relation triplets are decoded based on the predicted labels from the fine-grained classification. The experimental results demonstrate that the MD-SERel model performs well on both the Pesticide Registration Dataset (PRD) and the general DuIE dataset. On the PRD, compared to the optimal baseline model, the training time is 1.2 times faster, the inference time is 1.2 times faster, and the F1 score is improved by 1.5%, demonstrating its knowledge extraction capabilities in pesticide registration documents. On the DuIE dataset, the MD-SERel model also achieved better results compared to the baseline, demonstrating its strong generalization ability. These findings will provide technical support for the construction of pesticide knowledge bases. <a href="/1999-4893/17/12/559">Full article</a> </div> </div> <div class="belongsTo" style="margin-bottom: 10px;"> (This article belongs to the Special Issue <a href=" /journal/algorithms/special_issues/7KWL426950 ">Algorithms for Feature Selection (3rd Edition)</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/1999-4893/17/12/559/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1538001"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1538001"><i class="fa fa-caret-right"></i></div><div class="absgraph cycle-slideshow manual" data-cycle-fx="scrollHorz" data-cycle-timeout="0" data-cycle-next="#next1538001" data-cycle-prev="#prev1538001" data-cycle-progressive="#images1538001" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1538001-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/algorithms/algorithms-17-00559/article_deploy/html/images/algorithms-17-00559-g001-550.jpg?1733744587" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1538001" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1538001-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00559/article_deploy/html/images/algorithms-17-00559-g002-550.jpg?1733744588'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1538001-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00559/article_deploy/html/images/algorithms-17-00559-g003-550.jpg?1733744590'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-1538001-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00559/article_deploy/html/images/algorithms-17-00559-g004-550.jpg?1733744593'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-1538001-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00559/article_deploy/html/images/algorithms-17-00559-g005-550.jpg?1733744594'><p>Figure 5</p></div> --- <div class='openpopupgallery' data-imgindex='5' data-target='article-1538001-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00559/article_deploy/html/images/algorithms-17-00559-g006-550.jpg?1733744595'><p>Figure 6</p></div> --- <div class='openpopupgallery' data-imgindex='6' data-target='article-1538001-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00559/article_deploy/html/images/algorithms-17-00559-g007-550.jpg?1733744597'><p>Figure 7</p></div> --- <div class='openpopupgallery' data-imgindex='7' data-target='article-1538001-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00559/article_deploy/html/images/algorithms-17-00559-g008-550.jpg?1733744598'><p>Figure 8</p></div></script></div></div><div id="article-1538001-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00559/article_deploy/html/images/algorithms-17-00559-g001-550.jpg?1733744587" title=" <strong>Figure 1</strong><br/> <p>MD-SERel model.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/559'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00559/article_deploy/html/images/algorithms-17-00559-g002-550.jpg?1733744588" title=" <strong>Figure 2</strong><br/> <p>Self-attention mechanism architecture diagram.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/559'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00559/article_deploy/html/images/algorithms-17-00559-g003-550.jpg?1733744590" title=" <strong>Figure 3</strong><br/> <p>Syntactic dependency matrix. In the <a href="#algorithms-17-00559-f003" class="html-fig">Figure 3</a>, (<b>a</b>) is the result of semantic analysis of example sentences. (<b>b</b>) is a semantic adjacency matrix constructed from (<b>a</b>).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/559'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00559/article_deploy/html/images/algorithms-17-00559-g004-550.jpg?1733744593" title=" <strong>Figure 4</strong><br/> <p>HT-BES interactive annotation strategy.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/559'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00559/article_deploy/html/images/algorithms-17-00559-g005-550.jpg?1733744594" title=" <strong>Figure 5</strong><br/> <p>The type and quantity distribution of entities and relations.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/559'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00559/article_deploy/html/images/algorithms-17-00559-g006-550.jpg?1733744595" title=" <strong>Figure 6</strong><br/> <p>Entity lengths.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/559'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00559/article_deploy/html/images/algorithms-17-00559-g007-550.jpg?1733744597" title=" <strong>Figure 7</strong><br/> <p>The results for different overlapping patterns of triples.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/559'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00559/article_deploy/html/images/algorithms-17-00559-g008-550.jpg?1733744598" title=" <strong>Figure 8</strong><br/> <p>The results of different self-attention head numbers.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/559'>Full article</a></strong> "></a></div> </div> </div> </div> <div class="expanding-div collapsed"> <div class="generic-item article-item"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 13 pages, 7696 KiB   </span> <a href="/1999-4893/17/12/558/pdf?version=1733475199" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="From Stationary to Nonstationary UAVs: Deep-Learning-Based Method for Vehicle Speed Estimation" data-journal="algorithms"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Article</span></div> <a class="title-link" href="/1999-4893/17/12/558">From Stationary to Nonstationary UAVs: Deep-Learning-Based Method for Vehicle Speed Estimation</a> <div class="authors"> by <span class="inlineblock "><strong>Muhammad Waqas Ahmed</strong>, </span><span class="inlineblock "><strong>Muhammad Adnan</strong>, </span><span class="inlineblock "><strong>Muhammad Ahmed</strong>, </span><span class="inlineblock "><strong>Davy Janssens</strong>, </span><span class="inlineblock "><strong>Geert Wets</strong>, </span><span class="inlineblock "><strong>Afzal Ahmed</strong> and </span><span class="inlineblock "><strong>Wim Ectors</strong></span> </div> <div class="color-grey-dark"> <em>Algorithms</em> <b>2024</b>, <em>17</em>(12), 558; <a href="https://doi.org/10.3390/a17120558">https://doi.org/10.3390/a17120558</a> - 6 Dec 2024 </div> <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> The development of smart cities relies on the implementation of cutting-edge technologies. Unmanned aerial vehicles (UAVs) and deep learning (DL) models are examples of such disruptive technologies with diverse industrial applications that are gaining traction. When it comes to road traffic monitoring systems <a href="#" data-counterslink = "https://www.mdpi.com/1999-4893/17/12/558/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> The development of smart cities relies on the implementation of cutting-edge technologies. Unmanned aerial vehicles (UAVs) and deep learning (DL) models are examples of such disruptive technologies with diverse industrial applications that are gaining traction. When it comes to road traffic monitoring systems (RTMs), the combination of UAVs and vision-based methods has shown great potential. Currently, most solutions focus on analyzing traffic footage captured by hovering UAVs due to the inherent georeferencing challenges in video footage from nonstationary drones. We propose an innovative method capable of estimating traffic speed using footage from both stationary and nonstationary UAVs. The process involves matching each pixel of the input frame with a georeferenced orthomosaic using a feature-matching algorithm. Subsequently, a tracking-enabled YOLOv8 object detection model is applied to the frame to detect vehicles and their trajectories. The geographic positions of these moving vehicles over time are logged in JSON format. The accuracy of this method was validated with reference measurements recorded from a laser speed gun. The results indicate that the proposed method can estimate vehicle speeds with an absolute error as low as 0.53 km/h. The study also discusses the associated problems and constraints with nonstationary drone footage as input and proposes strategies for minimizing noise and inaccuracies. Despite these challenges, the proposed framework demonstrates considerable potential and signifies another step towards automated road traffic monitoring systems. This system enables transportation modelers to realistically capture traffic behavior over a wider area, unlike existing roadside camera systems prone to blind spots and limited spatial coverage. <a href="/1999-4893/17/12/558">Full article</a> </div> </div> <div class="belongsTo" style="margin-bottom: 10px;"> (This article belongs to the Section <a href="/journal/algorithms/sections/evolutionary_algorithms_and_machine_learning">Evolutionary Algorithms and Machine Learning</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/1999-4893/17/12/558/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1537961"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1537961"><i class="fa fa-caret-right"></i></div><div class="absgraph cycle-slideshow manual" data-cycle-fx="scrollHorz" data-cycle-timeout="0" data-cycle-next="#next1537961" data-cycle-prev="#prev1537961" data-cycle-progressive="#images1537961" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1537961-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/algorithms/algorithms-17-00558/article_deploy/html/images/algorithms-17-00558-g001-550.jpg?1733475304" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1537961" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1537961-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00558/article_deploy/html/images/algorithms-17-00558-g002-550.jpg?1733475306'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1537961-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00558/article_deploy/html/images/algorithms-17-00558-g003-550.jpg?1733475306'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-1537961-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00558/article_deploy/html/images/algorithms-17-00558-g004-550.jpg?1733475308'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-1537961-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00558/article_deploy/html/images/algorithms-17-00558-g005-550.jpg?1733475309'><p>Figure 5</p></div> --- <div class='openpopupgallery' data-imgindex='5' data-target='article-1537961-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00558/article_deploy/html/images/algorithms-17-00558-g006-550.jpg?1733475312'><p>Figure 6</p></div> --- <div class='openpopupgallery' data-imgindex='6' data-target='article-1537961-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00558/article_deploy/html/images/algorithms-17-00558-g007-550.jpg?1733475313'><p>Figure 7</p></div> --- <div class='openpopupgallery' data-imgindex='7' data-target='article-1537961-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00558/article_deploy/html/images/algorithms-17-00558-g008-550.jpg?1733475318'><p>Figure 8</p></div></script></div></div><div id="article-1537961-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00558/article_deploy/html/images/algorithms-17-00558-g001-550.jpg?1733475304" title=" <strong>Figure 1</strong><br/> <p>Showcases the methodological framework of the study.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/558'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00558/article_deploy/html/images/algorithms-17-00558-g002-550.jpg?1733475306" title=" <strong>Figure 2</strong><br/> <p>Feature-matching algorithm SIFT applied to input and template image. The highlighted markers depict the key points matched between the two images.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/558'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00558/article_deploy/html/images/algorithms-17-00558-g003-550.jpg?1733475306" title=" <strong>Figure 3</strong><br/> <p>Comparison of noisy and EMA-filtered trajectories with different alpha values.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/558'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00558/article_deploy/html/images/algorithms-17-00558-g004-550.jpg?1733475308" title=" <strong>Figure 4</strong><br/> <p>The mapped vehicle trajectories before and after EMA application.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/558'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00558/article_deploy/html/images/algorithms-17-00558-g005-550.jpg?1733475309" title=" <strong>Figure 5</strong><br/> <p>The fluctuations in velocity (in km/h) over time (in seconds) and the removal of errors using an EMA-based low-pass filter (α = 0.1). The single-point reference speed measured by the speed gun was 26 km/h.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/558'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00558/article_deploy/html/images/algorithms-17-00558-g006-550.jpg?1733475312" title=" <strong>Figure 6</strong><br/> <p>The pseudo tracks generated by the object tracking algorithm due to UAV movement.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/558'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00558/article_deploy/html/images/algorithms-17-00558-g007-550.jpg?1733475313" title=" <strong>Figure 7</strong><br/> <p>Extreme velocity (km/h) over time (s) with fluctuation resulting from pseudo tracks and their removal from the distance-based movement threshold (after introducing the distance threshold, the first measurement starts at 4.3 s). The single-point reference speed measured by the speed gun was 26 km/h.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/558'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00558/article_deploy/html/images/algorithms-17-00558-g008-550.jpg?1733475318" title=" <strong>Figure 8</strong><br/> <p>The method used for determining the positional accuracies of vehicle tracks on (<b>a</b>) tracks from stationary drone footage and (<b>b</b>) tracks from moving drone footage.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/558'>Full article</a></strong> "></a></div> </div> </div> </div> <div class="expanding-div collapsed"> <div class="generic-item article-item"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 17 pages, 5357 KiB   </span> <a href="/1999-4893/17/12/557/pdf?version=1733405967" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Integrating Explanations into CNNs by Adopting Spiking Attention Block for Skin Cancer Detection" data-journal="algorithms"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Article</span></div> <a class="title-link" href="/1999-4893/17/12/557">Integrating Explanations into CNNs by Adopting Spiking Attention Block for Skin Cancer Detection</a> <div class="authors"> by <span class="inlineblock "><strong>Inzamam Mashood Nasir</strong>, </span><span class="inlineblock "><strong>Sara Tehsin</strong>, </span><span class="inlineblock "><strong>Robertas Damaševičius</strong> and </span><span class="inlineblock "><strong>Rytis Maskeliūnas</strong></span> </div> <div class="color-grey-dark"> <em>Algorithms</em> <b>2024</b>, <em>17</em>(12), 557; <a href="https://doi.org/10.3390/a17120557">https://doi.org/10.3390/a17120557</a> - 5 Dec 2024 </div> <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> Lately, there has been a substantial rise in the number of identified individuals with skin cancer, making it the most widespread form of cancer worldwide. Until now, several machine learning methods that utilize skin scans have been directly employed for skin cancer classification, <a href="#" data-counterslink = "https://www.mdpi.com/1999-4893/17/12/557/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> Lately, there has been a substantial rise in the number of identified individuals with skin cancer, making it the most widespread form of cancer worldwide. Until now, several machine learning methods that utilize skin scans have been directly employed for skin cancer classification, showing encouraging outcomes in terms of enhancing diagnostic precision. In this paper, multimodal Explainable Artificial Intelligence (XAI) is presented that offers explanations that (1) address a gap regarding interpretation by identifying specific dermoscopic features, thereby enabling (2) dermatologists to comprehend them during melanoma diagnosis and allowing for an (3) evaluation of the interaction between clinicians and XAI. The specific goal of this article is to create an XAI system that closely aligns with the perspective of dermatologists when it comes to diagnosing melanoma. By building upon previous research on explainability in dermatology, this work introduces a novel soft attention mechanism, called Convolutional Spiking Attention Module (CSAM), to deep neural architectures, which focuses on enhancing critical elements and reducing noise-inducing features. Two instances of the proposed CSAM were placed inside the proposed Spiking Attention Block (SAB). The InceptionResNetV2, DenseNet201, and Xception architectures with and without the proposed SAB mechanism were compared for skin lesion classification. Pretrained networks with SAB outperform state-of-the-art methods on the HAM10000 dataset. The proposed method used the ISIC-2019 dataset for the crossdataset validation process. The proposed model provides attention regarding cancer pixels without using an external explainer, which proves the importance and significance of the SAB module. <a href="/1999-4893/17/12/557">Full article</a> </div> </div> <div class="belongsTo" style="margin-bottom: 10px;"> (This article belongs to the Special Issue <a href=" /journal/algorithms/special_issues/OO7YBT2SX1 ">Supervised and Unsupervised Classification Algorithms (2nd Edition)</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/1999-4893/17/12/557/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1537533"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1537533"><i class="fa fa-caret-right"></i></div><div class="absgraph cycle-slideshow manual" data-cycle-fx="scrollHorz" data-cycle-timeout="0" data-cycle-next="#next1537533" data-cycle-prev="#prev1537533" data-cycle-progressive="#images1537533" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1537533-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/algorithms/algorithms-17-00557/article_deploy/html/images/algorithms-17-00557-g001-550.jpg?1733406100" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1537533" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1537533-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00557/article_deploy/html/images/algorithms-17-00557-g002-550.jpg?1733406102'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1537533-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00557/article_deploy/html/images/algorithms-17-00557-g003-550.jpg?1733406104'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-1537533-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00557/article_deploy/html/images/algorithms-17-00557-g004-550.jpg?1733406106'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-1537533-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00557/article_deploy/html/images/algorithms-17-00557-g005-550.jpg?1733406108'><p>Figure 5</p></div> --- <div class='openpopupgallery' data-imgindex='5' data-target='article-1537533-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00557/article_deploy/html/images/algorithms-17-00557-g006-550.jpg?1733406109'><p>Figure 6</p></div> --- <div class='openpopupgallery' data-imgindex='6' data-target='article-1537533-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00557/article_deploy/html/images/algorithms-17-00557-g007-550.jpg?1733406110'><p>Figure 7</p></div> --- <div class='openpopupgallery' data-imgindex='7' data-target='article-1537533-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00557/article_deploy/html/images/algorithms-17-00557-g008-550.jpg?1733406112'><p>Figure 8</p></div> --- <div class='openpopupgallery' data-imgindex='8' data-target='article-1537533-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00557/article_deploy/html/images/algorithms-17-00557-g009-550.jpg?1733406113'><p>Figure 9</p></div> --- <div class='openpopupgallery' data-imgindex='9' data-target='article-1537533-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00557/article_deploy/html/images/algorithms-17-00557-g010-550.jpg?1733406115'><p>Figure 10</p></div> --- <div class='openpopupgallery' data-imgindex='10' data-target='article-1537533-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00557/article_deploy/html/images/algorithms-17-00557-g011-550.jpg?1733406118'><p>Figure 11</p></div> --- <div class='openpopupgallery' data-imgindex='11' data-target='article-1537533-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00557/article_deploy/html/images/algorithms-17-00557-g012-550.jpg?1733406118'><p>Figure 12</p></div> --- <div class='openpopupgallery' data-imgindex='12' data-target='article-1537533-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00557/article_deploy/html/images/algorithms-17-00557-g013-550.jpg?1733406119'><p>Figure 13</p></div> --- <div class='openpopupgallery' data-imgindex='13' data-target='article-1537533-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00557/article_deploy/html/images/algorithms-17-00557-g014-550.jpg?1733406121'><p>Figure 14</p></div> --- <div class='openpopupgallery' data-imgindex='14' data-target='article-1537533-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00557/article_deploy/html/images/algorithms-17-00557-g015-550.jpg?1733406123'><p>Figure 15</p></div> --- <div class='openpopupgallery' data-imgindex='15' data-target='article-1537533-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00557/article_deploy/html/images/algorithms-17-00557-g016-550.jpg?1733406124'><p>Figure 16</p></div> --- <div class='openpopupgallery' data-imgindex='16' data-target='article-1537533-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00557/article_deploy/html/images/algorithms-17-00557-g017-550.jpg?1733406127'><p>Figure 17</p></div></script></div></div><div id="article-1537533-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00557/article_deploy/html/images/algorithms-17-00557-g001-550.jpg?1733406100" title=" <strong>Figure 1</strong><br/> <p>Comparison of traditional CAD systems and XAI-based CAD systems.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/557'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00557/article_deploy/html/images/algorithms-17-00557-g002-550.jpg?1733406102" title=" <strong>Figure 2</strong><br/> <p>Overall architecture of the proposed CSAM model.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/557'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00557/article_deploy/html/images/algorithms-17-00557-g003-550.jpg?1733406104" title=" <strong>Figure 3</strong><br/> <p>Internal architecture of Spatial Attention Block and Channel Attention Block.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/557'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00557/article_deploy/html/images/algorithms-17-00557-g004-550.jpg?1733406106" title=" <strong>Figure 4</strong><br/> <p>Architecture of the proposed SAB.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/557'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00557/article_deploy/html/images/algorithms-17-00557-g005-550.jpg?1733406108" title=" <strong>Figure 5</strong><br/> <p>Building blocks of the InceptionResnetV2 model.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/557'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00557/article_deploy/html/images/algorithms-17-00557-g006-550.jpg?1733406109" title=" <strong>Figure 6</strong><br/> <p>Minimized architecture of the InceptionResnetV2 model.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/557'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00557/article_deploy/html/images/algorithms-17-00557-g007-550.jpg?1733406110" title=" <strong>Figure 7</strong><br/> <p>The proposed architecture of the InceptionResnetV2 model after embedding SAB.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/557'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00557/article_deploy/html/images/algorithms-17-00557-g008-550.jpg?1733406112" title=" <strong>Figure 8</strong><br/> <p>Building blocks of the DenseNet201 model.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/557'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00557/article_deploy/html/images/algorithms-17-00557-g009-550.jpg?1733406113" title=" <strong>Figure 9</strong><br/> <p>Minimized architecture of the DenseNet201 model.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/557'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00557/article_deploy/html/images/algorithms-17-00557-g010-550.jpg?1733406115" title=" <strong>Figure 10</strong><br/> <p>The proposed architecture of the DenseNet201 model after embedding SAB.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/557'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00557/article_deploy/html/images/algorithms-17-00557-g011-550.jpg?1733406118" title=" <strong>Figure 11</strong><br/> <p>Building blocks of the Xception model.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/557'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00557/article_deploy/html/images/algorithms-17-00557-g012-550.jpg?1733406118" title=" <strong>Figure 12</strong><br/> <p>Minimized architecture of the Xception model.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/557'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00557/article_deploy/html/images/algorithms-17-00557-g013-550.jpg?1733406119" title=" <strong>Figure 13</strong><br/> <p>The proposed architecture of the Xception model after embedding SAB.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/557'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00557/article_deploy/html/images/algorithms-17-00557-g014-550.jpg?1733406121" title=" <strong>Figure 14</strong><br/> <p>Performance increase using models that include the soft attention block.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/557'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00557/article_deploy/html/images/algorithms-17-00557-g015-550.jpg?1733406123" title=" <strong>Figure 15</strong><br/> <p>Attention maps generated using InRe+SAB on the HAM10000 dataset.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/557'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00557/article_deploy/html/images/algorithms-17-00557-g016-550.jpg?1733406124" title=" <strong>Figure 16</strong><br/> <p>Comparison of class performance using different split ratios.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/557'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00557/article_deploy/html/images/algorithms-17-00557-g017-550.jpg?1733406127" title=" <strong>Figure 17</strong><br/> <p>Attention maps generated using InRe + SAB when trained on the HAM10000 dataset and validated on the ISIC-2019 dataset.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/557'>Full article</a></strong> "></a></div> </div> </div> </div> <div class="expanding-div collapsed"> <div class="generic-item article-item"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 14 pages, 2052 KiB   </span> <a href="/1999-4893/17/12/556/pdf?version=1733388530" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Human Activity Recognition: A Comparative Study of Validation Methods and Impact of Feature Extraction in Wearable Sensors" data-journal="algorithms"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Article</span></div> <a class="title-link" href="/1999-4893/17/12/556">Human Activity Recognition: A Comparative Study of Validation Methods and Impact of Feature Extraction in Wearable Sensors</a> <div class="authors"> by <span class="inlineblock "><strong>Saeed Ur Rehman</strong>, </span><span class="inlineblock "><strong>Anwar Ali</strong>, </span><span class="inlineblock "><strong>Adil Mehmood Khan</strong> and </span><span class="inlineblock "><strong>Cynthia Okpala</strong></span> </div> <div class="color-grey-dark"> <em>Algorithms</em> <b>2024</b>, <em>17</em>(12), 556; <a href="https://doi.org/10.3390/a17120556">https://doi.org/10.3390/a17120556</a> - 5 Dec 2024 </div> <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> With the increasing availability of wearable devices for data collection, studies in human activity recognition have gained significant popularity. These studies report high accuracies on k-fold cross validation, which is not reflective of their generalization performance but is a result of the inappropriate <a href="#" data-counterslink = "https://www.mdpi.com/1999-4893/17/12/556/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> With the increasing availability of wearable devices for data collection, studies in human activity recognition have gained significant popularity. These studies report high accuracies on k-fold cross validation, which is not reflective of their generalization performance but is a result of the inappropriate split of testing and training datasets, causing these models to evaluate the same subjects that they were trained on, making them subject-dependent. This study comparatively discusses this validation approach with a universal approach, Leave-One-Subject-Out (LOSO) cross-validation which is not subject-dependent and ensures that an entirely new subject is used for evaluation in each fold, validated on four different machine learning models trained on windowed data and select hand-crafted features. The random forest model, with the highest accuracy of 76% when evaluated on LOSO, achieved an accuracy of 89% on k-fold cross-validation, demonstrating data leakage. Additionally, this experiment underscores the significance of hand-crafted features by contrasting their accuracy with that of raw sensor models. The feature models demonstrate a remarkable 30% higher accuracy, underscoring the importance of feature engineering in enhancing the robustness and precision of HAR systems. <a href="/1999-4893/17/12/556">Full article</a> </div> </div> <div class="belongsTo" style="margin-bottom: 10px;"> (This article belongs to the Section <a href="/journal/algorithms/sections/Algorithms_for_Multidisciplinary_Applications">Algorithms for Multidisciplinary Applications</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/1999-4893/17/12/556/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1537153"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1537153"><i class="fa fa-caret-right"></i></div><div class="absgraph cycle-slideshow manual" data-cycle-fx="scrollHorz" data-cycle-timeout="0" data-cycle-next="#next1537153" data-cycle-prev="#prev1537153" data-cycle-progressive="#images1537153" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1537153-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/algorithms/algorithms-17-00556/article_deploy/html/images/algorithms-17-00556-g001-550.jpg?1733388599" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1537153" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1537153-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00556/article_deploy/html/images/algorithms-17-00556-g002-550.jpg?1733388600'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1537153-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00556/article_deploy/html/images/algorithms-17-00556-g003-550.jpg?1733388601'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-1537153-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00556/article_deploy/html/images/algorithms-17-00556-g004-550.jpg?1733388602'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-1537153-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00556/article_deploy/html/images/algorithms-17-00556-g005-550.jpg?1733388604'><p>Figure 5</p></div> --- <div class='openpopupgallery' data-imgindex='5' data-target='article-1537153-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00556/article_deploy/html/images/algorithms-17-00556-g006-550.jpg?1733388605'><p>Figure 6</p></div> --- <div class='openpopupgallery' data-imgindex='6' data-target='article-1537153-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00556/article_deploy/html/images/algorithms-17-00556-g007-550.jpg?1733388606'><p>Figure 7</p></div></script></div></div><div id="article-1537153-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00556/article_deploy/html/images/algorithms-17-00556-g001-550.jpg?1733388599" title=" <strong>Figure 1</strong><br/> <p>The process of the activity recognition chain.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/556'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00556/article_deploy/html/images/algorithms-17-00556-g002-550.jpg?1733388600" title=" <strong>Figure 2</strong><br/> <p>Distribution of Activities in PAMAP2 dataset.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/556'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00556/article_deploy/html/images/algorithms-17-00556-g003-550.jpg?1733388601" title=" <strong>Figure 3</strong><br/> <p>Box plot of hand features.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/556'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00556/article_deploy/html/images/algorithms-17-00556-g004-550.jpg?1733388602" title=" <strong>Figure 4</strong><br/> <p>Variation in activities based on sensor readings.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/556'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00556/article_deploy/html/images/algorithms-17-00556-g005-550.jpg?1733388604" title=" <strong>Figure 5</strong><br/> <p>Plot of all models’ training vs. testing accuracies for k-fold and LOSO.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/556'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00556/article_deploy/html/images/algorithms-17-00556-g006-550.jpg?1733388605" title=" <strong>Figure 6</strong><br/> <p>Testing accuracy comparison of raw sensor models and feature-trained models validated with k-fold and LOSO on random forest and logistic regression classifiers.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/556'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00556/article_deploy/html/images/algorithms-17-00556-g007-550.jpg?1733388606" title=" <strong>Figure 7</strong><br/> <p>Training vs. testing accuracy in each subject in LOSO.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/556'>Full article</a></strong> "></a></div> </div> </div> </div> <div class="expanding-div collapsed"> <div class="generic-item article-item"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 19 pages, 6896 KiB   </span> <a href="/1999-4893/17/12/555/pdf?version=1733395837" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Optimization of Distribution Network Current Protection for Inverter-Based Distributed Power Access" data-journal="algorithms"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Article</span></div> <a class="title-link" href="/1999-4893/17/12/555">Optimization of Distribution Network Current Protection for Inverter-Based Distributed Power Access</a> <div class="authors"> by <span class="inlineblock "><strong>Chun Xiao</strong>, </span><span class="inlineblock "><strong>Qiong Cao</strong>, </span><span class="inlineblock "><strong>Yulu Ren</strong> and </span><span class="inlineblock "><strong>Xiaoqing Han</strong></span> </div> <div class="color-grey-dark"> <em>Algorithms</em> <b>2024</b>, <em>17</em>(12), 555; <a href="https://doi.org/10.3390/a17120555">https://doi.org/10.3390/a17120555</a> - 4 Dec 2024 </div> <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> In light of the accelerated development of renewable energy, inverter-based distributed power supply (IIDG) is assuming an increasingly pivotal role in contemporary power systems. This paper investigates the impact of inverter-based distributed power access on current protection in distribution networks and proposes an <a href="#" data-counterslink = "https://www.mdpi.com/1999-4893/17/12/555/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> In light of the accelerated development of renewable energy, inverter-based distributed power supply (IIDG) is assuming an increasingly pivotal role in contemporary power systems. This paper investigates the impact of inverter-based distributed power access on current protection in distribution networks and proposes an optimization method. Firstly, the IIDG power model is introduced for the purpose of analyzing the impact of distributed power supply access to an urban distribution network on the current magnitude of the distribution network. Subsequently, the issue of protection sensitivity following IIDG access is examined in the context of time-limited current flow protection and fixed-time overcurrent protection, respectively. To address the issue of inadequate sensitivity, a refined enhancement strategy for the real-time monitoring of the line current and modifying the action setting values is put forth. Finally, the proposed protection methodology is validated through PSCAD simulation, thereby verifying the effectiveness of the proposed optimization and improvement solutions in the real-time monitoring of the line current and adjustment of the action setting values. <a href="/1999-4893/17/12/555">Full article</a> </div> </div> <div class="belongsTo" style="margin-bottom: 10px;"> (This article belongs to the Section <a href="/journal/algorithms/sections/parallel_distributed_algorithms">Parallel and Distributed Algorithms</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/1999-4893/17/12/555/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1536780"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1536780"><i class="fa fa-caret-right"></i></div><div class="absgraph cycle-slideshow manual" data-cycle-fx="scrollHorz" data-cycle-timeout="0" data-cycle-next="#next1536780" data-cycle-prev="#prev1536780" data-cycle-progressive="#images1536780" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1536780-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/algorithms/algorithms-17-00555/article_deploy/html/images/algorithms-17-00555-g001-550.jpg?1733395901" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1536780" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1536780-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00555/article_deploy/html/images/algorithms-17-00555-g002-550.jpg?1733395902'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1536780-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00555/article_deploy/html/images/algorithms-17-00555-g003-550.jpg?1733395902'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-1536780-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00555/article_deploy/html/images/algorithms-17-00555-g004-550.jpg?1733395903'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-1536780-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00555/article_deploy/html/images/algorithms-17-00555-g005-550.jpg?1733395904'><p>Figure 5</p></div> --- <div class='openpopupgallery' data-imgindex='5' data-target='article-1536780-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00555/article_deploy/html/images/algorithms-17-00555-g006-550.jpg?1733395905'><p>Figure 6</p></div> --- <div class='openpopupgallery' data-imgindex='6' data-target='article-1536780-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00555/article_deploy/html/images/algorithms-17-00555-g007-550.jpg?1733395906'><p>Figure 7</p></div> --- <div class='openpopupgallery' data-imgindex='7' data-target='article-1536780-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00555/article_deploy/html/images/algorithms-17-00555-g008-550.jpg?1733395907'><p>Figure 8</p></div> --- <div class='openpopupgallery' data-imgindex='8' data-target='article-1536780-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00555/article_deploy/html/images/algorithms-17-00555-g009-550.jpg?1733395908'><p>Figure 9</p></div> --- <div class='openpopupgallery' data-imgindex='9' data-target='article-1536780-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00555/article_deploy/html/images/algorithms-17-00555-g010-550.jpg?1733395909'><p>Figure 10</p></div> --- <div class='openpopupgallery' data-imgindex='10' data-target='article-1536780-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00555/article_deploy/html/images/algorithms-17-00555-g011-550.jpg?1733395910'><p>Figure 11</p></div></script></div></div><div id="article-1536780-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00555/article_deploy/html/images/algorithms-17-00555-g001-550.jpg?1733395901" title=" <strong>Figure 1</strong><br/> <p>Equivalent circuit of IIDG access to grid.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/555'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00555/article_deploy/html/images/algorithms-17-00555-g002-550.jpg?1733395902" title=" <strong>Figure 2</strong><br/> <p>Single-source radial distribution network.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/555'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00555/article_deploy/html/images/algorithms-17-00555-g003-550.jpg?1733395902" title=" <strong>Figure 3</strong><br/> <p>Schematic diagram of a standard distribution network configuration.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/555'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00555/article_deploy/html/images/algorithms-17-00555-g004-550.jpg?1733395903" title=" <strong>Figure 4</strong><br/> <p>Adaptive full line current protection action logic diagram.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/555'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00555/article_deploy/html/images/algorithms-17-00555-g005-550.jpg?1733395904" title=" <strong>Figure 5</strong><br/> <p>Diagram of a 10 kV distribution network in a certain city.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/555'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00555/article_deploy/html/images/algorithms-17-00555-g006-550.jpg?1733395905" title=" <strong>Figure 6</strong><br/> <p>Comparison of minimum three-phase short circuit currents at different output levels (at the end of line BC).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/555'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00555/article_deploy/html/images/algorithms-17-00555-g007-550.jpg?1733395906" title=" <strong>Figure 7</strong><br/> <p>Sensitivity variations of time-limited overcurrent protection with different IIDG outputs.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/555'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00555/article_deploy/html/images/algorithms-17-00555-g008-550.jpg?1733395907" title=" <strong>Figure 8</strong><br/> <p>Minimum two-phase fault currents at the termination of line CD across different IIDG power outputs.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/555'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00555/article_deploy/html/images/algorithms-17-00555-g009-550.jpg?1733395908" title=" <strong>Figure 9</strong><br/> <p>Comparison of sensitivity before and after adjustment of Phase II current protection under different output conditions of IIDG.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/555'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00555/article_deploy/html/images/algorithms-17-00555-g010-550.jpg?1733395909" title=" <strong>Figure 10</strong><br/> <p>Comparison of sensitivity before and after adjustment of Phase II current protection under different output conditions of IIDG.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/555'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00555/article_deploy/html/images/algorithms-17-00555-g011-550.jpg?1733395910" title=" <strong>Figure 11</strong><br/> <p>Comparison of sensitivity before and after adjustment of Phase III current protection under different output conditions of IIDG.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/555'>Full article</a></strong> "></a></div> </div> </div> </div> <div class="expanding-div collapsed"> <div class="generic-item article-item"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 27 pages, 6325 KiB   </span> <a href="/1999-4893/17/12/554/pdf?version=1733390545" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Handling Exponentially Growing Strategies in Spatial Cooperative Games: The Case of the European Union" data-journal="algorithms"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Article</span></div> <a class="title-link" href="/1999-4893/17/12/554">Handling Exponentially Growing Strategies in Spatial Cooperative Games: The Case of the European Union</a> <div class="authors"> by <span class="inlineblock "><strong>Mehmet Küçükmehmetoğlu</strong>, </span><span class="inlineblock "><strong>Yasin Fahjan</strong> and </span><span class="inlineblock "><strong>Muhammed Ziya Paköz</strong></span> </div> <div class="color-grey-dark"> <em>Algorithms</em> <b>2024</b>, <em>17</em>(12), 554; <a href="https://doi.org/10.3390/a17120554">https://doi.org/10.3390/a17120554</a> - 4 Dec 2024 </div> <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> This paper introduces a comprehensive cooperative game theory framework to measure the significance of location and neighborhood relations in conjunction with the magnitude of players/parties. The significances of these relations are measured over the EU geography. In this case, there are (i) the <a href="#" data-counterslink = "https://www.mdpi.com/1999-4893/17/12/554/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> This paper introduces a comprehensive cooperative game theory framework to measure the significance of location and neighborhood relations in conjunction with the magnitude of players/parties. The significances of these relations are measured over the EU geography. In this case, there are (i) the test of availability of a core solution that satisfies all associated parties/players; (ii) the measurement of players’/parties’ rational minimal and maximal return expectations from the grand coalition regarding their all individual and sub-group strategies and associated return rationalities; (iii) the determination of the critical players/parties in the grand coalition. The study’s main contributions are the provision of a methodology that identifies spatially/geographically critical players/parties and the design of an algorithm for handling exponentially growing strategies alongside increasing numbers of players/parties. In sum, a comprehensive cooperative game theory framework is introduced to measure the significance of location and neighborhood relations in conjunction with the magnitude of the players/parties. The case of the EU has revealed the union’s geographically critical countries, with Germany being found to be the most influential. <a href="/1999-4893/17/12/554">Full article</a> </div> </div> <div class="belongsTo" style="margin-bottom: 10px;"> (This article belongs to the Section <a href="/journal/algorithms/sections/Algorithms_for_Multidisciplinary_Applications">Algorithms for Multidisciplinary Applications</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/1999-4893/17/12/554/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1536400"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1536400"><i class="fa fa-caret-right"></i></div><div class="absgraph cycle-slideshow manual" data-cycle-fx="scrollHorz" data-cycle-timeout="0" data-cycle-next="#next1536400" data-cycle-prev="#prev1536400" data-cycle-progressive="#images1536400" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1536400-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/algorithms/algorithms-17-00554/article_deploy/html/images/algorithms-17-00554-g001-550.jpg?1733390661" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1536400" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1536400-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00554/article_deploy/html/images/algorithms-17-00554-g002-550.jpg?1733390661'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1536400-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00554/article_deploy/html/images/algorithms-17-00554-g003-550.jpg?1733390662'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-1536400-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00554/article_deploy/html/images/algorithms-17-00554-g004-550.jpg?1733390663'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-1536400-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00554/article_deploy/html/images/algorithms-17-00554-g005-550.jpg?1733390663'><p>Figure 5</p></div> --- <div class='openpopupgallery' data-imgindex='5' data-target='article-1536400-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00554/article_deploy/html/images/algorithms-17-00554-g006-550.jpg?1733390665'><p>Figure 6</p></div> --- <div class='openpopupgallery' data-imgindex='6' data-target='article-1536400-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00554/article_deploy/html/images/algorithms-17-00554-g007-550.jpg?1733390667'><p>Figure 7</p></div> --- <div class='openpopupgallery' data-imgindex='7' data-target='article-1536400-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00554/article_deploy/html/images/algorithms-17-00554-g008-550.jpg?1733390670'><p>Figure 8</p></div> --- <div class='openpopupgallery' data-imgindex='8' data-target='article-1536400-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00554/article_deploy/html/images/algorithms-17-00554-g009-550.jpg?1733390672'><p>Figure 9</p></div> --- <div class='openpopupgallery' data-imgindex='9' data-target='article-1536400-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00554/article_deploy/html/images/algorithms-17-00554-g010-550.jpg?1733390674'><p>Figure 10</p></div></script></div></div><div id="article-1536400-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00554/article_deploy/html/images/algorithms-17-00554-g001-550.jpg?1733390661" title=" <strong>Figure 1</strong><br/> <p>An algorithm to eliminate redundant coalitions as well as the associated allocation constraints.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/554'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00554/article_deploy/html/images/algorithms-17-00554-g002-550.jpg?1733390661" title=" <strong>Figure 2</strong><br/> <p>Hypothetical three-player neighborhood design and the associated matrix [<a href="#B41-algorithms-17-00554" class="html-bibr">41</a>].</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/554'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00554/article_deploy/html/images/algorithms-17-00554-g003-550.jpg?1733390662" title=" <strong>Figure 3</strong><br/> <p>Model III application with the core and maximized <span class="html-italic">X<sub>A</sub></span> (10<sup>3</sup>): (<b>a</b>) Tax is zero (<span class="html-italic">Z</span> = 0): (<b>b</b>) Tax is two thousand (<span class="html-italic">Z</span> = 2000) [<a href="#B41-algorithms-17-00554" class="html-bibr">41</a>].</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/554'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00554/article_deploy/html/images/algorithms-17-00554-g004-550.jpg?1733390663" title=" <strong>Figure 4</strong><br/> <p>After tax (<span class="html-italic">Z</span>), the minimum expected individual benefits per unit base square meter area (<span class="html-italic">P_<sub>Min</sub>/P_<sub>Area</sub></span>) [<a href="#B41-algorithms-17-00554" class="html-bibr">41</a>].</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/554'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00554/article_deploy/html/images/algorithms-17-00554-g005-550.jpg?1733390663" title=" <strong>Figure 5</strong><br/> <p>After tax (<span class="html-italic">Z</span>), the maximum expected individual benefits per unit base square meter area (<span class="html-italic">P_<sub>Max</sub>/P_<sub>Area</sub></span>) [<a href="#B41-algorithms-17-00554" class="html-bibr">41</a>].</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/554'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00554/article_deploy/html/images/algorithms-17-00554-g006-550.jpg?1733390665" title=" <strong>Figure 6</strong><br/> <p>European Union countries and their neighborhood relations.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/554'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00554/article_deploy/html/images/algorithms-17-00554-g007-550.jpg?1733390667" title=" <strong>Figure 7</strong><br/> <p>After tax (<span class="html-italic">Z</span>), the minimum expected country benefits per unit GDP size (<span class="html-italic">X<sub>_Min</sub>/GDP_</span>)—Differentiated base GDP.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/554'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00554/article_deploy/html/images/algorithms-17-00554-g008-550.jpg?1733390670" title=" <strong>Figure 8</strong><br/> <p>After tax (<span class="html-italic">Z</span>), the maximum expected country benefits per unit GDP size (<span class="html-italic">X<sub>_Max</sub>/GDP_</span>)—Differentiated base GDP.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/554'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00554/article_deploy/html/images/algorithms-17-00554-g009-550.jpg?1733390672" title=" <strong>Figure 9</strong><br/> <p>After tax (<span class="html-italic">Z</span>), the minimum expected country benefits per unit GDP size (<span class="html-italic">X<sub>_Min</sub>/GDP_</span>)—Equal base GDP.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/554'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00554/article_deploy/html/images/algorithms-17-00554-g010-550.jpg?1733390674" title=" <strong>Figure 10</strong><br/> <p>After tax (<span class="html-italic">Z</span>), the maximum expected country benefits per unit GDP size (<span class="html-italic">X<sub>_Max</sub>/GDP_</span>)—Equal base GDP.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/554'>Full article</a></strong> "></a></div> </div> </div> </div> <div class="expanding-div collapsed"> <div class="generic-item article-item"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 11 pages, 2651 KiB   </span> <a href="/1999-4893/17/12/553/pdf?version=1733213485" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="m-QAM Receiver Based on Data Stream Spectral Clustering for Optical Channels Dominated by Nonlinear Phase Noise" data-journal="algorithms"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Article</span></div> <a class="title-link" href="/1999-4893/17/12/553">m-QAM Receiver Based on Data Stream Spectral Clustering for Optical Channels Dominated by Nonlinear Phase Noise</a> <div class="authors"> by <span class="inlineblock "><strong>Miguel Solarte-Sanchez</strong>, </span><span class="inlineblock "><strong>David Marquez-Viloria</strong>, </span><span class="inlineblock "><strong>Andrés E. Castro-Ospina</strong>, </span><span class="inlineblock "><strong>Erick Reyes-Vera</strong>, </span><span class="inlineblock "><strong>Neil Guerrero-Gonzalez</strong> and </span><span class="inlineblock "><strong>Juan Botero-Valencia</strong></span> </div> <div class="color-grey-dark"> <em>Algorithms</em> <b>2024</b>, <em>17</em>(12), 553; <a href="https://doi.org/10.3390/a17120553">https://doi.org/10.3390/a17120553</a> - 3 Dec 2024 </div> <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> Optical communication systems face challenges like nonlinear noises, particularly Kerr-induced phase noise, which worsens with higher-order m-QAM formats due to their dense data-symbol sets. Advanced signal processing, including machine learning, is increasingly used to enhance signal integrity during demodulation. This paper explores the <a href="#" data-counterslink = "https://www.mdpi.com/1999-4893/17/12/553/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> Optical communication systems face challenges like nonlinear noises, particularly Kerr-induced phase noise, which worsens with higher-order m-QAM formats due to their dense data-symbol sets. Advanced signal processing, including machine learning, is increasingly used to enhance signal integrity during demodulation. This paper explores the application of a spectral clustering algorithm adapted to deal with data streaming to mitigate nonlinear noise in long-haul optical channels dominated by nonlinear phase noise, offering a promising solution to a pressing issue. The spectral clustering algorithm was adapted to handle data streams, enabling potential real-time applications. Additionally, it was combined with a demapping process for m-QAM to resolve labeling inconsistencies when processing windowed data. We demonstrate that the spectral clustering algorithm outperforms the k-means algorithm in the face of nonlinear phase noise in −90, −100, and −110 dBc/Hz scenarios at 1 MHz in a simulated 10 GHz symbol rate channel. <a href="/1999-4893/17/12/553">Full article</a> </div> </div> <div class="belongsTo" style="margin-bottom: 10px;"> (This article belongs to the Section <a href="/journal/algorithms/sections/Algorithms_for_Multidisciplinary_Applications">Algorithms for Multidisciplinary Applications</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/1999-4893/17/12/553/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1535535"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1535535"><i class="fa fa-caret-right"></i></div><div class="absgraph cycle-slideshow manual" data-cycle-fx="scrollHorz" data-cycle-timeout="0" data-cycle-next="#next1535535" data-cycle-prev="#prev1535535" data-cycle-progressive="#images1535535" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1535535-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/algorithms/algorithms-17-00553/article_deploy/html/images/algorithms-17-00553-g001-550.jpg?1733213607" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1535535" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1535535-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00553/article_deploy/html/images/algorithms-17-00553-g002-550.jpg?1733213608'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1535535-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00553/article_deploy/html/images/algorithms-17-00553-g003-550.jpg?1733213610'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-1535535-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00553/article_deploy/html/images/algorithms-17-00553-g004-550.jpg?1733213611'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-1535535-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00553/article_deploy/html/images/algorithms-17-00553-g005-550.jpg?1733213612'><p>Figure 5</p></div> --- <div class='openpopupgallery' data-imgindex='5' data-target='article-1535535-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00553/article_deploy/html/images/algorithms-17-00553-g006-550.jpg?1733213613'><p>Figure 6</p></div> --- <div class='openpopupgallery' data-imgindex='6' data-target='article-1535535-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00553/article_deploy/html/images/algorithms-17-00553-g007-550.jpg?1733213614'><p>Figure 7</p></div> --- <div class='openpopupgallery' data-imgindex='7' data-target='article-1535535-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00553/article_deploy/html/images/algorithms-17-00553-g008-550.jpg?1733213615'><p>Figure 8</p></div></script></div></div><div id="article-1535535-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00553/article_deploy/html/images/algorithms-17-00553-g001-550.jpg?1733213607" title=" <strong>Figure 1</strong><br/> <p>Effect of spectral clustering algorithm on a synthetic data set.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/553'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00553/article_deploy/html/images/algorithms-17-00553-g002-550.jpg?1733213608" title=" <strong>Figure 2</strong><br/> <p>Effect of k-means and spectral clustering algorithm on a synthetic data set. (<b>a</b>) Clustering synthetic data by directly applying k-means. (<b>b</b>) Clustering of the data by applying k-means on the space described by the matrix Y. (<b>c</b>) Clustering of the original synthetic data set.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/553'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00553/article_deploy/html/images/algorithms-17-00553-g003-550.jpg?1733213610" title=" <strong>Figure 3</strong><br/> <p>Label inconsistency.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/553'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00553/article_deploy/html/images/algorithms-17-00553-g004-550.jpg?1733213611" title=" <strong>Figure 4</strong><br/> <p>Spectral clustering algorithm with demapping step.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/553'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00553/article_deploy/html/images/algorithms-17-00553-g005-550.jpg?1733213612" title=" <strong>Figure 5</strong><br/> <p>Relabeling data for demapping. (<b>a</b>) Mapping for 16-QAM constellation. (<b>b</b>) 16-QAM constellation with noise. (<b>c</b>) Demapping of 16-QAM constellation.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/553'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00553/article_deploy/html/images/algorithms-17-00553-g006-550.jpg?1733213613" title=" <strong>Figure 6</strong><br/> <p>SNR vs. BER performance of spectral clustering and k-means algorithm for 16-QAM.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/553'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00553/article_deploy/html/images/algorithms-17-00553-g007-550.jpg?1733213614" title=" <strong>Figure 7</strong><br/> <p>SNR vs. BER performance of spectral clustering and k-means algorithms for 32-QAM.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/553'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00553/article_deploy/html/images/algorithms-17-00553-g008-550.jpg?1733213615" title=" <strong>Figure 8</strong><br/> <p>SNR vs. BER performance of spectral clustering and k-means algorithms for 64-QAM.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/553'>Full article</a></strong> "></a></div> </div> </div> </div> <div class="expanding-div collapsed"> <div class="generic-item article-item"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 19 pages, 556 KiB   </span> <a href="/1999-4893/17/12/552/pdf?version=1733211201" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="A Temporal Graph Network Algorithm for Detecting Fraudulent Transactions on Online Payment Platforms" data-journal="algorithms"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Article</span></div> <a class="title-link" href="/1999-4893/17/12/552">A Temporal Graph Network Algorithm for Detecting Fraudulent Transactions on Online Payment Platforms</a> <div class="authors"> by <span class="inlineblock "><strong>Diego Saldaña-Ulloa</strong>, </span><span class="inlineblock "><strong>Guillermo De Ita Luna</strong> and </span><span class="inlineblock "><strong>J. Raymundo Marcial-Romero</strong></span> </div> <div class="color-grey-dark"> <em>Algorithms</em> <b>2024</b>, <em>17</em>(12), 552; <a href="https://doi.org/10.3390/a17120552">https://doi.org/10.3390/a17120552</a> - 3 Dec 2024 </div> <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> A temporal graph network (TGN) algorithm is introduced to identify fraudulent activities within a digital platform. The central premise is that digital transactions can be modeled via a graph network where various entities interact. The data used to build an event-based temporal graph <a href="#" data-counterslink = "https://www.mdpi.com/1999-4893/17/12/552/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> A temporal graph network (TGN) algorithm is introduced to identify fraudulent activities within a digital platform. The central premise is that digital transactions can be modeled via a graph network where various entities interact. The data used to build an event-based temporal graph (ETG) were sourced from an online payment platform and include details such as users, cards, devices, bank accounts, and features related to all these entities. Based on these data, seven distinct graphs were created; the first three represent individual interaction events (card registration, device registration, and bank account registration), while the remaining four are combinations of these graphs (card–device, card–bank account, device–bank account, and card–device–bank account registration). This approach was adopted to determine if the graph’s structure influenced the detection of fraudulent transactions. The results demonstrate that integrating more interaction events into the graph enhances the metrics, meaning graphs containing more interaction events yield superior fraud detection results than those based on individual events. In addition, the data used in this work correspond to Latin American payment transactions, which is relevant in the context of fraud detection since this region has the highest fraud rate in the world, yet few studies have focused on this issue. <a href="/1999-4893/17/12/552">Full article</a> </div> </div> <div class="belongsTo" style="margin-bottom: 10px;"> (This article belongs to the Section <a href="/journal/algorithms/sections/evolutionary_algorithms_and_machine_learning">Evolutionary Algorithms and Machine Learning</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/1999-4893/17/12/552/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1535492"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1535492"><i class="fa fa-caret-right"></i></div><div class="absgraph cycle-slideshow manual" data-cycle-fx="scrollHorz" data-cycle-timeout="0" data-cycle-next="#next1535492" data-cycle-prev="#prev1535492" data-cycle-progressive="#images1535492" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1535492-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/algorithms/algorithms-17-00552/article_deploy/html/images/algorithms-17-00552-ag-550.jpg?1733211326" alt="" style="border: 0;"><p>Graphical abstract</p></div><script id="images1535492" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1535492-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00552/article_deploy/html/images/algorithms-17-00552-g001-550.jpg?1733211322'><p>Figure 1</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1535492-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00552/article_deploy/html/images/algorithms-17-00552-g002-550.jpg?1733211323'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-1535492-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00552/article_deploy/html/images/algorithms-17-00552-g003-550.jpg?1733211324'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-1535492-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00552/article_deploy/html/images/algorithms-17-00552-g004a-550.jpg?1733211325'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='5' data-target='article-1535492-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00552/article_deploy/html/images/algorithms-17-00552-g004b-550.jpg?1733211326'><p>Figure 4 Cont.</p></div></script></div></div><div id="article-1535492-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00552/article_deploy/html/images/algorithms-17-00552-ag-550.jpg?1733211326" title=" <strong>Graphical abstract</strong><br/><strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/552'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00552/article_deploy/html/images/algorithms-17-00552-g001-550.jpg?1733211322" title=" <strong>Figure 1</strong><br/> <p>Diagram of the process followed by the MPTGNN. The process begins with the raw data provided by the payment platform. The data must be converted to an ETG to be processed by the algorithm. The steps described here summarize Algorithm 1.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/552'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00552/article_deploy/html/images/algorithms-17-00552-g002-550.jpg?1733211323" title=" <strong>Figure 2</strong><br/> <p>An example schema of the temporal graph with timestamps. The graph includes four distinct types of vertices: users (brown), devices (blue), cards (green), and bank accounts (orange). This particular graph represents the card–device–bank account registration, where each edge has an associated timestamp.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/552'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00552/article_deploy/html/images/algorithms-17-00552-g003-550.jpg?1733211324" title=" <strong>Figure 3</strong><br/> <p>Sample subgraph of the cards–devices–bank accounts graph. Gray vertices represent users, blue vertices are devices, green vertices are cards, and purple vertices are bank accounts. In this sample, it can be observed that users share devices (blue vertices) and bank accounts (purple vertices) more prominently than they do cards (green vertices). In addition, some users (gray vertices) act as bridges (long edges) between some connected components of the graph. Each edge has a corresponding timestamp (not shown in this image). This is not a general behavior; the figure is only intended as an example.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/552'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00552/article_deploy/html/images/algorithms-17-00552-g004a-550.jpg?1733211325" title=" <strong>Figure 4</strong><br/> <p>AUC vs. edge-to-vertex ratio for the graphs with 43 edge features and 118 edge features. The considered features are related to user behaviors and transaction information in the online payment platform. It can be seen that if the graph density increases, the AUC increases linearly. The coefficient of determination <math display="inline"><semantics> <msup> <mi>R</mi> <mn>2</mn> </msup> </semantics></math> is also reported. This also shows that aggregating more events (structural information) helps produce better metric results.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/552'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00552/article_deploy/html/images/algorithms-17-00552-g004b-550.jpg?1733211326" title=" <strong>Figure 4 Cont.</strong><br/> <p>AUC vs. edge-to-vertex ratio for the graphs with 43 edge features and 118 edge features. The considered features are related to user behaviors and transaction information in the online payment platform. It can be seen that if the graph density increases, the AUC increases linearly. The coefficient of determination <math display="inline"><semantics> <msup> <mi>R</mi> <mn>2</mn> </msup> </semantics></math> is also reported. This also shows that aggregating more events (structural information) helps produce better metric results.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/552'>Full article</a></strong> "></a></div> </div> </div> </div> <div class="expanding-div collapsed"> <div class="generic-item article-item"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 23 pages, 1530 KiB   </span> <a href="/1999-4893/17/12/551/pdf?version=1733207716" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Ellipsoidal K-Means: An Automatic Clustering Approach for Non-Uniform Data Distributions" data-journal="algorithms"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Article</span></div> <a class="title-link" href="/1999-4893/17/12/551">Ellipsoidal <i>K</i>-Means: An Automatic Clustering Approach for Non-Uniform Data Distributions</a> <div class="authors"> by <span class="inlineblock "><strong>Alaa E. Abdel-Hakim</strong>, </span><span class="inlineblock "><strong>Abdel-Monem M. Ibrahim</strong>, </span><span class="inlineblock "><strong>Kheir Eddine Bouazza</strong>, </span><span class="inlineblock "><strong>Wael Deabes</strong> and </span><span class="inlineblock "><strong>Abdel-Rahman Hedar</strong></span> </div> <div class="color-grey-dark"> <em>Algorithms</em> <b>2024</b>, <em>17</em>(12), 551; <a href="https://doi.org/10.3390/a17120551">https://doi.org/10.3390/a17120551</a> - 3 Dec 2024 </div> <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> Traditional <i>K</i>-means clustering assumes, to some extent, a uniform distribution of data around predefined centroids, which limits its effectiveness for many realistic datasets. In this paper, a new clustering technique, simulated-annealing-based ellipsoidal clustering (SAELLC), is proposed to automatically partition data into an <a href="#" data-counterslink = "https://www.mdpi.com/1999-4893/17/12/551/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> Traditional <i>K</i>-means clustering assumes, to some extent, a uniform distribution of data around predefined centroids, which limits its effectiveness for many realistic datasets. In this paper, a new clustering technique, simulated-annealing-based ellipsoidal clustering (SAELLC), is proposed to automatically partition data into an optimal number of ellipsoidal clusters, a capability absent in traditional methods. SAELLC transforms each identified cluster into a hyperspherical cluster, where the diameter of the hypersphere equals the minor axis of the original ellipsoid, and the center is encoded to represent the entire cluster. During the assignment of points to clusters, local ellipsoidal properties are independently considered. For objective function evaluation, the method adaptively transforms these ellipsoidal clusters into a variable number of global clusters. Two objective functions are simultaneously optimized: one reflecting partition compactness using the silhouette function (SF) and Euclidean distance, and another addressing cluster connectedness through a nearest-neighbor algorithm. This optimization is achieved using a newly-developed multiobjective simulated annealing approach. SAELLC is designed to automatically determine the optimal number of clusters, achieve precise partitioning, and accommodate a wide range of cluster shapes, including spherical, ellipsoidal, and non-symmetric forms. Extensive experiments conducted on UCI datasets demonstrated SAELLC’s superior performance compared to six well-known clustering algorithms. The results highlight its remarkable ability to handle diverse data distributions and automatically identify the optimal number of clusters, making it a robust choice for advanced clustering analysis. <a href="/1999-4893/17/12/551">Full article</a> </div> </div> <div class="belongsTo" style="margin-bottom: 10px;"> (This article belongs to the Section <a href="/journal/algorithms/sections/algorithms_analysis_complexity_theory">Analysis of Algorithms and Complexity Theory</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/1999-4893/17/12/551/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1535419"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1535419"><i class="fa fa-caret-right"></i></div><div class="absgraph cycle-slideshow manual" data-cycle-fx="scrollHorz" data-cycle-timeout="0" data-cycle-next="#next1535419" data-cycle-prev="#prev1535419" data-cycle-progressive="#images1535419" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1535419-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/algorithms/algorithms-17-00551/article_deploy/html/images/algorithms-17-00551-g001-550.jpg?1733207829" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1535419" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1535419-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00551/article_deploy/html/images/algorithms-17-00551-g002-550.jpg?1733207830'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1535419-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00551/article_deploy/html/images/algorithms-17-00551-g003-550.jpg?1733207831'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-1535419-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00551/article_deploy/html/images/algorithms-17-00551-g004-550.jpg?1733207832'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-1535419-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00551/article_deploy/html/images/algorithms-17-00551-g005-550.jpg?1733207833'><p>Figure 5</p></div> --- <div class='openpopupgallery' data-imgindex='5' data-target='article-1535419-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00551/article_deploy/html/images/algorithms-17-00551-g006-550.jpg?1733207834'><p>Figure 6</p></div> --- <div class='openpopupgallery' data-imgindex='6' data-target='article-1535419-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00551/article_deploy/html/images/algorithms-17-00551-g007-550.jpg?1733207835'><p>Figure 7</p></div> --- <div class='openpopupgallery' data-imgindex='7' data-target='article-1535419-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00551/article_deploy/html/images/algorithms-17-00551-g008-550.jpg?1733207836'><p>Figure 8</p></div> --- <div class='openpopupgallery' data-imgindex='8' data-target='article-1535419-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00551/article_deploy/html/images/algorithms-17-00551-g009-550.jpg?1733207837'><p>Figure 9</p></div> --- <div class='openpopupgallery' data-imgindex='9' data-target='article-1535419-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00551/article_deploy/html/images/algorithms-17-00551-g010-550.jpg?1733207839'><p>Figure 10</p></div> --- <div class='openpopupgallery' data-imgindex='10' data-target='article-1535419-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00551/article_deploy/html/images/algorithms-17-00551-g011-550.jpg?1733207840'><p>Figure 11</p></div> --- <div class='openpopupgallery' data-imgindex='11' data-target='article-1535419-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00551/article_deploy/html/images/algorithms-17-00551-g012-550.jpg?1733207842'><p>Figure 12</p></div></script></div></div><div id="article-1535419-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00551/article_deploy/html/images/algorithms-17-00551-g001-550.jpg?1733207829" title=" <strong>Figure 1</strong><br/> <p>(<b>a</b>) Ell_2_2 represents an artificial dataset comprising two groups of data points generated using a Gaussian distribution. (<b>b</b>) Three clusters resulting from the application of the silhouette function (SF) measure on Ell_2_2.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/551'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00551/article_deploy/html/images/algorithms-17-00551-g002-550.jpg?1733207830" title=" <strong>Figure 2</strong><br/> <p>(<b>a</b>) Ell_3_2 is a synthetic dataset consisting of three groups of data points resembling a percent mark generated by Gaussian distribution. (<b>b</b>) Dataset_3_2 is a synthetic dataset consisting of three clusters, as utilized in [<a href="#B28-algorithms-17-00551" class="html-bibr">28</a>].</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/551'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00551/article_deploy/html/images/algorithms-17-00551-g003-550.jpg?1733207831" title=" <strong>Figure 3</strong><br/> <p>Clustered Ell_2_2 for <math display="inline"><semantics> <mrow> <mi>K</mi> <mo>=</mo> <mn>2</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>K</mi> <mo>=</mo> <mn>3</mn> </mrow> </semantics></math> for Ell_3_2, (<b>a</b>,<b>c</b>) represent the data after applying 2-D transformation. (<b>b</b>,<b>d</b>) show the data after applying N-D transformation Equation (<a href="#FD13-algorithms-17-00551" class="html-disp-formula">13</a>).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/551'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00551/article_deploy/html/images/algorithms-17-00551-g004-550.jpg?1733207832" title=" <strong>Figure 4</strong><br/> <p>(<b>a</b>) Illustration of the computed two perpendicular vectors <math display="inline"><semantics> <mover accent="true"> <mi>r</mi> <mo>¯</mo> </mover> </semantics></math> on the major vector <math display="inline"><semantics> <mover accent="true"> <mi>v</mi> <mo>¯</mo> </mover> </semantics></math> of the cluster in three dimensions. There are also two symmetrical (reflected) vectors for <math display="inline"><semantics> <mover accent="true"> <mi>r</mi> <mo>¯</mo> </mover> </semantics></math>. (<b>b</b>) Illustration of how to calculate the optimal minor axis <span class="html-italic">b</span> for any cluster, ensuring that the ellipsoidal cluster does not interfere with an established spherical cluster if the ellipsoidal cluster’s diameter exceeds the exact minor axis.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/551'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00551/article_deploy/html/images/algorithms-17-00551-g005-550.jpg?1733207833" title=" <strong>Figure 5</strong><br/> <p>Results after applying <math display="inline"><semantics> <mrow> <mi>E</mi> <mi>S</mi> <mi>F</mi> </mrow> </semantics></math> on Dataset_3_2, (<b>a</b>) shows Dataset_3_2 clustered for two clusters without using the nearest-neighbor algorithm. (<b>b</b>) shows Dataset_3_2 clustered using the nearest-neighbor distance <math display="inline"><semantics> <mrow> <mi>E</mi> <mi>S</mi> <mi>F</mi> <mo>-</mo> <mi>N</mi> <mi>N</mi> </mrow> </semantics></math>.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/551'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00551/article_deploy/html/images/algorithms-17-00551-g006-550.jpg?1733207834" title=" <strong>Figure 6</strong><br/> <p>Performance comparison between SAELLC and six known clustering methods is then conducted for this four artificial datasets.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/551'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00551/article_deploy/html/images/algorithms-17-00551-g007-550.jpg?1733207835" title=" <strong>Figure 7</strong><br/> <p>Other four artificial datasets used in comparison.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/551'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00551/article_deploy/html/images/algorithms-17-00551-g008-550.jpg?1733207836" title=" <strong>Figure 8</strong><br/> <p>Automatically clustered Data_5_2 for <math display="inline"><semantics> <mrow> <mi>K</mi> <mo>=</mo> <mn>4</mn> </mrow> </semantics></math> and Data_10_2 for <math display="inline"><semantics> <mrow> <mi>K</mi> <mo>=</mo> <mn>10</mn> </mrow> </semantics></math> after application of SAELLC clustering technique; (<b>a</b>,<b>c</b>) illustrate transformed spherical clusters and (<b>b</b>,<b>d</b>) show points belonging to each cluster.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/551'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00551/article_deploy/html/images/algorithms-17-00551-g009-550.jpg?1733207837" title=" <strong>Figure 9</strong><br/> <p>Automatically clustered Mixed_3_2 for <math display="inline"><semantics> <mrow> <mi>K</mi> <mo>=</mo> <mn>3</mn> </mrow> </semantics></math> and Sym_3_2 for <math display="inline"><semantics> <mrow> <mi>K</mi> <mo>=</mo> <mn>3</mn> </mrow> </semantics></math> after application of SAELLC clustering technique; (<b>a</b>,<b>c</b>) illustrate transformed spherical clusters and (<b>d</b>,<b>b</b>) show points belonging to each cluster.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/551'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00551/article_deploy/html/images/algorithms-17-00551-g010-550.jpg?1733207839" title=" <strong>Figure 10</strong><br/> <p>Automatically clustered Ellip_2_2 for <math display="inline"><semantics> <mrow> <mi>K</mi> <mo>=</mo> <mn>2</mn> </mrow> </semantics></math> and Square1 for <math display="inline"><semantics> <mrow> <mi>K</mi> <mo>=</mo> <mn>4</mn> </mrow> </semantics></math> after application of SAELLC clustering technique; (<b>a</b>,<b>c</b>) illustrate transformed spherical clusters and (<b>b</b>,<b>d</b>) show points belonging to each cluster.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/551'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00551/article_deploy/html/images/algorithms-17-00551-g011-550.jpg?1733207840" title=" <strong>Figure 11</strong><br/> <p>Automatically clustered Square4 for <math display="inline"><semantics> <mrow> <mi>K</mi> <mo>=</mo> <mn>4</mn> </mrow> </semantics></math> and Sizes5 for <math display="inline"><semantics> <mrow> <mi>K</mi> <mo>=</mo> <mn>4</mn> </mrow> </semantics></math> after application of SAELLC clustering technique; (<b>a</b>,<b>c</b>) illustrate transformed spherical clusters and (<b>b</b>,<b>d</b>) show points belonging to each cluster.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/551'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00551/article_deploy/html/images/algorithms-17-00551-g012-550.jpg?1733207842" title=" <strong>Figure 12</strong><br/> <p>IoV deployment is a real-life example, which illustrates how traditional spherical clusters would fail to accommodate such diverse distribution patterns of data points.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/551'>Full article</a></strong> "></a></div> </div> </div> </div> <div class="expanding-div collapsed"> <div class="generic-item article-item"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 24 pages, 2247 KiB   </span> <a href="/1999-4893/17/12/550/pdf?version=1733365269" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Enhancing Intrusion Detection Systems with Dimensionality Reduction and Multi-Stacking Ensemble Techniques" data-journal="algorithms"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Article</span></div> <a class="title-link" href="/1999-4893/17/12/550">Enhancing Intrusion Detection Systems with Dimensionality Reduction and Multi-Stacking Ensemble Techniques</a> <div class="authors"> by <span class="inlineblock "><strong>Ali Mohammed Alsaffar</strong>, </span><span class="inlineblock "><strong>Mostafa Nouri-Baygi</strong> and </span><span class="inlineblock "><strong>Hamed Zolbanin</strong></span> </div> <div class="color-grey-dark"> <em>Algorithms</em> <b>2024</b>, <em>17</em>(12), 550; <a href="https://doi.org/10.3390/a17120550">https://doi.org/10.3390/a17120550</a> - 3 Dec 2024 </div> <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> The deployment of intrusion detection systems (IDSs) is essential for protecting network resources and infrastructure against malicious threats. Despite the wide use of various machine learning methods in IDSs, such systems often struggle to achieve optimal performance. The key challenges include the curse <a href="#" data-counterslink = "https://www.mdpi.com/1999-4893/17/12/550/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> The deployment of intrusion detection systems (IDSs) is essential for protecting network resources and infrastructure against malicious threats. Despite the wide use of various machine learning methods in IDSs, such systems often struggle to achieve optimal performance. The key challenges include the curse of dimensionality, which significantly impacts IDS efficacy, and the limited effectiveness of singular learning classifiers in handling complex, imbalanced, and multi-categorical traffic datasets. To overcome these limitations, this paper presents an innovative approach that integrates dimensionality reduction and stacking ensemble techniques. We employ the LogitBoost algorithm with XGBRegressor for feature selection, complemented by a Residual Network (ResNet) deep learning model for feature extraction. Furthermore, we introduce multi-stacking ensemble (MSE), a novel ensemble method, to enhance attack prediction capabilities. The evaluation on benchmark datasets such as CICIDS2017 and UNSW-NB15 demonstrates that our IDS surpasses current models across various performance metrics. <a href="/1999-4893/17/12/550">Full article</a> </div> </div> <div class="belongsTo" style="margin-bottom: 10px;"> (This article belongs to the Section <a href="/journal/algorithms/sections/evolutionary_algorithms_and_machine_learning">Evolutionary Algorithms and Machine Learning</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/1999-4893/17/12/550/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1535409"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1535409"><i class="fa fa-caret-right"></i></div><div class="absgraph cycle-slideshow manual" data-cycle-fx="scrollHorz" data-cycle-timeout="0" data-cycle-next="#next1535409" data-cycle-prev="#prev1535409" data-cycle-progressive="#images1535409" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1535409-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/algorithms/algorithms-17-00550/article_deploy/html/images/algorithms-17-00550-g001-550.jpg?1733365335" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1535409" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1535409-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00550/article_deploy/html/images/algorithms-17-00550-g002-550.jpg?1733365336'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1535409-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00550/article_deploy/html/images/algorithms-17-00550-g003-550.jpg?1733365338'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-1535409-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00550/article_deploy/html/images/algorithms-17-00550-g004-550.jpg?1733365339'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-1535409-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00550/article_deploy/html/images/algorithms-17-00550-g005-550.jpg?1733365340'><p>Figure 5</p></div> --- <div class='openpopupgallery' data-imgindex='5' data-target='article-1535409-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00550/article_deploy/html/images/algorithms-17-00550-g006-550.jpg?1733365342'><p>Figure 6</p></div> --- <div class='openpopupgallery' data-imgindex='6' data-target='article-1535409-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00550/article_deploy/html/images/algorithms-17-00550-g007-550.jpg?1733365344'><p>Figure 7</p></div></script></div></div><div id="article-1535409-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00550/article_deploy/html/images/algorithms-17-00550-g001-550.jpg?1733365335" title=" <strong>Figure 1</strong><br/> <p>The proposed method.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/550'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00550/article_deploy/html/images/algorithms-17-00550-g002-550.jpg?1733365336" title=" <strong>Figure 2</strong><br/> <p>Imbalance ratio of categories in <span class="html-italic">D1</span>.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/550'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00550/article_deploy/html/images/algorithms-17-00550-g003-550.jpg?1733365338" title=" <strong>Figure 3</strong><br/> <p>Imbalance ratio of categories in <span class="html-italic">D2</span>.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/550'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00550/article_deploy/html/images/algorithms-17-00550-g004-550.jpg?1733365339" title=" <strong>Figure 4</strong><br/> <p>Structure of ResNet.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/550'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00550/article_deploy/html/images/algorithms-17-00550-g005-550.jpg?1733365340" title=" <strong>Figure 5</strong><br/> <p>Structure of the residual block.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/550'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00550/article_deploy/html/images/algorithms-17-00550-g006-550.jpg?1733365342" title=" <strong>Figure 6</strong><br/> <p>Multiclass confusion matrix for <span class="html-italic">D1</span>.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/550'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00550/article_deploy/html/images/algorithms-17-00550-g007-550.jpg?1733365344" title=" <strong>Figure 7</strong><br/> <p>Multiclass confusion matrix for <span class="html-italic">D2</span>.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/550'>Full article</a></strong> "></a></div> </div> </div> </div> <div class="expanding-div collapsed"> <div class="generic-item article-item"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 17 pages, 1893 KiB   </span> <a href="/1999-4893/17/12/549/pdf?version=1733150955" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="New Insights into Fuzzy Genetic Algorithms for Optimization Problems" data-journal="algorithms"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Article</span></div> <a class="title-link" href="/1999-4893/17/12/549">New Insights into Fuzzy Genetic Algorithms for Optimization Problems</a> <div class="authors"> by <span class="inlineblock "><strong>Oleksandr Syzonov</strong>, </span><span class="inlineblock "><strong>Stefania Tomasiello</strong> and </span><span class="inlineblock "><strong>Nicola Capuano</strong></span> </div> <div class="color-grey-dark"> <em>Algorithms</em> <b>2024</b>, <em>17</em>(12), 549; <a href="https://doi.org/10.3390/a17120549">https://doi.org/10.3390/a17120549</a> - 2 Dec 2024 </div> <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> In this paper, we shed light on the use of two types of fuzzy genetic algorithms, which stand out from the literature due to the innovative ideas behind them. One is the Gendered Fuzzy Genetic Algorithm, where the crossover mechanism is regulated by <a href="#" data-counterslink = "https://www.mdpi.com/1999-4893/17/12/549/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> In this paper, we shed light on the use of two types of fuzzy genetic algorithms, which stand out from the literature due to the innovative ideas behind them. One is the Gendered Fuzzy Genetic Algorithm, where the crossover mechanism is regulated by the gender and the age of the population to generate offspring through proper fuzzy rules. The other one is the Elegant Fuzzy Genetic Algorithm, where the priority of the parent genome is updated based on the child’s fitness. Both algorithms present a significant computational burden. To speed up the computation, we propose to adopt a nearest-neighbor caching strategy. We first performed several experiments, using some well-known benchmark functions, and tried different types of membership functions and logical connectives. Afterward, some additional benchmarks were retrieved from the literature for a fair comparison against published results, which were obtained by means of former variants of fuzzy genetic algorithms. A real-world application problem, which was retrieved from the literature and dealt with rice production, was also tackled. All the numerical results show the potential of the proposed strategy. <a href="/1999-4893/17/12/549">Full article</a> </div> </div> <div class="belongsTo" style="margin-bottom: 10px;"> (This article belongs to the Special Issue <a href=" /journal/algorithms/special_issues/1XMDC2A8II ">Numerical Optimization and Algorithms: 2nd Edition</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/1999-4893/17/12/549/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1535217"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1535217"><i class="fa fa-caret-right"></i></div><div class="absgraph cycle-slideshow manual" data-cycle-fx="scrollHorz" data-cycle-timeout="0" data-cycle-next="#next1535217" data-cycle-prev="#prev1535217" data-cycle-progressive="#images1535217" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1535217-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/algorithms/algorithms-17-00549/article_deploy/html/images/algorithms-17-00549-g001-550.jpg?1733151098" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1535217" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1535217-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00549/article_deploy/html/images/algorithms-17-00549-g002-550.jpg?1733151099'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1535217-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00549/article_deploy/html/images/algorithms-17-00549-g003-550.jpg?1733151100'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-1535217-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00549/article_deploy/html/images/algorithms-17-00549-g004-550.jpg?1733151100'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-1535217-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00549/article_deploy/html/images/algorithms-17-00549-g005-550.jpg?1733151103'><p>Figure 5</p></div> --- <div class='openpopupgallery' data-imgindex='5' data-target='article-1535217-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00549/article_deploy/html/images/algorithms-17-00549-g006-550.jpg?1733151105'><p>Figure 6</p></div> --- <div class='openpopupgallery' data-imgindex='6' data-target='article-1535217-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00549/article_deploy/html/images/algorithms-17-00549-g007-550.jpg?1733151106'><p>Figure 7</p></div> --- <div class='openpopupgallery' data-imgindex='7' data-target='article-1535217-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00549/article_deploy/html/images/algorithms-17-00549-g008-550.jpg?1733151109'><p>Figure 8</p></div> --- <div class='openpopupgallery' data-imgindex='8' data-target='article-1535217-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00549/article_deploy/html/images/algorithms-17-00549-g009-550.jpg?1733151110'><p>Figure 9</p></div> --- <div class='openpopupgallery' data-imgindex='9' data-target='article-1535217-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00549/article_deploy/html/images/algorithms-17-00549-g010-550.jpg?1733151111'><p>Figure 10</p></div> --- <div class='openpopupgallery' data-imgindex='10' data-target='article-1535217-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00549/article_deploy/html/images/algorithms-17-00549-g011-550.jpg?1733151112'><p>Figure 11</p></div></script></div></div><div id="article-1535217-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00549/article_deploy/html/images/algorithms-17-00549-g001-550.jpg?1733151098" title=" <strong>Figure 1</strong><br/> <p>Linguistic variable, terms, and syntactic rules.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/549'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00549/article_deploy/html/images/algorithms-17-00549-g002-550.jpg?1733151099" title=" <strong>Figure 2</strong><br/> <p>A simple example of a min-heap scheme.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/549'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00549/article_deploy/html/images/algorithms-17-00549-g003-550.jpg?1733151100" title=" <strong>Figure 3</strong><br/> <p>Fitness linguistic variable with trapezoidal MFs (<math display="inline"><semantics> <mrow> <msub> <mi>N</mi> <mrow> <mi>f</mi> <mi>i</mi> <mi>t</mi> <mi>n</mi> <mi>e</mi> <mi>s</mi> <mi>s</mi> </mrow> </msub> <mo>=</mo> <mn>5</mn> </mrow> </semantics></math>).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/549'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00549/article_deploy/html/images/algorithms-17-00549-g004-550.jpg?1733151100" title=" <strong>Figure 4</strong><br/> <p>Diversity linguistic variable with trapezoidal MFs (<math display="inline"><semantics> <mrow> <msub> <mi>N</mi> <mrow> <mi>d</mi> <mi>i</mi> <mi>v</mi> <mi>e</mi> <mi>r</mi> <mi>s</mi> <mi>i</mi> <mi>t</mi> <mi>y</mi> </mrow> </msub> <mo>=</mo> <mn>5</mn> </mrow> </semantics></math>).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/549'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00549/article_deploy/html/images/algorithms-17-00549-g005-550.jpg?1733151103" title=" <strong>Figure 5</strong><br/> <p>First set of benchmark functions. Average fitness value for the following: (<b>a</b>) D = 5; (<b>b</b>) D = 1000.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/549'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00549/article_deploy/html/images/algorithms-17-00549-g006-550.jpg?1733151105" title=" <strong>Figure 6</strong><br/> <p>Rastrigin function (D = 5). Average fitness value after 500 generations with the following: (<b>a</b>) Pi-shaped MF; (<b>b</b>) Trapezoidal MF.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/549'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00549/article_deploy/html/images/algorithms-17-00549-g007-550.jpg?1733151106" title=" <strong>Figure 7</strong><br/> <p>Comparison against state-of-the-art approaches in [<a href="#B24-algorithms-17-00549" class="html-bibr">24</a>] (<math display="inline"><semantics> <mrow> <mi>D</mi> <mo>=</mo> <mn>1000</mn> </mrow> </semantics></math>; 1000 generations) for the following: (<b>a</b>) The Ackley function; (<b>b</b>) The Rastrigin function; (<b>c</b>) The Shwefel function.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/549'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00549/article_deploy/html/images/algorithms-17-00549-g008-550.jpg?1733151109" title=" <strong>Figure 8</strong><br/> <p>Average fitness value over the generations for the following: (<b>a</b>) The <math display="inline"><semantics> <msub> <mi>g</mi> <mn>1</mn> </msub> </semantics></math> function; (<b>b</b>) The <math display="inline"><semantics> <msub> <mi>g</mi> <mn>2</mn> </msub> </semantics></math> function.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/549'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00549/article_deploy/html/images/algorithms-17-00549-g009-550.jpg?1733151110" title=" <strong>Figure 9</strong><br/> <p>TSP: shortest path length by each approach.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/549'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00549/article_deploy/html/images/algorithms-17-00549-g010-550.jpg?1733151111" title=" <strong>Figure 10</strong><br/> <p><span class="html-italic">r</span> vs. <span class="html-italic">p</span>.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/549'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00549/article_deploy/html/images/algorithms-17-00549-g011-550.jpg?1733151112" title=" <strong>Figure 11</strong><br/> <p>The rice production problem: best solutions.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/549'>Full article</a></strong> "></a></div> </div> </div> </div> <div class="expanding-div collapsed"> <div class="generic-item article-item"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 22 pages, 2453 KiB   </span> <a href="/1999-4893/17/12/548/pdf?version=1733136017" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="A Tertiary Study for Process Mining" data-journal="algorithms"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Review</span></div> <a class="title-link" href="/1999-4893/17/12/548">A Tertiary Study for Process Mining</a> <div class="authors"> by <span class="inlineblock "><strong>Elia Kouzari</strong> and </span><span class="inlineblock "><strong>Ioannis Stamelos</strong></span> </div> <div class="color-grey-dark"> <em>Algorithms</em> <b>2024</b>, <em>17</em>(12), 548; <a href="https://doi.org/10.3390/a17120548">https://doi.org/10.3390/a17120548</a> - 2 Dec 2024 </div> <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> Background: This tertiary study lists the secondary studies published in the process mining domain and provides an analysis related to a set of research questions. It is the first tertiary study in this area. The objective is to provide information about the available <a href="#" data-counterslink = "https://www.mdpi.com/1999-4893/17/12/548/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> Background: This tertiary study lists the secondary studies published in the process mining domain and provides an analysis related to a set of research questions. It is the first tertiary study in this area. The objective is to provide information about the available secondary studies in process mining, respond to research questions relating to the thematic areas covered in the studies, as well as trends regarding their quality, and report on findings for publication venues, citations, guidelines used, and demographics. Method: A tertiary study based on systematic secondary studies published up to March 2023. A total of 25 secondary studies related to process mining have been identified following the application of inclusion/exclusion criteria and quality assessment. Results: The most popular thematic areas addressed are technologies and applications for process mining and healthcare. The secondary studies in process mining have a medium quality score of 3.5. The guidelines introduced by Kitchenham over the years are preferred in secondary studies in this field. There is no trend related to the number of primary studies included in secondary studies in process mining. Conclusion: Although numerous secondary studies exist for process mining, there is still room for more research, specifically in the areas highlighted in this study. Future researchers can use this study for reference, and they can also use the listed research topics to dive deep into the issues identified. <a href="/1999-4893/17/12/548">Full article</a> </div> </div> <div class="belongsTo" style="margin-bottom: 10px;"> (This article belongs to the Section <a href="/journal/algorithms/sections/Algorithms_for_Multidisciplinary_Applications">Algorithms for Multidisciplinary Applications</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/1999-4893/17/12/548/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1535008"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1535008"><i class="fa fa-caret-right"></i></div><div class="absgraph cycle-slideshow manual" data-cycle-fx="scrollHorz" data-cycle-timeout="0" data-cycle-next="#next1535008" data-cycle-prev="#prev1535008" data-cycle-progressive="#images1535008" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1535008-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/algorithms/algorithms-17-00548/article_deploy/html/images/algorithms-17-00548-g001-550.jpg?1733136093" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1535008" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1535008-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00548/article_deploy/html/images/algorithms-17-00548-g002-550.jpg?1733136094'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1535008-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00548/article_deploy/html/images/algorithms-17-00548-g003-550.jpg?1733136096'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-1535008-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00548/article_deploy/html/images/algorithms-17-00548-g004-550.jpg?1733136097'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-1535008-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00548/article_deploy/html/images/algorithms-17-00548-g005-550.jpg?1733136099'><p>Figure 5</p></div> --- <div class='openpopupgallery' data-imgindex='5' data-target='article-1535008-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/algorithms/algorithms-17-00548/article_deploy/html/images/algorithms-17-00548-g006-550.jpg?1733136100'><p>Figure 6</p></div></script></div></div><div id="article-1535008-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00548/article_deploy/html/images/algorithms-17-00548-g001-550.jpg?1733136093" title=" <strong>Figure 1</strong><br/> <p>The procedure followed in this Tertiary Study.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/548'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00548/article_deploy/html/images/algorithms-17-00548-g002-550.jpg?1733136094" title=" <strong>Figure 2</strong><br/> <p>The search procedure followed.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/548'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00548/article_deploy/html/images/algorithms-17-00548-g003-550.jpg?1733136096" title=" <strong>Figure 3</strong><br/> <p>Average quality score for the included studies throughout the years.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/548'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00548/article_deploy/html/images/algorithms-17-00548-g004-550.jpg?1733136097" title=" <strong>Figure 4</strong><br/> <p>The most preferred online search engines for identifying primary studies.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/548'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00548/article_deploy/html/images/algorithms-17-00548-g005-550.jpg?1733136099" title=" <strong>Figure 5</strong><br/> <p>The distribution of the number of primary studies for the 25 studies included in this Tertiary Study.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/548'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/algorithms/algorithms-17-00548/article_deploy/html/images/algorithms-17-00548-g006-550.jpg?1733136100" title=" <strong>Figure 6</strong><br/> <p>A worldwide distribution of secondary studies in Process Mining (Figure created using MS Excel map representation of data).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1999-4893/17/12/548'>Full article</a></strong> "></a></div> </div> </div> </div> </div> <div class="generic-item last-item"> <a class="bold" href="/search?q=&journal=algorithms&sort=pubdate&page_count=50">More Articles...</a> </div> </div> </div> </div> <div id="left-column" class="content__column large-3 large-pull-6 medium-3 medium-pull-6 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Algorithms | An Open Access Journal from MDPI