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<html><head><title>EAI Endorsed Transactions on Industrial Networks and Intelligent Systems - EUDL</title><link rel="icon" href="/images/favicon.ico"><link rel="stylesheet" type="text/css" href="/css/screen.css"><link rel="stylesheet" href="/css/zenburn.css"><meta http-equiv="Content-Type" content="charset=utf-8"><meta name="viewport" content="width=device-width, initial-scale=1.0"><meta name="Description" content="Visit the new journal website to submit and consult our contents: https://publications.eai.eu/index.php/inis/index"><script type="text/javascript" src="https://services.eai.eu//load-signup-form/EAI"></script><script type="text/javascript" src="https://services.eai.eu//ujs/forms/signup/sso-client.js"></script><script type="text/javascript">if (!window.EUDL){ window.EUDL={} };EUDL.cas_url="https://account.eai.eu/cas";EUDL.profile_url="https://account.eai.eu";if(window.SSO){SSO.set_mode('eai')};</script><script type="text/javascript" src="/js/jquery.js"></script><script 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src="/attachment/67281"></section><section class="issn"><strong>ISSN: </strong>2410-0218</section><section class="subscribe link"><a href="/journal/inis/subscribe">Subscribe</a></section><section class="escripts link"><a href="https://escripts.eai.eu/paper/submit">Submit Article</a></section><section class="instructions link"><a href="/instructions">Submission Instructions</a></section><section class="openaccess link"><a href="/openaccess">Open Access Information</a></section><section class="ethics link"><a href="/ethics">Ethics and Malpractice Statement</a></section><section class="most-recent link"><a href="/issue/inis/12/1">Most Recent Issue</a></section><section class="browse-filters"><div class="browse-by"><a class="browse-link">2024<span class="pointer"></span></a><div class="filters"><a href="/issue/inis/12/1" class="filter">Issue 1</a></div><a class="browse-link">2024<span class="pointer"></span></a><div class="filters"><a href="/issue/inis/11/4" class="filter">Issue 4</a><a 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class="cover"><a href="https://eai.eu/eai-sponsorship/?mtm_campaign=call%20for%20bids&mtm_kwd=bids&mtm_source=organize%20conference%20page&mtm_medium=eudl"><img src="https://eudl.eu/images/banner-outside.png"></a></section></section><section class="info-and-search"><div class="manage-menu"><h2 class="blurb">Visit the new journal website to submit and consult our contents: https://publications.eai.eu/index.php/inis/index</h2><section class="editors"><strong>Editor(s)-in-Chief: </strong><span class="editor">Trung Q. Duong</span>, <span class="editor">Le Nguyen Bao</span> and <span class="editor">Nguyen-Son Vo</span></section><section class="meta-tabs"><div class="tabs"><ul><li><a name="aims-and-scope">Aims & Scope</a></li><li><a name="Indexing">Indexing</a></li><li><a name="EditorialBoard">Editorial Board</a></li></ul></div><div class="content"><div name="aims-and-scope"><div class="abstract"><p>EAI Endorsed Transactions on Industrial Networks and Intelligent Systems is open access, a peer-reviewed scholarly journal focused on ubiquitous computing, cloud computing, and cyber-physical system, all kinds of networks in large-scale factories, including a lot of traditional and new industries. The journal publishes research articles, review articles, commentaries, editorials, technical articles, and short communications with a quarterly frequency (four issues per year). Authors are not charged for article submission and processing. This journal is co-organized, and managed by Duy Tan University, Vietnam.</p> <p>INDEXING: Scopus (CiteScore: 3.1), Compendex, DOAJ, ProQuest, EBSCO, DBLP</p> <ul> <li>Applications of wireless sensor networks, body area networks in large-scale industrial applications, such as fault theories of wireless networks, including routing, network control and management, reliable transmission and architectures, etc.</li> <li>Applications of social networking, big data, ubiquitous computing, mobile computing, and cloud computing in various industries and services (e.g., intelligent systems enhanced by social networking, cloud-based industrial networks, cloud-assisted intelligent systems, etc.)</li> <li>Analysis of industrial control and communication networks, including network lifetime, security, network scalability, reliability, stability, etc.</li> <li>Design and choice of industrial, intelligent, application-specific network protocols and algorithms (e.g., EtherNet/IP, Ethernet Powerlink, EtherCAT, Modbus-TCP, Profinet, SERCOS III, etc.) at any communication layer</li> <li>Opportunistic networks in the industry, such as underwater sensor networks in sewage treatment systems, including establishing a temporary data transmission structure using available devices (e.g., underwater robot, surface data station, surface sink and under water sink), optimizing horizontal multi-hop data links (e.g., 3D data transmission), etc.</li> <li>Applications of intelligent systems in various industries, including collaborative systems, quality control, optimization, decision support, planning, high-level control concepts (e.g., multi-agent and holonic systems, service-oriented architectures), low-level control concepts (e.g., IEC 61131-3 and IEC 61499-based control), advanced system engineering concepts (e.g., model-driven development, component-based design), supply chains, value chains, virtual organizations, and virtual societies, emergency preparedness, crisis management, business channels, electronic marketplaces, enterprise resources planning, etc.</li> <li>Design and analysis of real-time embedded industrial systems, including real-time computing, real-time operating systems, real-time communications, networked embedded systems technology, etc.</li> <li>Novel control techniques, with respect to process control, equipment control, supervisory control, adaptive control, motion control, etc.</li> <li>Automated manufacturing systems, regarding formal modeling and analysis of manufacturing systems, scheduling of manufacturing systems, queuing systems and petri nets in manufacturing systems, etc.</li> <li>Computational intelligence in automation, including neural, fuzzy, evolutionary approaches in automation, ant colonies optimization and swarm intelligence in automation, machine learning, expert systems, etc.</li> <li>Hardware and software design and development for intelligent systems, such as intelligent and humanized production monitoring and control, etc.</li> <li>Big data analysis and processing in various industries and services, including constructing data analysis models, providing data analysis and processing tools and designing various optimization algorithms based on data analysis.</li> <li>Crowd-sourced behavior analysis in various industry and services, such as measuring and calculating the diffusion direction and speed of gas in the petrochemical industry based on crowd-sourced data from a large number of and various types of sensors, as well as product and service evaluation.</li> <li>Simulation and testbed of current industrial networks and intelligent systems, including network performance analysis, automated manufacturing, intelligent monitoring, disaster prevention, etc.</li> <li>Vision of future smart factories, service, marketing, and their integration, incorporating current existing technologies.</li> <li>Multimedia applications, content management, process management and knowledge management for various industries, services, and engineering education: including multimedia processing, multimedia retrieval, multimedia indexing, image sensing, image processing, image coding, image recognition, etc.</li> <li>Pattern recognition methods for various industries and services: including statistical theory, clustering, similarity measures, unsupervised learning, supervised learning, etc.</li> <li>Survey, review and essay of current industrial networks researches and intelligent systems development.</li> </ul></div></div><div name="Indexing"><div class="abstract"><ul> <li><a href="https://www.scopus.com/sourceid/21101049547">Scopus</a></li> <li><a href="https://doaj.org/toc/2410-0218">DOAJ</a></li> <li><a href="https://dblp.uni-trier.de/db/journals/inis/">DBLP</a></li> <li><a href="https://search.crossref.org/?q=2410-0218">CrossRef</a></li> <li><a href="https://www.worldcat.org/search?q=eai+endorsed+transactions+on+industrial+networks&qt=owc_search">OCLC Discovery Services</a></li> <li><a href="https://europub.co.uk/journals/8120">EuroPub</a></li> <li><a href="https://publons.com/journal/29023/eai-endorsed-transactions-on-industrial-networks-a">Publons</a></li> <li><a href="https://app.dimensions.ai/discover/publication?or_facet_source_title=jour.1152852">Dimensions</a></li> <li><a href="https://www.proquest.com/products-services/Publicly-Available-Content-Database.html#overviewlinkSection">Publicly Available Content Database (ProQuest)</a></li> <li><a href="https://www.proquest.com/products-services/adv_tech_aero.html">Advanced Technologies & Aerospace Database (ProQuest)</a></li> <li><a href="https://www.proquest.com/products-services/adv_tech_aero.html">SciTech Premium Collection (ProQuest)</a></li> <li><a href="https://scholar.google.sk/scholar?as_ylo=2018&q=source:EAI+source:Endorsed+source:Transactions+source:on+source:Industrial+source:Networks+source:and+source:Intelligent+source:Systems&hl=es&as_sdt=0,5">Google Scholar</a></li> </ul></div></div><div name="EditorialBoard"><div class="abstract"><ul> <li>Ala Al-Fuqaha (Western Michigan University, USA)</li> <li>Al-Sakib Khan Pathan (Southeast University, Bangladesh)</li> <li>Ammar Rayes (Cisco Systems, USA)</li> <li>Antonino Masaracchia (IIT-CNR, Italy)</li> <li>Athanasios Maglaras (Dr, Prof . ofT.E.I. of Larissa)</li> <li>Berk Canberk (Northeastern University, USA)</li> <li>Ca V. Phan (Ho Chi Minh City University of Technology and Education, Vietnam)</li> <li>Chau Yuen (Singapore University of Technology and Design, Singapore)</li> <li>Chengfei Liu (Swinburne University of Technology, Australia)</li> <li>Chinmoy Kundu (University of Texas at Dallas, USA)</li> <li>Christer Carlsson (Åbo Akademi University, Finland)</li> <li>Chunsheng Zhu (University of British Columbia)</li> <li>Constandinos Mavromoustakis (University of Nicosia, Cyprus)</li> <li>Der-Jiunn Deng (National Changhua University of Education, Taiwan)</li> <li>Dickson Chiu (The University of Hong Kong)</li> <li>Eleanna Kafeza (Athens University of Economics and Business, Greece)</li> <li>Fu-ren Lin (National Tsing Hua University, Taiwan)</li> <li>Gerhard Hancke (University of London, UK)</li> <li>Guangjie Han (Hohai University, China)</li> <li>Guojun Wang (Central South University, China)</li> <li>Hacene Fouchal (University of Reims Champagne-Ardenne, France)</li> <li>Haklae Kim (Chung-Ang University, South Korea)</li> <li>Halil Yetgin (Bitlis Eren University, Turkey)</li> <li>Hideyasu Sasaki (Ritsumeikan University, Kyoto, Japan)</li> <li>Ho-fung Leung (Chinese University of Hong Kong, Hong Kong)</li> <li>Honggang Wang (University of Massachusetts Dartmouth, USA)</li> <li>Hua Hu (Hangzhou Dianzi University, China)</li> <li>Ibrahim Kushchu (Mobile Government Consortium International, UK)</li> <li>Irene Kafeza (Irene Law Office, Greece)</li> <li>Isabelle Comyn-Wattiau (ESSEC Business School Paris, France)</li> <li>Jaime Lloret- Mauri (Universitat Politècnica de València, Spain)</li> <li>Javier M. Aguiar (Universidad de Valladolid, Valladolid, Spain)</li> <li>Jesus Alonso-Zarate (Telecommunications Technology Center of Catalonia, Spain)</li> <li>Jian Yang (Macquarie University, Australia)</li> <li>Jiankun Hu (University of New South Wales, Australia)</li> <li>Jianmin Jiang (Shenzhen University)</li> <li>Jianwei Niu (Beihang University, China)</li> <li>Jinlei Jiang (Tsinghua University, China)</li> <li>Jinsong Wu (Bell Laboratory, China)</li> <li>Joel Rodrigues (Inst. Telecomunicações, Univ. of Beira Interior, Portugal)</li> <li>Juan Trujillo (University of Alicante, Spain)</li> <li>Jucheng Yang (Tianjing University of Technology, China)</li> <li>Junqing Zhang (Queen's University Belfast)</li> <li>KUN WANG (Nanjing University of Posts and Telecommunications)</li> <li>Kuo-Ming Chao (Leader – Distributed Systems and Modelling Research Group, UK)</li> <li>Leandros A. Maglaras (De Montfort University, UK)</li> <li>Lei Wang (Dalian University of Technology, China)</li> <li>Liang Zhou (Nanjing University of Posts and Telecommunications, China)</li> <li>Long D. Nguyen (Dong Nai University, Vietnam)</li> <li>Maggie M. Wang (The University of Hong Kong, Hong Kong)</li> <li>Nghia Duong-Trung (German Research Center for Artificial Intelligence, Germany)</li> <li>Ngo Hoang Tu (Seoul National University of Science and Technology, South Korea)</li> <li>Nguyen Van Nam (Viettel, Vietnam)</li> <li>Nicholas C Romano (Oklahoma State University, USA)</li> <li>Noel Crespi (Institut Mines-Telecom, Telecom SudParis, France)</li> <li>Panlong Yang (PLA University of Science and Technology, China)</li> <li>Pasi Tyrväinen (University of Jyväskylä, Finland)</li> <li>Patrick C.K. Hung (University of Ontario Institute of Technology, Canada)</li> <li>Periklis Chatzimisios (Alexander TEI of Thessaloniki, Greece)</li> <li>Pierluigi Siano (Università degli Studi di Salerno, Italy)</li> <li>Pirkko Walden (Abo Akademi University, Finland)</li> <li>Phuong Bui (Duy Tan University, Vietnam)</li> <li>Raymond Y.K Lau (City University of Hong Kong, Hong Kong)</li> <li>Richard Yu (Carleton University, Canada)</li> <li>Rong Yu (Guangdong University of Technology, China)</li> <li>Rose Hu (Utah State University, USA)</li> <li>Sammy Chan (City University of HongKong, HK)</li> <li>Shing-Chi Cheung (Hong Kong University of Science and Technology, Hong Kong)</li> <li>Stephen J. H. Yang (National Central University, Taiwan)</li> <li>Syed Hassan Ahmed (University of Central Florida, USA)</li> <li>Thanh-Phuong Nguyen (University of Toulon, France)</li> <li>Tran Trung Duy (PTIT, VietNam)</li> <li>Trang Hoang (Ho Chi Minh City University of Technology - Vietnam National University Ho Chi Minh City, Vietnam)</li> <li>Tuan-Minh Pham (Phenikaa University, Vietnam)</li> <li>Umar Zakir Abdul Hamid (Sensible 4 Oy, Helsinki)</li> <li>Victor Leung (The University of British Columbia)</li> <li>Vo Nguyen Son Dr. (Duy Tan University, Vietnam)</li> <li>Wai-Wa Fung (Information Security and Forensics Society, Hong Kong)</li> <li>Walid Gaaloul (Institut National des Télécommunications, France)</li> <li>Weiwei Jiang, (Beijing University of Posts and Telecommunications (BUPT), China)</li> <li>Wendy W. Y. Hui (University of Nottingham at Ningbo, China)</li> <li>William Cheung (Hong Kong Baptist University, Hong Kong)</li> <li>Xianfu Chen (VTT Technical Research Centre of Finland, Finland)</li> <li>Xiang Gui (Massey University, New Zealand)</li> <li>Xiaoling Wu (Chinese Academy of Sciences, China)</li> <li>Xu Wang (Heriot Watt University, UK)</li> <li>Yan Bai (University of Washington Tacoma, USA)</li> <li>Yan Zhang (Simula Research Laboratory and University of Oslo, Norway)</li> <li>Yi Zhuang (Zhejian Gongshang University, China)</li> <li>Yong Li (Tsinghua University, China)</li> <li>Yong Tang (South China Normal University, China)</li> <li>Yuanfang Chen (Institute Mines-Telecom, University Pierre and Marie Curie )</li> <li>Yuexing Peng (Beijing University of Posts and Telecommunications, China)</li> <li>Yuqing Sun (Shangdong University, China)</li> <li>Zakaria Maamar (Zayed University, UAE)</li> <li>Zhangbing Zhou (China University of Geosciences, China)</li> <li>Zhichao Sheng (Shanghai University, China)</li> <li>ZhiMing Cai (Macau University of Science and Technology, Macau)</li> <li>Mithun Mukherjee (Nanjing University of Information Science and Technology, China)</li> <li> </li> </ul></div></div></div></section><br><section class="article-tabs"><div class="tabs"><ul><li><a name="recent" onclick="handleRecentClick()">Recently Published</a></li><li><a name="popular" onclick="handlePopularClick()">Most Popular</a></li></ul></div><div class="contents"><div class="expandable-list"><ul class="results-list"><li class="result-item article-light recent"><h3><a href="../doi/10.4108/eetinis.v12i1.6794">Transformer Based Ship Detector: An Improvement on Feature Map and Tiny Training Set</a></h3><dl class="metadata"><dt class="title">Appears in: </dt><dd class="value">inis 24(1): </dd><br><dt class="title">Authors: </dt><dd class="value">Van-Linh Vo, Hoc-Phan , My-Ha Le , Duc-Dat Ngo, Manh Hung Nguyen</dd><br><dt class="title">Published: </dt><dd class="value"> 7th Nov 2024</dd><br><dt class="title">Abstract: </dt><dd class="value abstract">The exponential increment of commodity exchange has raised the need for maritime border security in recent years. One of the most critical tasks for naval border security is ship detection inside and outside the territorial sea. Conventionally, the task requires a substantial human workload. Fortunately, with the rapid growth of the digital camera and deep-learning technique, computer programs can handle object detection tasks well enough to replace human labor. Therefore, this paper studies how to apply recent state-of-the-art deep-learning networks to the ship detection task. We found that with a suitable number of object queries, the Deformable-DETR method will improve the performance compared to the state-of-the-art ship detector. Moreover, comprehensive experiments on different scale datasets prove that the technique can significantly improve the results when the training sample is limited. Last but not least, feature maps given by the method will focus well on key objects in the image. </dd><hr></dl></li><li class="result-item article-light recent"><h3><a href="../doi/10.4108/eetinis.v11i4.6193">A Secure Cooperative Image Super-Resolution Transmission with Decode-and-Forward Relaying over Rayleigh Fading Channels</a></h3><dl class="metadata"><dt class="title">Appears in: </dt><dd class="value">inis 24(4): </dd><br><dt class="title">Authors: </dt><dd class="value">Hien-Thuan Duong, Ca V. Phan, Quoc-Tuan Vien</dd><br><dt class="title">Published: </dt><dd class="value"> 3rd Sep 2024</dd><br><dt class="title">Abstract: </dt><dd class="value abstract">In addition to susceptibility to performance degradation due to hardware malfunctions and environmental influences, wireless image transmission poses risks of information exposure to eavesdroppers. This paper delves into the image communications within wireless relay networks (WRNs) and proposes a secure cooperative relaying (SCR) protocol over Rayleigh fading channels. In this protocol, a source node (referred to as Alice) transmits superior-resolution (SR) images to a destination node (referred to as Bob) with the assistance of a mediating node (referred to as Relay) operating in decode-and-forward mode, all while contending with the presence of an eavesdropper (referred to as Eve). In order to conserve transmission bandwidth, Alice firstly reduces the size of the original SR images before transmitting them to Relay and Bob. Subsequently, random linear network coding (RLNC) is employed by both Alice and Relay on the downscaled poor-resolution (PR) images to obscure the original images from Eve, thereby bolstering the security of the image communications. Simulation results demonstrate that the proposed SCR protocol surpasses both secure relaying transmission without a direct link and secure direct transmission without relaying links. Additionally, a slight reduction in image quality can be achieved by increasing the scaling factor for saving transmission bandwidth. Furthermore, the results highlight the SCR protocol’s superior effectiveness at Bob’s end when compared to Eve’s, which is due to Eve’s lack of access to the RLNC coefficient matrices and reference images utilised by Alice and Relay in the RLNC process. Finally, the evaluation of reference images, relay allocations and diversity reception over Rayleigh fading channels confirms the effectiveness of the SCR protocol for secure image communications in the WRNs. </dd><hr></dl></li><li class="result-item article-light recent"><h3><a href="../doi/10.4108/eetinis.v11i4.4734">Emotional Inference from Speech Signals Informed by Multiple Stream DNNs Based Non-Local Attention Mechanism</a></h3><dl class="metadata"><dt class="title">Appears in: </dt><dd class="value">inis 24(4): </dd><br><dt class="title">Authors: </dt><dd class="value">Oscal T.C. Chen, Duc-Chinh Nguyen, Long Quang Chan, Manh-Hung Ha</dd><br><dt class="title">Published: </dt><dd class="value"> 5th Aug 2024</dd><br><dt class="title">Abstract: </dt><dd class="value abstract">It is difficult to determine whether a person is depressed due to the symptoms of depression not being apparent. However, the voice can be one of the ways in which we can acknowledge signs of depression. Understanding human emotions in natural language plays a crucial role for intelligent and sophisticated applications. This study proposes deep learning architecture to recognize the emotions of the speaker via audio signals, which can help diagnose patients who are depressed or prone to depression, so that treatment and prevention can be started as soon as possible. Specifically, Mel-frequency cepstral coefficients (MFCC) and Short Time Fourier Transform (STFT) are adopted to extract features from the audio signal. The multiple streams of the proposed DNNs model, including CNN-LSTM based on an attention mechanism, are discussed within this research. Leveraging a pretrained model, the proposed experimental results yield an accuracy rate of 93.2% on the EmoDB dataset. Further optimization remains a potential avenue for future development. It is hoped that this research will contribute to potential application in the fields of medical treatment and personal well-being. </dd><hr></dl></li><li class="result-item article-light recent"><h3><a href="../doi/10.4108/eetinis.v11i4.5843">Efficient LDPC Code Design based on Genetic Algorithm for IoT Applications</a></h3><dl class="metadata"><dt class="title">Appears in: </dt><dd class="value">inis 24(4): </dd><br><dt class="title">Authors: </dt><dd class="value">Tan Do Duy, Thanh-Loc Nguyen-Van, Thien Huynh-The</dd><br><dt class="title">Published: </dt><dd class="value"> 2nd Aug 2024</dd><br><dt class="title">Abstract: </dt><dd class="value abstract">In this paper, we propose a low-density parity check (LDPC) code design scheme that improves the performance of the existing genetic algorithm-based LDPC scheme. In particular, we enhance the performance of the LDPC code by removing the girth-4 property of the parity check matrix and utilizing the min-sum decoding algorithm instead of the belief propagation decoding algorithm. In addition, we consider different short block-length scenarios, including 64-bit and 128-bit block length. Then, we evaluate the block error rate (BLER) of the LDPC code over the binary input additive white Gaussian noise (BI-AWGN) channel. Finally, extensive simulation results indicate that our proposed approach achieves more than 11% gain in terms of BLER compared with the benchmarked schemes. </dd><hr></dl></li><li class="result-item article-light recent"><h3><a href="../doi/10.4108/eetinis.v11i3.5221">ViMedNER: A Medical Named Entity Recognition Dataset for Vietnamese</a></h3><dl class="metadata"><dt class="title">Appears in: </dt><dd class="value">inis 24(4): </dd><br><dt class="title">Authors: </dt><dd class="value">Huy-The Vu, Pham Van Duong, Le Hoang Son, Minh Chuan Pham, Tien-Dat Trinh, Tran Manh Tuan, Minh-Tien Nguyen</dd><br><dt class="title">Published: </dt><dd class="value">11th Jul 2024</dd><br><dt class="title">Abstract: </dt><dd class="value abstract">Named entity recognition (NER) is one of the most important tasks in natural language processing, which identifies entity boundaries and classifies them into pre-defined categories. In literature, NER systems have been developed for various languages but limited works have been conducted for Vietnamese. This mainly comes from the limitation of available and high-quality annotated data, especially for specific domains such as medicine and healthcare. In this paper, we introduce a new medical NER dataset, named ViMedNER, for recognizing Vietnamese medical entities. Unlike existing works designed for common or too-specific entities, we focus on entity types that can be used in common diagnostic and treatment scenarios, including disease names, the symptoms of the diseases, the cause of the diseases, the diagnostic, and the treatment. These entities facilitate the diagnosis and treatment of doctors for common diseases. Our dataset is collected from four well-known Vietnamese websites that are professional in terms of drag selling and disease diagnostics and annotated by domain experts with high agreement scores. To create benchmark results, strong NER baselines based on pre-trained language models including PhoBERT, XLM-R, ViDeBERTa, ViPubMedDeBERTa, and ViHealthBERT are implemented and evaluated on the dataset. Experiment results show that the performance of XLM-R is consistently better than that of the other pre-trained language models. Furthermore, additional experiments are conducted to explore the behavior of the baselines and the characteristics of our dataset. </dd><hr></dl></li><li class="result-item article-light recent"><h3><a href="../doi/10.4108/eetinis.v11i3.5616">Resource-Efficient Deep Learning: Fast Hand Gestures on Microcontrollers</a></h3><dl class="metadata"><dt class="title">Appears in: </dt><dd class="value">inis 24(3): </dd><br><dt class="title">Authors: </dt><dd class="value">Minhhuy Le, Tuan Kiet Tran Mach, Khai Nguyen Van</dd><br><dt class="title">Published: </dt><dd class="value"> 3rd Jul 2024</dd><br><dt class="title">Abstract: </dt><dd class="value abstract">Hand gesture recognition using a camera provides an intuitive and promising means of human-computer interaction and allows operators to execute commands and control machines with simple gestures. Research in hand gesture recognition-based control systems has garnered significant attention, yet the deploying of microcontrollers into this domain remains relatively insignificant. In this study, we propose a novel approach utilizing micro-hand gesture recognition built on micro-bottleneck Residual and micro-bottleneck Conv blocks. Our proposed model, comprises only 42K parameters, is optimized for size to facilitate seamless operation on resource-constrained hardware. Benchmarking conducted on STM32 microcontrollers showcases remarkable efficiency, with the model achieving an average prediction time of just 269ms, marking a 7× faster over the state-of-art model. Notably, despite its compact size and enhanced speed, our model maintains competitive performance result, achieving an accuracy of 99.6% on the ASL dataset and 92% on OUHANDS dataset. These findings underscore the potential for deploying advanced control methods on compact, cost-effective devices, presenting promising avenues for future research and industrial applications. </dd><hr></dl></li><li class="result-item article-light recent"><h3><a href="../doi/10.4108/eetinis.v11i3.5237">Machine Learning in Cybersecurity: Advanced Detection and Classification Techniques for Network Traffic Environments</a></h3><dl class="metadata"><dt class="title">Appears in: </dt><dd class="value">inis 24(3): </dd><br><dt class="title">Authors: </dt><dd class="value">Samer El Hajj Hassan, Nghia Duong-Trung</dd><br><dt class="title">Published: </dt><dd class="value"> 1st Jul 2024</dd><br><dt class="title">Abstract: </dt><dd class="value abstract">In the digital age, the integrity of business operations and the smoothness of their execution heavily depend on cybersecurity and network efficiency. The need for robust solutions to prevent cyber threats and enhance network functionality has never been more critical. This research aims to utilize machine learning (ML) techniques for the meticulous analysis of network traffic, with the dual goals of detecting anomalies and categorizing network activities to bolster security and performance. Employing a detailed methodology, this study begins with data preparation and progresses through to the deployment of advanced ML models, including logistic regression, decision trees, and ensemble learning techniques. This approach ensures the accuracy of the analysis and facilitates a nuanced understanding of network dynamics. Our findings indicate a notable enhancement in identifying network inefficiencies and in the more accurate classification of network traffic. The application of ML models significantly reduces network delays and bottlenecks by providing a strong defence strategy against cyber threats and network shortcomings, thereby improving user satisfaction, and boosting the organizational reputation as a secure and effective service layer. Conclusively, the research highlights the pivotal role of machine learning in network traffic analysis, offering innovative insights and fresh perspectives on anomaly detection and the identification of malicious activities. It lays a foundation for future explorations and acts as an evaluation benchmark in the fields of cybersecurity and network management. </dd><hr></dl></li><li class="result-item article-light recent"><h3><a href="../doi/10.4108/eetinis.v11i3.5992">Distributed Spatially Non-Stationary Channel Estimation for Extremely-Large Antenna Systems</a></h3><dl class="metadata"><dt class="title">Appears in: </dt><dd class="value">inis 24(3): </dd><br><dt class="title">Authors: </dt><dd class="value">Yanqing Xu, Zhou Wang, Ruihong Jiang, Shuai Wang</dd><br><dt class="title">Published: </dt><dd class="value"> 7th May 2024</dd><br><dt class="title">Abstract: </dt><dd class="value abstract">This paper aims to develop a distributed channel estimation (CE) algorithm for spatially non-stationary (SNS) channels in extremely large aperture array systems, addressing the issues of high communication cost and computational complexity associated with traditional centralized algorithms. However, SNS channels differ from conventional spatially stationary channels, presenting new challenges such as varying sparsity patterns for different antennas. To overcome these challenges, we propose a novel distributed CE algorithm accompanied by a simple yet effective hard thresholding scheme. The proposed algorithm is not only suitable for uniform antenna arrays but also for irregularly deployed antennas. Simulation results demonstrate the advantages of the proposed algorithm in terms of estimation accuracy, communication cost, and computational complexity. </dd><hr></dl></li><li class="result-item article-light recent"><h3><a href="../doi/10.4108/eetinis.v11i3.4728">On the Performance of the Relay Selection in Multi-hop Cluster-based Wireless Networks with Multiple Eavesdroppers Under Equally Correlated Rayleigh Fading</a></h3><dl class="metadata"><dt class="title">Appears in: </dt><dd class="value">inis 24(3): </dd><br><dt class="title">Authors: </dt><dd class="value">Pham Minh Nam, Phong Ngo Dinh, Tu Lam-Thanh, Thuong Le-Tien, Nguyen Luong Nhat</dd><br><dt class="title">Published: </dt><dd class="value"> 2nd May 2024</dd><br><dt class="title">Abstract: </dt><dd class="value abstract">The performance of multi-hop cluster-based wireless networks under multiple eavesdroppers is investigated in the present work. More precisely, we derive the outage probability (OP) of the considered networks under two relay selection schemes: the channel-gain-based scheme and the random scheme. Although equally correlated Rayleigh fading is taken into consideration, the derived mathematical framework remains tractable. Specifically, we represent the exact expression of the OP under the channel-based scheme in series form, while the OP under the random scheme is computed in a closed-form expression. Additionally, we propose a novel power allocation for each transmitter that strictly satisfies the given intercept probability. Numerical results based on the Monte Carlo method are provided to verify the correctness of the derived framework. These results are also used to identify the influences of various parameters, such as the number of clusters, the number of relays per cluster, and the transmit power. </dd><hr></dl></li><li class="result-item article-light recent"><h3><a href="../doi/10.4108/eetinis.v11i2.5156">Improving Performance of the Typical User in the Indoor Cooperative NOMA Millimeter Wave Networks with Presence of Walls</a></h3><dl class="metadata"><dt class="title">Appears in: </dt><dd class="value">inis 24(2): e4</dd><br><dt class="title">Authors: </dt><dd class="value">Xuan Nam Tran, Sinh Cong Lam</dd><br><dt class="title">Published: </dt><dd class="value"> 8th Apr 2024</dd><br><dt class="title">Abstract: </dt><dd class="value abstract">INTRODUCTION: The beyond 5G millimeter wave cellular network system is expecting to provide the high quality of service in indoor areas. OBJECTIVES: Due to the high density of obstacles, the cooperative communication technique is employed to improve the user's desired signal power by finding more than one appropriate station to serve that user. METHODS: While the conventional system utilizes additional equipment such as Reconfigurable Intelligent Surfaces (RIS) and relays to enable the cooperative features, the paper introduces a new network paradigm that utilizes the second nearest Base Station (BS) of the typical user as the Decode and Forward (DF) relay. Thus, depends on the success of decoding the message from the user' serving BS of the second nearest BS, the typical user can work with and without assistance from the relay whose operation follows the discipline of the power-domain NOMA technique. In the case of with relay assistance, the Maximum Ratio Combining technique is utilized by the typical user to combine the desired signals. RESULTS: To examine the performance of the proposed system, the Nakagami-m and the newly developed path loss model, which considers the density of walls and their properties, are adopted to derive the coverage probability of the user with and without relay assistance. The closed-form expressions of this performance metric are derived by Gauss quadrature and Welch-Satterthwaite approximation. </dd><hr></dl></li><li class="result-item article-light popular"><h3><a href="../doi/10.4108/eai.6-8-2021.170560">Application of Artificial Intelligence for the Optimization of Hydropower Energy Generation</a></h3><dl class="metadata"><dt class="title">Appears in: </dt><dd class="value">inis 21(28): e1</dd><br><dt class="title">Authors: </dt><dd class="value">Krishna Kumar, R. P. Saini</dd><br><dt class="title">Downloads: </dt><dd class="value">3736</dd><br><dt class="title">Abstract: </dt><dd class="value abstract">Hydropower is one of the most promising sources of renewable energy. However, a substantial initial investment requires for the construction of large civil structures. Feasibility study, detailed project report preparation, construction planning, and timely execution of work are the important activities of a hydropower plant. Energy generation in hydropower plants are mainly depends on discharge and head. Therefore, an accurate estimation of discharge and head is important to decide the plant capacity. Erosion, cavitation, and operation & maintenance are the key challenges in hydropower energy generation. Artificial Intelligence (AI) has become popular, which can be utilized for site selection, parameters assessment, and operation & maintenance optimization. In this paper, a literature review on applications of AI in hydropower has been presented, and an attempt has also been made to identify the future potential areas of hydropower plants.</dd><hr></dl></li><li class="result-item article-light popular"><h3><a href="../doi/10.4108/eai.13-10-2021.171319">Classification of UNSW-NB15 dataset using Exploratory Data Analysis using Ensemble Learning</a></h3><dl class="metadata"><dt class="title">Appears in: </dt><dd class="value">inis 21(29): e4</dd><br><dt class="title">Authors: </dt><dd class="value">Narendra Singh Yadav, Neha Sharma, Saurabh Sharma</dd><br><dt class="title">Downloads: </dt><dd class="value">3350</dd><br><dt class="title">Abstract: </dt><dd class="value abstract">Recent advancements in machine learning have made it a tool of choice for different classification and analytical problems. This paper deals with a critical field of computer networking: network security and the possibilities of machine learning automation in this field. We will be doing exploratory data analysis on the benchmark UNSW-NB15 dataset. This dataset is a modern substitute for the outdated KDD’99 dataset as it has greater uniformity of pattern distribution. We will also implement several ensemble algorithms like Random Forest, Extra trees, AdaBoost, and XGBoost to derive insights from the data and make useful predictions. We calculated all the standard evaluation parameters for comparative analysis among all the classifiers used. This analysis gives knowledge, investigates difficulties, and future opportunities to propel machine learning in networking. This paper can give a basic understanding of data analytics in terms of security using Machine Learning techniques.</dd><hr></dl></li><li class="result-item article-light popular"><h3><a href="../doi/10.4108/eai.18-5-2020.164586">Control Algorithms for UAVs: A Comprehensive Survey</a></h3><dl class="metadata"><dt class="title">Appears in: </dt><dd class="value">inis 20(23): e5</dd><br><dt class="title">Authors: </dt><dd class="value">Anh M. Le, Minh T. Nguyen, Cuong V. Nguyen, Toan V. Quyen, Hoa T. Nguyen, Hoa T. Tran</dd><br><dt class="title">Downloads: </dt><dd class="value">2772</dd><br><dt class="title">Abstract: </dt><dd class="value abstract">The development of unmanned aerial vehicles (UAVs) has become a revolution in the ﬁelds of data collection, surveying, monitoring, and tracking objects in the ﬁeld. Many control and navigation algorithms are experimented and deployed for UAVs, especially quadrotors. Recent numerous approaches are geared towards reducing the inﬂuence of external disturbances to enhance the performance of UAVs. Nevertheless, designing cutting-edge controllers following the requirements of the applications is still a huge challenge. Based on the operating characteristics and movement principle of a quadrotor, this work reviews potential control algorithms of the current researches in the ﬁeld of the quadrotor ﬂight controller. Besides, a comparison has been made to provide an overview of the advantages and disadvantages of the mentioned methods. At last, the challenges and future directions of the quadrotor ﬂight controller are suggested.</dd><hr></dl></li><li class="result-item article-light popular"><h3><a href="../doi/10.4108/inis.2.2.e4">Smart Grid Attacks and Countermeasures</a></h3><dl class="metadata"><dt class="title">Appears in: </dt><dd class="value">inis 15(2): e4</dd><br><dt class="title">Authors: </dt><dd class="value"> Yang Xiao, Eric McCary</dd><br><dt class="title">Downloads: </dt><dd class="value">2761</dd><br><dt class="title">Abstract: </dt><dd class="value abstract">The term “Smart Grid” has been coined and used for several years to describe the efforts of the current power grid modernization effort. This effort plans to introduce self-healing, energy efficiency, reliability, and security using two-way digital communications and control technology, along with a host of other valuable attributes. As a bi-product of this modernization and newly gained systems interoperability, new communications and management interfaces are produced in both the cyber realm and physical domains. The increase of the public physical presence and cyber footprint opens up avenues for compromise to hackers and individuals with malicious intent. This survey paper will categorize and summarize vulnerabilities in the framework of the current power grid and the software and hardware which is currently being used to upgrade the grid. The paper will also detail known countermeasures which can be used to mitigate or eliminate attacks which exploit such vulnerabilities.</dd><hr></dl></li><li class="result-item article-light popular"><h3><a href="../doi/10.4108/eai.28-3-2019.157122">Innovative Application of 5G and Blockchain Technology in Industry 4.0</a></h3><dl class="metadata"><dt class="title">Appears in: </dt><dd class="value">inis 19(18): e4</dd><br><dt class="title">Authors: </dt><dd class="value">Sven Maček, Ivan Jovović, Siniša Husnjak, Ivan Forenbacher</dd><br><dt class="title">Downloads: </dt><dd class="value">2723</dd><br><dt class="title">Abstract: </dt><dd class="value abstract">The Industry 4.0 is experiencing significant challenges, including the need for an increased amount of data transmission with improved security, transparency and credibility. The 5th Generation Mobile Network (5G) and Blockchain are innovative emerging technologies that can respond to these needs. 5G will enable extremely large channel capacities and reduce data latency, while Blockchain's innovative data-sharing mode of operation delivers an improved high level of security, transparency, and credibility of stored data. Therefore, this paper presents a general survey of the potential application of 5G network and Blockchain technology in Industry 4.0. The results may be used by first-movers firms for gaining technological leadership on the market. </dd><hr></dl></li><li class="result-item article-light popular"><h3><a href="../doi/10.4108/eai.31-1-2020.162831">Wireless Power Transfer Near-field Technologies for Unmanned Aerial Vehicles (UAVs): A Review</a></h3><dl class="metadata"><dt class="title">Appears in: </dt><dd class="value">inis 20(22): e5</dd><br><dt class="title">Authors: </dt><dd class="value">Cuong V. Nguyen, Toan V. Quyen, Minh T. Nguyen, Linh H. Truong, Anh M. Le</dd><br><dt class="title">Downloads: </dt><dd class="value">2682</dd><br><dt class="title">Abstract: </dt><dd class="value abstract">Wireless power transfer (WPT) techniques are being popular currently with the development of midrange wireless powering and charging technology to gradually substitute the need for wired devices during charging. Unmanned Aerial Vehicles (UAVs) are also being used with many practical purposes for agriculture, surveillance, and healthcare, etc. There is a trade-off between the weight of the UAVs or their batteries and their flying time. In order to support those UAVs perform better in their tasks, WPT is applied in UAVs to recharge batteries which help to increase their working time. This paper highlights up-to-date studies that are specific to near-field WPT deploying into UAVs. The charging distances, the transfer efficiency, and transfer power, etc. are considered to provide an overview of all common problems in using and charging UAVs, especially for autonomous landing and charging. By classification and suggestions in specific problems will be provided opportunities and challenges with respect to apply near-field WPT techniques for charging the battery of UAVs and other applications in the real world.</dd><hr></dl></li><li class="result-item article-light popular"><h3><a href="../doi/10.4108/eai.29-9-2021.171188">Prediction of dogecoin price using deep learning and social media trends</a></h3><dl class="metadata"><dt class="title">Appears in: </dt><dd class="value">inis 21(29): e2</dd><br><dt class="title">Authors: </dt><dd class="value">Himanshu Airan, Priyanka Harjule, Parth Agarwal, Upkar Saraswat, Lakshit Chouhan, Basant Agarwal</dd><br><dt class="title">Downloads: </dt><dd class="value">2529</dd><br><dt class="title">Abstract: </dt><dd class="value abstract">INTRODUCTION: Cryptocurrency is a digital, decentralized form of money based on blockchain technology, which makes it the most secure method of making a transaction. There has been a huge increase in the number of cryptocurrencies in the past few years. Cryptocurrencies such as Bitcoin and Ethereum have become an interesting subject of study in fields such as finance. In 2021, over 4,000 cryptocurrencies are already listed. There are many past studies that focus on predicting the price of cryptocurrencies using machine learning, but the majority of them only focused on Bitcoin. Moreover, the majority of the models implemented for price prediction only used the historical market prices, and do not utilize social signals related to the cryptocurrency. OBJECTIVES: In this paper, we propose a deep learning model for predicting the prices of dogecoin cryptocurrency. The proposed model is based on historical market price data as well as social trends of Dogecoin cryptocurrency. METHODS: The market data of Dogecoin is collected from Kaggle on the granularity of a day and for the same duration the verified tweets have also been collected with hashtags “Dogecoin” and “Doge”. Experimental results show that the proposed model yields a promising prediction of future price of Dogecoin, a cryptocurrency that has recently become the talk of the town of the crypto market. RESULTS: Minimum achieved RMSE in predicted price of Dogecoin was 0.02 where the feature vector consisted of OCVP (Open, Close, Volume, Polarity) values from combined dataset. RESULTS: Experimental results show that the proposed approach performs efficiently.</dd><hr></dl></li><li class="result-item article-light popular"><h3><a href="../doi/10.4108/eai.19-12-2018.156079">Improving Customer Behaviour Prediction with the Item2Item model in Recommender Systems</a></h3><dl class="metadata"><dt class="title">Appears in: </dt><dd class="value">inis 18(17): e4</dd><br><dt class="title">Authors: </dt><dd class="value">T. T. S. Nguyen, P. M. T. Do, T. T. Nguyen</dd><br><dt class="title">Downloads: </dt><dd class="value">2314</dd><br><dt class="title">Abstract: </dt><dd class="value abstract">Recommender Systems are the most well-known applications in E-commerce sites. However, the trade-off between runtime and the accuracy in making recommendations is a big challenge. This work combines several traditional techniques to reduce the limitation of each single technique and exploits the Item2Item model to improve the prediction accuracy. As a case study, this paper focuses on user behaviour prediction in restaurant recommender systems and uses a public dataset including restaurant information and user sessions. Within this dataset, user behaviour can be discovered for the collaborative filtering, and restaurant information is extracted for the content-based filtering. The idea of the pre-trained word embedding in Natural Language Processing is utilized in the item-based collaborative filtering to find the similarity between restaurants based on user sessions. Experimental results have shown that the combination of these techniques makes valuable recommendations.</dd><hr></dl></li><li class="result-item article-light popular"><h3><a href="../doi/10.4108/eai.19-9-2018.155569">A Survey of System Level Power Management Schemes in the Dark-Silicon Era for Many-Core Architectures</a></h3><dl class="metadata"><dt class="title">Appears in: </dt><dd class="value">inis 18(15): e5</dd><br><dt class="title">Authors: </dt><dd class="value">Xiaohang Wang, Emmannuel Ofori-Attah, Michael Opoku Agyeman</dd><br><dt class="title">Downloads: </dt><dd class="value">2307</dd><br><dt class="title">Abstract: </dt><dd class="value abstract">Power consumption in Complementary Metal Oxide Semiconductor (CMOS) technology has escalated to a point that only a fractional part of many-core chips can be powered-on at a time. Fortunately, this fraction can be increased at the expense of performance through the dark-silicon solution. However, with many-core integration set to be heading towards its thousands, power consumption and temperature increases per time, meaning the number of active nodes must be reduced drastically. Therefore, optimized techniques are demanded for continuous advancement in technology. Existing eﬀorts try to overcome this challenge by activating nodes from diﬀerent parts of the chip at the expense of communication latency. Other eﬀorts on the other hand employ run-time power management techniques to manage the power performance of the cores trading-oﬀ performance for power. We found out that, for a signiﬁcant amount of power to saved and high temperature to be avoided, focus should be on reducing the power consumption of all the on-chip components. Especially, the memory hierarchy and the interconnect. Power consumption can be minimized by, reducing the size of high leakage power dissipating elements, turning-oﬀ idle resources and integrating power saving materials.</dd><hr></dl></li><li class="result-item article-light popular"><h3><a href="../doi/10.4108/eai.18-5-2020.165676">Correlation Analysis of Vital Signs to Monitor Disease Risks in Ubiquitous Healthcare System</a></h3><dl class="metadata"><dt class="title">Appears in: </dt><dd class="value">inis 20(24): e1</dd><br><dt class="title">Authors: </dt><dd class="value">Qammer H. Abbasi, Muhammad A. Imran, Sajjad Hussain, Huanlai Xing, Muhammad Azhar Iqbal, Hassan Murtaza</dd><br><dt class="title">Downloads: </dt><dd class="value">2270</dd><br><dt class="title">Abstract: </dt><dd class="value abstract">Healthcare systems for chronic diseases demand continuous monitoring of physiological parameters or vital signs of the patients’ body. Through these vital signs’ information, healthcare experts attempt to diagnose the behavior of a disease. Identifying the relationship between these vital signs is still a big question for the research community. We have proposed a sophisticated way to identify the affiliations between vital signs of three specific diseases i.e., Sepsis, Sleep Apnea, and Intradialytic Hypotension (IDH) through Pearson statistical correlation analysis. Vital signs data of about 32 patients were taken for analysis. Experimental results show significant affiliations of vital signs of Sepsis and IDH with average correlation coefficient of 0.9 and 0.58, respectively. The stability of the mentioned correlation is about 75% and 90%, respectively.</dd><hr></dl></li></ul></div></div></section><section class="publication-info"><dl class="metadata"><dt class="title">Publisher</dt> <dd class="value">EAI</dd> <dt class="title">ISSN</dt> <dd class="value">2410-0218</dd> <dt class="title">Number of Volumes</dt> <dd class="value">12</dd></dl><dl class="metadata"><dt class="title">Last Published</dt> <dd class="value">2024-11-06</dd></dl></section></div></section></form></section></section><div class="clear"></div><footer><div class="links"><a href="https://www.ebsco.com/" target="_blank"><img class="logo ebsco-logo" src="/images/ebsco.png" alt="EBSCO"></a><a href="https://www.proquest.com/" target="_blank"><img class="logo proquest-logo" src="/images/proquest.png" alt="ProQuest"></a><a href="https://dblp.uni-trier.de/db/journals/publ/icst.html" target="_blank"><img class="logo dblp-logo" src="/images/dblp.png" alt="DBLP"></a><a href="https://doaj.org/search?source=%7B%22query%22%3A%7B%22filtered%22%3A%7B%22filter%22%3A%7B%22bool%22%3A%7B%22must%22%3A%5B%7B%22term%22%3A%7B%22index.publisher.exact%22%3A%22European%20Alliance%20for%20Innovation%20(EAI)%22%7D%7D%5D%7D%7D%2C%22query%22%3A%7B%22query_string%22%3A%7B%22query%22%3A%22european%20alliance%20for%20innovation%22%2C%22default_operator%22%3A%22AND%22%2C%22default_field%22%3A%22index.publisher%22%7D%7D%7D%7D%7Dj" target="_blank"><img class="logo doaj-logo" src="/images/doaj.jpg" alt="DOAJ"></a><a href="https://www.portico.org/publishers/eai/" target="_blank"><img class="logo portico-logo" src="/images/portico.png" alt="Portico"></a><a href="http://eai.eu/" target="_blank"><img class="logo eai-logo" src="/images/eai.png"></a></div></footer></div><div class="footer-container"><div class="footer-width"><div class="footer-column logo-column"><a href="https://eai.eu/"><img src="https://eudl.eu/images/logo_new-1-1.png" alt="EAI Logo"></a></div><div class="footer-column"><h4>About EAI</h4><ul><li><a href="https://eai.eu/who-we-are/">Who We Are</a></li><li><a href="https://eai.eu/leadership/">Leadership</a></li><li><a href="https://eai.eu/research-areas/">Research Areas</a></li><li><a href="https://eai.eu/partners/">Partners</a></li><li><a href="https://eai.eu/media-center/">Media Center</a></li></ul></div><div class="footer-column"><h4>Community</h4><ul><li><a href="https://eai.eu/eai-community/">Membership</a></li><li><a href="https://eai.eu/conferences/">Conference</a></li><li><a href="https://eai.eu/recognition/">Recognition</a></li><li><a href="https://eai.eu/corporate-sponsorship">Sponsor Us</a></li></ul></div><div class="footer-column"><h4>Publish with EAI</h4><ul><li><a href="https://eai.eu/publishing">Publishing</a></li><li><a href="https://eai.eu/journals/">Journals</a></li><li><a href="https://eai.eu/proceedings/">Proceedings</a></li><li><a href="https://eai.eu/books/">Books</a></li><li><a href="https://eudl.eu/">EUDL</a></li></ul></div></div></div><script type="text/javascript" src="https://eudl.eu/js/gacode.js"></script><script src="/js/highlight.pack.js"></script><script>hljs.initHighlightingOnLoad();</script><script type="application/ld+json">{"@context":"http://schema.org","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"item":{"@id":"http://eudl.eu","name":"Home","image":null}},{"@type":"ListItem","position":2,"item":{"@id":"http://eudl.eu/journals","name":"Journals","image":null}},{"@type":"ListItem","position":3,"item":{"@id":"http://eudl.eu/journal/inis","name":"inis","image":null}}]}</script></body></html>