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AI</title> <meta name="description" content="Search results for: generative AI"> <meta name="keywords" content="generative AI"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none navbar-toggler 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id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="generative AI"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 172</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: generative AI</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">172</span> Generative AI in Higher Education: Pedagogical and Ethical Guidelines for Implementation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Judit%20Vilarmau">Judit Vilarmau</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Generative AI is emerging rapidly and transforming higher education in many ways, occasioning new challenges and disrupting traditional models and methods. The studies and authors explored remark on the impact on the ethics, curriculum, and pedagogical methods. Students are increasingly using generative AI for study, as a virtual tutor, and as a resource for generating works and doing assignments. This point is crucial for educators to make sure that students are using generative AI with ethical considerations. Generative AI also has relevant benefits for educators and can help them personalize learning experiences and promote self-regulation. Educators must seek and explore tools like ChatGPT to innovate without forgetting an ethical and pedagogical perspective. Eighteen studies were systematically reviewed, and the findings provide implementation guidelines with pedagogical and ethical considerations. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ethics" title="ethics">ethics</a>, <a href="https://publications.waset.org/abstracts/search?q=generative%20artificial%20intelligence" title=" generative artificial intelligence"> generative artificial intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=guidelines" title=" guidelines"> guidelines</a>, <a href="https://publications.waset.org/abstracts/search?q=higher%20education" title=" higher education"> higher education</a>, <a href="https://publications.waset.org/abstracts/search?q=pedagogy" title=" pedagogy"> pedagogy</a> </p> <a href="https://publications.waset.org/abstracts/179093/generative-ai-in-higher-education-pedagogical-and-ethical-guidelines-for-implementation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/179093.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">88</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">171</span> A Grounded Theory of Educational Leadership Development Using Generative Dialogue</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Elizabeth%20Hartney">Elizabeth Hartney</a>, <a href="https://publications.waset.org/abstracts/search?q=Keith%20Borkowsky"> Keith Borkowsky</a>, <a href="https://publications.waset.org/abstracts/search?q=Jo%20Axe"> Jo Axe</a>, <a href="https://publications.waset.org/abstracts/search?q=Doug%20Hamilton"> Doug Hamilton</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The aim of this research is to develop a grounded theory of educational leadership development, using an approach to initiating and maintaining professional growth in school principals and vice principals termed generative dialogue. The research was conducted in a relatively affluent, urban school district in Western Canada. Generative dialogue interviews were conducted by a team of consultants, and anonymous data in the form of handwritten notes were voluntarily submitted to the research team. The data were transcribed and analyzed using grounded theory. The results indicate that a key focus of educational leadership development is focused on navigating relationships within the school setting and that the generative dialogue process is helpful for principals and vice principals to explore how they might do this. Applicability and limitations of the study are addressed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=generative%20dialogue" title="generative dialogue">generative dialogue</a>, <a href="https://publications.waset.org/abstracts/search?q=school%20principals" title=" school principals"> school principals</a>, <a href="https://publications.waset.org/abstracts/search?q=grounded%20theory" title=" grounded theory"> grounded theory</a>, <a href="https://publications.waset.org/abstracts/search?q=leadership%20development" title=" leadership development"> leadership development</a> </p> <a href="https://publications.waset.org/abstracts/92456/a-grounded-theory-of-educational-leadership-development-using-generative-dialogue" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/92456.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">356</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">170</span> Next-Gen Solutions: How Generative AI Will Reshape Businesses</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Aishwarya%20Rai">Aishwarya Rai</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study explores the transformative influence of generative AI on startups, businesses, and industries. We will explore how large businesses can benefit in the area of customer operations, where AI-powered chatbots can improve self-service and agent effectiveness, greatly increasing efficiency. In marketing and sales, generative AI could transform businesses by automating content development, data utilization, and personalization, resulting in a substantial increase in marketing and sales productivity. In software engineering-focused startups, generative AI can streamline activities, significantly impacting coding processes and work experiences. It can be extremely useful in product R&D for market analysis, virtual design, simulations, and test preparation, altering old workflows and increasing efficiency. Zooming into the retail and CPG industry, industry findings suggest a 1-2% increase in annual revenues, equating to $400 billion to $660 billion. By automating customer service, marketing, sales, and supply chain management, generative AI can streamline operations, optimizing personalized offerings and presenting itself as a disruptive force. While celebrating economic potential, we acknowledge challenges like external inference and adversarial attacks. Human involvement remains crucial for quality control and security in the era of generative AI-driven transformative innovation. This talk provides a comprehensive exploration of generative AI's pivotal role in reshaping businesses, recognizing its strategic impact on customer interactions, productivity, and operational efficiency. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=generative%20AI" title="generative AI">generative AI</a>, <a href="https://publications.waset.org/abstracts/search?q=digital%20transformation" title=" digital transformation"> digital transformation</a>, <a href="https://publications.waset.org/abstracts/search?q=LLM" title=" LLM"> LLM</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20intelligence" title=" artificial intelligence"> artificial intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=startups" title=" startups"> startups</a>, <a href="https://publications.waset.org/abstracts/search?q=businesses" title=" businesses"> businesses</a> </p> <a href="https://publications.waset.org/abstracts/179625/next-gen-solutions-how-generative-ai-will-reshape-businesses" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/179625.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">76</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">169</span> Generative AI: A Comparison of Conditional Tabular Generative Adversarial Networks and Conditional Tabular Generative Adversarial Networks with Gaussian Copula in Generating Synthetic Data with Synthetic Data Vault</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lakshmi%20Prayaga">Lakshmi Prayaga</a>, <a href="https://publications.waset.org/abstracts/search?q=Chandra%20Prayaga.%20Aaron%20Wade"> Chandra Prayaga. Aaron Wade</a>, <a href="https://publications.waset.org/abstracts/search?q=Gopi%20Shankar%20Mallu"> Gopi Shankar Mallu</a>, <a href="https://publications.waset.org/abstracts/search?q=Harsha%20Satya%20Pola"> Harsha Satya Pola</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Synthetic data generated by Generative Adversarial Networks and Autoencoders is becoming more common to combat the problem of insufficient data for research purposes. However, generating synthetic data is a tedious task requiring extensive mathematical and programming background. Open-source platforms such as the Synthetic Data Vault (SDV) and Mostly AI have offered a platform that is user-friendly and accessible to non-technical professionals to generate synthetic data to augment existing data for further analysis. The SDV also provides for additions to the generic GAN, such as the Gaussian copula. We present the results from two synthetic data sets (CTGAN data and CTGAN with Gaussian Copula) generated by the SDV and report the findings. The results indicate that the ROC and AUC curves for the data generated by adding the layer of Gaussian copula are much higher than the data generated by the CTGAN. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=synthetic%20data%20generation" title="synthetic data generation">synthetic data generation</a>, <a href="https://publications.waset.org/abstracts/search?q=generative%20adversarial%20networks" title=" generative adversarial networks"> generative adversarial networks</a>, <a href="https://publications.waset.org/abstracts/search?q=conditional%20tabular%20GAN" title=" conditional tabular GAN"> conditional tabular GAN</a>, <a href="https://publications.waset.org/abstracts/search?q=Gaussian%20copula" title=" Gaussian copula"> Gaussian copula</a> </p> <a href="https://publications.waset.org/abstracts/183000/generative-ai-a-comparison-of-conditional-tabular-generative-adversarial-networks-and-conditional-tabular-generative-adversarial-networks-with-gaussian-copula-in-generating-synthetic-data-with-synthetic-data-vault" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/183000.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">82</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">168</span> Learning Traffic Anomalies from Generative Models on Real-Time Observations</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fotis%20I.%20Giasemis">Fotis I. Giasemis</a>, <a href="https://publications.waset.org/abstracts/search?q=Alexandros%20Sopasakis"> Alexandros Sopasakis</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study focuses on detecting traffic anomalies using generative models applied to real-time observations. By integrating a Graph Neural Network with an attention-based mechanism within the Spatiotemporal Generative Adversarial Network framework, we enhance the capture of both spatial and temporal dependencies in traffic data. Leveraging minute-by-minute observations from cameras distributed across Gothenburg, our approach provides a more detailed and precise anomaly detection system, effectively capturing the complex topology and dynamics of urban traffic networks. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=traffic" title="traffic">traffic</a>, <a href="https://publications.waset.org/abstracts/search?q=anomaly%20detection" title=" anomaly detection"> anomaly detection</a>, <a href="https://publications.waset.org/abstracts/search?q=GNN" title=" GNN"> GNN</a>, <a href="https://publications.waset.org/abstracts/search?q=GAN" title=" GAN"> GAN</a> </p> <a href="https://publications.waset.org/abstracts/193544/learning-traffic-anomalies-from-generative-models-on-real-time-observations" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/193544.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">8</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">167</span> A Deep Reinforcement Learning-Based Secure Framework against Adversarial Attacks in Power System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Arshia%20Aflaki">Arshia Aflaki</a>, <a href="https://publications.waset.org/abstracts/search?q=Hadis%20Karimipour"> Hadis Karimipour</a>, <a href="https://publications.waset.org/abstracts/search?q=Anik%20Islam"> Anik Islam</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Generative Adversarial Attacks (GAAs) threaten critical sectors, ranging from fingerprint recognition to industrial control systems. Existing Deep Learning (DL) algorithms are not robust enough against this kind of cyber-attack. As one of the most critical industries in the world, the power grid is not an exception. In this study, a Deep Reinforcement Learning-based (DRL) framework assisting the DL model to improve the robustness of the model against generative adversarial attacks is proposed. Real-world smart grid stability data, as an IIoT dataset, test our method and improves the classification accuracy of a deep learning model from around 57 percent to 96 percent. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=generative%20adversarial%20attack" title="generative adversarial attack">generative adversarial attack</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20reinforcement%20learning" title=" deep reinforcement learning"> deep reinforcement learning</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=IIoT" title=" IIoT"> IIoT</a>, <a href="https://publications.waset.org/abstracts/search?q=generative%20adversarial%20networks" title=" generative adversarial networks"> generative adversarial networks</a>, <a href="https://publications.waset.org/abstracts/search?q=power%20system" title=" power system"> power system</a> </p> <a href="https://publications.waset.org/abstracts/188908/a-deep-reinforcement-learning-based-secure-framework-against-adversarial-attacks-in-power-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/188908.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">37</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">166</span> Revolutionizing Gaming Setup Design: Utilizing Generative and Iterative Methods to Prop and Environment Design, Transforming the Landscape of Game Development Through Automation and Innovation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rashmi%20Malik">Rashmi Malik</a>, <a href="https://publications.waset.org/abstracts/search?q=Videep%20Mishra"> Videep Mishra</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The practice of generative design has become a transformative approach for an efficient way of generating multiple iterations for any design project. The conventional way of modeling the game elements is very time-consuming and requires skilled artists to design. A 3D modeling tool like 3D S Max, Blender, etc., is used traditionally to create the game library, which will take its stipulated time to model. The study is focused on using the generative design tool to increase the efficiency in game development at the stage of prop and environment generation. This will involve procedural level and customized regulated or randomized assets generation. The paper will present the system design approach using generative tools like Grasshopper (visual scripting) and other scripting tools to automate the process of game library modeling. The script will enable the generation of multiple products from the single script, thus creating a system that lets designers /artists customize props and environments. The main goal is to measure the efficacy of the automated system generated to create a wide variety of game elements, further reducing the need for manual content creation and integrating it into the workflow of AAA and Indie Games. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=iterative%20game%20design" title="iterative game design">iterative game design</a>, <a href="https://publications.waset.org/abstracts/search?q=generative%20design" title=" generative design"> generative design</a>, <a href="https://publications.waset.org/abstracts/search?q=gaming%20asset%20automation" title=" gaming asset automation"> gaming asset automation</a>, <a href="https://publications.waset.org/abstracts/search?q=generative%20game%20design" title=" generative game design"> generative game design</a> </p> <a href="https://publications.waset.org/abstracts/173936/revolutionizing-gaming-setup-design-utilizing-generative-and-iterative-methods-to-prop-and-environment-design-transforming-the-landscape-of-game-development-through-automation-and-innovation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/173936.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">70</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">165</span> The Impact of Generative AI Illustrations on Aesthetic Symbol Consumption among Consumers: A Case Study of Japanese Anime Style</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Han-Yu%20Cheng">Han-Yu Cheng</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study aims to explore the impact of AI-generated illustration works on the aesthetic symbol consumption of consumers in Taiwan. The advancement of artificial intelligence drawing has lowered the barriers to entry, enabling more individuals to easily enter the field of illustration. Using Japanese anime style as an example, with the development of Generative Artificial Intelligence (Generative AI), an increasing number of illustration works are being generated by machines, sparking discussions about aesthetics and art consumption. Through surveys and the analysis of consumer perspectives, this research investigates how this influences consumers' aesthetic experiences and the resulting changes in the traditional art market and among creators. The study reveals that among consumers in Taiwan, particularly those interested in Japanese anime style, there is a pronounced interest and curiosity surrounding the emergence of Generative AI. This curiosity is particularly notable among individuals interested in this style but lacking the technical skills required for creating such artworks. These works, rooted in elements of Japanese anime style, find ready acceptance among enthusiasts of this style due to their stylistic alignment. Consequently, they have garnered a substantial following. Furthermore, with the reduction in entry barriers, more individuals interested in this style but lacking traditional drawing skills have been able to participate in producing such works. Against the backdrop of ongoing debates about artistic value since the advent of artificial intelligence (AI), Generative AI-generated illustration works, while not entirely displacing traditional art, to a certain extent, fulfill the aesthetic demands of this consumer group, providing a similar or analogous aesthetic consumption experience. Additionally, this research underscores the advantages and limitations of Generative AI-generated illustration works within this consumption environment. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=generative%20AI" title="generative AI">generative AI</a>, <a href="https://publications.waset.org/abstracts/search?q=anime%20aesthetics" title=" anime aesthetics"> anime aesthetics</a>, <a href="https://publications.waset.org/abstracts/search?q=Japanese%20anime%20illustration" title=" Japanese anime illustration"> Japanese anime illustration</a>, <a href="https://publications.waset.org/abstracts/search?q=art%20consumption" title=" art consumption"> art consumption</a> </p> <a href="https://publications.waset.org/abstracts/173744/the-impact-of-generative-ai-illustrations-on-aesthetic-symbol-consumption-among-consumers-a-case-study-of-japanese-anime-style" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/173744.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">72</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">164</span> Improving Student Programming Skills in Introductory Computer and Data Science Courses Using Generative AI</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Genady%20Grabarnik">Genady Grabarnik</a>, <a href="https://publications.waset.org/abstracts/search?q=Serge%20Yaskolko"> Serge Yaskolko</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Generative Artificial Intelligence (AI) has significantly expanded its applicability with the incorporation of Large Language Models (LLMs) and become a technology with promise to automate some areas that were very difficult to automate before. The paper describes the introduction of generative Artificial Intelligence into Introductory Computer and Data Science courses and analysis of effect of such introduction. The generative Artificial Intelligence is incorporated in the educational process two-fold: For the instructors, we create templates of prompts for generation of tasks, and grading of the students work, including feedback on the submitted assignments. For the students, we introduce them to basic prompt engineering, which in turn will be used for generation of test cases based on description of the problems, generating code snippets for the single block complexity programming, and partitioning into such blocks of an average size complexity programming. The above-mentioned classes are run using Large Language Models, and feedback from instructors and students and courses’ outcomes are collected. The analysis shows statistically significant positive effect and preference of both stakeholders. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=introductory%20computer%20and%20data%20science%20education" title="introductory computer and data science education">introductory computer and data science education</a>, <a href="https://publications.waset.org/abstracts/search?q=generative%20AI" title=" generative AI"> generative AI</a>, <a href="https://publications.waset.org/abstracts/search?q=large%20language%20models" title=" large language models"> large language models</a>, <a href="https://publications.waset.org/abstracts/search?q=application%20of%20LLMS%20to%20computer%20and%20data%20science%20education" title=" application of LLMS to computer and data science education"> application of LLMS to computer and data science education</a> </p> <a href="https://publications.waset.org/abstracts/175778/improving-student-programming-skills-in-introductory-computer-and-data-science-courses-using-generative-ai" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/175778.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">58</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">163</span> Use of Generative Adversarial Networks (GANs) in Neuroimaging and Clinical Neuroscience Applications</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Niloufar%20Yadgari">Niloufar Yadgari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> GANs are a potent form of deep learning models that have found success in various fields. They are part of the larger group of generative techniques, which aim to produce authentic data using a probabilistic model that learns distributions from actual samples. In clinical settings, GANs have demonstrated improved abilities in capturing spatially intricate, nonlinear, and possibly subtle disease impacts in contrast to conventional generative techniques. This review critically evaluates the current research on how GANs are being used in imaging studies of different neurological conditions like Alzheimer's disease, brain tumors, aging of the brain, and multiple sclerosis. We offer a clear explanation of different GAN techniques for each use case in neuroimaging and delve into the key hurdles, unanswered queries, and potential advancements in utilizing GANs in this field. Our goal is to connect advanced deep learning techniques with neurology studies, showcasing how GANs can assist in clinical decision-making and enhance our comprehension of the structural and functional aspects of brain disorders. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=GAN" title="GAN">GAN</a>, <a href="https://publications.waset.org/abstracts/search?q=pathology" title=" pathology"> pathology</a>, <a href="https://publications.waset.org/abstracts/search?q=generative%20adversarial%20network" title=" generative adversarial network"> generative adversarial network</a>, <a href="https://publications.waset.org/abstracts/search?q=neuro%20imaging" title=" neuro imaging"> neuro imaging</a> </p> <a href="https://publications.waset.org/abstracts/188651/use-of-generative-adversarial-networks-gans-in-neuroimaging-and-clinical-neuroscience-applications" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/188651.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">33</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">162</span> Generating Swarm Satellite Data Using Long Short-Term Memory and Generative Adversarial Networks for the Detection of Seismic Precursors</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yaxin%20Bi">Yaxin Bi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Accurate prediction and understanding of the evolution mechanisms of earthquakes remain challenging in the fields of geology, geophysics, and seismology. This study leverages Long Short-Term Memory (LSTM) networks and Generative Adversarial Networks (GANs), a generative model tailored to time-series data, for generating synthetic time series data based on Swarm satellite data, which will be used for detecting seismic anomalies. LSTMs demonstrated commendable predictive performance in generating synthetic data across multiple countries. In contrast, the GAN models struggled to generate synthetic data, often producing non-informative values, although they were able to capture the data distribution of the time series. These findings highlight both the promise and challenges associated with applying deep learning techniques to generate synthetic data, underscoring the potential of deep learning in generating synthetic electromagnetic satellite data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=LSTM" title="LSTM">LSTM</a>, <a href="https://publications.waset.org/abstracts/search?q=GAN" title=" GAN"> GAN</a>, <a href="https://publications.waset.org/abstracts/search?q=earthquake" title=" earthquake"> earthquake</a>, <a href="https://publications.waset.org/abstracts/search?q=synthetic%20data" title=" synthetic data"> synthetic data</a>, <a href="https://publications.waset.org/abstracts/search?q=generative%20AI" title=" generative AI"> generative AI</a>, <a href="https://publications.waset.org/abstracts/search?q=seismic%20precursors" title=" seismic precursors"> seismic precursors</a> </p> <a href="https://publications.waset.org/abstracts/187478/generating-swarm-satellite-data-using-long-short-term-memory-and-generative-adversarial-networks-for-the-detection-of-seismic-precursors" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/187478.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">33</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">161</span> Artificial Intelligence for Generative Modelling</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shryas%20Bhurat">Shryas Bhurat</a>, <a href="https://publications.waset.org/abstracts/search?q=Aryan%20Vashistha"> Aryan Vashistha</a>, <a href="https://publications.waset.org/abstracts/search?q=Sampreet%20Dinakar%20Nayak"> Sampreet Dinakar Nayak</a>, <a href="https://publications.waset.org/abstracts/search?q=Ayush%20Gupta"> Ayush Gupta</a> </p> <p class="card-text"><strong>Abstract:</strong></p> As the technology is advancing more towards high computational resources, there is a paradigm shift in the usage of these resources to optimize the design process. This paper discusses the usage of ‘Generative Design using Artificial Intelligence’ to build better models that adapt the operations like selection, mutation, and crossover to generate results. The human mind thinks of the simplest approach while designing an object, but the intelligence learns from the past & designs the complex optimized CAD Models. Generative Design takes the boundary conditions and comes up with multiple solutions with iterations to come up with a sturdy design with the most optimal parameter that is given, saving huge amounts of time & resources. The new production techniques that are at our disposal allow us to use additive manufacturing, 3D printing, and other innovative manufacturing techniques to save resources and design artistically engineered CAD Models. Also, this paper discusses the Genetic Algorithm, the Non-Domination technique to choose the right results using biomimicry that has evolved for current habitation for millions of years. The computer uses parametric models to generate newer models using an iterative approach & uses cloud computing to store these iterative designs. The later part of the paper compares the topology optimization technology with Generative Design that is previously being used to generate CAD Models. Finally, this paper shows the performance of algorithms and how these algorithms help in designing resource-efficient models. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithm" title="genetic algorithm">genetic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=bio%20mimicry" title=" bio mimicry"> bio mimicry</a>, <a href="https://publications.waset.org/abstracts/search?q=generative%20modeling" title=" generative modeling"> generative modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=non-dominant%20techniques" title=" non-dominant techniques"> non-dominant techniques</a> </p> <a href="https://publications.waset.org/abstracts/145293/artificial-intelligence-for-generative-modelling" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/145293.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">149</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">160</span> Wearable Music: Generation of Costumes from Music and Generative Art and Wearing Them by 3-Way Projectors</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Noriki%20Amano">Noriki Amano</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The final goal of this study is to create another way in which people enjoy music through the performance of 'Wearable Music'. Concretely speaking, we generate colorful costumes in real- time from music and to realize their dressing by projecting them to a person. For this purpose, we propose three methods in this study. First, a method of giving color to music in a three-dimensionally way. Second, a method of generating images of costumes from music. Third, a method of wearing the images of music. In particular, this study stands out from other related work in that we generate images of unique costumes from music and realize to wear them. In this study, we use the technique of generative arts to generate images of unique costumes and project the images to the fog generated around a person from 3-way using projectors. From this study, we can get how to enjoy music as 'wearable'. Furthermore, we are also able to have the prospect of unconventional entertainment based on the fusion between music and costumes. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=entertainment%20computing" title="entertainment computing">entertainment computing</a>, <a href="https://publications.waset.org/abstracts/search?q=costumes" title=" costumes"> costumes</a>, <a href="https://publications.waset.org/abstracts/search?q=music" title=" music"> music</a>, <a href="https://publications.waset.org/abstracts/search?q=generative%20programming" title=" generative programming"> generative programming</a> </p> <a href="https://publications.waset.org/abstracts/98352/wearable-music-generation-of-costumes-from-music-and-generative-art-and-wearing-them-by-3-way-projectors" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/98352.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">173</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">159</span> Artificial Intelligence and the Next Generation Journalistic Practice: Prospects, Issues and Challenges</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shola%20Abidemi%20Olabode">Shola Abidemi Olabode</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The technological revolution over the years has impacted journalistic practice. As a matter of fact, journalistic practice has evolved alongside technologies of every generation transforming news and reporting, entertainment, and politics. Alongside these developments, the emergence of new kinds of risks and harms associated with generative AI has become rife with implications for media and journalism. Despite their numerous benefits for research and development, generative AI technologies like ChatGPT introduce new practical, ethical, and regulatory complexities in the practice of media and journalism. This paper presents a preliminary overview of the new kinds of challenges and issues for journalism and media practice in the era of generative AI, the implications for Nigeria, and invites a consideration of methods to mitigate the evolving complexity. It draws mainly on desk-based research underscoring the literature in both developed and developing non-western contexts as a contribution to knowledge. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=AI" title="AI">AI</a>, <a href="https://publications.waset.org/abstracts/search?q=journalism" title=" journalism"> journalism</a>, <a href="https://publications.waset.org/abstracts/search?q=media" title=" media"> media</a>, <a href="https://publications.waset.org/abstracts/search?q=online%20harms" title=" online harms"> online harms</a> </p> <a href="https://publications.waset.org/abstracts/170414/artificial-intelligence-and-the-next-generation-journalistic-practice-prospects-issues-and-challenges" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/170414.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">80</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">158</span> Crafting Robust Business Model Innovation Path with Generative Artificial Intelligence in Start-up SMEs</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ignitia%20Motjolopane">Ignitia Motjolopane</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Small and medium enterprises (SMEs) play an important role in economies by contributing to economic growth and employment. In the fourth industrial revolution, the convergence of technologies and the changing nature of work created pressures on economies globally. Generative artificial intelligence (AI) may support SMEs in exploring, exploiting, and transforming business models to align with their growth aspirations. SMEs' growth aspirations fall into four categories: subsistence, income, growth, and speculative. Subsistence-oriented firms focus on meeting basic financial obligations and show less motivation for business model innovation. SMEs focused on income, growth, and speculation are more likely to pursue business model innovation to support growth strategies. SMEs' strategic goals link to distinct business model innovation paths depending on whether SMEs are starting a new business, pursuing growth, or seeking profitability. Integrating generative artificial intelligence in start-up SME business model innovation enhances value creation, user-oriented innovation, and SMEs' ability to adapt to dynamic changes in the business environment. The existing literature may lack comprehensive frameworks and guidelines for effectively integrating generative AI in start-up reiterative business model innovation paths. This paper examines start-up business model innovation path with generative artificial intelligence. A theoretical approach is used to examine start-up-focused SME reiterative business model innovation path with generative AI. Articulating how generative AI may be used to support SMEs to systematically and cyclically build the business model covering most or all business model components and analyse and test the BM's viability throughout the process. As such, the paper explores generative AI usage in market exploration. Moreover, market exploration poses unique challenges for start-ups compared to established companies due to a lack of extensive customer data, sales history, and market knowledge. Furthermore, the paper examines the use of generative AI in developing and testing viable value propositions and business models. In addition, the paper looks into identifying and selecting partners with generative AI support. Selecting the right partners is crucial for start-ups and may significantly impact success. The paper will examine generative AI usage in choosing the right information technology, funding process, revenue model determination, and stress testing business models. Stress testing business models validate strong and weak points by applying scenarios and evaluating the robustness of individual business model components and the interrelation between components. Thus, the stress testing business model may address these uncertainties, as misalignment between an organisation and its environment has been recognised as the leading cause of company failure. Generative AI may be used to generate business model stress-testing scenarios. The paper is expected to make a theoretical and practical contribution to theory and approaches in crafting a robust business model innovation path with generative artificial intelligence in start-up SMEs. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=business%20models" title="business models">business models</a>, <a href="https://publications.waset.org/abstracts/search?q=innovation" title=" innovation"> innovation</a>, <a href="https://publications.waset.org/abstracts/search?q=generative%20AI" title=" generative AI"> generative AI</a>, <a href="https://publications.waset.org/abstracts/search?q=small%20medium%20enterprises" title=" small medium enterprises"> small medium enterprises</a> </p> <a href="https://publications.waset.org/abstracts/176774/crafting-robust-business-model-innovation-path-with-generative-artificial-intelligence-in-start-up-smes" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/176774.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">71</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">157</span> Image Inpainting Model with Small-Sample Size Based on Generative Adversary Network and Genetic Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jiawen%20Wang">Jiawen Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Qijun%20Chen"> Qijun Chen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The performance of most machine-learning methods for image inpainting depends on the quantity and quality of the training samples. However, it is very expensive or even impossible to obtain a great number of training samples in many scenarios. In this paper, an image inpainting model based on a generative adversary network (GAN) is constructed for the cases when the number of training samples is small. Firstly, a feature extraction network (F-net) is incorporated into the GAN network to utilize the available information of the inpainting image. The weighted sum of the extracted feature and the random noise acts as the input to the generative network (G-net). The proposed network can be trained well even when the sample size is very small. Secondly, in the phase of the completion for each damaged image, a genetic algorithm is designed to search an optimized noise input for G-net; based on this optimized input, the parameters of the G-net and F-net are further learned (Once the completion for a certain damaged image ends, the parameters restore to its original values obtained in the training phase) to generate an image patch that not only can fill the missing part of the damaged image smoothly but also has visual semantics. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=image%20inpainting" title="image inpainting">image inpainting</a>, <a href="https://publications.waset.org/abstracts/search?q=generative%20adversary%20nets" title=" generative adversary nets"> generative adversary nets</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithm" title=" genetic algorithm"> genetic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=small-sample%20size" title=" small-sample size"> small-sample size</a> </p> <a href="https://publications.waset.org/abstracts/126552/image-inpainting-model-with-small-sample-size-based-on-generative-adversary-network-and-genetic-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/126552.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">130</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">156</span> Generative Behaviors and Psychological Well-Being in Mexican Elders</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ana%20L.%20Gonzalez-Celis">Ana L. Gonzalez-Celis</a>, <a href="https://publications.waset.org/abstracts/search?q=Edgardo%20Ruiz-Carrillo"> Edgardo Ruiz-Carrillo</a>, <a href="https://publications.waset.org/abstracts/search?q=Karina%20Reyes-Jarquin"> Karina Reyes-Jarquin</a>, <a href="https://publications.waset.org/abstracts/search?q=Margarita%20Chavez-Becerra"> Margarita Chavez-Becerra</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Since recent decades, the aging has been viewed from a more positive perspective, where is not only about losses and damage, but also about being on a stage where you can enjoy life and live with well-being and quality of life. The challenge to feel better is to find those resources that seniors have. For that reason, psychological well-being has shown interest in the study of the affect and life satisfaction (hedonic well-being), while from a more recent tradition, focus on the development of capabilities and the personal growth, considering both as the main indicators of the quality of life. A resource that can be used in the later age is generativity, which refers to the ability of older people to develop and grow through activities that contribute with the improvement of the context in which they live and participate. In this way the generative interest is understood as a favourable attitude that contribute to the common benefit while strengthening and enriching the social institutions, to ensure continuity between generations and social development. On the other hand, generative behavior, differentiating from generative interest, is the expression of that attitude reflected in activities that make a social contribution and a benefit for generations to come. Hence the purpose of the research was to test if there is an association between the generative behaviour type and the psychological well-being with their dimensions. For this reason 188 Mexican adults from 60 to 94 years old (M = 69.78), 67% women, 33% men, completed two instruments: The Ryff’s Well-Being Scales to measure psychological well-being with 39 items with two dimensions (Hedonic and Eudaimonic well-being), and the Loyola’s Generative Behaviors Scale, grouped in five categories: Knowledge transmitted to the next generation, things to be remember, creativity, be productive, contribution to the community, and responsibility of other people. In addition, the socio-demographic data sheet was tested, and self-reported health status. The results indicated that the psychological well-being and its dimensions were significantly associated with the presence of generative behavior, where the level of well-being was higher when the frequency of some generative behaviour excelled; finding that the behavior with greater psychological well-being (M = 81.04, SD = 8.18) was "things to be remembered"; while with greater hedonic well-being (M = 73.39, SD = 12.19) was the behavior "responsibility of other people"; and with greater Eudaimonic well-being (M = 84.61, SD = 6.63), was the behavior "things to be remembered”. The most important findings highlight the importance of generative behaviors in adulthood, finding empirical evidence that the generativity in the last stage of life is associated with well-being. However, by finding differences in the types of generative behaviors at the level of well-being, is proposed the idea that generativity is not situated as an isolated construct, but needs other contextualized and related constructs that can simultaneously operate at different levels, taking into account the relationship between the environment and the individual, encompassing both the social and psychological dimension. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=eudaimonic%20well-being" title="eudaimonic well-being">eudaimonic well-being</a>, <a href="https://publications.waset.org/abstracts/search?q=generativity" title=" generativity"> generativity</a>, <a href="https://publications.waset.org/abstracts/search?q=hedonic%20well-being" title=" hedonic well-being"> hedonic well-being</a>, <a href="https://publications.waset.org/abstracts/search?q=Mexican%20elders" title=" Mexican elders"> Mexican elders</a>, <a href="https://publications.waset.org/abstracts/search?q=psychological%20well-being" title=" psychological well-being"> psychological well-being</a> </p> <a href="https://publications.waset.org/abstracts/65012/generative-behaviors-and-psychological-well-being-in-mexican-elders" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/65012.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">274</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">155</span> Enhancing Residential Architecture through Generative Design: Balancing Aesthetics, Legal Constraints, and Environmental Considerations</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Milena%20Nanova">Milena Nanova</a>, <a href="https://publications.waset.org/abstracts/search?q=Radul%20Shishkov"> Radul Shishkov</a>, <a href="https://publications.waset.org/abstracts/search?q=Damyan%20Damov"> Damyan Damov</a>, <a href="https://publications.waset.org/abstracts/search?q=Martin%20Georgiev"> Martin Georgiev</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This research paper presents an in-depth exploration of the use of generative design in urban residential architecture, with a dual focus on aligning aesthetic values with legal and environmental constraints. The study aims to demonstrate how generative design methodologies can innovate residential building designs that are not only legally compliant and environmentally conscious but also aesthetically compelling. At the core of our research is a specially developed generative design framework tailored for urban residential settings. This framework employs computational algorithms to produce diverse design solutions, meticulously balancing aesthetic appeal with practical considerations. By integrating site-specific features, urban legal restrictions, and environmental factors, our approach generates designs that resonate with the unique character of urban landscapes while adhering to regulatory frameworks. The paper places emphasis on algorithmic implementation of the logical constraint and intricacies in residential architecture by exploring the potential of generative design to create visually engaging and contextually harmonious structures. This exploration also contains an analysis of how these designs align with legal building parameters, showcasing the potential for creative solutions within the confines of urban building regulations. Concurrently, our methodology integrates functional, economic, and environmental factors. We investigate how generative design can be utilized to optimize buildings' performance, considering them, aiming to achieve a symbiotic relationship between the built environment and its natural surroundings. Through a blend of theoretical research and practical case studies, this research highlights the multifaceted capabilities of generative design and demonstrates practical applications of our framework. Our findings illustrate the rich possibilities that arise from an algorithmic design approach in the context of a vibrant urban landscape. This study contributes an alternative perspective to residential architecture, suggesting that the future of urban development lies in embracing the complex interplay between computational design innovation, regulatory adherence, and environmental responsibility. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=generative%20design" title="generative design">generative design</a>, <a href="https://publications.waset.org/abstracts/search?q=computational%20design" title=" computational design"> computational design</a>, <a href="https://publications.waset.org/abstracts/search?q=parametric%20design" title=" parametric design"> parametric design</a>, <a href="https://publications.waset.org/abstracts/search?q=algorithmic%20modeling" title=" algorithmic modeling"> algorithmic modeling</a> </p> <a href="https://publications.waset.org/abstracts/181135/enhancing-residential-architecture-through-generative-design-balancing-aesthetics-legal-constraints-and-environmental-considerations" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/181135.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">65</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">154</span> Semi-Supervised Outlier Detection Using a Generative and Adversary Framework</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jindong%20Gu">Jindong Gu</a>, <a href="https://publications.waset.org/abstracts/search?q=Matthias%20Schubert"> Matthias Schubert</a>, <a href="https://publications.waset.org/abstracts/search?q=Volker%20Tresp"> Volker Tresp</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In many outlier detection tasks, only training data belonging to one class, i.e., the positive class, is available. The task is then to predict a new data point as belonging either to the positive class or to the negative class, in which case the data point is considered an outlier. For this task, we propose a novel corrupted Generative Adversarial Network (CorGAN). In the adversarial process of training CorGAN, the Generator generates outlier samples for the negative class, and the Discriminator is trained to distinguish the positive training data from the generated negative data. The proposed framework is evaluated using an image dataset and a real-world network intrusion dataset. Our outlier-detection method achieves state-of-the-art performance on both tasks. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=one-class%20classification" title="one-class classification">one-class classification</a>, <a href="https://publications.waset.org/abstracts/search?q=outlier%20detection" title=" outlier detection"> outlier detection</a>, <a href="https://publications.waset.org/abstracts/search?q=generative%20adversary%20networks" title=" generative adversary networks"> generative adversary networks</a>, <a href="https://publications.waset.org/abstracts/search?q=semi-supervised%20learning" title=" semi-supervised learning"> semi-supervised learning</a> </p> <a href="https://publications.waset.org/abstracts/99065/semi-supervised-outlier-detection-using-a-generative-and-adversary-framework" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/99065.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">151</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">153</span> Generative Adversarial Network for Bidirectional Mappings between Retinal Fundus Images and Vessel Segmented Images</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Haoqi%20Gao">Haoqi Gao</a>, <a href="https://publications.waset.org/abstracts/search?q=Koichi%20Ogawara"> Koichi Ogawara</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Retinal vascular segmentation of color fundus is the basis of ophthalmic computer-aided diagnosis and large-scale disease screening systems. Early screening of fundus diseases has great value for clinical medical diagnosis. The traditional methods depend on the experience of the doctor, which is time-consuming, labor-intensive, and inefficient. Furthermore, medical images are scarce and fraught with legal concerns regarding patient privacy. In this paper, we propose a new Generative Adversarial Network based on CycleGAN for retinal fundus images. This method can generate not only synthetic fundus images but also generate corresponding segmentation masks, which has certain application value and challenge in computer vision and computer graphics. In the results, we evaluate our proposed method from both quantitative and qualitative. For generated segmented images, our method achieves dice coefficient of 0.81 and PR of 0.89 on DRIVE dataset. For generated synthetic fundus images, we use ”Toy Experiment” to verify the state-of-the-art performance of our method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=retinal%20vascular%20segmentations" title="retinal vascular segmentations">retinal vascular segmentations</a>, <a href="https://publications.waset.org/abstracts/search?q=generative%20ad-versarial%20network" title=" generative ad-versarial network"> generative ad-versarial network</a>, <a href="https://publications.waset.org/abstracts/search?q=cyclegan" title=" cyclegan"> cyclegan</a>, <a href="https://publications.waset.org/abstracts/search?q=fundus%20images" title=" fundus images"> fundus images</a> </p> <a href="https://publications.waset.org/abstracts/110591/generative-adversarial-network-for-bidirectional-mappings-between-retinal-fundus-images-and-vessel-segmented-images" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/110591.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">144</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">152</span> A Generative Adversarial Framework for Bounding Confounded Causal Effects</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yaowei%20Hu">Yaowei Hu</a>, <a href="https://publications.waset.org/abstracts/search?q=Yongkai%20Wu"> Yongkai Wu</a>, <a href="https://publications.waset.org/abstracts/search?q=Lu%20Zhang"> Lu Zhang</a>, <a href="https://publications.waset.org/abstracts/search?q=Xintao%20Wu"> Xintao Wu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Causal inference from observational data is receiving wide applications in many fields. However, unidentifiable situations, where causal effects cannot be uniquely computed from observational data, pose critical barriers to applying causal inference to complicated real applications. In this paper, we develop a bounding method for estimating the average causal effect (ACE) under unidentifiable situations due to hidden confounders. We propose to parameterize the unknown exogenous random variables and structural equations of a causal model using neural networks and implicit generative models. Then, with an adversarial learning framework, we search the parameter space to explicitly traverse causal models that agree with the given observational distribution and find those that minimize or maximize the ACE to obtain its lower and upper bounds. The proposed method does not make any assumption about the data generating process and the type of the variables. Experiments using both synthetic and real-world datasets show the effectiveness of the method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=average%20causal%20effect" title="average causal effect">average causal effect</a>, <a href="https://publications.waset.org/abstracts/search?q=hidden%20confounding" title=" hidden confounding"> hidden confounding</a>, <a href="https://publications.waset.org/abstracts/search?q=bound%20estimation" title=" bound estimation"> bound estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=generative%20adversarial%20learning" title=" generative adversarial learning"> generative adversarial learning</a> </p> <a href="https://publications.waset.org/abstracts/127808/a-generative-adversarial-framework-for-bounding-confounded-causal-effects" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/127808.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">191</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">151</span> Servitization in Machine and Plant Engineering: Leveraging Generative AI for Effective Product Portfolio Management Amidst Disruptive Innovations</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Till%20Gramberg">Till Gramberg</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the dynamic world of machine and plant engineering, stagnation in the growth of new product sales compels companies to reconsider their business models. The increasing shift toward service orientation, known as "servitization," along with challenges posed by digitalization and sustainability, necessitates an adaptation of product portfolio management (PPM). Against this backdrop, this study investigates the current challenges and requirements of PPM in this industrial context and develops a framework for the application of generative artificial intelligence (AI) to enhance agility and efficiency in PPM processes. The research approach of this study is based on a mixed-method design. Initially, qualitative interviews with industry experts were conducted to gain a deep understanding of the specific challenges and requirements in PPM. These interviews were analyzed using the Gioia method, painting a detailed picture of the existing issues and needs within the sector. This was complemented by a quantitative online survey. The combination of qualitative and quantitative research enabled a comprehensive understanding of the current challenges in the practical application of machine and plant engineering PPM. Based on these insights, a specific framework for the application of generative AI in PPM was developed. This framework aims to assist companies in implementing faster and more agile processes, systematically integrating dynamic requirements from trends such as digitalization and sustainability into their PPM process. Utilizing generative AI technologies, companies can more quickly identify and respond to trends and market changes, allowing for a more efficient and targeted adaptation of the product portfolio. The study emphasizes the importance of an agile and reactive approach to PPM in a rapidly changing environment. It demonstrates how generative AI can serve as a powerful tool to manage the complexity of a diversified and continually evolving product portfolio. The developed framework offers practical guidelines and strategies for companies to improve their PPM processes by leveraging the latest technological advancements while maintaining ecological and social responsibility. This paper significantly contributes to deepening the understanding of the application of generative AI in PPM and provides a framework for companies to manage their product portfolios more effectively and adapt to changing market conditions. The findings underscore the relevance of continuous adaptation and innovation in PPM strategies and demonstrate the potential of generative AI for proactive and future-oriented business management. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=servitization" title="servitization">servitization</a>, <a href="https://publications.waset.org/abstracts/search?q=product%20portfolio%20management" title=" product portfolio management"> product portfolio management</a>, <a href="https://publications.waset.org/abstracts/search?q=generative%20AI" title=" generative AI"> generative AI</a>, <a href="https://publications.waset.org/abstracts/search?q=disruptive%20innovation" title=" disruptive innovation"> disruptive innovation</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20and%20plant%20engineering" title=" machine and plant engineering"> machine and plant engineering</a> </p> <a href="https://publications.waset.org/abstracts/179281/servitization-in-machine-and-plant-engineering-leveraging-generative-ai-for-effective-product-portfolio-management-amidst-disruptive-innovations" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/179281.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">82</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">150</span> DISGAN: Efficient Generative Adversarial Network-Based Method for Cyber-Intrusion Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hongyu%20Chen">Hongyu Chen</a>, <a href="https://publications.waset.org/abstracts/search?q=Li%20Jiang"> Li Jiang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Ubiquitous anomalies endanger the security of our system con- stantly. They may bring irreversible damages to the system and cause leakage of privacy. Thus, it is of vital importance to promptly detect these anomalies. Traditional supervised methods such as Decision Trees and Support Vector Machine (SVM) are used to classify normality and abnormality. However, in some case, the abnormal status are largely rarer than normal status, which leads to decision bias of these methods. Generative adversarial network (GAN) has been proposed to handle the case. With its strong generative ability, it only needs to learn the distribution of normal status, and identify the abnormal status through the gap between it and the learned distribution. Nevertheless, existing GAN-based models are not suitable to process data with discrete values, leading to immense degradation of detection performance. To cope with the discrete features, in this paper, we propose an efficient GAN-based model with specifically-designed loss function. Experiment results show that our model outperforms state-of-the-art models on discrete dataset and remarkably reduce the overhead. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=GAN" title="GAN">GAN</a>, <a href="https://publications.waset.org/abstracts/search?q=discrete%20feature" title=" discrete feature"> discrete feature</a>, <a href="https://publications.waset.org/abstracts/search?q=Wasserstein%20distance" title=" Wasserstein distance"> Wasserstein distance</a>, <a href="https://publications.waset.org/abstracts/search?q=multiple%20intermediate%20layers" title=" multiple intermediate layers"> multiple intermediate layers</a> </p> <a href="https://publications.waset.org/abstracts/113103/disgan-efficient-generative-adversarial-network-based-method-for-cyber-intrusion-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/113103.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">129</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">149</span> Time Series Simulation by Conditional Generative Adversarial Net</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rao%20Fu">Rao Fu</a>, <a href="https://publications.waset.org/abstracts/search?q=Jie%20Chen"> Jie Chen</a>, <a href="https://publications.waset.org/abstracts/search?q=Shutian%20Zeng"> Shutian Zeng</a>, <a href="https://publications.waset.org/abstracts/search?q=Yiping%20Zhuang"> Yiping Zhuang</a>, <a href="https://publications.waset.org/abstracts/search?q=Agus%20Sudjianto"> Agus Sudjianto</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Generative Adversarial Net (GAN) has proved to be a powerful machine learning tool in image data analysis and generation. In this paper, we propose to use Conditional Generative Adversarial Net (CGAN) to learn and simulate time series data. The conditions include both categorical and continuous variables with different auxiliary information. Our simulation studies show that CGAN has the capability to learn different types of normal and heavy-tailed distributions, as well as dependent structures of different time series. It also has the capability to generate conditional predictive distributions consistent with training data distributions. We also provide an in-depth discussion on the rationale behind GAN and the neural networks as hierarchical splines to establish a clear connection with existing statistical methods of distribution generation. In practice, CGAN has a wide range of applications in market risk and counterparty risk analysis: it can be applied to learn historical data and generate scenarios for the calculation of Value-at-Risk (VaR) and Expected Shortfall (ES), and it can also predict the movement of the market risk factors. We present a real data analysis including a backtesting to demonstrate that CGAN can outperform Historical Simulation (HS), a popular method in market risk analysis to calculate VaR. CGAN can also be applied in economic time series modeling and forecasting. In this regard, we have included an example of hypothetical shock analysis for economic models and the generation of potential CCAR scenarios by CGAN at the end of the paper. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=conditional%20generative%20adversarial%20net" title="conditional generative adversarial net">conditional generative adversarial net</a>, <a href="https://publications.waset.org/abstracts/search?q=market%20and%20credit%20risk%20management" title=" market and credit risk management"> market and credit risk management</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20network" title=" neural network"> neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=time%20series" title=" time series"> time series</a> </p> <a href="https://publications.waset.org/abstracts/123535/time-series-simulation-by-conditional-generative-adversarial-net" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/123535.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">143</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">148</span> Leveraging Unannotated Data to Improve Question Answering for French Contract Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Touila%20Ahmed">Touila Ahmed</a>, <a href="https://publications.waset.org/abstracts/search?q=Elie%20Louis"> Elie Louis</a>, <a href="https://publications.waset.org/abstracts/search?q=Hamza%20Gharbi"> Hamza Gharbi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> State of the art question answering models have recently shown impressive performance especially in a zero-shot setting. This approach is particularly useful when confronted with a highly diverse domain such as the legal field, in which it is increasingly difficult to have a dataset covering every notion and concept. In this work, we propose a flexible generative question answering approach to contract analysis as well as a weakly supervised procedure to leverage unannotated data and boost our models’ performance in general, and their zero-shot performance in particular. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=question%20answering" title="question answering">question answering</a>, <a href="https://publications.waset.org/abstracts/search?q=contract%20analysis" title=" contract analysis"> contract analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=zero-shot" title=" zero-shot"> zero-shot</a>, <a href="https://publications.waset.org/abstracts/search?q=natural%20language%20processing" title=" natural language processing"> natural language processing</a>, <a href="https://publications.waset.org/abstracts/search?q=generative%20models" title=" generative models"> generative models</a>, <a href="https://publications.waset.org/abstracts/search?q=self-supervision" title=" self-supervision"> self-supervision</a> </p> <a href="https://publications.waset.org/abstracts/164182/leveraging-unannotated-data-to-improve-question-answering-for-french-contract-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/164182.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">194</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">147</span> Domain Adaptation Save Lives - Drowning Detection in Swimming Pool Scene Based on YOLOV8 Improved by Gaussian Poisson Generative Adversarial Network Augmentation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Simiao%20Ren">Simiao Ren</a>, <a href="https://publications.waset.org/abstracts/search?q=En%20Wei"> En Wei</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Drowning is a significant safety issue worldwide, and a robust computer vision-based alert system can easily prevent such tragedies in swimming pools. However, due to domain shift caused by the visual gap (potentially due to lighting, indoor scene change, pool floor color etc.) between the training swimming pool and the test swimming pool, the robustness of such algorithms has been questionable. The annotation cost for labeling each new swimming pool is too expensive for mass adoption of such a technique. To address this issue, we propose a domain-aware data augmentation pipeline based on Gaussian Poisson Generative Adversarial Network (GP-GAN). Combined with YOLOv8, we demonstrate that such a domain adaptation technique can significantly improve the model performance (from 0.24 mAP to 0.82 mAP) on new test scenes. As the augmentation method only require background imagery from the new domain (no annotation needed), we believe this is a promising, practical route for preventing swimming pool drowning. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=computer%20vision" title="computer vision">computer vision</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=YOLOv8" title=" YOLOv8"> YOLOv8</a>, <a href="https://publications.waset.org/abstracts/search?q=detection" title=" detection"> detection</a>, <a href="https://publications.waset.org/abstracts/search?q=swimming%20pool" title=" swimming pool"> swimming pool</a>, <a href="https://publications.waset.org/abstracts/search?q=drowning" title=" drowning"> drowning</a>, <a href="https://publications.waset.org/abstracts/search?q=domain%20adaptation" title=" domain adaptation"> domain adaptation</a>, <a href="https://publications.waset.org/abstracts/search?q=generative%20adversarial%20network" title=" generative adversarial network"> generative adversarial network</a>, <a href="https://publications.waset.org/abstracts/search?q=GAN" title=" GAN"> GAN</a>, <a href="https://publications.waset.org/abstracts/search?q=GP-GAN" title=" GP-GAN"> GP-GAN</a> </p> <a href="https://publications.waset.org/abstracts/163443/domain-adaptation-save-lives-drowning-detection-in-swimming-pool-scene-based-on-yolov8-improved-by-gaussian-poisson-generative-adversarial-network-augmentation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/163443.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">101</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">146</span> Theoretical Approaches to Graphic and Formal Generation from Evolutionary Genetics</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Luz%20Estrada">Luz Estrada</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The currents of evolutionary materialistic thought have argued that knowledge about an object is not obtained through the abstractive method. That is, the object cannot come to be understood if founded upon itself, nor does it take place by the encounter between form and matter. According to this affirmation, the research presented here identified as a problematic situation the absence of comprehension of the formal creation as a generative operation. This has been referred to as a recurrent lack in the production of objects and corresponds to the need to conceive the configurative process from the reality of its genesis. In this case, it is of interest to explore ways of creation that consider the object as if it were a living organism, as well as responding to the object’s experience as embodied in the designer since it unfolds its genesis simultaneously to the ways of existence of those who are involved in the generative experience. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=architecture" title="architecture">architecture</a>, <a href="https://publications.waset.org/abstracts/search?q=theoretical%20graphics" title=" theoretical graphics"> theoretical graphics</a>, <a href="https://publications.waset.org/abstracts/search?q=evolutionary%20genetics" title=" evolutionary genetics"> evolutionary genetics</a>, <a href="https://publications.waset.org/abstracts/search?q=formal%20perception" title=" formal perception"> formal perception</a> </p> <a href="https://publications.waset.org/abstracts/158586/theoretical-approaches-to-graphic-and-formal-generation-from-evolutionary-genetics" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/158586.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">117</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">145</span> Design and Implementation of Generative Models for Odor Classification Using Electronic Nose</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kumar%20Shashvat">Kumar Shashvat</a>, <a href="https://publications.waset.org/abstracts/search?q=Amol%20P.%20Bhondekar"> Amol P. Bhondekar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the midst of the five senses, odor is the most reminiscent and least understood. Odor testing has been mysterious and odor data fabled to most practitioners. The delinquent of recognition and classification of odor is important to achieve. The facility to smell and predict whether the artifact is of further use or it has become undesirable for consumption; the imitation of this problem hooked on a model is of consideration. The general industrial standard for this classification is color based anyhow; odor can be improved classifier than color based classification and if incorporated in machine will be awfully constructive. For cataloging of odor for peas, trees and cashews various discriminative approaches have been used Discriminative approaches offer good prognostic performance and have been widely used in many applications but are incapable to make effectual use of the unlabeled information. In such scenarios, generative approaches have better applicability, as they are able to knob glitches, such as in set-ups where variability in the series of possible input vectors is enormous. Generative models are integrated in machine learning for either modeling data directly or as a transitional step to form an indeterminate probability density function. The algorithms or models Linear Discriminant Analysis and Naive Bayes Classifier have been used for classification of the odor of cashews. Linear Discriminant Analysis is a method used in data classification, pattern recognition, and machine learning to discover a linear combination of features that typifies or divides two or more classes of objects or procedures. The Naive Bayes algorithm is a classification approach base on Bayes rule and a set of qualified independence theory. Naive Bayes classifiers are highly scalable, requiring a number of restraints linear in the number of variables (features/predictors) in a learning predicament. The main recompenses of using the generative models are generally a Generative Models make stronger assumptions about the data, specifically, about the distribution of predictors given the response variables. The Electronic instrument which is used for artificial odor sensing and classification is an electronic nose. This device is designed to imitate the anthropological sense of odor by providing an analysis of individual chemicals or chemical mixtures. The experimental results have been evaluated in the form of the performance measures i.e. are accuracy, precision and recall. The investigational results have proven that the overall performance of the Linear Discriminant Analysis was better in assessment to the Naive Bayes Classifier on cashew dataset. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=odor%20classification" title="odor classification">odor classification</a>, <a href="https://publications.waset.org/abstracts/search?q=generative%20models" title=" generative models"> generative models</a>, <a href="https://publications.waset.org/abstracts/search?q=naive%20bayes" title=" naive bayes"> naive bayes</a>, <a href="https://publications.waset.org/abstracts/search?q=linear%20discriminant%20analysis" title=" linear discriminant analysis"> linear discriminant analysis</a> </p> <a href="https://publications.waset.org/abstracts/58224/design-and-implementation-of-generative-models-for-odor-classification-using-electronic-nose" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/58224.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">387</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">144</span> A Furniture Industry Concept for a Sustainable Generative Design Platform Employing Robot Based Additive Manufacturing</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Andrew%20Fox">Andrew Fox</a>, <a href="https://publications.waset.org/abstracts/search?q=Tao%20Zhang"> Tao Zhang</a>, <a href="https://publications.waset.org/abstracts/search?q=Yuanhong%20Zhao"> Yuanhong Zhao</a>, <a href="https://publications.waset.org/abstracts/search?q=Qingping%20Yang"> Qingping Yang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The furniture manufacturing industry has been slow in general to adopt the latest manufacturing technologies, historically relying heavily upon specialised conventional machinery. This approach not only requires high levels of specialist process knowledge, training, and capital investment but also suffers from significant subtractive manufacturing waste and high logistics costs due to the requirement for centralised manufacturing, with high levels of furniture product not re-cycled or re-used. This paper aims to address the problems by introducing suitable digital manufacturing technologies to create step changes in furniture manufacturing design, as the traditional design practices have been reported as building in 80% of environmental impact. In this paper, a 3D printing robot for furniture manufacturing is reported. The 3D printing robot mainly comprises a KUKA industrial robot, an Arduino microprocessor, and a self-assembled screw fed extruder. Compared to traditional 3D printer, the 3D printing robot has larger motion range and can be easily upgraded to enlarge the maximum size of the printed object. Generative design is also investigated in this paper, aiming to establish a combined design methodology that allows assessment of goals, constraints, materials, and manufacturing processes simultaneously. ‘Matrixing’ for part amalgamation and product performance optimisation is enabled. The generative design goals of integrated waste reduction increased manufacturing efficiency, optimised product performance, and reduced environmental impact institute a truly lean and innovative future design methodology. In addition, there is massive future potential to leverage Single Minute Exchange of Die (SMED) theory through generative design post-processing of geometry for robot manufacture, resulting in ‘mass customised’ furniture with virtually no setup requirements. These generatively designed products can be manufactured using the robot based additive manufacturing. Essentially, the 3D printing robot is already functional; some initial goals have been achieved and are also presented in this paper. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=additive%20manufacturing" title="additive manufacturing">additive manufacturing</a>, <a href="https://publications.waset.org/abstracts/search?q=generative%20design" title=" generative design"> generative design</a>, <a href="https://publications.waset.org/abstracts/search?q=robot" title=" robot"> robot</a>, <a href="https://publications.waset.org/abstracts/search?q=sustainability" title=" sustainability"> sustainability</a> </p> <a href="https://publications.waset.org/abstracts/116707/a-furniture-industry-concept-for-a-sustainable-generative-design-platform-employing-robot-based-additive-manufacturing" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/116707.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">132</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">143</span> Turbulent Channel Flow Synthesis using Generative Adversarial Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=John%20M.%20Lyne">John M. Lyne</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20Andrea%20Scott"> K. Andrea Scott</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In fluid dynamics, direct numerical simulations (DNS) of turbulent flows require large amounts of nodes to appropriately resolve all scales of energy transfer. Due to the size of these databases, sharing these datasets amongst the academic community is a challenge. Recent work has been done to investigate the use of super-resolution to enable database sharing, where a low-resolution flow field is super-resolved to high resolutions using a neural network. Recently, Generative Adversarial Networks (GAN) have grown in popularity with impressive results in the generation of faces, landscapes, and more. This work investigates the generation of unique high-resolution channel flow velocity fields from a low-dimensional latent space using a GAN. The training objective of the GAN is to generate samples in which the distribution of the generated samplesis ideally indistinguishable from the distribution of the training data. In this study, the network is trained using samples drawn from a statistically stationary channel flow at a Reynolds number of 560. Results show that the turbulent statistics and energy spectra of the generated flow fields are within reasonable agreement with those of the DNS data, demonstrating that GANscan produce the intricate multi-scale phenomena of turbulence. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=computational%20fluid%20dynamics" title="computational fluid dynamics">computational fluid dynamics</a>, <a href="https://publications.waset.org/abstracts/search?q=channel%20flow" title=" channel flow"> channel flow</a>, <a href="https://publications.waset.org/abstracts/search?q=turbulence" title=" turbulence"> turbulence</a>, <a href="https://publications.waset.org/abstracts/search?q=generative%20adversarial%20network" title=" generative adversarial network"> generative adversarial network</a> </p> <a href="https://publications.waset.org/abstracts/141594/turbulent-channel-flow-synthesis-using-generative-adversarial-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/141594.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">206</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=generative%20AI&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=generative%20AI&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=generative%20AI&page=4">4</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=generative%20AI&page=5">5</a></li> <li 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