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Titlebook: Intelligent Systems Design and Applications; Deep Learning, Volum Ajith Abraham,Anu Bajaj,Tzung-Pei Hong Conference proceedings 2024 The Ed

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发表于 2025-3-21 18:36:24 | 显示全部楼层 |阅读模式
书目名称Intelligent Systems Design and Applications
副标题Deep Learning, Volum
编辑Ajith Abraham,Anu Bajaj,Tzung-Pei Hong
视频video
概述Includes recent research on intelligent systems design and applications.Presents the proceedings of the 23rd International Conference on Intelligent Systems Design and Applications ISDA 2023.Written b
丛书名称Lecture Notes in Networks and Systems
图书封面Titlebook: Intelligent Systems Design and Applications; Deep Learning, Volum Ajith Abraham,Anu Bajaj,Tzung-Pei Hong Conference proceedings 2024 The Ed
描述.This book highlights recent research on intelligent systems and nature-inspired computing. It presents 47 selected papers focused on Deep Learning from the 23rd International Conference on Intelligent Systems Design and Applications (ISDA 2023), which was held in 5 different cities namely Olten, Switzerland; Porto, Portugal; Kaunas, Lithuania; Greater Noida, India; Kochi, India, and in online mode. The ISDA is a premier conference in the field of artificial intelligence, and the latest installment brought together researchers, engineers, and practitioners whose work involves intelligent systems and their applications in industry. ISDA 2023 had contributions by authors from 64 countries. This book offers a valuable reference guide for all scientists, academicians, researchers, students, and practitioners in the field of artificial intelligence and deep learning..
出版日期Conference proceedings 2024
关键词Intelligent Systems; Intelligent Systems Design; Intelligent Systems Applications; ISDA; ISDA 2023
版次1
doihttps://doi.org/10.1007/978-3-031-64836-6
isbn_softcover978-3-031-64835-9
isbn_ebook978-3-031-64836-6Series ISSN 2367-3370 Series E-ISSN 2367-3389
issn_series 2367-3370
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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Deep Learning Approach for Autonomous Spacecraft Landing,Therefore in this research a simulation is created as realistic as possible considering required physics parameters. Data has been collected while landing a spacecraft, trained deep neural network, and deployed those DNN into simulator.
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OP-FedELM: One-Pass Privacy-Preserving Federated Classification via Evolving Clustering Method and cluster training datasets at the clients. Further, it will reduce the computational training time of ELM and communication overhead. At the server, we employed a meta-clustering algorithm to cluster the updates from the clients. We also proposed another one-shot FL algorithm called privacy-preservin
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Unveiling Deepfakes: Convolutional Neural Networks for Detection,visualize the training progress. This method contributes to the growing field of deepfake detection and offers a potential solution to combat the spread of manipulated media. The model obtains a validation accuracy of around 53.7% and a training accuracy of about 92%.
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