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Titlebook: Computational Intelligence Methods for Green Technology and Sustainable Development; Proceedings of the I Yo-Ping Huang,Wen-June Wang,Nguye

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發(fā)表于 2025-3-21 19:25:24 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Computational Intelligence Methods for Green Technology and Sustainable Development
副標題Proceedings of the I
編輯Yo-Ping Huang,Wen-June Wang,Nguyen-Le Hung
視頻videohttp://file.papertrans.cn/233/232395/232395.mp4
概述Presents recent Computational Intelligence Methods for Green Technology and Sustainable Development.Includes the proceedings of the 5th International Conference on Green Technology and Sustainable Dev
叢書名稱Advances in Intelligent Systems and Computing
圖書封面Titlebook: Computational Intelligence Methods for Green Technology and Sustainable Development; Proceedings of the I Yo-Ping Huang,Wen-June Wang,Nguye
描述.This book is a selected collection of 54 peer-reviewed original scientific research papers of the 5th International Conference on Green Technology and Sustainable Development (GTSD2020) organised in Vietnam in 2020. It highlights the importance of sustainability as well as promotes up-to-date innovation and research for green development in technologies, economics and education among countries. The conference provides an international platform for researchers, practitioners, policymakers and entrepreneurs to present their advances, knowledge and experience on various interdisciplinary topics related to the theme of “Green technology and sustainable development in industrial revolution 4.0”..The book is a valuable resource for researchers, analysts, engineers, practitioners and policymakers who are interested in the latest findings in artificial intelligence, cyber systems, robotics, green energy and power systems, mechanical and computational? mechanic models and advanced civil engineering. This book has 05 sessions consisting of both theoretical and practical aspects, and numerical and experimental analyses in various engineering disciplines..
出版日期Conference proceedings 2021
關鍵詞Internet of Things; Artificial Intelligence; Machine Learning; Computational Intelligence Autonomous Ro
版次1
doihttps://doi.org/10.1007/978-3-030-62324-1
isbn_softcover978-3-030-62323-4
isbn_ebook978-3-030-62324-1Series ISSN 2194-5357 Series E-ISSN 2194-5365
issn_series 2194-5357
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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發(fā)表于 2025-3-21 22:30:59 | 只看該作者
Mobile Data Traffic Offloading with Content Centric Networkingg CCN protocol to cache contents in the E-UTRAN Node B (eNodeB). With OPNET Modeler simulation tool, we conduct realistic mobile networks with a huge number of mobility LTE MSs access to the same service on a single server. The simulation results show that all MSs requests are responded successfully
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發(fā)表于 2025-3-22 00:46:12 | 只看該作者
A Computer-Aided Detection to Intracranial Hemorrhage by Using Deep Learning: A Case Study images. The deep-learning model based on MobileNetV2 architecture was trained on RSNA (Radiological Society of North America) Intracranial Hemorrhage dataset. Then it was validated on a dataset of ICH-Vietnamese cases collected from Vinh Long Province Hospital, Vietnam. The experiment indicated tha
地板
發(fā)表于 2025-3-22 04:57:32 | 只看該作者
A Novel Security Solution for Decentralized Web Systems with Real Time Hot-IPs Detectionh as (distributed) denial-of-service attacks or scanning Internet worm attacks. Detecting Hot-IPs in real time is the first important step that assists administrators in selecting security policies for the system.
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Proposed Novel Fish Freshness Classification Using Effective Low-Cost Threshold-Based and Neural Netample. The results of 8/9 models reach their 100% of accuracy on the training set and 7/9 at their 100% of accuracy on the testing set. These results confirm our four proposed feature assumptions and reveal the feasibility of the proposed models based on extracted features which are non-invasive, ra
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Malware Classification by Using Deep Learning Frameworkuch more advantageous due to the ability to model complex nonlinear functions compared to principal component analysis (PCA) which is restricted to a linear map. The compressed malware features are then classified with a deep neural network. Preliminary test results are quite promising, with 96% cla
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Attention Mechanism for Fashion Image Captioningion image which is able to cover both items and the relationship among the detailed attributes of items. We introduce an efficient framework for fashion image captioning that incorporates spatial attention inside the traditional encoder-decoder architecture. Our model generates fashion image caption
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Conference proceedings 2021en energy and power systems, mechanical and computational? mechanic models and advanced civil engineering. This book has 05 sessions consisting of both theoretical and practical aspects, and numerical and experimental analyses in various engineering disciplines..
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