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Titlebook: Internet of Things and Artificial Intelligence for Smart Environments; Hoe Tung Yew,Mazlina Mamat,Ervin Gubin Moung Book 2024 The Editor(s

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發(fā)表于 2025-3-21 20:04:32 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Internet of Things and Artificial Intelligence for Smart Environments
編輯Hoe Tung Yew,Mazlina Mamat,Ervin Gubin Moung
視頻videohttp://file.papertrans.cn/473/472479/472479.mp4
概述Reviews the application of smart technologies that utilizes Internet of Things (IoT) and Artificial Intelligence (AI).Highlights new findings on IoT and AI applications in smart environments.Provides
叢書名稱Internet of Things
圖書封面Titlebook: Internet of Things and Artificial Intelligence for Smart Environments; Hoe Tung Yew,Mazlina Mamat,Ervin Gubin Moung Book 2024 The Editor(s
描述.This book highlights the application of smart technologies that utilize the Internet of Things (IoT) and Artificial Intelligence (AI) to enhance efficiency and productivity across various industries. It provides a comprehensive review of the latest IoT and AI developments, including their applications, opportunities, and challenges in smart environments. This book is a valuable resource for researchers, engineers, academics, and students working on implementing smart technologies..
出版日期Book 2024
關鍵詞Wireless Networks; Food Shelf Life; Green House Management; Patient Monitoring; Image Processing; E-Walle
版次1
doihttps://doi.org/10.1007/978-981-97-1432-2
isbn_softcover978-981-97-1434-6
isbn_ebook978-981-97-1432-2Series ISSN 2199-1073 Series E-ISSN 2199-1081
issn_series 2199-1073
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor
The information of publication is updating

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IoT-Based Patient Monitoring System,he performance of the proposed system is benchmarked with the medical devices in the market. The average accuracies of measuring oxygen saturation (SpO.), heart rate, body temperature, and respiratory rate are 98.83%, 98.12%, 98.88%, and 97.61%, respectively. The accuracy, sensitivity, and specifici
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Artificial Intelligence in Time Series Prediction, Classification, and Sequence-to-Sequence Problempredicting the remaining shelf life, employing a sequence-to-sequence AI network. The innovative sequence-to-sequence network makes sequential predictions, dynamically adapting to yield accurate remaining shelf life estimates over time. Notable results include the LSTM model‘s consistent API predict
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Fuzzy-Based Handover Scheme for 5G Heterogeneous Networks,ates based on user preference and service requirements. In a comparative analysis, the proposed algorithm is assessed against the conventional TOPSIS algorithms. The simulation outcomes indicate that the proposed algorithm achieves a noteworthy reduction in handovers, with a decrease of up to 77.2%
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