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Titlebook: Next Generation Systems and Networks; Proceedings of BITS Hari Om Bansal,Pawan K. Ajmera,Chandra Shekhar Conference proceedings 2023 The E

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書目名稱Next Generation Systems and Networks
副標題Proceedings of BITS
編輯Hari Om Bansal,Pawan K. Ajmera,Chandra Shekhar
視頻videohttp://file.papertrans.cn/667/666236/666236.mp4
概述Presents recent advances on next-generation systems and networks.Discusses the outcomes of BITS EEE CON 2022, held at BITS Pilani, India.Serves as a reference guide for researchers and practitioners i
叢書名稱Lecture Notes in Networks and Systems
圖書封面Titlebook: Next Generation Systems and Networks; Proceedings of BITS  Hari Om Bansal,Pawan K. Ajmera,Chandra Shekhar Conference proceedings 2023 The E
描述The book is a collection of high-quality research papers presented at International Conference on Next Generation Systems and Networks (BITS EEE CON 2022), held at Birla Institute of Technology & Science, Pilani, Rajasthan, India, during November 4–5, 2022. This book provides reliable and efficient design solutions for the next-generation networks and systems. The book covers research areas in energy, power and control; communication and signal processing; and electronics and nanotechnology.
出版日期Conference proceedings 2023
關(guān)鍵詞5G/6G Communication; Intelligent Reflecting Surfaces; Smart Grid Communications; AI in Wireless Communi
版次1
doihttps://doi.org/10.1007/978-981-99-0483-9
isbn_softcover978-981-99-0482-2
isbn_ebook978-981-99-0483-9Series 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 Singapor
The information of publication is updating

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Nature-Inspired Optimization Algorithm-Assisted Optimal Sensor Node Positioning for Precision Agricsing nodes on the field. This is done by minimizing an innovatively designed cost function which is an indication of the un-scrutinized?field area and overlapping coverage area of the sensor nodes. Five different nature-inspired optimization algorithms are applied in this paper for the said task, an
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Stock Price Prediction Using GRU and BiLSTM Models,d comparing the performances of both models to predict future stock prices. The experiments were carried out using a freely available dataset of stock market, which contained the stock prices of various companies. It was observed that the BiLSTM model performed better than the GRU model. However, GR
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Intelligent Cost Estimation Engine for EV Charging Stations: A Deep Learning-Based Approach,-driven and deep learning-based intelligent cost estimation engine proposed here will leverage the IOT connectivity for EV to forecast the load demand at charging station and efficient price estimation for EV charging. EV will provide details like battery state parameters and user historical data, e
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