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Titlebook: Big Data, Machine Learning, and Applications; Proceedings of the 2 Malaya Dutta Borah,Dolendro Singh Laiphrakpam,Vale Conference proceeding

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期刊全称Big Data, Machine Learning, and Applications
期刊简称Proceedings of the 2
影响因子2023Malaya Dutta Borah,Dolendro Singh Laiphrakpam,Vale
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发行地址Presents high-quality research in the field of artificial intelligence and machine learning.Features the outcomes of the 2nd International Conference, BigDML 2021.Serves as a reference resource for re
学科分类Lecture Notes in Electrical Engineering
图书封面Titlebook: Big Data, Machine Learning, and Applications; Proceedings of the 2 Malaya Dutta Borah,Dolendro Singh Laiphrakpam,Vale Conference proceeding
影响因子.This book constitutes refereed proceedings of the Second International Conference on Big Data, Machine Learning, and Applications, BigDML 2021. The volume focuses on topics such as computing methodology; machine learning; artificial intelligence; information systems; security and privacy. This volume will benefit research scholars, academicians, and industrial people who work on data storage and machine learning..
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https://doi.org/10.1007/978-3-319-00894-3his paper provides a thorough review of supervised learning algorithms developed for SNNs categorically. We have divided the supervised learning algorithms into several categories based on the core principles for optimisation, such as gradient rule, asymmetric supervised Hebbian learning, remote supervision, and metaheuristics.
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An Insight on Drone Applications in Surveillance Domain,ms, controllers, and concern application(s). The inevitable usage of drones in Surveillance includes the evidence collection in the investigation process during the Forensic study/police investigations. The work can be extended to intelligent navigation with target mission-critical applications in military/defense.
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An Extensive Review of the Supervised Learning Algorithms for Spiking Neural Networks,his paper provides a thorough review of supervised learning algorithms developed for SNNs categorically. We have divided the supervised learning algorithms into several categories based on the core principles for optimisation, such as gradient rule, asymmetric supervised Hebbian learning, remote supervision, and metaheuristics.
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