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Titlebook: Hybrid Intelligent Systems; 20th International C Ajith Abraham,Thomas Hanne,Tzung-Pei Hong Conference proceedings 2021 The Editor(s) (if ap

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书目名称Hybrid Intelligent Systems
副标题20th International C
编辑Ajith Abraham,Thomas Hanne,Tzung-Pei Hong
视频video
概述Includes recent research on Hybrid Intelligent Systems.Presents the proceedings of the 20th International Conference on Hybrid Intelligent Systems (HIS 2020), which was held online from December 14 to
丛书名称Advances in Intelligent Systems and Computing
图书封面Titlebook: Hybrid Intelligent Systems; 20th International C Ajith Abraham,Thomas Hanne,Tzung-Pei Hong Conference proceedings 2021 The Editor(s) (if ap
描述.This book highlights the recent research on hybrid intelligent systems and their various practical applications. It presents 58 selected papers from the 20th International Conference on Hybrid Intelligent Systems (HIS 2020) and 20 papers from the 12th World Congress on Nature and Biologically Inspired Computing (NaBIC 2020), which was held online, from December 14 to 16, 2020. A premier conference in the field of artificial intelligence, HIS - NaBIC 2020 brought together researchers, engineers and practitioners whose work involves intelligent systems, network security and their applications in industry. Including contributions by authors from 25 countries, the book offers a valuable reference guide for all researchers, students and practitioners in the fields of science and engineering..
出版日期Conference proceedings 2021
关键词Hybrid Intelligent Systems; Hybridization of Intelligent Systems; Hybrid Computational Intelligence; HI
版次1
doihttps://doi.org/10.1007/978-3-030-73050-5
isbn_softcover978-3-030-73049-9
isbn_ebook978-3-030-73050-5Series 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
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Deep Convolutional Neural Network Based on Wavelet Transform for Super Image Resolution, is fed with only the approximated image while dismissing their details in order to remove noise. To validate the effectiveness of the proposed method, extensive experiments are performed using Set5, Set14, and urban 100 datasets. The obtained results demonstrated the efficiency of the proposed approach.
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Building IoT Analytics and Machine Learning with Open Source Software for Prediction of Environmentronmental parameters. We also implement data pipeline to serve machine learning model training as well as prediction. Initial prediction results for the ambient temperature parameter based on machine learning models and built-in IoT platforms can continue to be developed for many other environmental parameters.
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Solving Dynamic Bag-of-Tasks Scheduling Problem in Heterogeneous Multi-cloud Environment Using Hybrdel aims to minimize the makespan and the execution cost while taking into consideration budget constraints and guaranteeing load balancing between Cloud’s Virtual Machines. By performing experiments on synthetic data sets that we propose, we demonstrate the effectiveness of the algorithm.
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