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Titlebook: Data Engineering and Intelligent Computing; Proceedings of 5th I Vikrant Bhateja,Lai Khin Wee,T. M. Rajesh Conference proceedings 2022 The

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书目名称Data Engineering and Intelligent Computing
副标题Proceedings of 5th I
编辑Vikrant Bhateja,Lai Khin Wee,T. M. Rajesh
视频video
概述Presents recent innovative research in the field of intelligent computing and communication.Results of ICICC 2021 organized in Bengaluru, India, during November 2021.Serves as a reference for young sc
丛书名称Lecture Notes in Networks and Systems
图书封面Titlebook: Data Engineering and Intelligent Computing; Proceedings of 5th I Vikrant Bhateja,Lai Khin Wee,T. M. Rajesh Conference proceedings 2022 The
描述This book features a collection of high-quality, peer-reviewed papers presented at the Fifth International Conference on Intelligent Computing and Communication (ICICC 2021) organized by the Department of Computer Science and Engineering and the Department of Computer Science and Technology, Dayananda Sagar University, Bengaluru, India, on 26–27 November 2021. The book is organized in two volumes and discusses advanced and multi-disciplinary research regarding the design of smart computing and informatics. It focuses on innovation paradigms in system knowledge, intelligence and sustainability that can be applied to provide practical solutions to a number of problems in society, the environment and industry. Further, the book also addresses the deployment of emerging computational and knowledge transfer approaches, optimizing solutions in various disciplines of science, technology and health care.
出版日期Conference proceedings 2022
关键词Artificial Intelligence; Data Engineering; Intelligent Computing; Cloud Computing; Machine Learning; Sign
版次1
doihttps://doi.org/10.1007/978-981-19-1559-8
isbn_softcover978-981-19-1558-1
isbn_ebook978-981-19-1559-8Series 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
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Application-Oriented Content Quality Analysis of Data Using Python,sary to find out the potential consumers or clients so that marketing will be done in an effective manner with addition to that the business makers can gain loyalty of consumers which will be beneficial for them in various perspectives. So, the quality of data is extremely important for business mak
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An Improved Similarity Measure Based on Collaborative Filtering for Sparsity Problem in Recommenderlts for the people. Recommender systems (RSs) play a significant role in generating accurate recommendations. Among the different types of RS, collaborative filtering-based (CF) RS is the most used due to its ability to produce recommendations that can fit the user’s varying preferences over time. C
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Detection and Classification of Bird Pest Using Spectrogram, Physical Imagery, and Convolutional Nesing physical and acoustic spectrogram imagery and optimized convolutional neural network classifiers. Two CNN models are designed to automatically and hierarchically learn spatially image features using 5 convolution layers of different filters followed by max polling 3 and fully connected 10 NN la
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Scatter Index: An Alternative Measure of Dispersion Based on Relative Frequency of Occurrence of ObCV) is a widely used as a standardized measure of dispersion. However, CV is not advisable in applications where mean of dataset is close to zero. In this paper, we have derived an alternate measure of dispersion called . which is based on relative frequency of observations. The novelty of the study
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Heart Failure Survival Prediction using Various Machine Learning Approaches, 32% of those deaths occurring globally. Heart attack and stroke accounted for 85% of these deaths. We used chi2 distributor, quantile transformer, polynomial feature, and XGboosting as machine learning approaches in this paper. In addition, the suggested model is used in a variety of machine learni
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