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Titlebook: Machine Learning and Big Data Analytics(Proceedings of International Conference on Machine Learning ; Rajiv Misra,Rudrapatna K. Shyamasund

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发表于 2025-3-21 18:13:58 | 显示全部楼层 |阅读模式
书目名称Machine Learning and Big Data Analytics(Proceedings of International Conference on Machine Learning
编辑Rajiv Misra,Rudrapatna K. Shyamasundar,Rana Omer
视频video
概述Presents recent research on machine learning and big data analytics.Includes the Proceedings of ICMLBDA 2021, held on March 29–30, 2021, in Patna, India.Written by experts in the field
丛书名称Lecture Notes in Networks and Systems
图书封面Titlebook: Machine Learning and Big Data Analytics(Proceedings of International Conference on Machine Learning ;  Rajiv Misra,Rudrapatna K. Shyamasund
描述This edited volume on machine learning and big data analytics (Proceedings of ICMLBDA 2021) is intended to be used as a reference book for researchers and practitioners in the disciplines of computer science, electronics and telecommunication, information science, and electrical engineering. Machine learning and Big data analytics represent a key ingredients in the industrial applications for new products and services. Big data analytics applies machine learning for predictions by examining large and varied data sets—i.e., big data—to uncover hidden patterns, unknown correlations, market trends, customer preferences, and other useful information that can help organizations make more informed business decisions.
出版日期Conference proceedings 2022
关键词Machine Learning; Big Data; Big Data Analytics; ICMLBDA; ICMLBDA 2021; Intelligent Systems
版次1
doihttps://doi.org/10.1007/978-3-030-82469-3
isbn_softcover978-3-030-82468-6
isbn_ebook978-3-030-82469-3Series 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 Switzerl
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发表于 2025-3-22 03:45:33 | 显示全部楼层
https://doi.org/10.1007/978-3-030-82469-3Machine Learning; Big Data; Big Data Analytics; ICMLBDA; ICMLBDA 2021; Intelligent Systems
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Machine Learning and Big Data Analytics(Proceedings of International Conference on Machine Learning 978-3-030-82469-3Series ISSN 2367-3370 Series E-ISSN 2367-3389
发表于 2025-3-22 15:33:55 | 显示全部楼层
2367-3370 Patna, India.Written by experts in the fieldThis edited volume on machine learning and big data analytics (Proceedings of ICMLBDA 2021) is intended to be used as a reference book for researchers and practitioners in the disciplines of computer science, electronics and telecommunication, information
发表于 2025-3-22 18:02:59 | 显示全部楼层
Engagement Analysis of Students in Online Learning Environments,ship between eye-gaze and engagement intensity. OpenFace 2.0 toolbox abilities are leveraged for feature extraction. Experimental results on the test datasets give an accuracy of 55.64% on DAiSEE and an MSE of 0.0598 on Engagement in the Wild Dataset.
发表于 2025-3-23 00:07:25 | 显示全部楼层
Concurrent Vowel Identification Using the Deep Neural Network,n tested to predict the concurrent vowel scores for the other 5 F0 difference conditions. The proposed perceptron model was successful in qualitatively predicting the concurrent vowel scores across F0 differences, as observed in concurrent vowel data.
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Application of Artificial Intelligence to Predict the Degradation of Potential mRNA Vaccines Develoith the LSTM (Long Short Term Memory) and GRU (Gated Recurrent Unit) architectures to predict the degradation of each sequence in the given data, which comprised of sequences of mRNA. The performance of the model was evaluated using the MCRMSE (Mean Columnwise Root Mean Squared Error) as the scoring metric.
发表于 2025-3-23 06:40:04 | 显示全部楼层
Searching Pattern in DNA Sequence Using ECC-Diffie-Hellman Exchange Based Hash Function: An Efficiee is obtained and effective security with power consumption achieved. Experimentation results are carried out by using the publicly available dataset. The effectiveness is proved from the comparison results of the proposed and existing study.
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