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Titlebook: 2nd EAI International Conference on Big Data Innovation for Sustainable Cognitive Computing; BDCC 2019 Anandakumar Haldorai,Arulmurugan Ram

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发表于 2025-3-21 16:07:59 | 显示全部楼层 |阅读模式
期刊全称2nd EAI International Conference on Big Data Innovation for Sustainable Cognitive Computing
期刊简称BDCC 2019
影响因子2023Anandakumar Haldorai,Arulmurugan Ramu,Mu-Yen Chen
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发行地址Contains proceedings from 2nd EAI International Conference on Big Data Innovation for Sustainable Cognitive Computing (BDCC 2019), Coimbatore, India, December 12-13, 2019.Features topics ranging from
学科分类EAI/Springer Innovations in Communication and Computing
图书封面Titlebook: 2nd EAI International Conference on Big Data Innovation for Sustainable Cognitive Computing; BDCC 2019 Anandakumar Haldorai,Arulmurugan Ram
影响因子.This proceeding features papers discussing big data innovation for sustainable cognitive computing. The papers feature details on cognitive computing and its self-learning systems that use data mining, pattern recognition and natural language processing (NLP) to mirror the way the human brain works. This international conference focuses on cognitive computing technologies, from knowledge representation techniques and natural language processing algorithms to dynamic learning approaches. Topics covered include Data Science for Cognitive Analysis, Real-Time Ubiquitous Data Science, Platform for Privacy Preserving Data Science, and Internet-Based Cognitive Platform. The 2nd EAI International Conference on Big Data Innovation for Sustainable Cognitive Computing (BDCC 2019) took place in Coimbatore, India on December 12-13, 2019..Contains proceedings from 2nd EAI International Conference on Big Data Innovation for Sustainable Cognitive Computing (BDCC 2019), Coimbatore,India, December 12-13, 2019;.Features topics ranging from Data Science for Cognitive Analysis to Internet-Based Cognitive Platforms;.Includes contributions from researchers, academics, and professionals from around the w
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M. O. Thorner,M. L. Vance,R. M. Macleodon of recurrent neural network, i.e., long short-term memory neural network. We applied LSTM architecture to a RNN and trained the model using Amazon web service dataset in Microsoft Azure notebooks Jupyter. Through the performance test, we confirm that the deep learning approach, i.e., LSTM, is effective for sentiment recognition.
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Advances in Finance & Applied Economicsrgy utilization based on various QoS metrics that are employed in wireless networks. The proposed method improves the throughput ratio, packet delivery ratio, high broadcast power, and utilization of low energy utilization.
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Amrendra Pandey,Jagdish Shettigaronstrate that Hybrid Mesh Segmentation approach does not depend on complex attributes, and outperforms the existing state-of-the-art algorithms. The simulation reveals that Hybrid Mesh Segmentation achieves a promising performance with coverage of more than 95%.
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