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Titlebook: Big Data, Cloud Computing, and Data Science Engineering; Roger Lee Book 2020 Springer Nature Switzerland AG 2020 Computational Intelligenc

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期刊全稱Big Data, Cloud Computing, and Data Science Engineering
影響因子2023Roger Lee
視頻videohttp://file.papertrans.cn/186/185727/185727.mp4
發(fā)行地址Presents recent research in the field of computational science & computational intelligence.Includes the edited outcomes of the 4th IEEE/ACIS International Conference on Big Data, Cloud Computing, and
學(xué)科分類Studies in Computational Intelligence
圖書封面Titlebook: Big Data, Cloud Computing, and Data Science Engineering;  Roger Lee Book 2020 Springer Nature Switzerland AG 2020 Computational Intelligenc
影響因子This edited book presents the scientific outcomes of the 4th IEEE/ACIS International Conference on Big Data, Cloud Computing, Data Science & Engineering (BCD 2019) which was held on May 29–31, 2019 in Honolulu, Hawaii. The aim of the conference was to bring together researchers and scientists, businessmen and entrepreneurs, teachers, engineers, computer users and students to discuss the numerous fields of computer science and to share their experiences and exchange new ideas and information in a meaningful way..?.Presenting 15 of the conference’s most promising papers, the book discusses all aspects (theory, applications and tools) of computer and information science, the practical challenges encountered along the way, and the solutions adopted to solve them..
Pindex Book 2020
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A Feature Point Extraction and Comparison Method Through Representative Frame Extraction and Distors selected. The feature points are extracted from the selected representative frames by the distortion correction and the similarity can be compared in the subsequent query images. The proposed method is shown, through the experiments, to be superior in speed for the image comparison than other meth
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Release from the Curse of High Dimensional Data Analysis,esults. First, MNMs of six microarrays are zero (Fact3). Second, RIP could decompose microarray into many linearly separable gene subspaces (SMs) and noise subspace (Fact4). These two new facts indicate that we are free from the curse of high dimensional microarray data and complete the cancer gene
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