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Titlebook: Big Data, Machine Learning, and Applications; First International Ripon Patgiri,Sivaji Bandyopadhyay,Dalton Meitei T Conference proceeding

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樓主: Blandishment
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發(fā)表于 2025-3-23 12:38:39 | 只看該作者
978-3-030-62624-2Springer Nature Switzerland AG 2020
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發(fā)表于 2025-3-23 16:06:20 | 只看該作者
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發(fā)表于 2025-3-23 19:34:25 | 只看該作者
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發(fā)表于 2025-3-23 23:06:06 | 只看該作者
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發(fā)表于 2025-3-24 05:34:07 | 只看該作者
16#
發(fā)表于 2025-3-24 09:10:16 | 只看該作者
https://doi.org/10.1007/978-3-319-04150-6strained languages. In this work, we report the task of language-independent automatic extraction of locations from news articles using domain knowledge. The work is tested on four languages namely, English and three resource-constrained languages: Assamese, Manipuri and Mizo, the lingua francas of
17#
發(fā)表于 2025-3-24 14:40:01 | 只看該作者
https://doi.org/10.1007/978-3-030-80481-7vailability & usages of those device, huge data is flowing through various communication channels through different data sources. New demand from academic & industry includes analyzing generated data effectively to come up with fruitful insights that can be actionable either by systems or by humans.
18#
發(fā)表于 2025-3-24 16:36:05 | 只看該作者
https://doi.org/10.1007/978-3-030-80481-7 to wireless communication and self-organizing property ad hoc network is more vulnerable to several intrusions or attacks than the traditional system. Blackhole attack is an important routing disruption attack that malicious node advertises itself as part of a path to the destination. In this paper
19#
發(fā)表于 2025-3-24 21:56:42 | 只看該作者
Piermarco Cannarsa,Teresa D’Aprileet which leads to the generation of a tremendous amount of data which can be referred to as Big Data. Since the IoT is going to be expanded in a exponential manner, Big Data Issues becomes a threat for its faithful operation. Other than this, the existing IoT architecture has limitations with respec
20#
發(fā)表于 2025-3-24 23:22:02 | 只看該作者
Piermarco Cannarsa,Teresa D’Aprileed on various learning strategies have been proposed for multi class DLD classification. Due to imbalance in DLD class distribution the mis-classification probability of minority class is higher when compared to the majority class. To overcome the affects of imbalance in class distribution, the samp
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