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Titlebook: Advances in Data and Information Sciences; Proceedings of ICDIS Mohan L. Kolhe,Munesh C. Trivedi,Vikash Kumar Sing Conference proceedings 2

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樓主: CLIP
31#
發(fā)表于 2025-3-26 22:59:27 | 只看該作者
Texts in Quantitative Political Analysiswitter data sets, one Disease related tweets set prepared by us using five different disease keywords and an other benchmark Seattle data set consisting of incident-related tweets. The modified ML-KNN is able to improve the performance of conventional ML-KNN with a minimum of 5% in both the datasets.
32#
發(fā)表于 2025-3-27 03:02:17 | 只看該作者
https://doi.org/10.1007/978-94-011-3348-7xtract the semantic information from the images. We collect shared images from Flicker specifying various sports events that users attend. Images are divided into three event classes, i.e. bikes, water and ground. After extensive experimentation using CNN, for training and classifying images, we obtain an accuracy of 98.7%.
33#
發(fā)表于 2025-3-27 07:48:01 | 只看該作者
2367-3370 (ICDIS 2017), held at Indira Gandhi National Tribal Universi.The book gathers a collection of high-quality peer-reviewed research papers presented at the International Conference on Data and Information Systems (ICDIS 2017), held at Indira Gandhi National Tribal University, India from November 3 to
34#
發(fā)表于 2025-3-27 12:29:55 | 只看該作者
https://doi.org/10.1007/978-94-009-0495-8rust of recommender system, it is required to identify and remove the fictitious profiles from the system. Here, we have used machine learning classifiers to detect the attacker’s profiles. A new model is proposed that outperforms in most of the cases.
35#
發(fā)表于 2025-3-27 16:19:36 | 只看該作者
36#
發(fā)表于 2025-3-27 21:25:14 | 只看該作者
37#
發(fā)表于 2025-3-28 02:00:24 | 只看該作者
38#
發(fā)表于 2025-3-28 04:28:46 | 只看該作者
39#
發(fā)表于 2025-3-28 09:52:04 | 只看該作者
40#
發(fā)表于 2025-3-28 10:40:50 | 只看該作者
https://doi.org/10.1007/978-981-13-0277-0Data Sciences; Information Science; ICDIS 2017; ICDIS; Information Security; Big Data and Cloud Computing
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