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Titlebook: ICT Based Innovations; Proceedings of CSI 2 A. K. Saini,A. K. Nayak,Ram Krishna Vyas Conference proceedings 2018 Springer Nature Singapore

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樓主: Addendum
31#
發(fā)表于 2025-3-27 00:43:46 | 只看該作者
32#
發(fā)表于 2025-3-27 04:28:30 | 只看該作者
2194-5357 unication technologies (ICTs) are the key players in improviThis volume comprises the select proceedings of the annual convention of the Computer Society of India. Divided into 10 topical volumes, the proceedings present papers on state-of-the-art research, surveys, and succinct reviews. The volumes
33#
發(fā)表于 2025-3-27 05:36:03 | 只看該作者
34#
發(fā)表于 2025-3-27 13:08:51 | 只看該作者
Proposed ICT-Based Transportation Model: EEG, on the roofs of the building, we are using them on concrete roads. Another big thing that we are proposing is how to avoid congestion on toll booths by making use of smart wallet in order to save time.
35#
發(fā)表于 2025-3-27 14:26:01 | 只看該作者
36#
發(fā)表于 2025-3-27 20:06:44 | 只看該作者
Comparing the Behavior of Oversampling and Undersampling Approach of Class Imbalance Learning by Co every real data in one form or another. This paper compares the oversampling and undersampling approaches of class imbalance learning in noisy environment and tries to find out which is the better approach in such case.
37#
發(fā)表于 2025-3-28 01:53:30 | 只看該作者
38#
發(fā)表于 2025-3-28 04:10:53 | 只看該作者
Design and Development of the Agricultural Model: A Way to Connect Farmer Community to Agriculture production and other farm management activities. Thus, such generated knowledge from this model helps the farmer community in its decision making at the time of crop cultivation and reducing risk in their agri-business and thereby helps in rural development.
39#
發(fā)表于 2025-3-28 09:05:39 | 只看該作者
BharataNatyam Dance Classification with Rough Set Tools,ising and have higher accuracy for about 500 instances. A comparative study has been done with two different tools namely WEKA (Waikato Environment for Knowledge Analysis) 3.6.11 and RSES (Rough Set Exploration System) 2.2.2 which has given 87.42% and about 76.8% classifier accuracy, respectively.
40#
發(fā)表于 2025-3-28 13:58:37 | 只看該作者
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