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Titlebook: Data Science and Intelligent Applications; Proceedings of ICDSI Ketan Kotecha,Vincenzo Piuri,Rajan Patel Conference proceedings 2021 The Ed

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樓主: architect
31#
發(fā)表于 2025-3-26 23:19:15 | 只看該作者
Performance Analysis of Indian Stock Market via Sentiment Analysis and Historical Data,historic prices of the stock to predict the stock recital. For combining the above approaches, we are using the decision tree approach of machine learning for classification and prediction for more accurate prophecy. The proposed algorithm gives above 70% accuracy for the given data.
32#
發(fā)表于 2025-3-27 03:25:58 | 只看該作者
D-Lotto: The Lottery DApp with Verifiable Randomness,tem to achieve properties like decentralization, transparency, and immutability. These properties combined with randomness and verifiability lead to this significant lottery design to be unique of its kind.
33#
發(fā)表于 2025-3-27 07:13:22 | 只看該作者
34#
發(fā)表于 2025-3-27 13:01:42 | 只看該作者
35#
發(fā)表于 2025-3-27 14:40:04 | 只看該作者
Big Data and Its Application in Healthcare and Medical Field,e time of operation and diagnosing the patients based on the symptoms. In this paper, we develop a specialist framework through which the specialists and patients can be associated for all intents and purposes, alongside that searching for answer for storing this huge information in a progressively compelling and packed structure.
36#
發(fā)表于 2025-3-27 21:01:15 | 只看該作者
37#
發(fā)表于 2025-3-27 23:56:33 | 只看該作者
Rawls, Stability and Public Justificationrom the microblog tweets. The evaluation metrics that were used are precision, recall and F-score. We have observed that support vector machine (SVM) has the highest accuracy in classification of tweets based on pre-defined retrieval criteria.
38#
發(fā)表于 2025-3-28 02:11:45 | 只看該作者
39#
發(fā)表于 2025-3-28 10:09:40 | 只看該作者
40#
發(fā)表于 2025-3-28 14:19:55 | 只看該作者
Rawls, Stability and Public Justificationdata like Hive and Pig are popularly known to handle such type of data. But these tools and techniques individually are inadequate for mining data efficiently. We have reviewed the research in the area of mining rainfall data and identified the gaps in the existing approaches.
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