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Titlebook: Advances in Computing and Data Sciences; 6th International Co Mayank Singh,Vipin Tyagi,Tuncer ?ren Conference proceedings 2022 The Editor(s

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樓主: radionuclides
41#
發(fā)表于 2025-3-28 17:30:32 | 只看該作者
COVID-19 Epidemiology and Virus Dynamicson technique. The analysis is done using the PROMISE data set, based on the performance metrics such as specificity, sensitivity, and accuracy. The specificity, sensitivity, and accuracy of the proposed method are found to be 98.6248%, 93.5694%, and 92.0506% in accordance with the training percentage.
42#
發(fā)表于 2025-3-28 19:23:24 | 只看該作者
https://doi.org/10.1007/978-981-99-3153-8Moreover, the chatbot design is programmed to answer all the queries about first aid prescriptions and offer a reminder for users. As a result, the proposed implementation leads to a framework for integrating intelligent conversational systems with the Android operating system.
43#
發(fā)表于 2025-3-28 23:30:54 | 只看該作者
44#
發(fā)表于 2025-3-29 06:01:32 | 只看該作者
45#
發(fā)表于 2025-3-29 07:27:31 | 只看該作者
Conference proceedings 2022 Sciences, ICACDS 2022, which was held in Kurnool, India in April 2022..The total of 69 full papers presented in the proceedings was carefully reviewed and selected from 411 submissions. The papers focus on?advances of next generation computing technologies in the areas of advanced computing and dat
46#
發(fā)表于 2025-3-29 13:21:33 | 只看該作者
47#
發(fā)表于 2025-3-29 19:03:44 | 只看該作者
48#
發(fā)表于 2025-3-29 23:34:37 | 只看該作者
49#
發(fā)表于 2025-3-30 00:49:13 | 只看該作者
https://doi.org/10.1007/978-3-030-97178-6erformed using the techniques tokenization, stopword removal, stemming and POS tagging. Features are extracted from the preprocessed data and classification is performed using the bald eagle optimization enabled BiLSTM classifier. The experiment shows that the proposed method works more efficient with a precision of 92.65%, and recall of 93.82%.
50#
發(fā)表于 2025-3-30 04:13:17 | 只看該作者
COVID-19 Experience in the Philippinesdetect spam messages. In this paper, our objective is to detect spam messages in a dataset using vectorization along with various machine learning algorithms and compare their results to find out the best classifier for detecting spam messages.
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