Titlebook: Advances in Intelligent Computing Techniques and Applications; Intelligent Systems, Faisal Saeed,Fathey Mohammed,Yousef Fazea Conference pr

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https://doi.org/10.1007/3-540-29288-8ere trained using the TF-IDF text representation. This choice aimed to ensure a fair comparison between the algorithms. The evaluation of each model is conducted using topic coherence as the metric. The results indicate that both NMF and Bertopic give an excellent performance.

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https://doi.org/10.1007/3-540-29288-8ained model was evaluated using the root mean square error (RMSE) and mean absolute error (MAE) metrics. Based on the experiment results, the Bi-LSTM model with RMSprop optimizer and 0.0001 learning rate could provide the best results with an RMSE value of 16.68 and an MAE of 12.76. As the best mode

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Obverse

https://doi.org/10.1007/3-540-29288-8achine Learning domain. With Scrum, we can assess the accuracy improvement of the data sets during each sprint, providing an effective means of reviewing the sprint process. The goal is to develop a system capable of identifying new viruses and disseminating that information to all mobile devices, t

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Classical Methods of Statisticsg F1-score. The F1-score, a widely recognized measure of a model‘s accuracy, balances precision and recall. Specifically, it considers both false positives and false negatives, offering a nuanced evaluation of the model‘s performance. In the context of flood prediction, where the consequences of bot

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Classical Methods of Statisticsase. Additionally, a cutting-edge model named BILSTM is introduced, which capitalizes on processing word sequences to predict text; this model has demonstrated superior performance compared to LSTM and GRU models in the decoding stage. The findings of this study, as measured by the Bleu performance

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