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Titlebook: Artificial Intelligence and Industrial Applications; Artificial Intellige Tawfik Masrour,Ibtissam El Hassani,Anass Cherrafi Conference proc

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51#
發(fā)表于 2025-3-30 09:57:22 | 只看該作者
SQL Generation from Natural Language Using Supervised Learning and Recurrent Neural Networks, embedding and Recurrent Neural Networks (RNN), precisely on Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU) cells. We present also the DataSet used for training and testing our models, based on WikiSQL, and finally we show where we arrived in terms of accuracy.
52#
發(fā)表于 2025-3-30 12:52:29 | 只看該作者
Epigenetics in Crop Improvementuency-Inverse Document Frequency (TF-IDF). In this research paper, we provide a comparison of the three classifiers. The Experimental results have demonstrated that K-NN achieved a high performance based on four measuring factors namely: Precision, Recall, F1-score and Accuracy in both datasets Enron and LingSpam.
53#
發(fā)表于 2025-3-30 18:21:22 | 只看該作者
54#
發(fā)表于 2025-3-30 22:33:44 | 只看該作者
55#
發(fā)表于 2025-3-31 02:03:47 | 只看該作者
https://doi.org/10.1007/978-981-97-4742-9ssing unit, data aggregation-analysis unit, and monitoring-visualization unit. Each level optimizing the solution of a particular task to achieve smart telemonitoring of the knee telerehabilitation process.
56#
發(fā)表于 2025-3-31 05:12:31 | 只看該作者
Spam Filtering System Based on Nearest Neighbor Algorithms,uency-Inverse Document Frequency (TF-IDF). In this research paper, we provide a comparison of the three classifiers. The Experimental results have demonstrated that K-NN achieved a high performance based on four measuring factors namely: Precision, Recall, F1-score and Accuracy in both datasets Enron and LingSpam.
57#
發(fā)表于 2025-3-31 12:08:13 | 只看該作者
58#
發(fā)表于 2025-3-31 17:15:27 | 只看該作者
EduBot: An Unsupervised Domain-Specific Chatbot for Educational Institutions,eprocessing the training data. User’s inputs were similarly processed and then tf-idf based cosine similarity applied to retrieve the best answer. Later, a user-centric evaluation metric was used to evaluate the model and as per the metric, our current model showed approximately 80% accuracy.
59#
發(fā)表于 2025-3-31 19:33:26 | 只看該作者
Conceptual Architecture of AI-Enabled IoT System for Knee Rehabilitation Exercises Telemonitoring,ssing unit, data aggregation-analysis unit, and monitoring-visualization unit. Each level optimizing the solution of a particular task to achieve smart telemonitoring of the knee telerehabilitation process.
60#
發(fā)表于 2025-3-31 23:35:36 | 只看該作者
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