Morose
发表于 2025-3-25 07:16:10
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大笑
发表于 2025-3-25 07:34:43
Identifying Handwriting Difficulties in Children in Devanagari Script Using Machine Learning, identify children with dysgraphia and without dysgraphia in Devanagari script based on their performance characteristics as per the BHK algorithm. 52 students handwriting samples were collected, and feature analysis is done based on BHK and machine learning model using the KNN algorithm for the predictions.
联邦
发表于 2025-3-25 12:29:02
https://doi.org/10.1007/978-3-031-45249-9ining purposes, deep learning classifier bidirectional long short-term memory (BiLSTM) network is used. Results established that despite using few channels, recognition accuracy does not vary to a large extent. We achieved almost the same accuracy level by selecting less number of optimal frontal channels.
PET-scan
发表于 2025-3-25 18:49:41
2194-5357 he environment and industry. Further, the book also addresses the deployment of emerging computational and knowledge transfer approaches, optimizing solutions in various disciplines of science, technology and health care.978-981-16-0170-5978-981-16-0171-2Series ISSN 2194-5357 Series E-ISSN 2194-5365
macabre
发表于 2025-3-25 20:16:46
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尖
发表于 2025-3-26 01:12:42
Lecture Notes in Computer Scienceinput. The convolutional neural networks-based classification model proves accuracy of 93% in discriminate from malware and benign files. The convolutional neural network-based malware detection model has higher performance when compared with deep neural network classification model trained with GIS
在驾驶
发表于 2025-3-26 05:40:18
Lecture Notes in Computer Scienceie reviews. Sentiment analysis is a natural language processing approach to identify the emotional tone behind the opinion text from negative to a positive level. This technique uses fine-grained sentiment analysis that provides a more precise level of polarity by breaking it from very negative to v
拥护者
发表于 2025-3-26 10:48:16
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Ptsd429
发表于 2025-3-26 15:01:50
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虚情假意
发表于 2025-3-26 18:54:38
Clinical In Vitro Fertilizationoal is to leverage deep learning visualization techniques for better interpretation of our results. Overall, our proposed model achieved a competitive MAE of 7.61 months on the test set provided by Radiological Society of North America (RSNA).