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Titlebook: Intelligent Systems Design and Applications; 22nd International C Ajith Abraham,Sabri Pllana,Anu Bajaj Conference proceedings 2023 The Edit

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31#
發(fā)表于 2025-3-27 00:17:10 | 只看該作者
32#
發(fā)表于 2025-3-27 04:55:28 | 只看該作者
33#
發(fā)表于 2025-3-27 09:02:53 | 只看該作者
New Approach in LPR Systems Using Deep Learning to Classify Mercosur License Plates with Perspectivt method, against 93.00% without the adjustment, and 87.71% and 87.46% character recognition without and with perspective adjustment respectively. Thus, presenting great effectiveness compared to the work found in the literature.
34#
發(fā)表于 2025-3-27 10:56:02 | 只看該作者
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發(fā)表于 2025-3-27 15:26:00 | 只看該作者
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發(fā)表于 2025-3-27 20:27:57 | 只看該作者
,An MDA Approach for?Extending Functional Dimension for?Sensitive Business Processes Execution,d the PSM level (Plateform Specific Model) corresponds to the execution of sensitive business processes. Thus, the modeling and deployment of such sensitive processes requires the development and implementation of a specific business process management system that supports the defined extensions.
37#
發(fā)表于 2025-3-28 00:10:04 | 只看該作者
2367-3370 he book offers a valuable reference guide for all researchers, students, and practitioners in the fields of computer science and engineering.. .?..?.978-3-031-35506-6978-3-031-35507-3Series ISSN 2367-3370 Series E-ISSN 2367-3389
38#
發(fā)表于 2025-3-28 04:15:55 | 只看該作者
,A Machine Learning Framework for?Cereal Yield Forecasting Using Heterogeneous Data,showed that the Pearson correlation r, Root-Mean-Square Error (RMSE) and the Mean Absolute Error (MAE) values were 0.97, 208.492, and 105.910, respectively. This paper showed the best results and it can be used to address national food security challenges.
39#
發(fā)表于 2025-3-28 09:28:26 | 只看該作者
,Semantic Segmentation Using MSRF-NET for Ultrasound Breast?Cancer,ing ultrasound breast cancer. We will investigate the influence of batch size, epochs, input size, learning rate on training and testing. Best performing model achieved a 93.95% in Dice Coefficient, 96.1% in specificity, 94.2% in precision, and 93.7% in recall. Lastly, we compare the performance of
40#
發(fā)表于 2025-3-28 11:27:37 | 只看該作者
,PU Matrix Completion Based Multi-label Classification with?Missing Labels,in labels is constrained by a nuclear norm. Finally, the proposed algorithm is optimization problem which is convex but not smooth, so Alternating Direction Multiplier Method is design for handling the optimization problem by dividing the problem into multiple subproblems. Experimental results on fo
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