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Titlebook: Intelligent Learning for Computer Vision; Proceedings of Congr Harish Sharma,Mukesh Saraswat,Jagdish Chand Bansal Conference proceedings 20

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樓主: Malnutrition
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發(fā)表于 2025-3-23 11:44:37 | 只看該作者
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發(fā)表于 2025-3-23 15:19:42 | 只看該作者
2367-4512 d practitioners in academia and industryThis book is a collection of selected papers presented at the First Congress on Intelligent Systems (CIS 2020), held in New Delhi, India, during September 5–6, 2020. It includes novel and innovative work from experts, practitioners, scientists, and decision-ma
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發(fā)表于 2025-3-23 19:20:38 | 只看該作者
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發(fā)表于 2025-3-24 00:05:29 | 只看該作者
Conference proceedings 2021mber 5–6, 2020. It includes novel and innovative work from experts, practitioners, scientists, and decision-makers from academia and industry. It covers selected papers in the area of computer vision. This book covers new tools and technologies in some of the important areas of medical science like
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發(fā)表于 2025-3-24 04:59:35 | 只看該作者
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發(fā)表于 2025-3-24 06:36:01 | 只看該作者
An Improved Inception Layer-Based Convolutional Neural Network for Identifying Rice Leaf Diseases,CNN model. All the models are tested and compared by varying the learning rate of 0.01, 0.001, 0.0001 and using different optimizers such as SGD and Adam. The proposed I-CNN achieved the highest accuracy of 81.25%, whereas the best accuracies of AlexNet and VGG16 are 72.5 and 62.5%, respectively.
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發(fā)表于 2025-3-24 12:26:47 | 只看該作者
Designing Controller Parameter of Wind Turbine Emulator Using Artificial Bee Colony Algorithm,a WTE. The parameters of PI controller installed in WTE are designed using artificial bee colony algorithm (ABC) algorithm. The ABC is selected as it is performing well to solve design optimization problems.
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發(fā)表于 2025-3-24 16:43:10 | 只看該作者
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發(fā)表于 2025-3-24 20:24:00 | 只看該作者
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發(fā)表于 2025-3-25 01:52:33 | 只看該作者
Comparative Study of Supervised Machine Learning Algorithms for Healthcare Dataset Using Orange,Bayes algorithm shown better results as compared to other algorithms of average accuracy of 81.23% and 79.65%. Support vector machine does not fit with these types of classification and performed accuracy of 56.34%. As an inference, SVM should not be implemented on such classification problems.
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