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Titlebook: Classification Applications with Deep Learning and Machine Learning Technologies; Laith Abualigah Book 2023 The Editor(s) (if applicable)

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11#
發(fā)表于 2025-3-23 12:06:08 | 只看該作者
Jerzy Korczak,Aleksander Fafu?attack possibility) dataset, freely available on kagle. The data was divided into three categories consisting of (303, 909, 1808) instances which were analyzed on the WEKA platform. The results showed that the RFC was the best performer.
12#
發(fā)表于 2025-3-23 15:34:49 | 只看該作者
Mango Varieties Classification-Based Optimization with Transfer Learning and Deep Learning Approachollected and obtain a deep learning model which is able to classify four types of mango (Alampur Baneshan, Alphonso, Harum Manis and Keitt) automatically. In summary, the objective in this paper is to develop a deep learning algorithm to automatically classify four types of mango cultivar.
13#
發(fā)表于 2025-3-23 20:12:38 | 只看該作者
A Novel Big Data Classification Technique for Healthcare Application Using Support Vector Machine, ttack possibility) dataset, freely available on kagle. The data was divided into three categories consisting of (303, 909, 1808) instances which were analyzed on the WEKA platform. The results showed that the RFC was the best performer.
14#
發(fā)表于 2025-3-24 00:23:16 | 只看該作者
15#
發(fā)表于 2025-3-24 02:42:43 | 只看該作者
Research in Soviet Social Psychologyvert into jpg format and augmentation. Based on the accuracy result from the model, the best model for the salak classification is ResNet50 which gave an accuracy of 84% followed by VGG16 that gave an accuracy of 77% and CNN which gave 31%.
16#
發(fā)表于 2025-3-24 10:07:55 | 只看該作者
17#
發(fā)表于 2025-3-24 13:54:32 | 只看該作者
Iryna Zolotaryova,Anna Khodyrevskawith a higher accuracy. In the proposed work, we also inspected two transfer learning methods in the classification of markisa which are VGG-16 and InceptionV3. The results showed that the performance of the first proposed CNN model outperforms VGG-16 (95% accuracy) and InceptionV3 (65% accuracy).
18#
發(fā)表于 2025-3-24 17:34:11 | 只看該作者
19#
發(fā)表于 2025-3-24 21:56:22 | 只看該作者
20#
發(fā)表于 2025-3-25 01:02:55 | 只看該作者
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