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

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书目名称Classification Applications with Deep Learning and Machine Learning Technologies影响因子(影响力)




书目名称Classification Applications with Deep Learning and Machine Learning Technologies影响因子(影响力)学科排名




书目名称Classification Applications with Deep Learning and Machine Learning Technologies网络公开度




书目名称Classification Applications with Deep Learning and Machine Learning Technologies网络公开度学科排名




书目名称Classification Applications with Deep Learning and Machine Learning Technologies被引频次




书目名称Classification Applications with Deep Learning and Machine Learning Technologies被引频次学科排名




书目名称Classification Applications with Deep Learning and Machine Learning Technologies年度引用




书目名称Classification Applications with Deep Learning and Machine Learning Technologies年度引用学科排名




书目名称Classification Applications with Deep Learning and Machine Learning Technologies读者反馈




书目名称Classification Applications with Deep Learning and Machine Learning Technologies读者反馈学科排名

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Jerzy Korczak,Aleksander Fafuła We also compared the performance of optimizers and three levels of epoch on the performance of the model. In general, transfer learning with a pre-trained VGG16 neural network provides higher performance for the dataset; the dataset performed better with an optimizer of SGD, compared with ADAM.

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Research in Security Sector Reform Policyption. Effects of variables, i.e., hidden layers, perceptrons, filter number, optimizers, and learning rate, on the proposed model are also investigated in this study. The best performing model in this study is the new proposed model with 2 CNN layers (12, 96 filters) and 6 dense layers with 147 perceptrons, achieving an accuracy of 87%.

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Book 2023elps them study, formulate, and design their research goal by aligning the latest technologies studies’ image and data classifications. The early start-up can use it to work with product or prototype design requirement analysis and its design and development..

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https://doi.org/10.1007/978-3-319-16348-2ollected 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.