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

Consonant

書目名稱Classification Applications with Deep Learning and Machine Learning Technologies影響因子(影響力)




書目名稱Classification Applications with Deep Learning and Machine Learning Technologies影響因子(影響力)學科排名




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書目名稱Classification Applications with Deep Learning and Machine Learning Technologies網(wǎng)絡(luò)公開度學科排名




書目名稱Classification Applications with Deep Learning and Machine Learning Technologies被引頻次




書目名稱Classification Applications with Deep Learning and Machine Learning Technologies被引頻次學科排名




書目名稱Classification Applications with Deep Learning and Machine Learning Technologies年度引用




書目名稱Classification Applications with Deep Learning and Machine Learning Technologies年度引用學科排名




書目名稱Classification Applications with Deep Learning and Machine Learning Technologies讀者反饋




書目名稱Classification Applications with Deep Learning and Machine Learning Technologies讀者反饋學科排名

拋棄的貨物

GILD

Jacket

Jerzy Korczak,Aleksander Fafu?a 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.

Inflamed

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品嘗你的人

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%.

affect

Conserve

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.