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31 |
Deep learning-based malaria parasite detection: convolutional neural networks model for accurate species identification of Plasmodium falciparum and Plasmodium vivax
Enthalten in Scientific reports Bd. 15, 30.1.2025, Nr. 1, date:12.2025: 1-11
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32 |
Deep-learning: investigating deep neural networks hyper-parameters and comparison of performance to shallow methods for modeling bioactivity data
Enthalten in Journal of cheminformatics Bd. 9, 28.6.2017, Nr. 1, date:12.2017: 1-13
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33 |
Design of tomato picking robot detection and localization system based on deep learning neural networks algorithm of Yolov5
Enthalten in Scientific reports Bd. 15, 20.2.2025, Nr. 1, date:12.2025: 1-16
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34 |
Electron energy loss spectroscopy database synthesis and automation of core-loss edge recognition by deep-learning neural networks
Enthalten in Scientific reports Bd. 12, 23.12.2022, Nr. 1, date:12.2022: 1-11
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35 |
Enhanced detection of surface deformations in LPBF using deep convolutional neural networks and transfer learning from a porosity model
Enthalten in Scientific reports Bd. 14, 6.11.2024, Nr. 1, date:12.2024: 1-15
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36 |
Enhancing parkinson disease detection through feature based deep learning with autoencoders and neural networks
Enthalten in Scientific reports Bd. 15, 13.3.2025, Nr. 1, date:12.2025: 1-28
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37 |
Modeling and effect analysis of machining parameters for surface roughness and specific energy consumption during TC18 machining using deep reinforcement learning and neural networks
Enthalten in Artificial intelligence review Bd. 58, 11.4.2025, Nr. 7, date:7.2025: 1-31
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38 |
Modelling and parameter identification of coefficient of friction for deep-drawing quality steel sheets using the CatBoost machine learning algorithm and neural networks
Enthalten in The international journal of advanced manufacturing technology Bd. 124, 8.12.2022, Nr. 7-8, date:2.2023: 2229-2259
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39 |
Neural networks and arbitrage in the VIX
Enthalten in Digital finance Bd. 2, 13.8.2020, Nr. 1-2, date:9.2020: 97-115
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40 |
Prediction of activity and specificity of CRISPR-Cpf1 using convolutional deep learning neural networks
Enthalten in BMC bioinformatics Bd. 20, 13.6.2019, Nr. 1, date:12.2019: 1-10
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