Deep Neural Networks: Theory, Algorithms and Applications
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Computational and Applied Mathematics".
Deadline for manuscript submissions: 31 March 2025 | Viewed by 1904
Special Issue Editors
Interests: computer vision; augmented reality; mechatronics
Special Issues, Collections and Topics in MDPI journals
Interests: artificial intelligence; neural networks; computer vision; augmented reality
Interests: big data classification; meta-learning; class imbalance; time series; ensembles, neural networks
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Machine Learning (ML) algorithms, as a central branch of Artificial Intelligence (AI), are changing how automatic classification tasks are solved. Moreover, thanks to the progress in hardware technologies, the researchers are experimenting with a significant change oriented to using Deep Learning (DL) techniques instead of traditional ML. Nowadays, DL can be implemented not only in powerful computers but also in mobile devices.
One example of DL is a Deep Neural Network (DNN). A DNN comprises many hidden layers between the input and output layers. Using a DNN, it is less challenging to encounter responses to problems that only offered answers in the past.
This Special Issue aims to help the scientific community disseminate new theories, advances, and applications regarding Deep Neural Networks. We welcome theoretical and practical papers. Topics of interest include, but are not limited to:
- Novel Deep Neural Networks Architectures.
- Theoretical Explanations of Deep Neural Networks.
- Applications of Deep Neural Networks.
- Transfer Learning in Deep Neural Networks.
- Managing Extensive and Short Data Sets with Deep Neural Networks.
- Integration of Hybrid Models.
- Hardware implementation of Deep Neural Networks.
Prof. Dr. Osslan Osiris Vergara Villegas
Prof. Dr. Vianey Guadalupe Cruz Sánchez
Prof. Dr. Vicente García
Guest Editors
Manuscript Submission Information
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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- deep learning
- convolutional neural networks
- deep believe networks
- deep reinforcement learning
- generative adversarial networks
- recursive neural networks
- transformers
