New Insights in Machine Learning and Deep Neural Networks
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematics and Computer Science".
Deadline for manuscript submissions: closed (15 March 2023) | Viewed by 51888
Special Issue Editors
Interests: computer science; generative adversarial networks; synthetic data; NPL; fake news identification; data mining; text mining; machine learning; social network analysis; data visualization; eLearning
Special Issues, Collections and Topics in MDPI journals
Interests: biomedical signal processing; biomedical imaging; deep learning
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
You are cordially invited to submit an original research article or a comprehensive review to this Special Issue on "New Insights in Machine Learning and Deep Neural Networks”.
The focus of this Special Issue is primarily on theoretical results and applications of machine learning and/or deep neural network models that aim to describe systems that can better learn and generalize. Deep learning is an emerging branch of machine learning, especially in terms of neural architecture design and parameter tuning difficulty. Although such models are increasingly used in complex problems, they are sometimes designed based on general and not-so-well-founded assumptions, which can limit their use in applications. Here, novel models based on nonclassical assumptions are particularly welcome. We are seeking research based on mathematical and algorithmic approaches, as well as statistical and computational methods, with a specific interest in applications related to complex systems and challenging research areas (such as biology and medicine, computer science, economics and finance, social media analysis, marketing, epidemiology, and information theory).
Dr. Álvaro Figueira
Dr. Francesco Renna
Guest Editors
Manuscript Submission Information
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Keywords
- generative adversarial networks
- data augmentation
- object identification and scene classification
- medical imaging
- detecting fake news on social media
- facial expression recognition
- automatic feature selection
- text and narrative representation
- image and video reconstruction
- prediction analysis
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