New Technological Advancements and Applications of Deep Learning

Dear Colleagues,

Currently, deep learning models have become an alternative approach applied to many challenging real-world environments ranging from medical imaging, agriculture, and bioinformatics, among others. Specifically, deep learning has overcome many of the drawbacks of traditional machine learning techniques. Despite its great success in dealing with challenging tasks, it still has some drawbacks and pitfalls to be addressed, such as the lack of training data and imbalanced data. Many more suitable network architectures are being proposed.

This Special Issue aims to present novel works regarding the proposal of new approaches dealing with challenging applications of deep learning such as medical information processing, cybersecurity, natural language processing, informatics, robotics and control, among many others. Moreover, new contributions focused on hardware solutions are encouraged, e.g., based on FPGA and GPU. Furthermore, high-quality review and survey papers are welcomed. The papers considered for possible publication may focus on, but are not necessarily be limited to, the following areas:

Deep learning,

Convolutional neural networks,

Deep learning algorithm/architectures/theory,

Deep learning applications,

Image classification,

Image segmentation,

Image registration,

Supervised learning,

Unsupervised learning,

FPGA- and GPU-based solutions,

Overfitting,

Imbalanced data,

Small datasets for training.

Dr. Laith Alzubaidi
Dr. Jinglan Zhang
Prof. Dr. Ye Duan
Prof. Dr. Jose Santamaria Lopez
Guest Editors

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