Signal and Image Processing Based on Machine/Deep Learning Techniques

Dear Colleagues,

Through the development of different types of sensors, various types of signals and images/videos can transmit the daily and social activities of individuals. Therefore, large amounts of signals and images need to be analyzed rapidly and accurately, This requires high scientific programming for dealing with these noisy data that are corrupted by various sources of interference. Given the enormous quantity and diversity of data generated and stored by modern intelligent systems, the need for accurate and fast efficient algorithms is becoming increasingly important. Several machine learning (ML) approaches are developed to automatically interpret these diverse data, using different methods of feature extraction and classification schemes. More advanced scientific programming methods, such as deep learning (DL), are widely used and have shown their great capacity in signal processing and image processing. DL approaches such as deep generative models, convolution neural networks (CNN), deep belief networks, the autoencoder and its variants, long short-term memory (LSTM), and deep generative models, have been applied for big data efficiently. Using these new ML and DL methods for signal and image processing will help to provide accurate and fast interpretation and deal with noisy and corrupted types of data.

This Special Issue aims to collect original research and review articles that describe the contributions of ML and DL technologies to the development of advanced signal processing and image processing for various applications.

Possible topics include but are not limited to the following:

• Machine and deep learning for signal and image/video processing
• Advanced computational intelligence for signal and image presentation
• Deep learning vs. traditional machine learning comparative analysis of signals and images
• Reviews on different machine and deep learning for signals and images
• Machine and deep learning in biomedical signal processing
• Machine and deep learning for medical image processing
• Machine and deep learning for medical and health informatics
• Computational intelligence for big data
• Multimodal learning algorithms for signal and image processing
• Machine and deep learning for fault diagnosis and proactive manintatinace
• Signal and image transformations using machine learning and deep learning
• Machine and deep learning for automatic detection and segmentation
• Machine and deep learning for ambient intelligence and assistive living
• Machine and deep learning for human activity and motion recognition

Dr. Omneya Attallah
Dr. Kadir Sabancı
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. Applied Sciences 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 2400 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.

• machine and deep learning for signal and image/video processing
• advanced computational intelligence for signal and image presentation
• deep learning vs. traditional machine learning comparative analysis of signals and images
• reviews on different machine and deep learning for signals and images
• machine and deep learning in biomedical signal processing
• machine and deep learning for medical image processing
• machine and deep learning for medical and health informatics
• computational intelligence for big data
• multimodal learning algorithms for signal and image processing
• machine and deep learning for fault diagnosis and proactive manintatinace
• signal and image transformations using machine learning and deep learning
• machine and deep learning for automatic detection and segmentation
• machine and deep learning for ambient intelligence and assistive living
• machine and deep learning for human activity and motion recognition