Deep Learning and Big Data for Healthcare and Industry (Industry 5.0)

Dear Colleagues,

Deep learning, also called deep structured learning or hierarchical learning, is an essential member of the family's machine learning method. It allows computational models composed of multiple processing layers to be fed with raw data and automatically learn various abstract representations of data for detection and classification.

Healthcare is now an open field to get advantageous use of deep learning. Big data advances and challenges are open to provide systems that can be accurate enough to be useful to the clinician and the patient in the health itinerary. Industrial data is also massive and can be efficiently and effectively utilized for the industry's early warning system.

The goal of this Special Issue is to put together relevant contributions of deep learning and big data applications in healthcare and industry (Industry 5.0). On the one hand, the applications can include works with medical images (magnetic resonance, radioscopy, and tomography, echography, nuclear medicine), contributions to signal processing (cardiac, neural, long-term monitoring, wellness devices), data from large forms (primary attention, specialized medicine, clinical practice, electronic health recordings, hospital information systems, interoperability), or industrial data for an early warning system in assembly lines.

This Special Issue will bring together researchers from diverse fields and specialization, such as healthcare engineering, bioinformatics, medical doctors, computer engineering, computer science, information technology, statistics, production engineering, operations management, manufacturing, and mathematics. To sum up, feature interpretation remains an open issue in deep learning and the state of the art of big data. Still, it takes particular relevance in healthcare and industry applications, so contributions, including insights into this hot and open topic, are welcomed.

Dr. Muhammad Fazal Ijaz
Prof. Dr. Marcin Woźniak
Dr. Imran Razzak
Guest Editors

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