Advanced Patient-Monitoring using Machine-Learning, Deep-Learning and Medical Big-Data Evaluation

Submission Deadline: 20 August 2022 (closed)

Guest Editors

Dr. Seifedine Kadry, Noroff University College, Norway.
Dr. Venkatesan Rajinikanth, St. Joseph's College of Engineering; India.
Dr. Oana Geman, Stefan cel Mare University of Suceava, Romania.


The current advancements in science and technology helped to access modern computation facilities to handle a variety of tasks in different domains. The employment of recent computational facilities also improved the medical facilities from diagnosis to treatment. The modern healthcare systems existing in multi-specialty hospitals are employed in diagnosing a huge number of patients and therefore the disease-associated data which is to be evaluated to identify the illness will be large. The current advancements, such as web-based monitoring, Artificial Intelligence (AI) based disease prediction and health-app integrated with local and cloud supported monitoring devices further help to enhance the patient handling capability in modern hospitals.

The increase in disease incidence rate creates a massive diagnostic burden and therefore most modern hospitals are equipped with AI-supported methodologies. The recent advancements such as the traditional and modern machine-learning and deep-learning methods are widely employed in academic research and clinical research works to examine a variety of medical data ranging from biomedical signals to medical images collected using different methods. Further, the integration of the machine-learning and deep-learning methods helped to develop advanced diagnostic systems which detect the disease and its cause with better accuracy.

This Special Issue aims to encourage academic researchers and medical experts to present their pioneering work related to; machine-learning, deep-learning, integrated machine/deep learning frameworks, heuristic algorithm associated procedures, medical big-data processing, and medical cloud and fog computing facilities. This issue also welcomes real clinical case studies which implement AI-supported screening and decision making. Integration of AI with the Internet of Things (IoT) to handle the medical big-data is also within the scope of this special issue.


The probable topic of interest of this call includes the following
• Pre-processing of medical signals and images with heuristic algorithm supported methods
• Recent feature extraction and feature reduction related to medical big-data
• Development of AI framework for automatic diagnosis and decision making
• Machine-Learning supported medical signal and image examination
• Pre-trained and customary Deep-Learning supported medical signal and image examination
• Advanced machine/deep learning scheme for; data pre-processing, post-processing, disease detection, and decision making
• Integrated AI with cloud computing for online patient monitoring
• IoT and AI-supported medical Big-Data analytics
• Automation of Intensive Care Unit (ICU) with Integrated AI and medical cloud
• Advanced deep-learning approaches for medical big-data handling through cloud

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