Special Issue "Intelligent Decision Support Systems for Complex Healthcare Applications"

Submission Deadline: 31 January 2021
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Guest Editors
Dr. K. Shankar, Alagappa University, India.
Dr. Gyanendra Prasad Joshi, Sejong University, South Korea.
Dr. Gia Nhu Nguyena, Duy Tan University, Viet Nam.


Intelligent decision support system (IDSS) for Complex healthcare applications investigates the massive quantity of complex medical data to help physicians, academicians, pathologists, doctors and other healthcare professionals. Decision support system (DSS) is an intelligent system, which offers excellent assistant in diverse levels of health-related disease diagnosis. Besides, it is a dynamic information model as important data is added on a uniform basis. Internet of Things, embedded devices, sensors, mobile applications, manual data entry and online sources are few complex data sources for IDSS. The data supported by IDSS considerably aid in early diagnosis of diseases and corresponding treatments. Intelligent DSS make use of artificial intelligence (AI) techniques to improvise the process of complex making decisions. AI tools such as Metaheuristic, Fuzzy Logic, Case based Reasoning, Artificial Neural Networks, and Intelligent Agents can be integrated to DSS for healthcare diagnosis.

Meta-heuristics optimization algorithm can handle real-world application such as machine learning, artificial intelligence, data mining, data analysis, image processing etc. Those algorithms are developed from the behavior of birds, animals, insects, or from any specific characteristics. To reduce the complexity of research work, recently algorithms are used for the purpose of prediction, identification, classification, and detection of diseases via various analysis tools. This special issue focuses on the development of latest and advanced metaheuristic algorithms for intelligent DSS in complex healthcare applications. It serves as a platform for dissemination as well as sharing of the latest scientific contributions from metaheuristic algorithms. We invite authors to contribute original research articles as well as review articles on recent advances in these active research areas.

Topics of interest include, but are not limited to:

• Advances in Metaheuristic Optimization Algorithms based IDSS for Complex Disease prediction methods and techniques.

• Advances in Metaheuristic Optimization Algorithms based IDSS for Complex Data mining and knowledge discovery algorithms.

• Intelligent decision-making systems for Computer-aided diagnostic system.

• Advances in Swarm intelligence based IDSS models for Complex healthcare applications.

• Advances in Nature-inspired metaheuristic optimization based IDSS models for Complex healthcare applications.

• Advances in Metaheuristic Optimization Algorithms based IDSS for Big healthcare and rehabilitation data analytics.

• Advances in Metaheuristic based clinical imaging techniques for Complex disease diagnosis.

• Collection, integration, and analysis of Complex clinical data using machine learning techniques with advanced Metaheuristic Algorithms.

• Modified or improved metaheuristic optimization based IDSS models for Complex healthcare applications.

• New metaheuristic optimization based IDSS models for Complex healthcare applications.

• Advances in Hybrid metaheuristic optimization based IDSS models for Complex healthcare applications.

Advances in Metaheuristic Optimization Algorithms, Intelligent Decision Support Systems, Complex Data mining and knowledge discovery algorithms, Computer-aided diagnostic system, Complex healthcare applications, Complex healthcare applications, Big healthcare and rehabilitation data analytics, Machine learning techniques, Deep Learning

Published Papers
  • Fog-Based Secure Framework for Personal Health Records Systems
  • Abstract The rapid development of personal health records (PHR) systems enables an individual to collect, create, store and share his PHR to authorized entities. Health care systems within the smart city environment require a patient to share his PRH data with a multitude of institutions’ repositories located in the cloud. The cloud computing paradigm cannot meet such a massive transformative healthcare systems due to drawbacks including network latency, scalability and bandwidth. Fog computing relieves the load of conventional cloud computing by availing intermediate fog nodes between the end users and the remote servers. Assuming a massive demand of PHR data within… More
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  • Deep Learning Based Intelligent and Sustainable Smart Healthcare Application in Cloud-Centric IoT
  • Abstract Recent developments in information technology can be attributed to the development of smart cities which act as a key enabler for next-generation intelligent systems to improve security, reliability, and efficiency. The healthcare sector becomes advantageous and offers different ways to manage patient information in order to improve healthcare service quality. The futuristic sustainable computing solutions in e-healthcare applications depend upon Internet of Things (IoT) in cloud computing environment. The energy consumed during data communication from IoT devices to cloud server is significantly high and it needs to be reduced with the help of clustering techniques. The current research article presents… More
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  • Artificial Intelligence-Based Semantic Segmentation of Ocular Regions for Biometrics and Healthcare Applications
  • Abstract Multiple ocular region segmentation plays an important role in different applications such as biometrics, liveness detection, healthcare, and gaze estimation. Typically, segmentation techniques focus on a single region of the eye at a time. Despite the number of obvious advantages, very limited research has focused on multiple regions of the eye. Similarly, accurate segmentation of multiple eye regions is necessary in challenging scenarios involving blur, ghost effects low resolution, off-angles, and unusual glints. Currently, the available segmentation methods cannot address these constraints. In this paper, to address the accurate segmentation of multiple eye regions in unconstrainted scenarios, a lightweight outer… More
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  • Emergency Prioritized and Congestion Handling Protocol for Medical Internet of Things
  • Abstract Medical Internet of Things (MIoTs) is a collection of small and energyefficient wireless sensor devices that monitor the patient’s body. The healthcare networks transmit continuous data monitoring for the patients to survive them independently. There are many improvements in MIoTs, but still, there are critical issues that might affect the Quality of Service (QoS) of a network. Congestion handling is one of the critical factors that directly affect the QoS of the network. The congestion in MIoT can cause more energy consumption, delay, and important data loss. If a patient has an emergency, then the life-critical signals must transmit with… More
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