Research Topic

Fuzzy Systems and Computational Intelligence for Biomedical Data Analysis - New Directions, Challenges and Applications

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About this Research Topic

Real-life phenomena involve uncertainty, impreciseness, vagueness, incompleteness, inconsistency, and indeterminacy. To address such ambiguous real-world challenges, it is therefore essential to introduce new logic representations for effective modeling. Fuzzy sets and logic are gaining significant attention in this context. These approaches have been applied in computational intelligence, clustering, control, data analysis & mining, decision making & support, design, human factors engineering & ergonomics, image processing, information processing & retrieval, knowledge representation & reasoning, marketing, medical diagnosis, optimization, pattern classification, production planning & scheduling, quality control, etc.

Medicine is undergoing a sector-wide transformation thanks to the advances in computing, networking, and artificial intelligence technologies. Core-research at the intersection of these fields is also pushing forward the boundaries of innovation in real-world industrial applications. The massive volume of data generated in the biomedical sector and the need to make sense of it are driving new optimization research trends. These are mainly based on machine learning and artificial intelligence but also cross-pollinated by advances in biomedical imaging, data analysis, and systems development.

This Research Topic focuses on the role of fuzzy theory in Biomedical Data Analysis and provides a unique opportunity for disseminating to the research community recent relevant and impactful research on applications, practices, and methodologies of fuzzy sets and systems for handling emerging problems. We welcome authors to present new techniques, methodologies, mixed-method approaches, and research directions regarding unsolved issues. Topics of interest include, but are not limited to:

• Soft computing models in advanced Fuzzy sets (Intuitionistic Fuzzy Set, Picture Fuzzy Set, Neutrosophic Set, Hesitant Fuzzy Set, etc.) for medical decision support with Health Ontologies;
• Intelligent context-aware applications, hybrid systems, including intelligent knowledge-based systems, and intelligent decision-support systems;
• Advanced fuzzy rules and rule-based systems for emerging eHealth IoT applications;
• Fuzzy Big Data processing: Fuzzy processing for Hadoop/Mapreduce/Sparks etc.;
• Fuzzy AI tools in healthcare;
• Dynamic and adaptive models in applications including machine learning with evolutionary systems and artificial neural networks for eHealth;
• Models to enable intelligent forecasting, monitoring, and prediction in research and real-world applications;
• Fuzzy techniques in mining complex patterns for healthcare monitoring;
• Fuzzy techniques in classification, regression, clustering, pattern mining, and real-time learning for healthcare monitoring;
• Fuzzy systems in bioinformatics, health, and medical analytics;
• Neutrosophic set-based applications and case studies for Health care logistics;
• The fusion models of fuzzy sets, rough sets and soft sets for Medical decision support systems;
• Hybrid intelligent decision support models and applications for IoT in healthcare applications;
• Emergency medical service systems;
• Optimization of Healthcare Systems and Data Transmission;
• Data mining and exploration of health data;
• Computational intelligence towards efficient diagnosis;
• Remote healthcare and health monitoring.


Keywords: Fuzzy Logic, Fuzzy Theory, Biomedical Data Analysis, Fuzzy Sets, Health Ontologies


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

Real-life phenomena involve uncertainty, impreciseness, vagueness, incompleteness, inconsistency, and indeterminacy. To address such ambiguous real-world challenges, it is therefore essential to introduce new logic representations for effective modeling. Fuzzy sets and logic are gaining significant attention in this context. These approaches have been applied in computational intelligence, clustering, control, data analysis & mining, decision making & support, design, human factors engineering & ergonomics, image processing, information processing & retrieval, knowledge representation & reasoning, marketing, medical diagnosis, optimization, pattern classification, production planning & scheduling, quality control, etc.

Medicine is undergoing a sector-wide transformation thanks to the advances in computing, networking, and artificial intelligence technologies. Core-research at the intersection of these fields is also pushing forward the boundaries of innovation in real-world industrial applications. The massive volume of data generated in the biomedical sector and the need to make sense of it are driving new optimization research trends. These are mainly based on machine learning and artificial intelligence but also cross-pollinated by advances in biomedical imaging, data analysis, and systems development.

This Research Topic focuses on the role of fuzzy theory in Biomedical Data Analysis and provides a unique opportunity for disseminating to the research community recent relevant and impactful research on applications, practices, and methodologies of fuzzy sets and systems for handling emerging problems. We welcome authors to present new techniques, methodologies, mixed-method approaches, and research directions regarding unsolved issues. Topics of interest include, but are not limited to:

• Soft computing models in advanced Fuzzy sets (Intuitionistic Fuzzy Set, Picture Fuzzy Set, Neutrosophic Set, Hesitant Fuzzy Set, etc.) for medical decision support with Health Ontologies;
• Intelligent context-aware applications, hybrid systems, including intelligent knowledge-based systems, and intelligent decision-support systems;
• Advanced fuzzy rules and rule-based systems for emerging eHealth IoT applications;
• Fuzzy Big Data processing: Fuzzy processing for Hadoop/Mapreduce/Sparks etc.;
• Fuzzy AI tools in healthcare;
• Dynamic and adaptive models in applications including machine learning with evolutionary systems and artificial neural networks for eHealth;
• Models to enable intelligent forecasting, monitoring, and prediction in research and real-world applications;
• Fuzzy techniques in mining complex patterns for healthcare monitoring;
• Fuzzy techniques in classification, regression, clustering, pattern mining, and real-time learning for healthcare monitoring;
• Fuzzy systems in bioinformatics, health, and medical analytics;
• Neutrosophic set-based applications and case studies for Health care logistics;
• The fusion models of fuzzy sets, rough sets and soft sets for Medical decision support systems;
• Hybrid intelligent decision support models and applications for IoT in healthcare applications;
• Emergency medical service systems;
• Optimization of Healthcare Systems and Data Transmission;
• Data mining and exploration of health data;
• Computational intelligence towards efficient diagnosis;
• Remote healthcare and health monitoring.


Keywords: Fuzzy Logic, Fuzzy Theory, Biomedical Data Analysis, Fuzzy Sets, Health Ontologies


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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