About this Research Topic
Soft Computing (SC) is an Artificial Intelligence (AI) approach that is more effective at solving real-life problems than traditional computing models. Soft Computing models are tolerant of partial truths, impressions, uncertainty, and approximation, in handling and providing useable solutions to complex problems. The applications of Soft Computing and Machine Learning (inclusive of Deep Learning), which are prominent subfields of AI, have been dominant in recent times. Thus, the possibilities of engaging these computational techniques for greater efficiency and effectiveness in healthcare systems have increased. The potential of intelligent systems to add value to many facets of human endeavor has been widely acknowledged by researchers. Generally, AI systems have recorded impressive performance, particularly in bioinformatics, health informatics, medicine, image recognition and biomedical analysis. As a result, there is a growing belief that Intelligent systems that are based on Soft Computing methods and Machine Learning can provide the needed leverage to improve processes and operations of healthcare systems. The massive application of AI approaches in tackling the COVID-19 pandemic, with over 23,900 published in online databases between 2020 - November 2022, attests to this fact.
The focus of this Research Topic is to provide a platform for researchers and practitioners to present the theoretical, conceptual, and practical applications of Soft Computing and Machine Learning for problem-solving in the healthcare sector. Soft Computing embraces the group of computational techniques based on AI and natural selection to enable complex problems for which analytical formulations are not feasible to be solved quickly and effectively. Typical Soft Computing techniques include Artificial Neural Networks, Fuzzy logic, Evolutionary algorithms, Swarm intelligence, and other computational methods that are based on approximate reasoning and approximate modelling. Similarly, Machine Learning enables complex problems that defy symbolic logic and expert systems to be solved by learning patterns hidden in data.
Submissions on the following are welcomed but not limited to:
- Soft Computing and Machine Learning Techniques for Medical and Health Informatics
- Intelligent Systems Applications for Healthcare Management
- Big Data Analytics for Medical Image Computing
- Bioinformatics Applications
- Data Mining Techniques & Knowledge Discovery in Medical Data
- Medical Image Analysis, Classification and Segmentation
- Deep Learning Applications to Healthcare Systems
- Pattern Detection in Medical Data
The focus of this Research Topic is to provide a platform for researchers and practitioners to present the theoretical, conceptual, and practical applications of Soft Computing and Machine Learning for problem-solving in the healthcare sector. Soft Computing embraces the group of computational techniques based on AI and natural selection to enable complex problems for which analytical formulations are not feasible to be solved quickly and effectively. Typical Soft Computing techniques include Artificial Neural Networks, Fuzzy logic, Evolutionary algorithms, Swarm intelligence, and other computational methods that are based on approximate reasoning and approximate modelling. Similarly, Machine Learning enables complex problems that defy symbolic logic and expert systems to be solved by learning patterns hidden in data.
Submissions on the following are welcomed but not limited to:
- Soft Computing and Machine Learning Techniques for Medical and Health Informatics
- Intelligent Systems Applications for Healthcare Management
- Big Data Analytics for Medical Image Computing
- Bioinformatics Applications
- Data Mining Techniques & Knowledge Discovery in Medical Data
- Medical Image Analysis, Classification and Segmentation
- Deep Learning Applications to Healthcare Systems
- Pattern Detection in Medical Data
Keywords: Machine Learning, Artificial Intelligence, Healthcare Systems, Bioinformatics, Health Informatics
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.
