Advances in Computational Intelligence and Soft Computing (CISC) Paradigms: Applications for Environment and Health

Dear Colleagues,

Computational intelligence and soft computing (CISC) paradigms encompass a number of nature-inspired computational methodologies that encompass three main systems—artificial neural networks (ANNs), fuzzy sets, and evolutionary algorithms (EA) including genetic algorithms (EA/GAs)—and their hybridizations, such as neuro-fuzzy computing and neo-fuzzy systems. Based on their ability to capture the uncertainty, complexity and stochastic nature of the underlying processes, these systems have produced valuable, timely, robust, high quality and human-competitive results that have contributed to artificial intelligence research breakthroughs ranging from deep learning to genetic programming.

These powerful methodologies can be used to address a wide range of data analysis problems from environmental forecasting to health, industrial, business, financial, scientific, government and social media applications. The recent advances and success of computational intelligence methods and techniques in big data analysis applications suggests they can also be applied successfully in the analysis of large-scale raw data in complex public health and environmental applications. In this context, computational intelligence and soft computing (CISC) paradigms comprising numerous branches including neural networks, swarm intelligence, expert systems, evolutionary computing, fuzzy systems, and artificial immune systems, can play a vital role in handling the different aspects of public health and environmental systems.

The analogies and abstractions developed in the CISC fields have been able to provide valuable insights for successful algorithmic design and improvement, in many cases outperforming traditional search and heuristics. These techniques and algorithms have been particularly successful when specifically designed for, or applied to, solving complex real-world problems in data analytics and pattern recognition, by means of state-of-the-art methods with general applicability; domain-specific solutions; or hybrid algorithms that integrate CISC tools with traditional numerical and mathematical methods.

In this Special Issue, we invite researchers to contribute high-quality articles and surveys focusing on CISC methods for a wide range of application areas. The relevant topics of this Special Issue include but are not limited to:

• Computational intelligence and soft computing solutions for environmental challenges
• Computational intelligence and soft computing in mobile-cloud based computing for social networks
• Big data analytics for environmental and health prediction, management, and decision-making
• Fuzzy system theory in health and environmental applications
• Socio-environmental data analytical approaches using computational methods
• Deep learning and machine learning algorithms for efficient indexing and retrieval in public health systems
• Intelligent techniques for smart surveillance and security in public health systems
• Modeling, data mining, and public opinion analysis based on big data
• Crowd computing-assisted access control and digital rights management for health systems
• Evolutionary algorithms for data analysis and recommendations
• Crowd intelligence and computing paradigms
• Applied soft computing for content security, vulnerability and forensics in public health
• Computational intelligence in multimedia computing and context-aware recommendation
• Scalable, incremental learning and understanding of big data with its real-world applications for visualization, human-computer interactions, and virtual reality community
• Crowd intelligence-assisted ubiquitous, personal, and mobile social media applications
• Artificial intelligence and pattern recognition technologies for recommendations in healthcare
• Deep learning and computational intelligence based medical data analysis for smart healthcare services
• Parallel and distributed computing
• Computer vision and image processing
• Autonomous systems and industrial processes optimization
• Extreme and intelligent manufacturing
• Biomedical applications
• Big data analytics

Prof. Dr. Jason K. Levy
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2500 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.