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

A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601). This special issue belongs to the section "Digital Health".

Deadline for manuscript submissions: 31 December 2019.

Special Issue Editor

Guest Editor
Prof. Dr. Jason K. Levy Website E-Mail
Disaster Preparedness and Emergency Management, University of Hawaii, Kapolei, HI 96707, USA
Interests: disaster risk governance; sustainable hazard mitigation; stochastic and statistical hydrology; sociohydrology; fluvial and marine disasters; global climate change, computational intelligence for water management; hydrologic resilience; process-based modeling of coupled human–water systems; inundation; economics of water resources management; drought

Special Issue Information

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

This Special Issue will feature the best papers presented at the 2019 International Conference on Computational Intelligence and Communication Networks (CICN 2019) in Honolulu, USA on 3–6 January, 2019. The CICN 2019 conference series brings together worldwide leading researchers, developers, practitioners and educators to advance the state of the art in computational intelligence and communication networks. The conference covers all aspects of soft computing, communication networks, computational intelligence and artificial intelligence. Since this research field draws on a diverse range of paradigms and influences, the article types considered include original articles and authoritative reviews pertaining to algorithmic design, data analytics, case histories, empirical studies, conceptual–theoretical investigations, policy perspectives, institutional analysis, and risk analysis, among others. Conference papers may be accepted if they have been substantially extended.

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 papers will be 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 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. International Journal of Environmental Research and Public Health is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 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.

Keywords

  • Computational intelligence
  • Soft computing
  • Fuzzy systems
  • Evolutionary algorithms
  • Neural networks
  • Big data analytics
  • Pattern recognition
  • Hybrid algorithms
  • Numerical and mathematical methods
  • Biomedical applications
  • Extreme computing
  • Intelligent manufacturing
  • Autonomous systems and industrial process optimization
  • Computer vision and image processing
  • Parallel and distributed computing

Published Papers (2 papers)

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Research

Open AccessArticle
The Korea Cancer Big Data Platform (K-CBP) for Cancer Research
Int. J. Environ. Res. Public Health 2019, 16(13), 2290; https://doi.org/10.3390/ijerph16132290 - 28 Jun 2019
Abstract
Data warehousing is the most important technology to address recent advances in precision medicine. However, a generic clinical data warehouse does not address unstructured and insufficient data. In precision medicine, it is essential to develop a platform that can collect and utilize data. [...] Read more.
Data warehousing is the most important technology to address recent advances in precision medicine. However, a generic clinical data warehouse does not address unstructured and insufficient data. In precision medicine, it is essential to develop a platform that can collect and utilize data. Data were collected from electronic medical records, genomic sequences, tumor biopsy specimens, and national cancer control initiative databases in the National Cancer Center (NCC), Korea. Data were de-identified and stored in a safe and independent space. Unstructured clinical data were standardized and incorporated into cancer registries and linked to cancer genome sequences and tumor biopsy specimens. Finally, national cancer control initiative data from the public domain were independently organized and linked to cancer registries. We constructed a system for integrating and providing various cancer data called the Korea Cancer Big Data Platform (K-CBP). Although the K-CBP could be used for cancer research, the legal and regulatory aspects of data distribution and usage need to be addressed first. Nonetheless, the system will continue collecting data from cancer-related resources that will hopefully facilitate precision-based research. Full article
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Open AccessArticle
An Ensemble Classifier with Case-Based Reasoning System for Identifying Internet Addiction
Int. J. Environ. Res. Public Health 2019, 16(7), 1233; https://doi.org/10.3390/ijerph16071233 - 06 Apr 2019
Abstract
Internet usage has increased dramatically in recent decades. With this growing usage trend, the negative impacts of Internet usage have also increased significantly. One recurring concern involves users with Internet addiction, whose Internet usage has become excessive and disrupted their lives. In order [...] Read more.
Internet usage has increased dramatically in recent decades. With this growing usage trend, the negative impacts of Internet usage have also increased significantly. One recurring concern involves users with Internet addiction, whose Internet usage has become excessive and disrupted their lives. In order to detect users with Internet addiction and disabuse their inappropriate behavior early, a secure Web service-based EMBAR (ensemble classifier with case-based reasoning) system is proposed in this study. The EMBAR system monitors users in the background and can be used for Internet usage monitoring in the future. Empirical results demonstrate that our proposed ensemble classifier with case-based reasoning (CBR) in the proposed EMBAR system for identifying users with potential Internet addiction offers better performance than other classifiers. Full article
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