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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: closed (31 August 2020) | Viewed by 23003

Special Issue Editor

Prof. Dr. Jason Levy

E-Mail Website
Guest Editor
Disaster Preparedness and Emergency Management, University of Hawaii, 2540 Dole Street, Honolulu, HI 96822, USA
Interests: epidemiology and prevention of congenital anomalies; psychosis and affective psychosis; cancer epidemiology and prevention; molecular and human genome epidemiology; evidence synthesis related to public health and health services research
Special Issues, Collections and Topics in MDPI journals

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

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 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 monthly 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 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.

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

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (5 papers)

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Research

29 pages, 3694 KiB  
Article
Modeling Network Public Opinion Propagation with the Consideration of Individual Emotions
by Peihua Fu, Bailu Jing, Tinggui Chen, Jianjun Yang and Guodong Cong
Int. J. Environ. Res. Public Health 2020, 17(18), 6681; https://doi.org/10.3390/ijerph17186681 - 14 Sep 2020
Cited by 16 | Viewed by 4153
Abstract
The occurrence of popular social events causes fluctuations and changes of public emotions, while the rapid development of online social platforms and networks has made individual interactions more intense and further escalated public emotions into public opinion. However, there is a lack of [...] Read more.
The occurrence of popular social events causes fluctuations and changes of public emotions, while the rapid development of online social platforms and networks has made individual interactions more intense and further escalated public emotions into public opinion. However, there is a lack of consideration of individual emotions in the current research on online public opinion. Based on this, this paper firstly expounds the quantitative representation of attitude and emotion, analyzes the formation and propagation process of online public opinion by combining individual’s expression willingness, individual’s expression ability, attitude perception value, attitude change probability and other factors, and constructs a network public opinion propagation model that takes individual emotion into consideration. Finally, the main factors affecting the formation and propagation of network public opinion are discussed through simulation experiments. The results demonstrate that: (1) fear is conducive to the formation of online public opinion, but the speed is relatively slow; sadness is not conducive to the formation, but once enough people participate in the exchange of views, the formation of online public opinion will be faster; (2) the influence of online public opinion on individual emotions expands with the increase of the number of individual interactions; (3) different network structures impact differently on the propagation of public opinion. Among them, BA (BA network is a scale-free network model proposed by Barabasi and Albert in order to explain the generation mechanism of power law, BA model has two characteristics: growth and priority connection mechanism) and ER (ER network is a network with random connectivity proposed by Erdös-Renyi) random networks can promote the propagation of online public opinion, which is prone to “one-sided” online public opinion. WS small-world networks (proposed by Watts and Strogatz. It is a kind of network with short average path length and high clustering coefficient) and fully-connected networks have an inhibitory effect on the spread of online public opinion, easily maintaining the multi-dimensional nature of online public opinion. Full article
(This article belongs to the Special Issue Advances in Computational Intelligence and Soft Computing (CISC) Paradigms: Applications for Environment and Health)
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34 pages, 995 KiB  
Article
Strategies for Heterogeneous R&D Alliances of In Vitro Diagnostics Firms in Rapidly Catching-Up Economies
by Chi-Yo Huang and I-Ling Tung
Int. J. Environ. Res. Public Health 2020, 17(10), 3688; https://doi.org/10.3390/ijerph17103688 - 23 May 2020
Cited by 6 | Viewed by 4050
Abstract
Most developed countries already have high-quality in vitro diagnostic (IVD) techniques for diseases, but developing countries often do not have access to these technologies and cannot afford them. Enabling firms to leverage external resources to optimize their research and development (R&D) performance has [...] Read more.
Most developed countries already have high-quality in vitro diagnostic (IVD) techniques for diseases, but developing countries often do not have access to these technologies and cannot afford them. Enabling firms to leverage external resources to optimize their research and development (R&D) performance has become one of the most critical issues for small and medium-sized late-coming IVD firms. R&D alliances, especially heterogeneous alliances, are necessary for releasing the resource limitations of late-coming small and medium-sized enterprises (SMEs) and reaching the metaoptimum of the R&D performances. However, to the authors’ knowledge, a few, if any, previous studies have investigated the key success factors and strategies of heterogeneous alliances in the IVD industry. Therefore, the authors aim to define the critical factors for evaluating and selecting strategies for heterogeneous alliances in the IVD industry. A Decision-Making Trial and Evaluation Laboratory (DEMATEL)-based analytic network process (DANP) was proposed to prioritize the weights associated with the evaluation criteria. Then, a heterogeneous R&D alliance strategy was derived from the compromise ranking based on the modified VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method. An empirical study of major Taiwanese IVD firms’ evaluation and selection of heterogeneous R&D alliance strategies will be used to reveal the practicability of the analytic framework. Based on the analytic results, the joint venture strategy is the most suitable heterogeneous R&D alliance strategy for IVD firms in rapidly catching-up economies. These results can serve as the basis for heterogeneous R&D alliance strategy definitions in the IVD industry in the future. Full article
(This article belongs to the Special Issue Advances in Computational Intelligence and Soft Computing (CISC) Paradigms: Applications for Environment and Health)
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29 pages, 14742 KiB  
Article
Public Opinion Polarization by Individual Revenue from the Social Preference Theory
by Tinggui Chen, Qianqian Li, Peihua Fu, Jianjun Yang, Chonghuan Xu, Guodong Cong and Gongfa Li
Int. J. Environ. Res. Public Health 2020, 17(3), 946; https://doi.org/10.3390/ijerph17030946 - 4 Feb 2020
Cited by 33 | Viewed by 5456
Abstract
Social conflicts occur frequently during the social transition period and the polarization of public opinion happens occasionally. By introducing the social preference theory, the target of this paper is to reveal the micro-interaction mechanism of public opinion polarization. Firstly, we divide the social [...] Read more.
Social conflicts occur frequently during the social transition period and the polarization of public opinion happens occasionally. By introducing the social preference theory, the target of this paper is to reveal the micro-interaction mechanism of public opinion polarization. Firstly, we divide the social preferences of Internet users (network nodes) into three categories: egoistic, altruistic, and fair preferences, and adopt the revenue function to define the benefits obtained by individuals with different preferences among their interaction process so as to analyze their decision-making behaviors driven by the revenue. Secondly, the revenue function is used to judge the exit rules of nodes in a network, and then a dynamic network of spreading public opinion with the node (individual) exit mechanism is built based on a BA scale-free network. Subsequently, the influences of different social preferences, as well as individual revenue on the effect of public opinion polarization, are analyzed through simulation experiments. The simulation results show that (1) Different social preferences demonstrate different influences on the evolution of public opinions, (2) Individuals tend to interact with ones with different preferences, (3) The network with a single preference or a high aggregation is more likely to form public opinion polarization. Finally, the practicability and effectiveness of the proposed model are verified by a real case. Full article
(This article belongs to the Special Issue Advances in Computational Intelligence and Soft Computing (CISC) Paradigms: Applications for Environment and Health)
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13 pages, 2959 KiB  
Article
The Korea Cancer Big Data Platform (K-CBP) for Cancer Research
by Hyo Soung Cha, Jip Min Jung, Seob Yoon Shin, Young Mi Jang, Phillip Park, Jae Wook Lee, Seung Hyun Chung and Kui Son Choi
Int. J. Environ. Res. Public Health 2019, 16(13), 2290; https://doi.org/10.3390/ijerph16132290 - 28 Jun 2019
Cited by 20 | Viewed by 5103
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
(This article belongs to the Special Issue Advances in Computational Intelligence and Soft Computing (CISC) Paradigms: Applications for Environment and Health)
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17 pages, 3114 KiB  
Article
An Ensemble Classifier with Case-Based Reasoning System for Identifying Internet Addiction
by Wen-Huai Hsieh, Dong-Her Shih, Po-Yuan Shih and Shih-Bin Lin
Int. J. Environ. Res. Public Health 2019, 16(7), 1233; https://doi.org/10.3390/ijerph16071233 - 6 Apr 2019
Cited by 10 | Viewed by 3262
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
(This article belongs to the Special Issue Advances in Computational Intelligence and Soft Computing (CISC) Paradigms: Applications for Environment and Health)
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