Journal Information
Applied Computational Intelligence and Soft Computing (ACISC)
https://www.hindawi.com/journals/acisc/
Publisher:
Hindawi
ISSN:
1687-9724
Viewed:
390
Tracked:
0

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Call For Papers
Applied Computational Intelligence and Soft Computing will focus on the disciplines of computer science, engineering, and mathematics. The scope of the journal includes developing applications related to all aspects of natural and social sciences by employing the technologies of computational intelligence and soft computing. The new applications of using computational intelligence and soft computing are still in development. Although computational intelligence and soft computing are established fields, the new applications of using computational intelligence and soft computing can be regarded as an emerging field, which is the focus of this journal.

The application areas of interest include, but are not limited to:

    Interval Analysis (Real Interval Arithmetics, Complex Interval Arithmetics, Interval Equations, etc.)
    Interval Mathematics (Metric Topology for Intervals, Interval Integrals, Interval Differential Equations, etc.)
    Interval Computation (Matrix Computation with Intervals, Systems of Interval Equations, etc.)
    Fuzzy Sets (Fuzzy Numbers, Extension Principle, Fuzzy Rough Sets, Fuzzy Competence Sets, etc.)
    Fuzzy Systems (Fuzzy Control, Fuzzy Neural Networks, Genetic Fuzzy Systems, Hybrid Intelligent Systems, etc.)
    Fuzzy Logics (Many-Valued Logics, Type-2 Fuzzy Logics, Intuitionistic Fuzzy Logics, etc.)
    Fuzzy Mathematics (Fuzzy Differential Equations, Fuzzy Real Analysis, Fuzzy Topology, Fuzzy Algebra, etc.)
    Fuzzy Optimization (Possibilistic Programming, Fuzzy Linear and Nonlinear Programming, Fuzzy Stochastic Optimization, etc.)
    Fuzzy Statistical Analysis (Fuzzy Random Variables, Fuzzy Regression Analysis, Fuzzy Reliability Analysis, Fuzzy Times Series, etc.)
    Operations Research (Fuzzy Games, Fuzzy Inventory Models, Fuzzy Queueing Theory, Fuzzy Scheduling Problems, etc.)
    Heuristics (Ant Colony Optimization, Artificial Immune Systems, Genetic Algorithms, Particle Swarm Intelligence, Simulated Annealing, Tabu Search, etc.)
    Hybrid Systems (The fusion of Fuzzy Systems and Computational Intelligence)
    Approximate Reasoning (Possibility Theory, Mathematical Theory of Evidence, Fuzzy Common Knowledge, etc.)
    Miscellaneous (Fuzzy Data Mining, Fuzzy Biomedical Systems, Pattern Recognition, Fuzzy Clustering, Information Retrieval, Chaotic Systems, etc.)
Last updated by Dou Sun in 2017-04-30
Special Issues
Special Issue on Applications of Evolutionary Computation on Solving Combinatorial Optimization Problems in Retailing, Logistics, and Supply Chain Management
Submission Date: 2017-07-28

Evolutionary computation (EC) is a computational intelligence subfield involving solving complex problems. It is a group of population-based problem-solving techniques which are on the base of biological evolution, such as genetic inheritance and natural selection. It has been used to address a wide variety of challenging real-world problems in recent years. Due to its powerful performance, it has received great interest from many researchers and practitioners. Combinatorial optimization problems in retailing, logistics, and supply chain management, because of their inherent complexity, are very suitable to employ EC to solve a number of related problems in these fields. The aim of this special issue is to discuss recent advances in applications of EC for retailing, logistics, and supply chain management problems. We invite authors to contribute high-quality research as well as review articles that demonstrate successful applications of EC methodologies and approaches in these areas. Potential topics include but are not limited to the following: Decisions on closing or opening convenience stores Combinatorial optimization of goods and product mix optimization Strategic sourcing and supplier selection Product line selection Location/site selection Revenue management Planning for production/manufacturing Planning for distribution Production scheduling Warehouse management Green transportation Green supply chain Green logistics Fast delivery Emergency supply chain management Global logistics management Cold chain or low-temperature supply chain management Integrated supply chain Resource allocation and management Supply chain collaboration Genetic algorithm Simulated annealing Ant colony optimization algorithm Immune optimization algorithm Other evolutionary computation algorithms Other topics related to retailing, logistics, and supply chain management Authors can submit their manuscripts through the Manuscript Tracking System at http://mts.hindawi.com/submit/journals/acisc/rls/. Manuscript Due Friday, 28 July 2017 First Round of Reviews Friday, 20 October 2017 Publication Date Friday, 15 December 2017
Last updated by Dou Sun in 2017-04-30
Special Issue on Rule Extraction from Neural Models: From Small Data to Big Data
Submission Date: 2017-07-28

The primary disadvantage of artificial neural networks (ANNs) is that they have no clear declarative representation of knowledge. In addition, ANNs have considerable difficulty generating the necessary explanation structures, which limits their full potential because the ability to provide detailed characterizations of classification strategies would promote their acceptance. Expert systems benefit from a clear declarative representation of knowledge about the problem domain; therefore, a natural means to elucidate the knowledge embedded within ANNs is to extract symbolic rules, even though this problem is known to be NP-hard. Most current rule extraction algorithms are applied to multilayer perceptrons (MLPs) with one hidden layer. However, surprisingly, very little work has been conducted in relation to deep ANNs. Bridging this gap could be expected to contribute to the real-world utility of both deep MLPs and deep learning networks. Rule extraction from NNs can also be considered an optimization problem because it involves a clear trade-off between accuracy and comprehensibility; although higher number of rules typically provides better accuracy, it also reduces comprehensibility. Another clear trade-off can be seen between the numbers of rules and uncovered samples. Specifically, in addition to reducing comprehensibility, higher number of rules reduces the number of uncovered samples. Rule extraction from neural models therefore remains an area in need of further innovation. Potential topics include but are not limited to the following: Machine learning and computational intelligence applied to rule extraction Machine learning and computational intelligence applied to transparency of deep learning networks Rule extraction from medical, financial, and industrial Big Data Rule extraction from decision tree ensembles Accuracy-interpretability dilemma: high performance classifiers versus rule extraction Authors can submit their manuscripts through the Manuscript Tracking System at http://mts.hindawi.com/submit/journals/acisc/renm/. Manuscript Due Friday, 28 July 2017 First Round of Reviews Friday, 20 October 2017 Publication Date Friday, 15 December 2017
Last updated by Dou Sun in 2017-04-30
Special Issue on Soft Computing Methods for Social Sciences: Modeling and Analyzing the Issues for Social Sciences
Submission Date: 2017-08-25

Opinions, evaluations, perceptions, expectations, and so on are just main data that have been collected and evaluated by social science disciplines. They are gathered either in the form of words or in the form of sentences which need to be evaluated. So far several statistical methods are employed in order to analyze those types of data by transforming them into Likert-type scores. However, those data representing another type of uncertainty called impression carry the potential of being suitably modeled by soft computing tools such as Fuzzy Set Theory, Belief Functions, Evolutionary Computing, Fuzzy Logic, Rough Set Theory, and Dempster-Shafer Theory. These approaches could bring more flexibility and more interpretability, just as those theories have brought them into computationally oriented fields of science such as engineering, decision science, and statistics. By doing so, it is expected to provide twofold benefits within social science disciplines ranging from education and tourism to political science and physiology. One of them is based on uncovering new empirical findings by employing those methods, for instance, interactions among verbal statements having impact on some other attributes. The second one is to provide grounding for the development of novel methods when social scientists collaborate with researchers working in the fields of soft computing. All accepted papers are expected to either have implementation of data sets benefiting from those methods in order to show that they provide deeper understanding the subject of interest or propose novel methods. Potential topics include but are not limited to the following: Fuzzy Set Theory in humanities and social sciences including economics Fuzzy Logic in humanities and social sciences including economics Belief Theory in humanities and social sciences including economics Evolutionary Computing in humanities and social sciences including economics Dempster-Shafer Theory in humanities and social sciences including economics Rough Set Theory in humanities and social sciences including economics Authors can submit their manuscripts through the Manuscript Tracking System at https://mts.hindawi.com/submit/journals/acisc/scmss/. Manuscript Due Friday, 25 August 2017 First Round of Reviews Friday, 17 November 2017 Publication Date Friday, 12 January 2018
Last updated by Dou Sun in 2017-04-30
Special Issue on Computational Intelligence in Evaluation and Management for Internet Services and Applications
Submission Date: 2017-09-15

The emerging synthesis of intelligence technologies, network technologies, and information technologies is one of the most promising approaches towards Internet service support for the next generation. Generally, computational intelligence provides a set of nature-inspired computational methodologies and approaches to address complex real-world problems, such as service evaluation and management in which the processes contain some uncertainties and are too complex for mathematical reasoning. Although service evaluation and management have made a lot of research including research on Quality of Service (QoS) and Quality of Experience (QoE), researchers have started to integrate computational intelligence in the design of the quality monitoring, performance evaluation, and resource management of network infrastructures and services in order to enable better service experiences. However, because of the additional complexities in content generation, processing, distribution, and display, as well as emerging new forms of media technologies and services, many challenges remain in developing effective and practical evaluation and management methods with computational intelligence. This special issue aims to become a valuable information source for state-of-the-art research and development in services and applications in future Internet. It also aims to serve as an outlet for facilitating computational intelligent among service computing researchers, practitioners, and professionals from across academics, government, and industry. Finally, it aims to foster the dissemination of high quality research in new ideas, methods, theories, techniques, and applications of evaluation and management for improving services. Original research articles are solicited in all aspects, including theoretical studies, practical applications, and experimental prototypes. Potential topics include but are not limited to the following: Computation intelligence for service evaluation and management Theoretical and computational models for service/application Soft computing, fuzzy logic, and artificial neural networks for service/application Data mining and data warehousing for service evaluation and management Grid, cloud, and high speed/performance and green computing for service/application Computation intelligence for service selection, composition, and provisioning Architectures and algorithms in service evaluation and management IoT and cloud platforms for service evaluation and management QoS/QoE evaluation metrics, methodologies, frameworks, and testbeds Managing QoS and QoE levels in wireless or 5G network Authors can submit their manuscripts through the Manuscript Tracking System at https://mts.hindawi.com/submit/journals/acisc/cem/. Manuscript Due Friday, 15 September 2017 First Round of Reviews Friday, 8 December 2017 Publication Date Friday, 2 February 2018
Last updated by Dou Sun in 2017-04-30
Special Issue on Recent Applications of Computational Intelligence and Soft Computing In Remote Sensing and Geographic Information System and Sciences
Submission Date: 2017-09-29

Remote Sensing (RS) and Geographical Information System (GIS) are two of the important sources for providing, extracting, and managing valuable information regarding the earth. For providing, extracting, and managing valuable information, researchers and scientists employ several methods, algorithms, and techniques. Among existing methods, algorithms, and techniques, computational intelligence and soft computing ones are very popular, efficient, and robust. Computational intelligence and soft computing are powerful paradigms to provide, extract, and manage earth’s information. Computational intelligence and soft computing comprise a wide range of methods including evolutionary and bioinspired computation, stochastic methods, machine learning, neural networks, deep learning, and fuzzy logic. In this special issue, we attempt to integrate novel computational intelligence and soft computing techniques on earth sciences, especially RS and GIS. We invite authors to contribute high quality researches that demonstrate successful application of computational intelligence methods in RS and GIS sciences. Potential topics include but are not limited to the following: Extracting and providing information from optical remotely sensed data using computational intelligence and soft computing techniques Deep learning and its application in RS and GIS Extracting and providing information from hyperspectral remotely sensed data using computational intelligence and soft computing techniques The application of augmented and virtual reality in RS Extracting and providing information from Synthetic Aperture Radar (SAR) remotely sensed data using computational intelligence and soft computing techniques Extracting and providing information from Unmanned Aerial Vehicle (UAV) remotely sensed data using computational intelligence and soft computing techniques Managing and extracting information and knowledge from GISs Soft computing-based classification and decision making systems The application of computational intelligence and soft computing techniques in managing natural hazards The application of computational intelligence and soft computing techniques in urban and nonurban areas Extracting and providing information from high resolution remotely sensed data using Geographic-Object-Based Image Analysis (GEOBIA) Authors can submit their manuscripts through the Manuscript Tracking System at https://mts.hindawi.com/submit/journals/acisc/racis/. Manuscript Due Friday, 29 September 2017 First Round of Reviews Friday, 22 December 2017 Publication Date Friday, 16 February 2018
Last updated by Dou Sun in 2017-04-30
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