Journal Information
Information Sciences
http://www.journals.elsevier.com/information-sciences/
Impact Factor:
4.305
Publisher:
ELSEVIER
ISSN:
0020-0255
Viewed:
14719
Tracked:
61

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Call For Papers
Information Sciences will publish original, innovative and creative research results. A smaller number of timely tutorial and surveying contributions will be published from time to time.

The journal is designed to serve researchers, developers, managers, strategic planners, graduate students and others interested in state-of-the art research activities in information, knowledge engineering and intelligent systems. Readers are assumed to have a common interest in information science, but with diverse backgrounds in fields such as engineering, mathematics, statistics, physics, computer science, cell biology, molecular biology, management science, cognitive science, neurobiology, behavioural sciences and biochemistry.

The journal publishes high-quality, refereed articles. It emphasizes a balanced coverage of both theory and practice. It fully acknowledges and vividly promotes a breadth of the discipline of Informations Sciences.

Topics include:

Foundations of Information Science:
Information Theory, Mathematical Linguistics, Automata Theory, Cognitive Science, Theories of Qualitative Behaviour, Artificial Intelligence, Computational Intelligence, Soft Computing, Semiotics, Computational Biology and Bio-informatics.

Implementations and Information Technology:
Intelligent Systems, Genetic Algorithms and Modelling, Fuzzy Logic and Approximate Reasoning, Artificial Neural Networks, Expert and Decision Support Systems, Learning and Evolutionary Computing, Expert and Decision Support Systems, Learning and Evolutionary Computing, Biometrics, Moleculoid Nanocomputing, Self-adaptation and Self-organisational Systems, Data Engineering, Data Fusion, Information and Knowledge, Adaptive ad Supervisory Control, Discrete Event Systems, Symbolic / Numeric and Statistical Techniques, Perceptions and Pattern Recognition, Design of Algorithms, Software Design, Computer Systems and Architecture Evaluations and Tools, Human-Computer Interface, Computer Communication Networks and Modelling and Computing with Words

Applications:
Manufacturing, Automation and Mobile Robots, Virtual Reality, Image Processing and Computer Vision Systems, Photonics Networks, Genomics and Bioinformatics, Brain Mapping, Language and Search Engine Design, User-friendly Man Machine Interface, Data Compression and Text Abstraction and Summarization, Virtual Reality, Finance and Economics Modelling and Optimisation
Last updated by Dou Sun in 2018-07-15
Special Issues
Special Issue on Advances in Industrial Artificial Intelligence (AIAI)
Submission Date: 2019-09-30

In general,Industrial Artificial Intelligence(IAI)refers to the application of artificial intelligence to industrial automation. Different from general artificial intelligence, industrialAInarrows downthe scope ofAIresearch fields to building intelligent systems that resolve engineering problems with human-like intelligence. IndustrialAIstresses more applications rather thanAIconcept or framework development and highlights the value ofAIsolution to specific process industry, including causality identification between input-output variables, state or product quality estimate, retrieval and reasoning of interesting cases, motif discovery from the closed-loop system for monitoring, fault diagnosis and detection, soft-sensing, parameter, structure and process optimization, planning and coordination, dynamical system modelling and control. IAIhas received sufficiently technical supports from sensing techniques, more powerful computing facilities and stronger communication infrastructure. However, one should be aware that these technologies mentioned above may create some business values only if the problems in industry can be well studied and formulated. Understanding the domain-based AI (DBAI) concept is important and meaningful toAIcommunity, and one cannot expect much ofAItechnologies without knowing the application background in depth, the data nature, and dynamics and constraints of the variables.IAI, as a member ofDBAIfamily, will play a key role in contributing to product and service innovation, process improvement, and insightful discovery, and will eventually become an unstoppable driver for the transformation of economy and business opportunities. This special issue aims to highlightIAIconcept, research scopes and recently technical advancements in industrial data analytics, and make theIAIconcept more visible inAIcommunity. Original contributions, including industrial data driven machine learning techniques, advanced fuzzy logic systems, online optimization algorithms, real-world case studies on industrial applications, and comprehensive surveys with directions, are cordially welcome. Through this special issue, some fundamental concepts and associatedAItechniques forIAIwill be further focused and promoted. About the issue The topics of this special issue include, but are not limited to: Identification of input-output causality from noisy big industrial data Computational intelligence and machine learning techniques for soft-sensors and predictive modelling Time-series forecasting, and interval estimate for industrial data Learning-based reasoning techniques for industrial applications AI-driven operational optimization and decision-making AI-based methods for process monitoring, abnormality detection and fault analysis AI-based planning and scheduling for process industries Case studies of AI technology for problem solving in process industries, chemical engineering, power systems, industrial robotics, maritime engineering, transportation engineering, civil engineering, and intelligent software engineering
Last updated by Dou Sun in 2019-07-06
Special Issue on Theory and Applications for Learning Guided Evolutionary Optimization and Fitness Landscape Analysis
Submission Date: 2019-11-30

Evolutionary algorithms are highly flexible in terms of handling constraints, dynamic changes, and multiple conflicting objectives. In real-world applications, many complex optimization problems do not have an analytical objective function available during the optimization process. Learning guided evolutionary optimization (LGEO) utilizes statistical and machine learning techniques to assist the evolutionary algorithms. The area of LGEO has attracted attention of researchers over the recent years due to its applicability and interesting computational aspects. With the growth of new technologies and models in machine learning, researchers in this field have to continuously face with new challenges, such as which learning techniques can be used and how to use learning techniques to help design optimization algorithms. Being Influenced by biological evolution, researchers began the fitness landscape research early in the field of evolutionary optimization, whose purpose is to understand the behavior of evolutionary algorithms to solve optimization problems. Fitness landscape analysis (FLA) can be used to many real-world problems by analyzing the underlying search space in terms of the objectives to be optimized. There have been many recent advances in the FLA field in the development of methods and measures that have been shown to be effective in the understanding of algorithm behavior, the prediction of meta-heuristic performance and the selection of algorithms.. This special issue aims to provide a platform for bringing together researchers to discuss new and existing issues in these areas, and invite researchers to submit original and previously unpublished research and application papers. Topics of Interest: Topics include, but are not limited to the following: Therotical analysis on learning guided evolutionary computation Therotical analysis on fitness landscape analysis Evolutionary learning methods on scheduling problems Learning guided evolutionary strategy design Fitness landscape analysis techniques for evolutionary algorithms Advanced data-driven evolutionary algorithms Multi-objective data-driven optimization methods Surrogate models in evolutionary algorithms Deep learning in learning guided evolutionary optimization Knowledge mining techniques for learning guided evolutionary optimization problems Learning guided evolutionary alogrithms in scheduling optimization Fitness landscape analysis techniques for continuous optimisation problems Learning guided evolutionary alogrithms in dynamic/real-time/nondeterministic systems
Last updated by Dou Sun in 2019-07-11
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