Journal Information
Information Sciences
Impact Factor:

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

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 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
Special Issue on New Parallel Distributed Technology for Big Data and AI
Submission Date: 2019-11-30

The improvement of computation power brings opportunities to big data and Artificial Intelligence (AI), however, new architectures, such as heterogeneous CPU-GPU, FPGA, etc., also bring great challenges to large-scale data and AI applications. Parallel Computing (PC), Machine Learning (ML), AI, and Big Data (BD) have grown substantially in popularity in recent years. Much research has been done in both academia and industry, with applications in many areas. For example, deep learning has achieved great success. AI/ML have been used to successfully play games such as Chess, Go, Atari, and Jeopardy. Many companies have been using AI and ML in areas including health care, natural resource management, and advertisement. Most of the PC/ML/AI/BD technologies and applications require intensive use of high-performance computers and accelerators for efficient processing. Parallel computing, distributed computing, cloud computing, and high-performance computing (HPC) are key components of these systems. Clusters of computers and accelerators (e.g., GPUs) are routinely used to train and run models, both in research and industry. On the other hand, HPC, ML, AI, and BD have also led to key applications for parallel computing, distributed computing, and HPC. Consequently, these issues have driven much of research in this area. The objective of this special issue is to bring together the parallel and distributed computing and PC/ML/AI/BD communities to present and discuss methodologies, solutions, and applications to performance issues, to present how PC/ML/AI/BD can be used to solve performance problems. Topics of interest include, but are not limited to: Large-scale Neural Computing , Natural Computing, Fuzzy Computing Large-scale Data Mining, Machine Learning, and Artificial Intelligence Parallel Computing Architectures for Neural Computing, Machine Learning Distributed Computing Architectures / Algorithms / Models for Neural Computing High-performance Computing Architectures for Neural Computing / Natural Computing High-performance Computing (HPC) Architectures for Machine Learning / Data Mining Scalable Natural Computing Algorithms and Applications Scalable Fuzzy Computing Algorithms and Applications Uncertainty Management in Machine Learning Novel HPC algorithms for AI/ML/BD Non-traditional HPC algorithms for AI/ML/BD
Last updated by Dou Sun in 2019-10-14
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