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

Special Issue on Combining Machine Learning and Metaheuristics for Optimizing Complex Intelligent Systems
截稿日期: 2024-06-15

With advancements in different domains, intelligent systems have become increasingly complex. It is challenging to ensure that these systems are resilient and adaptable in uncertain and dynamic environments. To design intelligent systems in such environments, integrating machine learning and metaheuristic algorithms provides a strong foundation. This approach has significantly improved the efficiency of intelligent systems, leading to more impactful solutions. This special issue seeks contributions that explore the convergence of machine learning and metaheuristics, with a focus on optimizing complex intelligent systems. We encourage submissions that cover novel algorithmic developments, comparative studies, real-world case analyses, and theoretical advancements at the intersection of these two methodologies.

Guest editors:

Prof. Jian Wang (Executive Guest Editor)
China University of Petroleum (East China), China
Email: wangjiannl@upc.edu.cn 
Areas of Expertise: computational intelligence, machine learning, pattern recognition, deep learning, differential programming, clustering, fuzzy systems, and evolutionary computation
 
Assoc. Prof. Chanjuan Liu
Dalian University of Technology, China
Email: chanjuanliu@dlut.edu.cn 
Areas of Expertise: Intelligent Decision Making and Optimization

Prof. Jacek Mańdziuk
Warsaw University of Technology
Email: mandziuk@mini.pw.edu.pl 
Areas of Expertise: application of Computational Intelligence and Artificial Intelligence methods to games, dynamic and bilevel optimization problems, and human-machine cooperation in problem solving
Special issue information:
Topics of Interest
The following areas are of particular interest, although not limited to:

Parameter and structure optimization of machine learning models based on metaheuristicalgorithms.
Solution initialization, selection and generation of metaheuristics, and parameter optimization of MHs using data-driven insights from ML.
The integrated solution of MHs and ML contributes to designing more flexible, robust, and adaptive intelligent systems.
Benchmark Datasets and Evaluation Metrics: Contributions focused on developing new benchmark datasets and evaluation metrics tailored to ML and MHs.

Special Issue on Explainable Artificial Intelligence for Security and Privacy in Recommender Systems
截稿日期: 2024-07-31

Recommender Systems (RS) have become one of the most effective approaches to quickly extract insightful information from big data and are not widely applied to various fields such as Smart Healthcare, E-commerce, Intelligent Tourism, Smart Transportation, etc. The characteristics of big data, such as multi-source property and data diversity, require that a recommender system can quickly integrate the data distributed across multiple parties so as to make comprehensive and accurate recommendation decisions. In particular, to protect business secrets and obey laws, securing user data and preserving user privacy during the abovementioned data integration process are very important but challenging requirements in practice.

Machine learning powered Artificial Intelligence (AI) has recently emerged as one of the key technologies to realize multi-source data analyses and knowledge utilization. Therefore, AI has provided a promising way to achieve the abovementioned security and privacy goals in RS. However, current AI-based security and privacy research in RS still falls short in providing a good explanation of how the AI algorithms or models can balance a series of conflicting recommendation criteria well, e.g., security, accuracy, robustness, privacy, efficiency, etc. Therefore, the adaptation of explainable AI models and technologies is highly demanded to achieve their full potentials in guaranteeing user security and privacy in RS.

This special issue focuses on the challenges and problems in Explainable Artificial Intelligence for Security and Privacy in Recommender Systems. It aims to share and discuss recent advances and future trends of secure, privacy-preserving and explainable AI for RS, and to bring academic researchers and industry developers together.

Guest editors:

Prof. Jinjun Chen (Executive Guest Editor)
Swinburne University of Technology, Australia
Email: jinjun.chen@gmail.com

Prof. Lianyong Qi
China University of Petroleum (East China), China
Email: lianyongqi@gmail.com

Dr. Hayford Perry Fordson
Cornell University, USA
Email: perryfordson@cornell.edu

Special issue information:

The topics of interest include, but are not limited to:

Empirical studies of secure and explainable AI for RS 
Explainability of AI models/algorithms in dependable RS
Explainable AI for Privacy techniques/protocols in RS
Adversarial attack and defense in RS with explainable AI
Blockchain-based security solutions for RS with explainable AI
Authentication and Anonymity for explainable AI-based RS
Novel explainable AI techniques or applications to distributed RS
Explainable AI to detect potential biases for secure RS
Novel evaluation frameworks of explainable AI for RS
Explainability of federated learning for cross-platform RS
Lightweight security and privacy solutions with explainable AI for RS