Journal Information
Information Sciences
http://www.journals.elsevier.com/information-sciences/
Impact Factor:
6.795
Publisher:
Elsevier
ISSN:
0020-0255
Viewed:
50474
Tracked:
157
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 2022-01-29
Special Issues
Special Issue on Explainable Artificial Intelligence for Security and Privacy in Recommender Systems
Submission Date: 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
Last updated by Dou Sun in 2024-04-16
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