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 Big Data Analytics for Intelligent Transportation Systems: Emerging Practice and Advanced Methodologies
Submission Date: 2021-11-30

Topic of Interest

The topic of interest includes the following but not limited to:
- Information extraction methodology of big data in transportation systems
- Advanced big data analytics technique in transportation systems
- Prediction technique with big data in transportation systems
- Big-data-driven operational analysis for transportation management
- Big-data-driven decision making methodology for transportation management
- Innovative applications of big data analytics in public transport (bus, subway, light rail, etc.)
- Innovative applications of big data analytics in shared transport (car-sharing, bike-sharing, ride-hailing, etc.)
- Innovative applications of big data analytics in road/air/marine freight transportation 

Special Issue on Modeling imprecise information and knowledge to improve explanability in AI
Submission Date: 2022-01-28

Topics of interest include, but are not limited to:
- Foundational and philosophical aspects of imprecision in information and knowledge
- Theoretical advancements in imprecision modeling in AI
- Imprecision modeling methods to improve explanability in AI New technologies for representing and processing imprecision in XAI systems
- Real-world applications and case studies that demonstrate explanability improvements through imprecision management 

Special Issue on Innovative applications of computational intelligence and neuroscience for blockchain internet of medical things
Submission Date: 2022-12-30

Since the Internet of Things (IoT) has emerged, it has provided a smart industry that is run with data-driven decision-making (Shao et al., 2021; Wu et al., 2021b). On the other hand, insufficient data security and trust in the currently running IoT have significantly limited its acceptance and application. At the moment, many different devices connect with IoT to provide a variety of services and applications, such as healthcare and medical industries (Alzubi, 2021). Such growth of a huge size has caused users to be seriously concerned about their security and privacy, particularly in the case of the internet of medical things (IoMT) (Jain et al., 2021; Khan and Akhunzada, 2021; Mahmoud et al., 2021), which demands special considerations. One of the over disputed examples of this issue in healthcare systems is unauthorized access of third parties to massive amounts of the patients’ sensitive information such as their medical/personal records that are applicable to making extremely significant decisions. Some other life-threatening or malevolent activities that might happen are making unauthorized changes to health-care-related data, gaining access to hospital networks, hijacking medical devices, and exploiting stored/exchanged information (Alsubaei et al., 2019; Kumar and Tripathi, 2021). Such issues necessitate further research for finding optimum solutions to address such threats and attacks on IoMT effectively.

Blockchain is one of the security mechanisms that can be an appropriate substitute to the traditional methods in dealing with security- and privacy-related issues (Wu et al., 2021a; Zhan et al., 2021). It is recognized as the backbone of future IoMT applications. Blockchain has been found with different benefits such as improving security level, decreasing costs, being traceable, enhancing speed, and improving the efficiency of mechanisms. Since blockchain was introduced, researchers have concentrated on applying this technology to bring security to healthcare systems and applications (Büyüközkan and Tüfekçi, 2021; Garg et al., 2020; Shahzad et al., 2021; Zhou et al., 2020). Unfortunately, though, this integration cannot be easily done because these two technologies have different requirements.

In addition, the emerging blockchain technologies can affect the security- and privacy-related issues arising in IoMT. If blockchain is integrated with security mechanisms like digital signature and asymmetric cryptographic schemes, it can significantly improve IoMT by providing high-quality security protection. Moreover, decentralized blockchain systems can mitigate the risk of failures resulting from malicious attacks and single-point failures. Furthermore, when privacy preservation mechanisms like differential privacy and homomorphic obfuscations are completely introduced, blockchain will preserve the IoMT data privacy. Furthermore, blockchain holds some inherent merits, e.g., immutability and traceability (Ahmad et al., 2021); these two characteristics can further enhance the IoMT data provenance. For that reason, it can be said that blockchain is a perfect carrier for IoMT. Thus, if IoMT and blockchain are deeply integrated, the IoMT systems can be further improved. Therefore, the main questions of this special issue are how blockchain technology makes a more trustable and secure IoMT model by using computational intelligence and neuroscience models and algorithms? What are the main challenges to integrate blockchain technology and the IoMT in terms of security? What are the main requirements for the implementation of blockchain technology in the IoMT? To answer these questions, an attempt will be made to leverage the IoMT and blockchain technology integration by using innovative computational intelligence and neuroscience models, algorithms, and tools in this special issue. Therefore, the main objectives of this special issue include:
- To deliver vast contributions to the body of knowledge by discussing the role of computational intelligence and neuroscience models and algorithms for implementing blockchain technology in the IoMT.
- To present the state‐of‐the‐art literature of blockchain technology and establishing a reliable connection between blockchain technology and the IoMT in the healthcare domain using computational intelligence and neuroscience models and tools.

In this special issue, we address the innovative technologies, developments, and related challenges of blockchain for IoT in medical and healthcare topics. We solicit original works that have not been published nor under consideration in other publication venues. Only articles that clearly related to the IoT and blockchain topics with significant contributions in medical and healthcare will be considered. Researchers and practitioners are invited to submit their original research, novel algorithms, innovative models, and critical survey (except bibliometrics analysis) manuscripts with IoMT and blockchain technology on the following potential topics and applications, but not limited to:
- Computational Intelligence and IoMT-blockchain
- Intelligent systems using blockchain and IoMT
- Information technology and IoMT-blockchain technologies
- Cloud-based IoMT enabled platforms using blockchain
- Learning and evolutionary computing, biometrics for IoMT-blockchain
- Securing majority-attack in IoMT-blockchain technologies
- IoT security using blockchain technology in healthcare
- Mobile robots for IoMT-blockchain
- Blockchain technologies for IoMT data quality using edge computing technologies
- Blockchain mechanisms for IoMT security and privacy
- IoMT-blockchain using fuzzy sets theory
- Innovative applications of artificial neural networks for IoMT-blockchain
- Information systems and IoMT-blockchain technologies
- Machine learning for IoMT-blockchain technologies
- IoMT-blockchain using decision-making methods
- Networking- IoMT using blockchain technology
- Blockchain-based access control system in IoMT
- IoMT-enabled big data
- Industrial IoMT-blockchain
- Deep learning for IoMT-blockchain technologies
- IoMT and blockchain in supply chain management
- Industrial IoMT-blockchain and industry 4.0 

Special Issue on Big Data Science and Data-Driven Methods in Finance
Submission Date: 2022-12-31

The increasing availability of large datasets is the ground of a scientific debate on methodological instruments for big data exploration and on the possible applications of such techniques.

In this framework, finance is undoubtedly one of the most challenging application contexts. Indeed, the variety of the financial instruments and the nonstandard behavior of the related patterns suggest that financial data can be seen as the realizations of a complex system. This explains why recent years have witnessed the relevance of machine learning methods and advanced statistical theory in the areas like financial risk management, optimal allocation models, trading rules and investment strategies.

Importantly, the abundance of data of different nature has led also to a growing attention on fields of research of interdisciplinary nature, reading finance under the perspective of disciplines like text mining, pattern recognition, cluster analysis, fuzzy logic, symbolic statistics and complex networks. The presence of such interconnections point to the relevance of the big data and data-driven methods for the interpretation of several financial contexts in the light of highly impacting socio-economic phenomena.

This special issue enters this debate. It seeks high-quality contributions positioned at the frontier of the research on theoretical advancements on big data science and data-driven methods and on their challenging applications in the financial environment. Papers addressing interesting real-world applications are especially encouraged.

Topics of interest include, but are not limited to,

· Behavioral data finance
· Big data and risk management
· Financial sentiment analysis and text mining
· Data-driven methods for optimal allocation models
· Big data clustering in finance
· Data analysis for trading strategies
· Complex networks in finance
· High-frequency financial data
· Interval-valued series in finance
· Fuzzy theory for financial forecasting
· Machine learning algorithms for financial risk management
· Big financial data regularities assessment
· Rank-size analysis of financial big data