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 2022-01-29
Special Issues
Special Issue on Recent Advances in Fuzzy Deep Learning for Uncertain Medicine Data
Submission Date: 2022-06-30

This special issue focuses on the integration of both techniques with a focus on medicine application, especially on designing the efficient and effective integrated fuzzy and deep learning model, algorithm, and system to improve reasoning and intelligent epidemic monitoring, control, and treatment of uncertain medicine data. This special issue aims at providing an opportunity for collecting some advanced work in the above common research areas, including compilation of the latest research, development, and practical experiences as well as up-to-date issues, reviewing accomplishments, assessing future directions and challenges in this field. Guest editors: Weiping Ding Nantong University, China Email: Jun Liu Ulster University, United Kingdom Email: Chin‐Teng Lin University of Technology Sydney, Australia Email: Dariusz Mrozek Silesian University of Technology, Poland Email: For inquiries regarding this Special Issue, please contact: Weiping Ding ( ) Special issue information: Currently, digital platforms have been increasingly utilized to assemble and structure a large-scale and wide variety of medical data that pose various challenges for data analytics, such as large volume, high dimensionality, significant heterogeneity, class imbalance, and in some cases, low numbers of samples. In addition, the nature of medical data causes many uncertainties in medical decision-making resulting from the lack of information, imprecise information, and contradictory nature, e.g., limited understanding of biological mechanisms; imprecise test measurements; highly subjective and imprecise medical history; inconsistency from different sources; missing information in some cases. Although the current research in this field has shown promising results, there is an urgent need to explore and develop advanced intelligent medicine decision models that are capable of handling the above challenges, especially in medical areas such as epidemic monitoring, virus tracking, prevention, control and treatment, and resource allocation. Deep learning has demonstrated to provide powerful models in representing complex relationships using multilevel structures to make highly accurate predictions from complex data sources, especially in object classification and detection within the imagery. Therefore, it is effective in medicine information processing and has already been in use in specialties such as radiology, pathology, dermatology, and recently ophthalmology. However, there are many problems with deep learning, including the over-fitting/under-fitting problem, the lack of robustness, especially the lack of intelligibility/ interpretability, and the limit in handling uncertain or imprecise circumstances. These problems fundamentally restrict the utility of such tools in the medicine areas mentioned above. Fuzzy set theory is a branch of AI capable of analyzing complex medical data, which has been one of the state of the art methodologies, leading to the enhanced performance in various medical applications to prevent, diagnose, and treat diseases. Compared to the traditional data analytics and decision support techniques, fuzzy set and their extensions are effective white-box tools for representing and explaining the complexity and vagueness of the information, especially to reduce uncertainty. However, the relatively low learning efficiency and performance also hinder their applications in the medical domain. Therefore, in the last few years, integrating deep learning and fuzzy systems has been an emerging and promising topic with applications in different domains. This special issue focuses on the integration of both techniques with a focus on medicine application, especially on designing the efficient and effective integrated fuzzy and deep learning model, algorithm, and system to improve reasoning and intelligent epidemic monitoring, control, and treatment of uncertain medicine data. This special issue aims at providing an opportunity for collecting some advanced work in the above common research areas, including compilation of the latest research, development, and practical experiences as well as up-to-date issues, reviewing accomplishments, assessing future directions and challenges in this field. It will bring both researchers from academia and practitioners from industry to discuss the latest progress, new research topics, and potential epidemic diseases application domains. Papers for the special issue are invited on but not limited to any of the topics listed below. The topics of this special issue include, but not limited to: Fuzzy deep learning models for feature extraction of medicine data Fuzzy deep learning approaches for functional brain imaging processing Fuzzy deep learning models for monitoring/predicting the spread of epidemic diseases Multilayer/Multistage/Multilevel fuzzy deep learning for medical image analysis Advanced fuzzy deep learning techniques for the risk prediction of COVID-19 Multi-objective fuzzy deep learning systems for handling epidemic disease tracking Focused fuzzy deep learning algorithms for infectious disease modelling Evolutionary fuzzy deep learning for scheduling and combinatorial optimisation tasks Distributed fuzzy deep learning for widespread monitoring medical diseases Explainable fuzzy deep learning for prediction of healthcare variations Hybrid fuzzy decision support system for medicine and health care Fusion of fuzzy deep learning and big data for future challenges Real-world applications of fuzzy deep learning for uncertain medicine data We highly recommend the submission of multimedia associated with each article as it significantly increases the visibility, downloads, and citations of articles. Manuscript submission information: Submission format Papers will be evaluated based on their originality, presentation, relevance, and contribution to Recent Advances in Fuzzy Deep Learning for Uncertain Medicine Data, as well as their suitability and quality in terms of both technical contribution and writing. The submitted papers must be written in English and describe original research which has not been published nor currently under review by other journals or conferences. Previously published conference papers should be clearly identified by the authors (at the submission stage). An explanation should be provided about how the papers have been extended to be considered for this special issue. Guest Editors will make an initial judgment of the suitability of submissions to this special issue. Papers that either lack originality, clarity in presentation, or fall outside the scope of the special issue will not be sent for review, and the authors will be promptly informed in such cases. Author guidelines for preparation of manuscript can be found at Guide for authors - Information Sciences - ISSN 0020-0255 ( Submission guidelinesAll manuscripts and any supplementary material should be submitted through Elsevier Editorial System (EES). The authors must select “VSI: FDLUMD” when they identify the “Article Type” step in the submission process. The EES website is located at Guide for authorsThis site will guide you stepwise through the creation and uploading of your article. The guide for authors can be found on the journal homepage. Important dates: Deadline of submission: June 30, 2022 Revised version submission: October 31, 2022 Acceptance notification: November 30, 2022 Final manuscripts due: December 31, 2022 Anticipated publication: January 31, 2023 Learn more about the benefits of publishing in a special issue: Interested in becoming a guest editor? Discover the benefits of guest editing a special issue and the valuable contribution that you can make to your field:
Last updated by Dou Sun in 2022-01-29
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
Last updated by Dou Sun in 2021-10-16
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
Last updated by Dou Sun in 2021-10-16
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