Journal Information
Information Sciences
http://www.journals.elsevier.com/information-sciences/
Impact Factor:
4.832
Publisher:
ELSEVIER
ISSN:
0020-0255
Viewed:
8830
Tracked:
21

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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 Xin Yao in 2017-08-21
Special Issues
Special Issue on Distributed Event-Triggered Control and Estimation in Resource-Constrained Cooperative Networks
Submission Date: 2018-01-15

In many practical applications, such as environmental monitoring systems, smart grids, intelligent transportation systems, and wireless robotics, there is a fundamental need to accomplish coordinated tasks, such as consensus estimation, agreement at a common point or move in an anticipated formation pattern, across time and space that cannot be achieved by a single node or agent. The ever-increasing demand for cooperative networks of nodes or agents has stimulated widespread research interest in developing distributed estimation and control strategies that guarantee coordinated tasks. The benefits of employing cooperative networks lie in several aspects including flexibility, manipulability and scalability that are beyond the capability of an individual node or an agent. However, in the context of distributed estimation and control, nodes or agents usually possess limited sensing, computing and communication capabilities, and network bandwidth sometimes may also be restricted. These constraints pose significant challenges to the analysis and design of resource-constrained cooperative networks. Thus, it is of great significance in both theory and practice to regulate the sampling, communication or actuation frequencies among interacting nodes or agents such that over-utilization of the available computation and communication resources can be effectively reduced while preserving desired estimation and control performance for such cooperative networks. Event-triggered mechanisms, which abandon the conventional periodic updating of task commands, have been proven to be an effective and promising tool capable of alleviating network bandwidth occupancy and reducing computation and communication cost. This special issue aims at advancing the event-triggered technology and methodology and further promote the research activities in distributed event-triggered estimation and control for cooperative networks, such as sensor networks, complex networks and multi-agent networks. The special issue seeks original work to address some emerging issues and challenges from distributed event-triggered estimation and control and their applications to areas, such as power systems, robotics, vehicular networks, and camera networks. Topics of interest include but not limited to:[WP1] - Distributed event-triggered control and estimation - Event-triggered control of networked control systems - Event-triggered estimation of networked systems - Consensus and diffusion estimation of sensor networks - Distributed event-triggered estimation of complex networks - Synchronization and pinning control of complex networks - Consensus of multi-agent systems - Formation control of multi-agent systems - Containment control of multi-agent systems - Applications of event-triggered control and estimation in smart grids, wireless robotics, unmanned aerial vehicles, autonomous underwater vehicles, vehicular networks, and camera networks and so on
Last updated by Dou Sun in 2017-11-04
Special Issue on Advanced Methods for Evolutionary Many Objective Optimization
Submission Date: 2018-02-01

Multi-objective optimization problems (MOPs) arise regularly in real-world where multiple objectives are required to be optimized at the same time. So far, Evolutionary multi-objective optimization (EMO) algorithms have been demonstrated as effective in addressing MOPs with two and three objectives. However, they tend to face difficulties on addressing MOPs with four or more objectives, the so called Many-objective Optimization Problems (MaOPs). Challenges to evolutionary algorithms and other metaheuristics in solving MaOPs include the inability of dominance based MOEAs to converge to the Pareto frontier while maintaining good diversity, the prohibitively high computational complexity for EMO algorithms based on performance indicators, and the difficulty for human users or decision makers to clearly understand the relationship between objectives and articulate preferences. Finally, visualization of the solutions of MaOPs also becomes a grand challenge. This special issue-- Advanced Methods for Evolutionary Many Objective Optimization, aims to discuss the philosophical changes needed in tackling MaOPs using evolutionary algorithms and in evaluating the quality of the solution sets they achieved. It will present most recent advances in theory, algorithm development and applications of evolutionary algorithms for MaOPs. We cordially invite you to submit high-quality original research to this SI at Information Sciences (https://www.journals.elsevier.com/information-sciences ) addressing various topics related to many-objective optimization, but are not limited to: - Algorithm design for many-objective optimization - Benchmarks and performance indicators for many-objective optimization; - Dimensionality reduction, visualization techniques of many-objective optimization; - Constraint handling methods for many-objective optimization; - Preference articulation and decision making methods for many-objective optimization; - Hybrid algorithms for many-objective optimization; - Many-objective optimization in combinatorial/discrete, large-scale problems; - Many-objective optimization in dynamic environments; - Many-objective real-world optimization problems
Last updated by Dou Sun in 2017-11-06
Special Issue on Business Analytics – Emerging Trends and Challenges
Submission Date: 2018-03-30

We are living in a world characterized by an abundance of many different kinds of data. The importance of analyzing information contained therein has been already recognized by academia and practitioners. As a result, we have witnessed a rapidly growing number of products and solutions by the respective solution providers. What is still missing, however, is an adequate interpretation and modeling of the ever growing data sets to create intelligent systems that are able to propose advanced solutions to complex problems. As a consequence, this special issue aims at moving towards the next step and foster the development of advanced techniques coming from machine learning, artificial intelligence, uncertainty modeling, and data science, among others, to establish emerging trends in business analytics capable of facing the current challenges. In this special issue we understand the term “Business Analytics” in a rather broad sense, covering a spectrum of different application areas. Topics relevant for this special issue include, but are not limited to: - Business Analytics for Financial Modeling - Forecasting - Human Resources (HR) Analytics - Healthcare Analytics - Learning Analytics - Fraud Detection and Cybersecurity - Privacy-preserving and ethics in Business Analytics
Last updated by Dou Sun in 2017-11-04
Special Issue on Business Analytics – Emerging Trends and Challenges
Submission Date: 2018-03-30

We are living in a world characterized by an abundance of many different kinds of data. The importance of analyzing information contained therein has been already recognized by academia and practitioners. As a result, we have witnessed a rapidly growing number of products and solutions by the respective solution providers. What is still missing, however, is an adequate interpretation and modeling of the ever growing data sets to create intelligent systems that are able to propose advanced solutions to complex problems. As a consequence, this special issue aims at moving towards the next step and foster the development of advanced techniques coming from machine learning, artificial intelligence, uncertainty modeling, and data science, among others, to establish emerging trends in business analytics capable of facing the current challenges. In this special issue we understand the term “Business Analytics” in a rather broad sense, covering a spectrum of different application areas. Topics relevant for this special issue include, but are not limited to: - Business Analytics for Financial Modeling - Forecasting - Human Resources (HR) Analytics - Healthcare Analytics - Learning Analytics - Fraud Detection and Cybersecurity - Privacy-preserving and ethics in Business Analytics
Last updated by Dou Sun in 2017-11-04
Special Issue on Privacy Computing: Principles and Applications
Submission Date: 2018-05-30

While more and more data including personal information is being hosted online such as cloud infrastructure, privacy leakage is becoming one of most challenging concerns in information collection, sharing or analysis. In practice, different temporal, spatial or application cases often demand different privacy protection solutions. Accordingly, most of traditional approaches are case by case or based on a specific application circumstance. It is on demand for a systematic and quantized privacy characterization towards systematic computing model describing the relationships between protection level, profit and loss as well as the complexity of integrated privacy protection models because real-world applications with privacy are changing across time, space and different domains. This special issue focuses on the new paradigm of privacy computing for principles and applications. High quality publications are solicited from engineers and scientists in academia, industry, and government to address the resulting profound challenges on principles and applications of privacy computing. Topics of interests include, but are not limited to: - Fundamental principles for privacy computing - Privacy principles for information sensing, collection, engineering and distribution - Privacy protection based information hiding and sharing principles - Privacy preserving data publishing principles - Privacy information integration, synergy and storage - Privacy operation and modelling methodologies - Privacy protection methodologies and principles - Privacy applications in cloud, social networks, IoT and Industrial Internet - Privacy, security, trust, autonomy, reliability, fault-tolerance – association principles - Privacy, AI, Machine Learning, Data Mining and Knowledge Discovery – association principles
Last updated by Dou Sun in 2017-11-04
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