Journal Information
Computers & Electrical Engineering
http://www.journals.elsevier.com/computers-and-electrical-engineering/
Impact Factor:
2.663
Publisher:
Elsevier
ISSN:
0045-7906
Viewed:
19906
Tracked:
39
Call For Papers
The impact of computers has nowhere been more revolutionary than in electrical engineering. The design, analysis, and operation of electrical and electronic systems are now dominated by computers, a transformation that has been motivated by the natural ease of interface between computers and electrical systems, and the promise of spectacular improvements in speed and efficiency.

Published since 1973, Computers & Electrical Engineering provides rapid publication of topical research into the integration of computer technology and computational techniques with electrical and electronic systems. The journal publishes papers featuring novel implementations of computers and computational techniques in areas like signal and image processing, high-performance computing, parallel processing, and communications. Special attention will be paid to papers describing innovative architectures, algorithms, and software tools.

Specific topics of interest include:

    Applications of high-performance computing and novel computing systems

    Internet-based, multimedia, and wireless networks and applications

    Communications, especially wireless

    Signal processing architectures, algorithms, and applications

    Green technologies in information, computing, and communication systems

    Multi-disciplinary areas, including robotics, embedded systems, and security
Last updated by Dou Sun in 2021-03-09
Special Issues
Special Issue on Securing IoT-based Critical Infrastructure (VSI-cei)
Submission Date: 2021-05-15

Overview Critical Energy Infrastructure (CEI) refers to specific engineering information about proposed or existing critical infrastructure. Modern infrastructures are increasingly moving to distributed and complex cyber-physical systems which requires proactive protection and fast restoration to mitigate physical or cyber attacks, espically, combined physical-cyber attacks, that are much more challenging task and it is expected to become the most intrusive attack. This case is particularly true for the Critical Energy Infrastructures (CEI), e.g., the US Industrial Control Systems Cyber Emergency Response Team responded to 245 plus incidents during 2015, and 32% of these incidents were from the Energy sector. Considering the importance of energy in our life and its impact on other critical infrastructures, CEI requires significant attention comparatively. Machine learning allows the data to remain on-premise in the infrastructure and used to provide a robust defense mechanism for critical infrastructures. For example, wind-turbine system is considered one of the most complex cyber-physical infrastructures causing huge cascading effects to other critical energy infrastructures, such as transportation, healthcare sector, communications, industry finance and electrical power systems. Such threats to infrastructure enable the responsible authorities to consider the advantages of machine learning, IoT and simultaneously protecting their privacy i.e. there is always a possibility of attacks against these infrastructures which can be predicted and detected efficiently. This special section aims to stimulate discussion on the design, use and evaluation of machine learning models for Critical Energy Infrastructure towards the improvement of the privacy and security. We invite theoretical work and review articles on practical use-cases of Federated Learning in CEI that discuss adding a layer of trust to powerful algorithms for delivering near real-time intelligence. Topics: This special section will respond to the research challenges by encouraging researchers in the computing world to bring to bear novel techniques, combinations of tools, and so forth to build effective ways to Enhancing the Security of Critical Energy Infrastructures. We solicit papers covering various topics of interest that include: Securing Critical Infrastructure Federated Learning for Critical Infrastructure Data privacy solutions for critical infrastructure Automated Protection to CEI Security and privacy of big data in Energy Enhancing the Security of CEI Cyber Attacks on C EI Machine Learning in Energy Sector Model and Infrastructure for Federated Learning in Energy Advances and Open Problems in Critical Infra Structure Scalable Federated in Energy sector Securing Federated learning Federated Learning for Crisis in Critical Infrastructure Management of Cloud-based critical infrastructure Deep learning for Industrial control systems Submission Guidelines: New papers, or extended versions of papers presented at related conferences, are welcome. Submissions must not be currently under review for publication elsewhere. Conference papers may be submitted only if they are substantially extended (more than 50%), and must be referenced. All submitted papers will be peer-reviewed using the normal standards of CAEE, and accepted based on quality, originality, novelty, and relevance to the theme of the special section. By submitting a paper to this issue, the authors agree to referee one paper (if asked) within the time frame of the special issue. Before submission, authors should carefully read the Guide for Authors available at https://www.elsevier.com/journals/computers-and-electrical-engineering/0045-7906/guide-for-authors Authors should submit their papers through the journal's web submission tool at https://www.editorialmanager.com/compeleceng/default.aspx by selecting “VSI-cei” under the “Issues” tab. For additional questions, contact the Main Guest Editor. Schedule: Submission of manuscript: May. 15, 2021 First notification: July 30, 2020 Submission of revised manuscript: August 28, 2021 Notification of the re-review: October 28, 2021 Final notification: November 16, 2020 Final paper due: December 1, 2021 Publication: March. 2022 Guest Editors: Imran Razzak (Main Guest Editor) Senior Lecturer, School of Information Technology, Deakin University, Geelong, Australia, 3220 imran.razzak@deakin.edu.au
Last updated by Dou Sun in 2021-03-09
Special Issue on Advances of Machine Learning in Cybersecurity (VSI-mlsec)
Submission Date: 2021-05-31

With the rapid advancement of emerging technologies such as Internet of Things (IoT) and cloud computing, a huge amount of data is generated and processed in our daily life. As these technologies are based on the internet, security issues are continuously increasing due to the presence of numerous hackers and malicious users. They always try to hack users’ personal and confidential data by using security attacks. Sometimes, they replace the authentic data by their fake data. The situation becomes more critical, when a large number of users access and store their personal data outside their own domain at the same time. Attackers mainly target financial, healthcare and defence sectors. Therefore, there must be a strong security technique to protect confidential or personal data against the hackers and malicious users. Currently, Machine Learning (ML) algorithms are used in the cybersecurity field by many researchers. Machine learning is the study of mathematical model-based algorithms that improve automatically through past experience. ML algorithms are based on data to make decisions without being explicitly programmed to do so. There are many applications of ML in daily life, such as smart email categorization, chatbot, marketing, healthcare, gaming, plagiarism check, autonomous vehicles, and many more. Nowadays, ML is used in industry and academia due to the data-driven feature for achieving enhanced security and privacy. As new attacks are being developed every day by the attackers and malicious users, it is very difficult to detect them by using the traditional intrusion detection techniques. ML algorithms can be developed to train a system for detecting sophisticated attacks, which are similar to the already defined known attacks. It is important to improve the algorithms so that there is a good trade-off between learning cost and detection accuracy. Recent research has also shown the negative impact of ML as these advanced fields support new attack tools by using adversarial ML techniques to develop new attacks. Attackers and malicious users can also hack ML algorithms by altering the training data and modifying the classification function of ML, which can directly affect the detection accuracy of a system. These types of threats are very critical. Therefore, novel techniques of cybersecurity must be developed to protect the system. This special section gives a platform for researchers, academicians and industry professionals to present their research on ML in the cybersecurity field. It aims to address the challenges and issues of applying ML in cybersecurity. Theoretical as well as experimental research works on the mentioned topics are within the scope of this special section. Topics: Suggested topics include: Adversarial pedagogy, adversarial models and minimum deterrence level Machine learning trends in maintaining security and privacy Deep learning trends in maintaining security and privacy Security threats, intrusions and malware detection exploiting machine learning methods Challenges of black-box attacks in machine learning methods ML driven attack model generation and specification ML based cryptanalysis of cryptographic protocols Use of machine learning in forensics and threat intelligence ML driven software testing and threat anticipation ML driven security architectures ML based secure social media ML for multimedia data security ML for big data security/cloud security/IoT security Emerging technologies and future work directions in cybersecurity
Last updated by Dou Sun in 2021-01-01
Special Issue on Intelligent Approaches in Security and Privacy Computing (VSI-spc)
Submission Date: 2021-06-02

Overview: Conferences on the security of information and networks address a wide range of academic, technical, and practical aspects of security and privacy. Recently, there has been an interest in how to solve ordinary as well as advanced computation needs in security and privacy using smart approaches. Collating effective attempts employing artificial intelligence for a wide span of research interests in security and privacy is likely to serve to highlight existing solutions and advance similar approaches. Topics: Suggested topics include themes of artificial intelligence, machine learning, and other intelligent approaches to computation for smart security and privacy, especially in the following areas: Network Security and Protocols, Security of Cyber-Physical Systems, Intrusion Detection and Remediation, Cryptographic Techniques, Key Management, Computational Intelligence Techniques in Security, Cryptographic Protocol Security, and Formal Verification Techniques. Submission Guidelines: High-quality new or extended papers presented at related conferences, especially SIN 2020 and SIN 2019 (International Conference on Security of Information and Networks, www sinconf.org), are welcome. Submissions must not be currently under review for publication elsewhere. Conference papers may be submitted only if they are substantially extended (more than 50%), and the original papers must be referenced. All submitted papers will be peer-reviewed using the normal standards of CAEE and accepted based on quality, originality, novelty, and relevance to the theme of the special section. By submitting a paper to this issue, the authors agree to referee one paper (if asked) within the time frame of the special issue. Before submission, authors should carefully read the Guide for Authors available at https://www.elsevier.com/journals/computers-and-electrical-engineering/0045-7906/guide-for-authors Authors should submit their papers through the journal's web submission tool at https://www.editorialmanager.com/compeleceng/default.aspx by selecting “VSI-spc” under the “Issues” tab. For additional questions, contact the Main Guest Editor. Schedule: Submission of manuscript: June 2, 2021 First notification: Aug 4, 2021 Submission of revised manuscript: Sept 1, 2021 Notification of the re-review: Oct 6, 2021 Final notification: Nov 10, 2021 Final paper due: Dec 10, 2021 Publication: April 2022 Guest Editors: Atilla ELCI (Main Guest Editor): Email addresses: atilla.elci@gmail.com, atilla.elci@hku.edu.tr
Last updated by Dou Sun in 2021-03-09
Special Issue on Security and Privacy in IoT and Cloud (VSI-spiot)
Submission Date: 2021-06-30

Overview With the changing industrial and economic landscape based on the Internet, the individuals and enterprises are becoming more used to storing and processing of personal and organizational data on the cloud platforms. The cloud and IoT infrastructures are becoming more capable to serve the emerging needs of users. The client and the IoT devices are acquiring data from the environment and sending them to the cloud to process. But this transmission of data faces challenges like privacy, integrity, and authentication. While the data owner stores or processes the data on the cloud, it needs to be encrypted; the most important challenge is processing the data on the cloud without decrypting, which can be assured by homomorphic cryptosystems. Further, the devices on the network are heterogeneous and embedded in the case of IoT devices. Most IoT devices have limited resources like memory, energy, and processing power. Hence, they need lightweight and ultra-lightweight encryption algorithms, suitable for hardware implementation. This special section plans to address the above security challenges by inviting original research, tools, techniques, algorithms, and designs for meeting security challenges in cloud and IoT infrastructure. Topics: Suggested topics include: Homomorphic encryption techniques for cloud Homomorphic encryption techniques for Surveillance Lightweight encryption algorithms for IoT network Ultra-lightweight block cipher Low-latency block cipher Embedded and FPGA implemented security solutions for IoT network Hardware designed new cryptosystem for cloud and IoT devices Security vulnerabilities in cyber physical systems Lightweight authentication for cyber physical systems Adversarial neural cryptography for cloud and IoT Blockchain in cyber physical systems Secure solutions for healthcare, smart city, smart grid, etc. Cyber forensics Submission Guidelines: New papers, or extended versions of papers presented at related conferences, are welcome. Submissions must not be currently under review for publication elsewhere. Conference papers may be submitted only if they are substantially extended (more than 50%) and must be referenced. All submitted papers will be peer-reviewed using the normal standards of CAEE, and accepted based on quality, originality, novelty, and relevance to the theme of the special section. By submitting a paper to this issue, the authors agree to referee one paper (if asked) within the time frame of the special issue. Before submission, authors should carefully read the Guide for Authors available at https://www.elsevier.com/journals/computers-and-electrical-engineering/0045-7906/guide-for-authors Authors should submit their papers through the journal's web submission tool at https://www.editorialmanager.com/compeleceng/default.aspx by "VSI-spiot" under the “Issues” tab. For additional questions, contact the Main Guest Editor. Schedule: Submission of manuscript: 30th June 2021 First notification: 31st August 2021 Submission of revised manuscript: 15th October 2021 Notification of the re-review: 15th December 2021 Final paper due: 15th Feb. 2022 Publication: June 2022 Guest Editors: Dr. Bhaskar Mondal, (Main Guest Editor) Assistant Professor, Dept. of Computer Science and Engineering, National Institute of Technology Patna, Patna, India Email: bhaskar.cs@nitp.ac.in Prof. Yu-Chen Hu Distinguished Professor, Dept. of Computer Science and Information Management, Providence University, Taiwan, R.O.C. Email: ychu@pu.edu.tw
Last updated by Dou Sun in 2021-03-09
Special Issue on Artificial Intelligence and Machine Learning in Industry 4.0 (VSI-mli4)
Submission Date: 2021-10-15

Industry 4.0 refers to the introduction of digital technologies and development of skills, resources and high-tech for the evolution of Industrial Factories. The concepts of Artificial Intelligence (AI), Machine Learning (ML) and its applications in Industry 4.0 are popular among researchers. Further development is crucial to the future of the Industry. Several industrial applications are being designed and deployed using AI and ML. Besides, numerous researchers from diversified domains are working towards the amalgamation of these technologies. Different types of industries and research outputs require to work in Industry 4.0 platforms, including the use and integration of AI, ML, Big Data and the Internet of Things (IoT). Therefore, there is an urgent need to develop future-proof types of AI and ML applications, services, architectures and proofs-of-concept. The primary scope of this special section is to cover the areas of AI and ML for Industry 4.0. We invite researchers from academia as well as industry to describe the current state of technologies to harness the power of Artificial Intelligence in the long term. This special section is intended to report high quality, recent and original research work on Industrial applications using AI and ML methods to design new data models and applications for Industry. Best paper winners and top authors from IoTBDS 2021 (http://iotbds.org/) and COMPLEXIS 2021 (http://www.complexis.org/), to be held 23-25 April, 2021 online streaming and IIoTBDSC 2021 http://iiotbdsc.com/ (24-26 August, 2021, Macao, SAR of China or virtual) shall be invited. We also strongly welcome authors of unpublished work and high-quality outputs to submit. Topics: The topics of interest include New technologies, digitalization and analytics for industrial solutions with AI and ML Soft Computing Models with AI and ML for Big Data and Internet of Things (IoT) Advanced ML and intelligent algorithms for Smart Industry Solutions Industry 4.0 based data analytics platforms with innovative AI approaches Advanced Computational Intelligence and Visual Intelligence for Industry 4.0 Innovative AI and ML with Big Data Analytics and Industrial IoT for Industry 4.0 Real-world Case Studies and Solutions with applications using AI and ML
Last updated by Dou Sun in 2021-01-01
Special Issue on Recent Advances in Deep Learning (VSI-radl)
Submission Date: 2021-12-15

Overview Deep learning (DL) is one of the most promising artificial intelligence (AI) methods that tries to imitate the workings of the human brain in processing information, and automatically generates patterns for decision making and other complicated tasks. DL is able to learn with/without human supervision, drawing from data, even unstructured and/or unlabelled. However, the achievements of DL techniques do not stop at only arriving and outperforming the results of other AI algorithms: DL’s accomplishments are generally better than human results for tasks like image recognition or game playing, thus beyond the expectations of the experts. The aim of this special section is to provide a diverse, but complementary, set of contributions to demonstrate new developments and applications of DL to solve problems in diverse fields. The ultimate goal is to promote research and development of DL by publishing high-quality survey and research articles in this rapidly growing field. Topics: The topics of interest include New architectures, theories, analytics for DL Deep convolutional neural network Deep graph neural network DL with attention mechanism Deep auto-encoders Reinforcement learning DL applications, e.g., IoT, medical image analysis, multimedia technology, and enhanced learning Submission Guidelines: New papers, or extended versions of papers presented at related conferences, are welcome. Submissions must not be currently under review for publication elsewhere. Conference papers may be submitted only if they are substantially extended (more than 50%) and must be referenced. All submitted papers will be peer-reviewed using the normal standards of CAEE, and accepted based on quality, originality, novelty, and relevance to the theme of the special section. By submitting a paper to this issue, the authors agree to referee one paper (if asked) within the time frame of the special issue. Before submission, authors should carefully read the Guide for Authors available at https://www.elsevier.com/journals/computers-and-electrical-engineering/0045-7906/guide-for-authors Authors should submit their papers through the journal's web submission tool at https://www.editorialmanager.com/compeleceng/default.aspx by selecting “VSI-radl” under the “Issues” tab. For additional questions, contact the Main Guest Editor. Schedule : Submission of manuscript: Dec. 15, 2021 First notification: March 15, 2022 Submission of revised manuscript: April 15, 2022 Notification of the re-review: May 15, 2022 Final notification: June 15, 2022 Final paper due: July 15, 2022 Publication: Oct 15, 2022 Guest Editors: Yu-Dong Zhang (Main Guest Editor) University of Leicester, Leicester, UK Email: yudongzhang@ieee.org Juan Manuel Gorriz University of Granada, Spain Email: gorriz@ugr.es Yuankai Huo Vanderbilt University, USA Email: yuankai.huo@vanderbilt.edu
Last updated by Dou Sun in 2021-03-09
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