Journal Information
Computers & Electrical Engineering
http://www.journals.elsevier.com/computers-and-electrical-engineering/
Impact Factor:
2.189
Publisher:
Elsevier
ISSN:
0045-7906
Viewed:
18966
Tracked:
38

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 2019-11-24
Special Issues
Special Issue on Data Security and Privacy in Big Data era (VSI-spbd)
Submission Date: 2021-02-15

Overview: Recent technologies, such as IoT, social networks, cloud computing, and data analytics, create a huge amount of data. However, for this data to be used to its full power, security and privacy are critical. Data security and privacy have been widely investigated over the past years. However, today we face new issues in securing and protecting data, that result in new challenging research directions. Some of these challenges arise from the increasing privacy concerns with respect to the use of such data, and from the need of reconciling privacy with the use of data. Other challenges arise regarding the deployments of new data collection and processing devices, such as those used in IoT systems, which cuse security concerns. In this special issue, we discuss relevant concepts and approaches for Big Data security and privacy, and identify research challenges to be addressed to achieve comprehensive solutions. The purpose is to make security and privacy communities realize the challenges and tasks that we face in Big Data. We focus on exploring the security and privacy aspects of Big Data as supporting and indispensable elements of the emerging Big Data research. Topics: The areas of interest include: Security technologies for collecting of Big Data Intrusion detection and transmission surveillance of Big Data Storage and system security for Big Data Big Data forensics Integrity protection and authentication of Big Data Access control of Big Data Privacy-aware analysis and retrieval of Big Data Privacy-aware data fusion of Big Data
Last updated by Dou Sun in 2020-11-03
Special Issue on Resiliency techniques in cloud computing infrastructures and applications (VSI-rtcc)
Submission Date: 2021-03-20

Overview Today’s businesses increasingly rely on cloud computing, which brings both great opportunities and challenges. One of the critical challenges is resiliency to cloud system disruptions, which often result in significant revenue loss. Such failures may originate at any component in a cloud system, including application host servers, networks that connect servers to the cloud, as well as the business application itself. This research area combines topics of cloud computing architectures, network, and application components. A key concept of this is virtualization of infrastructure and components in both physical and virtual layers to improve their resilience. Resilient cloud computing infrastructure refers to the design and operation of facilities, servers, networks and integration, including the middleware infrastructure. This special section aims to present current state-of-the-art research addressing network resiliency and analysis. Topics Suggested topics include: Resilient ubiquitous system IP networks and service resilience Resilient distributed systems and algorithms Middleware infrastructure for application and integration Intrusion-resilient middleware design and validation High availability, resilient and survivable infrastructure design Detection and response to vulnerabilities and attacks on the internet on IT components in critical infrastructure Models and algorithms of survivable networks design and modelling Methods for measurement, evaluation, or validation of resilience Optimization issues in resilient networks design Resilient cloud computing architectures Resilient content-oriented networks architectures and solutions
Last updated by Dou Sun in 2020-11-03
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 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
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