Impact Score 5.94
Aims and Scope:
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 We also strongly welcome authors of unpublished work and high-quality outputs to submit.
The topics of interest include
Submission of manuscripts:
All articles will be peer-reviewed and accepted based on quality, originality, novelty, and relevance to the theme of the special section. Before submission, authors should carefully read over the journal's Author Guidelines, which is available at http://www.elsevier.com/wps/find/journaldescription.cws_home/367/authorinstructions.
Authors should submit their papers through the journal's web submission tool at evise.com/profile/#/COMPELECENG/login by selecting VSI-mli4 from the “Issues” pull-down menu during the submission process. For additional questions, please contact the guest editors.
Submission of manuscript: Oct 15, 2021
First notification: Dec 15, 2021
Submission of revised manuscript: Jan 15, 2022
Notification of the re-review: March 15, 2022
Final notification: April 15, 2022
Final paper due: May 15, 2022
Publication: Sep 2022
Note: The decision on paper acceptance will be made as a cluster, not by individual papers, according to the above schedule.
Special Section Editors:
Dr. Harleen Kaur
Jamia Hamdard, New Delhi, India
Email: [email protected]
Supervising Associate Editor: Prof Fatos Xhafa
Guest editors’ short biography:
Prof. Victor Chang is currently a Full Professor of Data Science and Information Systems at the School of Computing, Engineering and Digital Technologies, Teesside University, Middlesbrough, UK, since September 2019. He currently co-leads and leads two Research Groups at Teesside University. He won many awards and achievements. He is widely regarded as one of the most active and influential young scientists and experts in IoT/Data Science/Cloud/security/AI/IS, as he has the experience to develop ten different services for multiple disciplines. His publications: https://scholar.google.com/citations?hl=en&user=IqIYZ14AAAAJ&view_op=list_works&sortby=pubdate
Dr. Harleen Kaur is a faculty at the Department of Computer Science and Engineering, Jamia Hamdard, New Delhi, India. She is currently working as Principal Investigator on Indo-Poland bilateral International project funded by the Ministry of Science and Technology, India, and the Ministry of Polish, Poland. She has published more than 100 publications in SCI, referred Journals, and esteemed Conferences. Her publications: https://scholar.google.com/citations?hl=en&user=NcnUvt0AAAAJ&view_op=list_works&sortby=pubdate