Big Data and Machine Learning for Energy 4.0: Shaping the Future of Energy Conversion Technologies for Sustainable Power Generation
Guest Editors:
Dr. Muhammad Farhan
Assistant Professor of Computer Science, COMSATS University Islamabad,
Sahiwal Campus, Pakistan.
Mail: farhan@ciitsahiwal.edu.pk, farhanmuhammad.dr@gmail.com
Google Scholar: https://scholar.google.com/citations?user=flDECaEAAAAJ&hl=en
ORCID: https://orcid.org/0000-0002-3649-5717
Researcher ID: F-8071-2011
Scopus Author ID: 56823258900
Loop profile: 1913951
Sci Profiles: 15286
Dr. Khalid Mahmood
Future Technology Research Center
National Yunlin University of Science and Technology
Yunlin, Taiwan.
Gmail: khalid@yuntech.edu.tw
ORCID: https://orcid.org/0000-0001-5046-7766
Google Scholar: https://scholar.google.com.pk/citations?user=Hvjr9voAAAAJ&hl=en
Researcher ID: AAE-9552-2020
Scopus Author ID: 57342911900
Sci Profiles: 2658687
Dr. Sohail Jabbar
College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU),
Riyadh 11432, Saudi Arabia.
Gmail: sjjabar@imamu.edu.sa
Google Scholar:
https://0-scholar-google-com.brum.beds.ac.uk/citations?user=5DrhXKQAAAAJ&hl=ru
Web of Science: https://www.webofscience.com/wos/author/rid/E-3052-2012
ORCID: https://orcid.org/0000-0002-2127-1235
Scopus Author ID: 35179598300
Researcher ID: E-3052-2012
Sci Profiles: 83693
Topics
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Data-driven big data-assisted machine learning for Sustainable Power Generation
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Decentralized edge computing for advanced ML for Sustainable Power Generation in Energy 4.0
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Big data assisted user-centric intelligent networking for Sustainable Power Generation urban informatics
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Real-time data analysis and management for Sustainable Power Generation in Energy 4.0
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Big data-enabled ML for behavioural decision-making in Energy 4.0
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Deep learning-based big data analytics for Sustainable Power Generation in Energy 4.0
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Big data-enabled ML in Green manufacturing for Sustainable Power Generation in Energy 4.0
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ML-based prescriptive, predictive, and descriptive-analytical approaches for Sustainable Power Generation in Energy 4.0
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Big data-enabled ML industrial IoT for Sustainable Power Generation in Energy 4.0
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Risk Management analysis using Big data-enabled ML in Sustainable Power Generation for Energy 4.0
Submission procedure
Prospective authors are invited to submit their papers by following the instructions on the website of Journal of New Materials for Electrochemical Systems (JNMES):
https://www.iieta.org/Journals/JNMES/Instructions%20for%20Authors
You can send directly your manuscript to the guest editor: Dr. Muhammad Farhan;
Email: farhan@ciitsahiwal.edu.pk, or farhanmuhammad.dr@gmail.com
The submitted manuscripts should not have been previously published, nor should they be currently under consideration for publication elsewhere.
Important dates
• Manuscript submissions due: 2024-03-15
• First round of reviews completed: 2024-04-15
• Revised manuscripts due: 2024-06-21
• Final manuscripts due: 2024-10-27