Topical Collection on "Leveraging Machine Learning and Deep Learning for Advancements in Operational Technology"

A topical collection of Digital Technologies Research and Applications (DTRA) (E-ISSN: 2754-5687).
Deadline for manuscript submissions: 31 December 2024

Collection Editors:

Dr. Abdul Rehman
School of Computer Science and Engineering, Kyungpook National University, 41566, Daegu, South Korea
Research Interests: IoT & SIoT; small-world problem; big data; artificial intelligence; networking and security

Dr. Faisal Saeed
School of Computer Science and Engineering, Kyungpook National University, Daegu 41566, South Korea
Research Interests: object detection and classification; deep neural networks

Topical Collection Information:

Dear Colleagues,

The rapidly evolving landscape of Operational Technology (OT) is increasingly intertwined with the innovative realms of Machine Learning (ML) and Deep Learning (DL). Our upcoming Topical Collection aims to explore this synergy, focusing on the transformative impact of ML and DL on OT. We invite researchers and industrial practitioners to contribute their insights, findings, and real-world applications that highlight the integration and enhancement of OT through ML and DL techniques.

This Topical Collection seeks to delve into how ML and DL can revolutionize traditional OT systems, making them more efficient, predictive, and adaptive. Key areas of interest include but are not limited to, predictive maintenance using ML algorithms, quality control through DL-based computer vision, energy management optimization, and supply chain enhancements. Contributions may also explore the role of ML in enhancing safety and security protocols within industrial settings, optimizing complex industrial processes, and driving data-driven decision-making.

We are particularly interested in papers demonstrating ML and DL's application in advancing robotics and automation within OT, showcasing how these technologies contribute to precision and automation levels in various industrial processes. Additionally, research highlighting the utilization of ML/DL for customization and personalization in manufacturing and the implementation of anomaly detection systems is highly encouraged.

Our goal is to create a comprehensive collection of research that advances academic understanding and offers practical solutions and strategies for industry professionals. We aim to bridge the gap between theoretical research and real-world industrial applications, providing a platform for sharing innovative ideas, methodologies, and technologies that redefine the potential of OT.

This Topical Collection presents a unique opportunity for researchers and industry experts to showcase their work, contribute to the field's growth, and collaborate on forging paths toward a more efficient, reliable, and intelligent operational technology landscape. We eagerly await your submissions and are excited to witness the contributions that will shape the future of OT.

Dr. Abdul Rehman
Dr. Faisal Saeed
Collection Editors

Keywords:

operational technology
machine learning
deep learning
predictive maintenance
industrial automation
quality control algorithms
energy management optimization
supply chain analytics
safety and security in OT
process optimization techniques
robotics in manufacturing
data-driven decision making
anomaly detection systems
customization in industrial processes
real-world applications of ML/DL in OT

Manuscript Submission Information:

Please visit the Submissions Guidelines page before submitting a manuscript. Submitted papers should be well formatted and use good English. Manuscripts should be submitted online through the online manuscript submission and editorial system. Additionally, please include a cover letter specifying that the manuscript is intended for the Topical Collection "Leveraging Machine Learning and Deep Learning for Advancements in Operational Technology" when submitting it online. Manuscripts can be submitted until the submission deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal and will be listed together on the Topical Collection website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract can be sent to the Editorial Office dtra@ukscip.com for announcement on this website.

The Article Processing Charge (APC) for publication in this open access journal is 300 USD. Authors who are unable to cover this cost or those who are invited to submit papers may be eligible for discounts or waivers.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a double-blind peer-review process.

Published Papers:

This Topical Collection is now open for submission.