Important Dates
- Submission Deadline: 28 January 2024
- Publication: November/December 2024
Technological innovation is the bedrock of attempts to improve strategic sustainability goals, including resource and energy efficiency. Industrialization would be impossible without technological advancement. Early acquirers of integrated and interdependent innovations in the manufacturing industries are reaping the economic advantages of increased efficiency improvements, economic output, protection, cost reductions, revenue growth, and client satisfaction and retention as they work toward Industrial Transformation for Sustainability. Among all technological advances are those associated with the Industrial Transformation for Sustainability movement, including Artificial Intelligence, deep learning, business intelligence, Internet of Things (IoT) communication, and blockchain technology. To identify a few, industrial production, power generation, utility services, automobile industries, and aerospace businesses have benefited from investment opportunities in such ground-breaking technologies. Additionally, global warming requiring additional transition to low-carbon communities necessitates a sea difference in how emerging markets and industries operate.
With regards to intelligent urban as methodologies for Industrial Transformation toward a Smart and Sustainable Society, experimental computational intelligence has been increasingly comprehended. Carbon-intensive processes that underlie the world economy could help influence a much more sustainable future across innovation, predictive analysis, computerization, electricity generation, and operational efficiencies.
Despite the numerous benefits of integrating computational intelligence methodologies into numerous Industrial Transformation for Sustainability initiatives, the effective implementation of the AI model presents numerous difficulties, including information amount and complexity, interoperability, and the precision of qualitative findings from the gathered information. Technological improvements through AI-enabled techniques, such as enhanced pattern recognition and natural language processing, have opened up numerous new research directions for evaluating the computational intelligence conceptual model by observing factual data and sensed information. Additionally, advances in computational competence methodologies like decentralized and federated learning should be used to empower the multiple edge devices and generate an optimization method under the supervision of a vital network edge.
In recent years, academics and practitioners have seen significant interest in innovative computational intelligence technologies for Industrial Transformation for Sustainability. Besides that, industry 4.0 was indeed reshaping how businesses manufacture, enhance, and distribute their consumer goods. Likewise, industrial automation integration of advanced sensing devices, integrated development environment, and robotic systems that collect information and enable more informed decision-making is critical in achieving Industrial Transformation toward a Smart and Sustainable Society. These digital technologies enable enhanced automation, preventative analysis, self-optimization of operational efficiencies, and, most importantly, a previously unattainable threshold of responsiveness and efficiency to customers. Topics of interest include, but are not limited to:
- Advanced computational using green communication for Industrial Transformation toward a Smart and Sustainable Society
- Green edge platform using caching techniques for Industrial Transformation towards Smart and Sustainable Society
- Low latency and ultra-reliable communication protocol for Industrial Transformation towards Smart and Sustainable Society
- Collaborative computational intelligence using ML/DL models for Industrial Transformation
- AI-enabled Federated/distributed learning for Industrial Transformation
- Enhanced computational intelligence progression in Industry 4.0 for Industrial Transformation towards Smart and Sustainable Society
- Blockchain-based computational intelligence for Industrial Transformation towards Smart and Sustainable Society
- Progress in Digital twin for Industrial Transformation towards Smart and Sustainable Society
- Green computing for energy-aware data centers for Industrial Transformation
- Deep learning for acquiring advanced computational intelligence for studying sustainability challenges
Submission Guidelines
Only submissions that describe previously unpublished, Contemporary research and practice that are not currently under review by a conference or another journal will be considered. Extended versions of conference papers must be at least 30 percent different from the original conference works. Feature articles should be no longer than 4,200 words and have no more than 20 references (with tables and figures counting as 300 words each). Articles should be understandable by a broad audience of computer science and engineering professionals, avoiding unnecessary theory, mathematics, jargon, or abstract concepts. For author guidelines, see the Author Information page.
All manuscripts must be submitted to ScholarOne Manuscripts by the deadline, making sure that the specific Special Issue is selected in order to be considered for publication under this Call for Papers. Submissions are subject to peer review on both technical merit and relevance to IT Professional’s readership.
The use of artificial intelligence (AI)–generated text in an article should be disclosed in the acknowledgements section, while the sections of the paper that present AI-generated text verbatim should be quoted within quotation marks and provide a citation to the AI system used to generate the text.
IT Professional magazine is a hybrid publication, allowing either traditional manuscript submission or author-paid Open Access manuscript submission.
Questions?
Contact the guest editors at it6-2024@computer.org.
- Editorial Board Member: Hassan Keshavaz, University of Technology Malaysia
- M. M. Kamruzzaman, College of Computer and Information Sciences, Saudi Arabia
- Shehzad Ashraf Chaudhry, Istanbul Gelisim University, Turkey
- Ahmed A. Abd El-Latif H. Jafari, Menoufia University, Egypt
