IJACSA Special Issue on Sustainable AI: Energy-Efficient Machine Learning and Deep Learning (SUSAI-EE)

Objective and Scope

With the growing adoption of machine learning (ML) and deep learning (DL) across diverse fields, the computational cost and environmental impact of training and deploying these models have become critical concerns. The demand for larger datasets and increasingly complex models, such as transformers and generative AI, has led to skyrocketing energy consumption and carbon footprints. This special issue aims to address the urgent need for sustainable AI practices by focusing on energy-efficient ML and DL methodologies, frameworks, and applications.
The objective is to advance the state of the art in developing green AI solutions that balance performance with environmental sustainability. Topics of interest include innovative algorithms, energy-optimized hardware, and frameworks that enable practitioners and researchers to design AI systems with reduced resource consumption while maintaining or enhancing their effectiveness.

We invite original research papers, reviews, and case studies that contribute to energy-efficient AI under the following themes:

• Energy Optimization in ML and DL Algorithms
• Novel training algorithms that reduce computational complexity
• Transfer learning and pre-trained models for energy savings
• Meta-learning techniques for minimizing retraining efforts
• Hardware-Software Co-Design for Sustainable AI
• Low-power AI implementation on edge devices and IoT
• Optimization techniques for AI deployment on constrained hardware
• Green AI Frameworks and Benchmarks
• Development of metrics and benchmarks for evaluating energy efficiency
• Standardized frameworks for measuring carbon footprints of AI systems
• Distributed AI Systems for Sustainability
• Resource allocation strategies for AI in multi-cloud and hybrid environments
• AI-powered solutions for climate change, renewable energy, and waste reduction
• Sustainable practices in industries like healthcare, agriculture, and transportation
• Case studies on energy-efficient AI in real-world scenarios
• Policy and Ethical Implications of Sustainable AI
• Policy frameworks to promote green AI initiatives
• Ethical considerations in balancing AI performance and environmental impact
• Incentivizing the adoption of sustainable AI technologies

Submission Guidelines:

Authors must follow the journal guidelines available at http://thesai.org/Publications/Guidelines.

Research articles, review articles as well as communications are invited.

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

There is a fixed publication fee of GBP 500 for all accepted manuscripts. Only registered manuscripts will be part of the published special issue.

Authors may submit their manuscripts as per the following schedule:

• Submission Deadline: 31 July 2025
• Review Notification: 30 September 2025
• Registration Deadline: 15 October 2025
• Camera Ready Submission: 31 October 2025
• Final publication: December 2025

Manuscripts should be submitted via email to gopal@vips.edu. Subject line should be: "IJACSA Special Issue on Sustainable AI: Energy-Efficient Machine Learning and Deep Learning (SUSAI-EE)".

Guest Editors

Dr. Gopal Chaudhary is an Assistant Professor at VIPS-TC, School of Engineering and Technology, Delhi, India, affiliated with Guru Gobind Singh Indraprastha University. He holds a Ph.D. in Biometrics from Netaji Subhas Institute of Technology, University of Delhi, India. With over 75 publications in reputed journals and conferences (Elsevier, Springer, Inderscience), his research focuses on soft computing, intelligent systems, information fusion, and pattern recognition. Dr. Chaudhary completed his B.E. in Electronics and Communication Engineering in 2009 and M.Tech. in Microwave and Optical Communication from Delhi Technological University in 2012. His professional journey includes serving as an Associate Professor and Training and Placement Officer at Bharati Vidyapeeth’s College of Engineering, New Delhi. He has also organized several conferences and acted as a guest editor for special issues in leading publishers such as Taylor & Francis and Springer.

Dr. Manju Khari is a Professor in the School of Computer and Systems Sciences at Jawaharlal Nehru University, New Delhi, India. She holds a Ph.D. in Computer Science and Engineering from NIT Patna and an M.Tech. in Information Security from Guru Gobind Singh Indraprastha University. With over 100 publications in top-tier journals and conferences, her research encompasses information security, software testing, IoT, and deep learning. Dr. Khari has co-authored NCERT books, edited 10 volumes for Springer and Wiley, and served as a reviewer for renowned journals like IEEE and ACM. She has actively contributed to government-funded projects, collaborating with CSIR, DBT, and DST. A recognized expert, she also holds associate editorial roles and frequently participates in international conferences.

Prof. Athina Vakali is a distinguished professor in the School of Informatics at Aristotle University, Greece, where she leads the Laboratory on Data and Web Science. She holds a Ph.D. in Informatics (Aristotle University), an M.Sc. in Computer Science (Purdue University, USA), and a B.Sc. in Mathematics. Her research focuses on big data analytics, social network mining, and decentralized data management. With over 190 publications and an h-index of 42, her work has garnered more than 10,000 citations. She has supervised 12 Ph.D. theses and coordinated over 25 international and EU-funded research projects. Prof. Vakali has served as an editor for ACM Computing Surveys and co-chaired prestigious conferences like ACM/IEEE Web Intelligence 2019. Renowned for her academic excellence, she has significantly contributed to future internet and data science initiatives globally.