The Sustainable Machine Intelligence Journal (SMIJ) is an international, double-blind peer-reviewed, open-access journal published quarterly (see Publishing Schedule for the journal’s volume/issue model), both online and in print. The journal is committed to advancing research in sustainable machine intelligence, integrating innovative approaches in artificial intelligence, machine learning, and intelligent systems with a focus on environmental, social, and economic sustainability.
SMIJ explores emerging concepts such as energy-efficient algorithms, ethical AI frameworks, resource-aware computational models, and sustainable decision-making systems, emphasizing their real-world applications in diverse domains. It also features studies in advanced areas like green machine learning, sustainable data processing, and intelligent optimization methodologies, fostering innovation in developing responsible and future-ready intelligent systems.
The journal serves as a platform for publishing high-quality research addressing challenges in creating intelligent systems that balance performance with sustainability. It promotes advancements in areas such as sustainable AI-driven optimization, eco-friendly computational models, intelligent data management, and green decision support systems, aiming to solve critical problems in modern technology and society.
SMIJ seeks to encourage interdisciplinary collaboration and innovation, making it a vital resource for academics, industry experts, and policymakers striving for a sustainable future powered by intelligent technologies.
SMIJ was established in 2022, and the first issue was published in October 2022. The journal publishes articles in English.
For a detailed overview of the journal’s scope and focus area, please refer to Aims and Scope homepage.
Current Issue: Volume 14, Issue 3 (2026)
Original Articles
Evaluation of Deep Learning Techniques for Intrusion Detection in Cybersecurity
Doaa El-Shahat, Mohamed Mahmoud Abdelaziz, Waseem Akram, and Muhammad Jabir Khan
Leakage-Safe Boosting Ensembles With SHAP Explainability for Alzheimer's Disease Risk Prediction: A Rigorous Multi-Classifier Benchmark on Global Structured Clinical Data
Hadeer Mahmoud and Fady Salama
Explainable Deep Learning for Forensic Wound Analysis: A Survey of Injury Classification, Gunshot Wound Interpretation, and Toward Court-Admissible AI
Zakaria Ahmed and Zeshan Aslam Khan
Swarm-Calibrated Ordinal Forests for Risk-Aware Forecasting in Non-Stationary Intraday Financial Time Series
Ahmed S. Salama and Ahmad M. Nagm
Anti-Fuzzy Hypergraphs and Superhypergraphs: Max-Oriented Uncertainty Models for Higher-Order and Hierarchical Networks
Takaaki Fujita, Ajoy Kanti Das, Suman Das, and Sankar Prasad Mondal
HAF-BiTrans: A Heterogeneity-Aware Federated BiLSTM-Transformer Framework for Privacy-Preserving Detection of Multi-Stage APT Behaviors in IoT Networks
Tareef S Alkellezli and Nariman A. Khalil