Aims & Scope
Aims & Scope
Sustainable Machine Intelligence Journal (SMIJ), proudly affiliated with the distinguished portfolio of Sciences Force Publisher, stands as an eminent conduit for the advancement of scholarly discourse at the nexus of artificial intelligence, sustainability, and ethical technological paradigms. Positioned within the domain of computer science, the journal is resolutely committed to propelling pioneering research endeavors and groundbreaking machine intelligence solutions that contribute to applied sustainable development in real-world.
Mission Statement:
At the forefront of our mandate is the solemn mission to establish the Sustainable Machine Intelligence Journal (SMIJ) as a preeminent open-access platform. Our purpose is to provide an intellectual locus for academics, researchers, and practitioners deeply engaged in the development and application of machine intelligence solutions that ardently contribute to global sustainability imperatives, environmental preservation, and conscientious technological deployment.
Scope of the Journal:
The purview of the journal is expansive, encompassing a spectrum of themes within the domain of sustainable machine intelligence, including, though not exhaustively, the following areas:
- Sustainability-aware Deep Reinforcement Learning (DRL) for energy, mobility, and resource optimization
- Sustainable Graph Neural Networks (GNNs) for climate, infrastructure, and ecological systems modeling
- Sustainability-oriented Quantum Machine Learning (QML) and quantum-enhanced learning for efficient computation
- Meta-learning for sustainability adaptation, enabling robust learning under changing environmental conditions
- Energy-efficient Neural Architecture Search (NAS) and model compression for sustainable deployment
- Federated Learning for sustainable and privacy-preserving intelligence in distributed sensing and smart ecosystems
- Explainable AI (XAI) for sustainability-critical decisions, emphasizing transparency, accountability, and auditability
- Fuzzy, uncertainty-aware, and neutrosophic machine intelligence for sustainability, enabling robust modeling, decision-making, and risk-sensitive optimization under incomplete, ambiguous, or conflicting sustainability data.
- Sustainable Swarm Intelligence in eco-friendly sensor networks and environmental monitoring
- Sustainability-driven Generative Adversarial Networks (GANs) for data augmentation in environmental and Earth-observation contexts
- Quantum-enhanced machine intelligence for sustainability, focusing on efficiency, scalability, and real-world feasibility
- Lifelong and continual learning for sustainability, supporting long-term adaptation with reduced retraining cost
- Sustainable Knowledge Graphs and structured intelligence for biodiversity informatics and environmental governance
- Self-supervised learning for sustainability data, including low-label or sparse ecological and geospatial datasets
- Sustainable Natural Language Processing (NLP) for climate policy analysis, environmental reporting, and sustainability disclosures
- Hopfield and associative memory models with sustainable optimization perspectives and efficient inference
- Geospatial intelligence for sustainability, including land-use, climate-risk mapping, and environmental change detection
- Adversarial machine intelligence and robustness in sustainability-critical systems (e.g., monitoring, forecasting, control)
- Attention mechanisms for sustainability modeling, improving interpretability and efficiency in large-scale environmental data pipelines
- Cross-cutting sustainability themes (encouraged across all submissions):
- Energy- and carbon-aware model training and inference
- Sustainable computing, efficient AI pipelines, and green AI evaluation practices
- Responsible AI: safety, ethics, governance, and societal impacts in sustainability applications
- Reproducibility, benchmarking, and real-world validation in sustainability-oriented settings
Applications:
The Sustainable Machine Intelligence Journal (SMIJ) welcomes application-driven contributions that demonstrate how machine intelligence can be designed and deployed to advance sustainability objectives while addressing ethical, safety, and governance considerations. Submissions should emphasize methodological clarity, responsible deployment, and, where feasible, measurable impacts on environmental performance, resource efficiency, or societal benefit. The journal particularly encourages studies that translate research innovations into practical systems, decision-support tools, and industrial solutions across sustainability-relevant sectors, including (but not limited to) the following application areas:
- Eco-friendly energy grid management and demand-response optimization
- Smart grids for renewable integration and distributed energy coordination
- Renewable energy forecasting and uncertainty-aware power planning
- Green data centers and sustainable cloud computing, including energy-aware scheduling
- Carbon footprint reduction in transportation, routing, and fleet management
- Green logistics and transportation planning for low-emission supply operations
- Sustainable urban mobility solutions and eco-friendly transportation networks
- Climate change prediction models and climate-risk early warning systems
- Pollution monitoring and control using intelligent sensing and analytics
- Ocean health monitoring systems and marine environmental intelligence
- Intelligent water resource management for allocation, leakage detection, and quality monitoring
- Precision farming with AI for sustainable agriculture and reduced input waste
- Sustainable fisheries management and ecosystem-respecting harvesting strategies
- Sustainable supply chain optimization, traceability, and resilience planning
- Environmental impact assessment in urban planning and climate-responsive infrastructure design
- Green building design and energy efficiency, including adaptive control systems
- AI-assisted circular economy strategies, reuse planning, and lifecycle optimization
- Intelligent recycling and waste sorting systems for higher recovery and lower contamination
- Sustainable materials discovery through AI, prioritizing low-impact alternatives
- Carbon trading and market intelligence for monitoring, forecasting, and compliance support
- AI-guided environmental policy modeling and decision-support under uncertainty
- Conservation drones and AI-driven conservation robotics for wildlife monitoring and habitat protection
- Wildlife trafficking prevention using multimodal detection and network analysis
- Eco-conscious consumer behavior analytics for sustainability-aware services and interventions
- Learning analytics for sustainability education and environmental decision-making
