Focus & Scope
The journal's scope encompasses, but is not limited to, the following key areas:
1. Core AI Theory & Methodologies:
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Machine Learning (Deep Learning, Reinforcement Learning, Supervised/Unsupervised Learning)
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Neural Networks and Architectures
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Natural Language Processing (NLP) and Computational Linguistics
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Computer Vision and Image Processing
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Robotics and Autonomous Systems
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Knowledge Representation and Reasoning
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Search, Optimization, and Evolutionary Algorithms
2. AI for Sustainable Development & Global Challenges:
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AI for Climate Change, Environmental Monitoring, and Conservation
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AI in Renewable Energy Systems and Resource Management
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AI for Smart Agriculture and Food Security
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AI applications in support of the UN Sustainable Development Goals (SDGs)
3. Next-Generation & Emerging AI Paradigms:
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Quantum Artificial Intelligence (Quantum Machine Learning, Quantum Algorithms)
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Neuromorphic Computing and Brain-Inspired AI
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Explainable AI (XAI) and Interpretable Machine Learning
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Edge AI and Distributed Intelligence
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AI at the intersection of other disruptive technologies (e.g., Blockchain, IoT)
4. AI in Engineering & Intelligent Systems:
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AI in Power Electronics and Drive Systems
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AI for Smart Grids, Energy Forecasting, and Load Management
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AI in Industrial Automation, Predictive Maintenance, and Industry 4.0/5.0
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AI in Civil, Mechanical, and Aerospace Engineering
5. Applied AI & Cross-Disciplinary Applications:
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AI in Healthcare, Biomedicine, and Genomics
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AI in Finance, Business, and E-Commerce
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AI in Education and Applied Learning Technologies (Personalized Learning, Adaptive Systems)
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AI in Smart Cities, Urban Planning, and Transportation
6. Ethical, Societal, & Human-Centric AI:
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AI Ethics, Fairness, Accountability, and Transparency (FAccT)
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Bias Mitigation and Algorithmic Justice
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Policy, Governance, and Regulatory Frameworks for AI
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Societal Impact, Future of Work, and Human-AI Collaboration
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Privacy, Security, and Trustworthy AI Systems
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