Aims

Acadlore Transactions on AI and Machine Learning (ATAIML) is a peer-reviewed open-access journal dedicated to advancing theoretical foundations, algorithmic innovations, and practical applications in artificial intelligence and machine learning. The journal emphasizes the scientific rigor, reproducibility, and responsible development of AI technologies that contribute to societal, industrial, and scientific advancement.

ATAIML provides a multidisciplinary and transparent forum for publishing original research, review articles, and technical developments. The journal encourages contributions addressing both core methods—such as model design, optimization, explainability, and computational efficiency—and the ethical, reliable deployment of AI in real-world contexts.

Published quarterly by Acadlore, ATAIML is committed to maintaining high editorial standards, strengthening research integrity, and promoting the global exchange of knowledge across the AI and ML communities.

Key features of ATAIML include:

• A strong focus on advancing fundamental theories, algorithms, and architectures in artificial intelligence and machine learning;

• Support for research that enhances model explainability, robustness, security, and trustworthy deployment in real-world systems;

• Emphasis on interdisciplinary innovation that bridges AI with engineering, natural sciences, and socio-economic domains;

• Promotion of responsible AI development, addressing ethical, legal, and societal implications to ensure human-centered progress;

• Commitment to a rigorous and transparent peer-review and publication process, enabling fair evaluation and global knowledge dissemination.

Scope

ATAIML welcomes high-quality submissions in all areas of AI and ML, including but not limited to:

Foundations and Models

• Deep learning architectures and optimization algorithms

• Graph neural networks and representation learning

• Probabilistic and Bayesian learning frameworks

• Computational learning theory and statistical ML

• Reinforcement learning and sequential decision-making

• Transfer learning, domain adaptation, federated learning, and meta-learning

• Evolutionary computation and swarm intelligence

Systems, Infrastructure, and Engineering

• Scalable, distributed, and edge intelligence systems

• AI for IoT and cyber-physical systems

• Efficient training systems and model lifecycle management (MLOps)

• High-performance and neuromorphic computing for AI

Data-Centric and Multimodal AI

• Data governance, quality, uncertainty quantification

• Synthetic, self-supervised, and weakly supervised learning

• Multimodal learning and data fusion

• Knowledge graphs and symbolic–neural integration

Trustworthy and Responsible AI

• Explainable, interpretable, and reliable AI

• Robustness, safety, fairness, privacy, and security in ML systems

• Legal, ethical, and societal implications of AI deployments

Applied AI Across Domains

• Robotics, autonomous systems, and smart manufacturing

• Healthcare analytics, medical imaging, and bioinformatics

• Smart cities, climate intelligence, and sustainability

• Computer vision, natural language and speech processing

• AI for finance, education, agriculture, and public services

• Human–AI interaction, creativity support, and cultural computing

Emerging and Future Paradigms

• Generative AI and foundation models

• Quantum machine learning and optimization

• Bio-inspired and cognitive computing

• AI-empowered mixed, augmented, and extended reality technologies