Aims

Acadlore Transactions on AI and Machine Learning (ATAIML) is a peer-reviewed open-access journal that publishes original research in artificial intelligence and machine learning, with an emphasis on theoretical analysis, algorithmic development, and carefully designed empirical studies.

The journal is primarily interested in work that offers clear methodological contributions, theoretical insights, or well-supported experimental findings, rather than papers that report only incremental applications of existing techniques.

ATAIML aims to provide a venue for research that connects foundational ideas in AI and ML with engineering practice and real-world systems, while maintaining a strong focus on scientific rigor, reproducibility, and transparency in reporting.

The journal also welcomes critical discussions on the reliability, interpretability, and broader implications of AI technologies, including their ethical and social aspects, where these issues are addressed with appropriate technical or analytical depth.

Published quarterly by Acadlore, ATAIML follows a structured peer-review process and standard editorial procedures to ensure consistency and transparency in its publication practices.

Key features of ATAIML include:

• An emphasis on research that contributes to theoretical understanding and methodological development in artificial intelligence and machine learning;

• A particular interest in work addressing model interpretability, robustness, reliability, and security in learning systems;

• Contributions that connect AI methods with engineering practice, scientific domains, or socio-economic contexts in a technically grounded way;

• Studies that examine ethical, legal, or societal aspects of AI where these are supported by clear analytical or technical frameworks;

• A standard peer-review and editorial process intended to support consistency, transparency, and fairness in the evaluation of submissions.

Scope

ATAIML welcomes submissions across a broad range of topics in artificial intelligence and machine learning, including, but not limited to, the areas outlined below:

Foundations and Models

• Deep learning architectures and related optimisation methods

• Graph neural networks and representation learning

• Probabilistic and Bayesian approaches to learning

• Computational learning theory and statistical learning methods

• Reinforcement learning and sequential decision models

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

• Evolutionary computation and swarm-based methods

Systems, Infrastructure, and Engineering

• Scalable, distributed, and edge-based learning systems

• AI for Internet of Things and cyber-physical systems

• Training infrastructure, deployment, and lifecycle management (MLOps)

• High-performance and neuromorphic computing for AI workloads

Data-Centric and Multimodal AI

• Data governance, quality assessment, and uncertainty modelling

• Synthetic data, self-supervised, and weakly supervised learning

• Multimodal learning and data fusion techniques

• Knowledge graphs and symbolic–neural hybrid approaches

Trustworthy and Responsible AI

• Explainability, interpretability, and reliability of learning systems

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

• Ethical, legal, and societal aspects of AI use and deployment

Applied AI Across Domains

• Robotics, autonomous systems, and intelligent manufacturing

• Healthcare analytics, medical imaging, and bioinformatics

• Smart cities, climate-related modelling, and sustainability applications

• Computer vision, natural language processing, and speech technologies

• AI applications in finance, education, agriculture, and public services

• Human–AI interaction and computational support for creativity and culture

Emerging and Future Paradigms

• Generative models and foundation architectures

• Quantum approaches to learning and optimisation

• Bio-inspired and cognitive computing

• Intelligent systems for mixed, augmented, and extended reality