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