Aims & Scope

Interdisciplinary Journal of AI, Machine Learning & Data Science (IJAIMLDS)

The Interdisciplinary Journal of AI, Machine Learning & Data Science (IJAIMLDS) publishes original, peer-reviewed research that advances theory, algorithms, and data-driven methods in artificial intelligence, machine learning, and data science.

The journal provides an academic forum for researchers and practitioners to share work that demonstrates originality, methodological rigor, and relevance to contemporary research problems.

Aim

Interdisciplinary Journal of AI, Machine Learning & Data Science (IJAIMLDS) aims to promote interdisciplinary research and knowledge exchange by publishing high-quality scholarly work that contributes to the advancement of artificial intelligence, machine learning, and data science through theoretical, algorithmic, and data-centric approaches.

Scope

Interdisciplinary Journal of AI, Machine Learning & Data Science (IJAIMLDS) welcomes submissions in the following areas, among others:

  • Artificial Intelligence: automated reasoning, knowledge representation and reasoning, cognitive intelligence and modeling, learning theory
  • Machine Learning: supervised, unsupervised, and reinforcement learning; deep learning architectures; learning algorithms; model optimization; neural networks and neural computing
  • Computational Intelligence: fuzzy logic and fuzzy systems, genetic algorithms, evolutionary and swarm intelligence, evolutionary robotics, and computational intelligence methodologies
  • Data Science & Analytics: data mining, data and web mining, statistical learning, big data analytics, data visualization, and decision analytics
  • Multi-Agent & Distributed AI: multi-agent systems, cooperative and competitive learning, and simulation-based studies
  • Perception & Representation Learning: mapping, localization, and tracking, representation learning, and machine perception
  • Explainable & Responsible AI: explainability, interpretability, fairness, transparency, and accountability in AI and machine learning models
  • Interdisciplinary Research: applications of AI, machine learning, and data science across domains, provided that the work makes clear methodological or analytical contributions

Sectors

The journal also welcomes research applying AI, machine learning, and data science methods to the following sectors, where emphasis is placed on algorithms, models, and data analysis rather than system-level implementations:

  • Healthcare and biomedical analytics
  • Finance, economics, and financial data analysis
  • Manufacturing and industrial analytics
  • Robotics and autonomous behaviour modelling
  • Transportation and mobility analytics
  • Urban and smart-environment data analytics
  • Energy, sustainability, and environmental analytics
  • Social and computational social science
  • Cybersecurity and anomaly detection
  • Scientific computing and data-intensive research

Article Types

The journal accepts the following types of submissions:

  • Research Articles
  • Review Articles
  • Short Communications
  • Technical Notes

All submissions must demonstrate clear theoretical, algorithmic, or data-driven contributions in artificial intelligence, machine learning, or data science. Papers focused solely on system architectures, platforms, or implementation details are outside the scope of the journal.