About
The Interdisciplinary Journal of AI, Machine Learning & Data Science (IJAIMLDS) is a peer-reviewed, open access scholarly journal publishing original research and review articles in artificial intelligence, machine learning, and data science.
Aims and Scope
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 theoryMachine Learning: supervised, unsupervised, and reinforcement learning; deep learning architectures; learning algorithms; model optimization; neural networks and neural computingComputational Intelligence: fuzzy logic and fuzzy systems, genetic algorithms, evolutionary and swarm intelligence, evolutionary robotics, and computational intelligence methodologiesData Science & Analytics: data mining, data and web mining, statistical learning, big data analytics, data visualization, and decision analyticsMulti-Agent & Distributed AI: multi-agent systems, cooperative and competitive learning, and simulation-based studiesPerception & Representation Learning: mapping, localization, and tracking, representation learning, and machine perceptionExplainable & Responsible AI: explainability, interpretability, fairness, transparency, and accountability in AI and machine learning modelsInterdisciplinary Research: applications of AI, machine learning, and data science across domains, provided that the work makes clear methodological or analytical contributions
