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Machine Learning and Applications: An International Journal (MLAIJ)
ISSN : 2394 - 0840
Scope & Topics

Machine Learning and Applications: An International Journal (MLAIJ) is a quarterly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the machine learning. The journal is devoted to the publication of high quality papers on theoretical and practical aspects of machine learning and applications.The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on machine learning advancements, and establishing new collaborations in these areas. Original research papers, state-of-the-art reviews are invited for publication in all areas of machine learning.
Authors are solicited to contribute to the journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of machine learning.

Topics of interest
Foundations of Machine Learning
  • Statistical Learning Theory and Generalization
  • Optimization for ML (Convex, Non Convex, Large Scale)
  • Probabilistic Modeling, Bayesian Learning and Graphical Models
  • Causal Inference, Causal ML and Counterfactual Reasoning
  • Online Learning, Meta Learning and Continual Learning
  • Multi Task Learning, Transfer Learning and Domain Adaptation
  • Theory of Deep Learning and Emergent Behaviors
    • Deep Learning and Representation Learning
  • Neural Network Architectures and Training Techniques
  • Self Supervised Learning and Contrastive Learning
  • Generative Models (GANs, Diffusion Models, VAEs)
  • Diffusion Models for Images, Text, Time Series, Molecules and Graphs
  • Foundation Models, LLMs, Vision Language Models and Multimodal Models
  • Efficient Deep Learning (Pruning, Quantization, Distillation)
  • Representation Learning for Structured, Temporal and Graph Data
    • Reinforcement Learning, Decision Making and Embodied AI
  • Deep Reinforcement Learning and Policy Optimization
  • Multi Agent RL, Game Theory and Coordination
  • Offline RL, Safe RL and Risk Sensitive RL
  • World Models, Embodied AI and Interactive Learning
  • RL for Robotics, Control Systems and Real World Deployment
  • Hierarchical RL and Skill Discovery
  • Planning Augmented Models and Decision Transformers
    • Natural Language Processing, Speech and Multimodal AI
  • Large Language Models and Instruction Tuned Models
  • Retrieval Augmented Generation (RAG) and Knowledge Grounded Models
  • Long Context Models, Memory Augmented Models and Tool Using LLMs
  • Text Generation, Summarization and Dialogue Systems
  • Speech Recognition, Speech Synthesis and Audio Language Models
  • Vision Language Models, Video Language Models and Multimodal Fusion
  • NLP for Low Resource Languages and Cross Lingual Learning
    • Computer Vision, Perception and Graphics
  • Image Classification, Detection and Segmentation
  • 3D Vision, Scene Understanding and SLAM
  • Vision Transformers and Diffusion Based Vision Models
  • Video Understanding, Action Recognition and Motion Prediction
  • Generative Vision Models, Neural Rendering and 3D Generation
  • Embodied Perception and Interactive Vision
  • Vision Language Action Models for Robotics
    • Data Mining, Knowledge Discovery and Graph Learning
  • Graph Neural Networks (GNNs) and Graph Representation Learning
  • Knowledge Graphs, Reasoning and Neuro Symbolic AI
  • Large Scale Data Mining and Pattern Discovery
  • Time Series Forecasting, Anomaly Detection and Predictive Modeling
  • Simulation Based ML and Synthetic Data Generation
  • ML for Structured, Relational and Heterogeneous Data
    • Trustworthy, Explainable and Responsible AI
  • Explainable AI (XAI) and Mechanistic Interpretability
  • Fairness, Accountability, Transparency and Ethics in ML
  • Robust ML, Adversarial Attacks and Defenses
  • Jailbreak Resistant LLMs and Safety Evaluation
  • Privacy Preserving ML (Differential Privacy, Federated Learning, Secure ML)
  • Safety Critical ML and Reliability
  • AI Governance, Risk Assessment and Policy Aligned ML
    • ML Systems, Hardware Acceleration and Efficient Computing
  • Distributed and Parallel ML Systems
  • Training and Inference Optimization for Foundation Models
  • ML Compilers, Optimization and Deployment Frameworks
  • Edge ML, TinyML and On Device Learning
  • Edge Native Foundation Models and Distributed Inference
  • Neuromorphic Computing and Brain Inspired ML
  • Energy Efficient ML, Green AI and Carbon Aware ML Pipelines
    • Applied Machine Learning and Domain Specific ML
  • Healthcare and Life Sciences
  • Medical Imaging, Diagnostics and Clinical Decision Support
  • Computational Biology, Genomics and Drug Discovery
  • Digital Health, Wearables and Personalized Medicine
  • ML for Neuroscience and Cognitive Modeling
  • ML for Digital Therapeutics and Clinical Decision Automation
    • Science and Engineering
  • ML for Physics, Chemistry, Materials Science and Climate Modeling
  • Physics Informed ML and Scientific Machine Learning (SciML)
  • Differentiable Physics, Neural Simulators and ML Accelerated Simulation
  • ML for Robotics, Autonomous Systems and Control
  • ML for Smart Cities, IoT and Cyber Physical Systems
    • Business, Finance and Social Systems
  • ML for Finance, Risk Modeling and Fraud Detection
  • Recommender Systems, Personalization and User Modeling
  • Social Network Analysis and Computational Social Science
  • ML for Policy Simulation and Societal Impact Modeling
    • Emerging Trends
  • Agentic AI, Autonomous AI Systems and Multi Agent LLM Ecosystems
  • Tool Using AI, Planning Augmented LLMs and Autonomous Agents
  • Program Synthesis, AI for Code and ML Guided Theorem Proving
  • Quantum Machine Learning and Quantum Inspired Algorithms
  • AutoML, Neural Architecture Search (NAS) and Hyperparameter Optimization
  • ML for Foundation Model Alignment, Safety and Governance
  • ML for Autonomous Scientific Discovery and Robot Scientists
  • ML for Synthetic Biology, Bio Inspired Algorithms and Living Systems

    • Important Dates
    calendar_todaySubmission Deadline : April 20, 2026

    calendar_todayAuthors Notification : May 19, 2026

    calendar_todayRegistration & Camera-Ready Paper Due : May 26, 2026