Machine Learning and Applications: An International Journal (MLAIJ)

ISSN : 2394 - 0840

Citations, h-index, i10-index of MLAIJ

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 include but are not limited to, the following

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
  

Paper Submission

Authors are invited to submit papers for this journal through E-mail: mlaijjournal@airccse.org or through Submission System. Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this Journal.

Important Dates

Related Journal

• International Journal of Artificial Intelligence & Applications (IJAIA)

Editor In Chief

• Natarajan Meghanathan, Jackson State University, USA

Associate Editor

• Tatiana Tambouratzis, University of Piraeus, Greece

Editorial Board Members

• Verma A.K, Thapar University, India
• Abdulkadir Ozcan, Karatay University, Turkey
• Arman Sargolzaei, Florida International University, USA
• Arvind Kumar, Amity university Noida, India
• Ashutosh Kumar Dubey, Trinity Institute of Technology & Research, India

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