Aims and Scope

Aim of JMLIC

The Journal of Machine Learning and Intelligent Computing (JMLIC) aims to provide an international, peer-reviewed platform for high-quality research in machine learning and intelligent computing, fostering the development and dissemination of novel algorithms, models, and computational methods that advance the state of the art in AI and data-driven intelligence. It encourages interdisciplinary collaboration among computer scientists, data scientists, engineers, and domain experts to address real-world challenges, supporting research that bridges theoretical advances in machine learning with practical applications across diverse fields. The journal promotes transparency, reproducibility, and ethical practices in intelligent computing, facilitating the open sharing of knowledge, datasets, and methods to enable global collaboration and accelerate innovation. By publishing original research, comprehensive surveys, case studies, and application-oriented works, JMLIC contributes to both academic understanding and practical implementation, ultimately advancing robust, scalable, and impactful intelligent computing solutions for technological progress and societal benefit. 

 Scope of JJMLIC

JMLIC welcomes submissions in (but is not limited to) the following broad subject areas:

1. Supervised and unsupervised learning — classification, regression, clustering, dimensionality reduction.
2. Deep learning and neural network architectures — CNNs, RNNs, transformers, GANs.
3. Reinforcement learning, sequential decision making, and reward-based learning systems.
4. Hybrid intelligent systems — combining machine learning with rule-based, symbolic, or expert systems.
5. Natural language processing (NLP), text analytics, and language-based intelligent computing.
6. Computer vision, image and video analysis, pattern recognition, and multimedia analytics.
7. Big data analytics, data mining, and large-scale data processing for intelligence extraction.
8. Time-series analysis, forecasting, anomaly detection, and predictive analytics.
9. Explainable AI (XAI), model interpretability, fairness, and ethical AI research.
10. Machine learning applications in engineering, healthcare, finance, environment, and other domain-specific areas.
11. Data engineering, feature engineering, data preprocessing, data augmentation, and data quality methods.
12. Graph analytics, network analysis, graph neural networks, and relational data modeling.
13. Distributed machine learning, edge computing, federated learning, and scalable AI systems.
14. Reinforcement and multi‑agent systems, autonomous agents, and intelligent decision support systems.