5th International Conference on Machine Learning, NLP and Data Mining (MLDA 2026)

Program Committee

Accepted Papers

Virtual Conference

Scope
5^th International Conference on Machine Learning, NLP and Data Mining (MLDA 2026) provides a premier international forum for researchers, practitioners and industry experts to share their latest findings, methodologies and innovations in Machine Learning, Natural Language Processing and Data Mining. As these fields continue to evolve rapidly and shape the future of intelligent systems, MLDA 2026 aims to bring together diverse perspectives that advance both theoretical foundations and real world applications.

Call for Papers

The conference welcomes high quality submissions that present original research results, innovative projects, comprehensive surveys and industrial case studies. Contributions may address significant advances in learning algorithms, language technologies, data centric methods, scalable systems and interdisciplinary applications. While the conference highlights key areas within ML, NLP and Data Mining, it remains open to emerging topics and novel ideas that push the boundaries of intelligent data driven research.

MLDA 2026 encourages participation from academia, industry and government, fostering collaboration and knowledge exchange across disciplines. By providing a platform for discussing breakthroughs, challenges and future directions, the conference aims to support the continued growth and impact of machine learning, natural language computing and data mining in science, technology and society.

Topics of interest include, but are not limited to, the following

Machine learning theory, optimization and probabilistic modeling
Bayesian learning, causal inference and causal representation learning
Semi supervised, weakly supervised and self supervised learning
Continual, lifelong, transfer, meta learning and few shot learning
NeuroAI and neuroscience inspired machine learning
Deep neural architectures and efficient deep learning (pruning, quantization, distillation)
Multimodal representation learning across text, vision, audio and sensor data
Graph neural networks, graph transformers and temporal graph learning
Large scale, distributed and parallel training of ML models
Pretraining, fine tuning and adaptation of large language models (LLMs)
Retrieval augmented generation (RAG), memory augmented models and long context modeling
LLM agents, tool using models, multi step reasoning and autonomous language agents
Hallucination mitigation, LLM evaluation, safety, alignment and responsible LLMs
Information extraction, information retrieval and retrieval enhanced NLP
Text mining, text classification, topic modeling and semantic parsing
Argumentation mining, opinion mining and sentiment analysis
Corpus linguistics, multilingual NLP and cross lingual modeling
Dialogue systems, conversational agents, question answering andchatbots
Speech recognition, speech synthesis and affective speech technologies
Data mining foundations, pattern mining, sequence mining and knowledge discovery
Data centric AI, dataset auditing, bias detection and data governance
Synthetic data generation, fidelity evaluation and data augmentation pipelines
Big data analytics, scalable data processing and distributed data systems
Knowledge graphs, semantic reasoning and linked data technologies

Predictive learning, forecasting and advanced time series machine learning
Time series modeling for irregular, multivariate and high frequency data
Recommendation systems, personalization and user modeling
Machine learning for business intelligence, decision support and knowledge intensive systems
Image and video understanding, scene analysis and object recognition
Vision language models, multimodal fusion and embodied perception
Deep reinforcement learning, multi agent RL, offline RL and imitation learning
RL for NLP, dialogue systems, planning and sequential decision making
Explainable AI, interpretability, fairness, accountability and transparency
Robustness, adversarial machine learning and secure model training
Privacy preserving ML, federated learning and differential privacy
ML pipelines, automation, deployment, monitoring and optimization
MLOps, scalable ML systems, distributed computing and infrastructure for LLMs
Model integrity, watermarking, secure inference and model protection
ML and NLP for IoT, edge computing, TinyML and ubiquitous intelligence
Semantic web technologies, ontologies, conceptual modeling and knowledge integration
Code generation, program synthesis, automated debugging and software engineering ML
ML for healthcare, bioinformatics, computational biology and medical applications
ML for finance, economics, business analytics and risk modeling
ML for education, learning analytics and personalized learning systems
ML for environmental modeling, sustainability and climate related applications
ML for engineering, scientific discovery and simulation driven research
Social network analysis, web mining and web intelligence
Human AI collaboration, human in the loop learning and interactive ML
Affective computing, emotion aware systems and user centric AI design

Paper Submission

Authors are invited to submit papers through the conference Submission System by May 16, 2026. Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this conference. The proceedings of the conference will be published by International Journal on Cybernetics & Informatics (IJCI) (Confirmed).

Selected papers from MLDA 2026, after further revisions, will be published in the special issue of the following journal.

Important Dates

Submission Deadline

May 16, 2026

Authors Notification

June 20, 2026

Registration & camera - Ready Paper Due

June 27, 2026

Proceedings

The soft copy of the proceedings will be available on Journal web pages.

The Registration fee is 250 USD for accepted article Authors. Atleast one author of accepted paper is required to register at the full registration rate.