7th International Conference on Big Data, Machine Learning and IoT (BMLI 2026)
September 26 ~ 27, 2026, Toronto, Canada
https://nlaim2026.org/bmli/index
Scope
7th International Conference on Big Data, Machine Learning and IoT (BMLI 2026) serves as a premier global forum for presenting innovative ideas, cutting edge research, advanced methodologies and real world applications across the rapidly evolving domains of Big Data, Machine Learning and the Internet of Things. As data driven intelligence continues to transform industries, scientific discovery and society at large, BMLI 2026 brings together researchers, practitioners and thought leaders to exchange insights, foster collaboration and shape the next generation of intelligent systems.
The conference welcomes high quality contributions that demonstrate significant advances, including theoretical developments, experimental results, system designs, industrial case studies and comprehensive.
Topics of interest include, but are not limited to, the following
· Distributed and cloud native data systems
· Streaming, real time analytics and event processing
· AI native databases, vector search and semantic retrieval
· Foundation models, multimodal AI and self supervised learning
· Transformers, MoE models and efficient inference
· Generative AI, diffusion models and synthetic data
· RAG systems, LLM augmentation and knowledge integration
· LLM agents, autonomous AI and tool use models
· Graph ML, GNNs and large scale graph systems
· Federated, distributed and privacy preserving ML
· Causal ML, counterfactual reasoning and robust inference
· Continual, lifelong and meta learning
· Neural architecture search and automated ML
· Multi agent reinforcement learning and cooperative AI
· AI safety, alignment and adversarial robustness
· Large scale training, parallel ML and AI compilers
· ML for code, program synthesis and automated reasoning
· Knowledge graphs, semantic systems and reasoning
· Multimodal analytics: text, vision, audio, sensor data
· Neural fields (NeRFs, Gaussian Splatting) and 3D reconstruction
· Embodied AI, robotic foundation models and simulation
· Next gen IoT: 5G/6G, LPWAN, satellite IoT and NTN
· Ultra low power IoT, neuromorphic edge and energy harvesting
· Wireless and RF sensing, CSI based perception
· IoT localization, tracking and cooperative perception
· Digital twins, cyber physical systems and industrial IoT
· LLMs at the edge (TinyLLMs, EdgeLLMs)
· LLM powered IoT orchestration and autonomous device control
· RAG for sensor data, time series and IoT streams
· AI for cybersecurity, threat intelligence and LLM red teaming
· Quantum ML, SciML and AI for scientific discovery
· AI for climate, sustainability and environmental systems
· Spatial computing, AR/VR/XR and neural rendering
· Responsible AI: fairness, ethics and governance
Paper Submission
Authors are invited to submit papers through the conference Submission System by August 01, 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 Computer Science Conference Proceedings in Computer Science & Information Technology (CS & IT) series (Confirmed).
Selected papers from BMLI 2026, after further revisions, will be published in the special issue of the following journal.
· The International Journal of Database Management Systems (IJDMS)
· International Journal of Data Mining & Knowledge Management Process (IJDKP)
· International Journal on Web Service Computing (IJWSC)
· Machine Learning and Applications: An International Journal (MLAIJ)
· Advances in Vision Computing: An International Journal (AVC)
· Information Technology in Industry (ITII)
Important Dates
· Submission Deadline: August 01, 2026
· Authors Notification: August 25, 2026
· Registration & camera - Ready Paper Due: September 01, 2026
Contact Us
Here's where you can reach us : bmli@nlaim2026.org (or) confbmli@gmail.com
Submission URL: https://inwes2026.org/submission/index.php
