7th International conference on Big Data, Machine learning and Applications (BIGML 2026)
\nNovember 21 ~ 22, 2026, London, United Kingdom
\nhttps:\/\/aisc2026.org\/bigml\/index<\/p>\n
Scope & Topics
\n7th International conference on Big Data, Machine learning and Applications (BIGML 2026) will act as a major forum for the presentation of innovative ideas, approaches, developments, and research projects in the areas of Big Data and Machine Learning. It will also serve to facilitate the exchange of information between researchers and industry professionals to discuss the latest issues and advancement in the area of Big Data and Machine Learning.<\/p>\n
Authors are solicited to contribute to the Conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of Computer Science and Information Technology.<\/p>\n
Topics of interest include, but are not limited to, the following:
\n\u2022 Big Data Systems, Lakehouse Architectures and Cloud Native Pipelines
\n\u2022 Streaming, Real\u2011Time, Edge and IoT Big Data Analytics
\n\u2022 Large\u2011Scale Data Mining, Pattern Discovery and Graph Analytics
\n\u2022 Machine Learning and Deep Learning for Massive Datasets
\n\u2022 Self\u2011Supervised, Contrastive, Graph and Sequential ML
\n\u2022 Time\u2011Series Foundation Models, SSMs and Anomaly Detection
\n\u2022 Foundation Models, LLMs, RAG and Multimodal AI for Big Data
\n\u2022 Generative AI, Synthetic Data and Simulation\u2011Driven Data Generation
\n\u2022 Causal ML, Causal Discovery and Counterfactual Reasoning
\n\u2022 Neural Retrieval, Vector Databases and Web\u2011Scale Search
\n\u2022 Data\u2011Centric AI, Weak Supervision and Data Quality Engineering
\n\u2022 ML Safety, Robustness, Alignment and Privacy\u2011Preserving ML
\n\u2022 Adversarial ML, Threat Intelligence and Fraud Detection
\n\u2022 Code\u2011LLMs, Software Analytics and Automated Code Intelligence
\n\u2022 Recommender Systems, Personalization and Graph Based Recommendations
\n\u2022 ML for Optimization, Supply Chain, Routing and Operations Research
\n\u2022 Scientific ML, Physics Informed ML and Climate\/Sustainability Analytics
\n\u2022 MLOps, DataOps, HPC and Hardware Accelerated ML
\n\u2022 Energy\u2011Efficient ML, Carbon\u2011Aware Computing and Efficient Model Training<\/p>\n
Paper Submission
\nAuthors are invited to submit papers through the conference Submission System by September 26, 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).<\/p>\n
Selected papers from BIGML 2026, after further revisions, will be published in the special issues of the following journals
\n\u2022 Machine Learning and Applications: An International Journal (MLAIJ)
\n\u2022 International Journal of Computer Networks & Communications (IJCNC) – Scopus ERA, WJCI Indexed
\n\u2022 International Journal of Wireless & Mobile Networks (IJWMN) – INSPEC WJCI Indexed
\n\u2022 International Journal of Mobile Network Communications & Telematics (IJMNCT)
\n\u2022 Advances in Vision Computing: An International Journal (AVC)
\n\u2022 Information Technology in Industry (ITII)<\/p>\n
Important Dates
\n\u2022 Submission Deadline : September 26, 2026
\n\u2022 Authors Notification : October 24, 2026
\n\u2022 Registration & Camera-Ready Paper Due : October 31, 2026<\/p>\n
Contact Us<\/p>\n
Here’s where you can reach us : bigml@ccsit2026.org (or) bigmlconf@yahoo.com<\/p>\n
Submission Link : https:\/\/aisc2026.org\/submission\/index.php<\/p>\n
<\/p>\n
This event is not affiliated with Invest Ottawa and is independently organized by a third party.<\/em><\/p>\n","protected":false},"parent":0,"template":"","class_list":["post-140213","event","type-event","status-publish","hentry","event-category-other-events"],"acf":[],"yoast_head":"\n