7-8 November 2025, Hybrid format
The Department of Economics, Entrepreneurship and Business Administration co-hosts the International Conference on Machine Learning and Big Data Analytics ICMLBDA 2025, held in a hybrid format on 7-8 November 2025.
More details are available on the conference homepage and in the promotional materials
We invite everyone to participate in the conference, especially in the session “Data-Driven Models and Analytics for Circular Economy and Sustainable Innovations.”
Detailed session info is provided
Submission deadline for papers and participation is 30 September 2025.
Conference proceedings will be published in the Springer PROMS series indexed by Scopus (Archive)
Submission guidelines are available via link
The template for paper formatting is available here
The 5th International Conference on Machine Learning and Big Data Analytics (ICMLBDA) 2025 provides a platform for researchers and professionals to share research and reports on new technologies and applications in ML and Big Data Analytics such as biometric recognition systems, medical diagnosis, industry, telecommunications, AI Petri Nets model-based diagnosis, gaming, stock trading, intelligent aerospace systems, robot control, law, remote sensing, scientific discovery agents, multi-agent systems, and natural language and web intelligence.
The conference theme is “Embracing Innovations” for secure Next Generation Computing and Communication Systems.
The rapid advancement of technologies and rising expectations require researchers to continuously reinvent themselves through new investigations and product development. The conference aims to bring together leading academicians, industrialists, government bodies, scientists, research scholars, and students on one platform to present their work, discuss, and adopt innovations for secure next-generation systems.
We encourage submission of works spanning theory to practice, including case studies, works-in-progress, and conceptual explorations.
This special session explores the role of data-driven models, machine learning, and advanced analytics in providing strategies and solutions for sustainable innovation across different fields. It aims to highlight methodologies supporting eco-innovation, predictive life cycle management, sustainable product design, green logistics, and quantitative policy evaluation.
The session emphasizes applied case studies and interdisciplinary research demonstrating the potential of data-driven tools to generate actionable insights and sustainable outcomes.
Relevant topics include (but are not limited to):
- Modeling resource use and waste reduction
- Predictive tools for managing product life cycles
- Using data to support eco-innovation and green design
- Machine learning for tracking environmental and social performance
- Analytics for optimizing sustainable logistics or energy systems
- Evaluating the impact of sustainability policies with quantitative methods
Session Chairs:
- Inna Koblianska, i.koblianska@biem.sumdu.edu.ua, Sumy State University, Ukraine
- Borys Kuzikov, b.kuzikov@sl.sumdu.edu.ua, Sumy State University, Ukraine
- Anna Masłoń-Oracz, amaslon@sgh.waw.pl, Warsaw School of Economics, Poland
More session details