Ensemble Learning-Driven Models for Managerial System Modelling and Applications

Dear Colleagues,

We are pleased to invite submissions to a Special Issue titled “Ensemble Learning-Driven Models for Managerial System Modelling and Applications” in Systems. The developments of ensemble learning in system modeling and applications are crucial due to the growing complexity and uncertainty in modern management systems. Traditional single models often struggle to address these challenges effectively, leading to suboptimal predictions and decisions. Ensemble learning combines multiple models to leverage their respective strengths, and offers a powerful solution by improving predictive accuracy, robustness, and adaptability. By integrating diverse models, ensemble learning reduces biases and variances inherent in single models, providing more reliable and precise results.

Furthermore, ensemble learning’s broad applicability across diverse and complex systems, ranging from engineering and economics to social and ecological systems, fosters interdisciplinary innovation and collaboration. By advancing research in ensemble learning for system modeling, we can address the pressing demands of modern management challenges, providing both academic insights and practical solutions that enhance the effectiveness and reliability of decision-making processes.

The aim of this Special Issue is to explore the latest developments and applications of ensemble learning techniques in the context of managerial systems. This Special Issue seeks high-quality papers that address both theoretical and practical aspects of ensemble learning in managerial systems. We welcome original research articles on topics including, but not limited to the following:

• Methods for combining ensemble learning techniques to optimize renewable energy sources management.
• Techniques for enhancing load forecasting accuracy in smart grids through ensemble learning approaches.
• Innovations in ensemble learning algorithms tailored for managerial applications.
• Ensemble methods for decision making in supply chain management.
• Forecasting and optimization using ensemble models in financial management systems.
• Application of ensemble learning in decision support systems.
• Risk management and fraud detection in corporate environments using ensemble techniques.
• Applications of ensemble learning in business intelligence systems.
• Integration of ensemble learning with business intelligence and analytics platforms.
• Exploration of ensemble learning methods in intelligent production management.

Dr. Zhenkun Liu
Dr. Yan Hao
Dr. Pei Du
Prof. Dr. Jianzhou Wang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Systems is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• ensemble learning-driven models
• managerial system modeling
• renewable energy sources management
• supply chain management
• financial management systems
• decision support systems
• business intelligence systems