Deadline Date: 30 August 2026
The special issue "Modern Statistical Models and Machine Learning Models and Their Applications in Different Sciences" brings together innovative research that connects statistical theory and machine learning to practical applications in a variety of scientific fields. This issue highlights the evolving landscape where traditional statistical approaches are being enhanced or reimagined through machine learning techniques to tackle complex, high-dimensional, and non-linear problems. The selected papers cover a broad spectrum of disciplines including medicine, environmental science, finance, engineering, and social sciences, showcasing how these models improve prediction accuracy, decision-making, and pattern recognition.
A key aspect of the issue is the integration of interpretability and robustness into the development of models, ensuring that these advanced tools can be trusted and used effectively in real-world scenarios. Contributions range from theoretical advancements in statistical modeling to practical case studies demonstrating the real-world benefits of machine learning algorithms such as deep learning, ensemble methods, and probabilistic models. By fostering interdisciplinary collaboration, this special issue serves as a platform for researchers and practitioners to share insights, methodologies, and novel applications. It emphasizes the importance of model validation, ethical AI, and data-driven discovery, making it a valuable resource for those interested in the intersection of statistics, machine learning, and applied sciences.
The special issue "Modern Statistical Models and Machine Learning Models and Their Applications in Different Sciences" brings together innovative research that connects statistical theory and machine learning to practical applications in a variety of scientific fields. This issue highlights the evolving landscape where traditional statistical approaches are being enhanced or reimagined through machine learning techniques ... show more
Editorial board
