Special Issue: Applied Mathematics in the Age of Intelligent Systems and Data Science

Abstract

The increasing complexity of intelligent systems and data-driven technologies has elevated the role of applied mathematics in addressing modern computational challenges. This special issue focuses on recent mathematical innovations and their applications in machine learning, artificial intelligence (AI), data science, and computational operations research (OR) in finance. By bridging theoretical mathematics with real-world applications, this issue aims to highlight advanced methodologies that drive progress in computational and data-centric fields.      

We invite contributions that explore mathematical modeling and simulation techniques, emphasizing their role in improving the accuracy, efficiency, and scalability of intelligent systems. Applied mathematics provides the theoretical foundation through linear algebra, probability theory, and optimization, while AI-driven models such as deep learning, reinforcement learning, and generative models automate and refine data mining processes.

By exploring the intersection of applied mathematics and AI, this special issue will highlight novel methodologies that improve data mining in interdisciplinary domains such as healthcare, finance, cybersecurity, and bioinformatics. Key areas of focus include mathematical optimization techniques, uncertainty quantification, probabilistic modeling, and scalable AI architectures for big data analytics. The issue welcomes contributions from researchers and practitioners, fostering interdisciplinary collaboration to advance theoretical and practical developments in data mining. By bringing together innovations in applied mathematics and AI, this special issue aims to shape the future of intelligent data analysis and decision-making systems.

 

Key topics include but are not limited to:

• Numerical methods and their applications in computing
• Optimization techniques and their computational implementations
• Machine learning algorithms and mathematical foundations
• Uncertainty Quantifications
• Computational Operations Research in finance  
• Mathematical aspects of artificial intelligence                                                                      
• Computational Bioinformatics
• Machine learning algorithms and their mathematical underpinnings
  • Probabilistic modeling and uncertainty quantification
  • Explainable AI and interpretable machine learning in data mining
  • Data-driven decision-making using mathematical models
  • Scalable AI architectures for big data analytics
  • Applications of data mining in healthcare, finance, and cybersecurity.

 

Guest Editors:

Dr. Saurav Mallik, Research Scientist, Department of Pharmacology & Toxicology, University of Arizona, Tucson, AZ, USA

Dr. Priyanka Roy, Assistant Professor, Mathematics division, School of Advanced Sciences and Languages, VIT Bhopal University, Sehore-466114, M.P, India

Dr. Sandeep Kumar Mathivanan, Assistant Professor, School of Computing Science & Engineering, Galgotias University, Greater Noida, Uttar Pradesh-203201, India

Dr. Hong Qin, Associate professor at the School of Data Science, Department of Computer Science, Old Dominion University, USA

Dr. Ruba Abu Khurma, Assistant Professor in the Dept. of Information Technology department in Al-Balqa Applied University, Jordan

 

Submission Information

Submit it online: http://ojs.wiserpub.com/index.php/CM/user/register

Or send it to the email address: wendy@wiserpub.com

 

Submission Guideline

https://ojs.wiserpub.com/index.php/CM/about/submissions

 

For any inquiries about this Special Issue, please contact the Editors via wendy@wiserpub.com