Applied Mathematics in Artificial Intelligence: Methods, Algorithms, and Applications

Dear Colleagues,

Applied mathematics in artificial intelligence (AI) leverages mathematical methods to develop and understand AI algorithms and their applications, equipping intelligent systems with real-world problem-solving capabilities. Recognizing the transformative potential of this field, we aim to aggregate cutting-edge research in applied mathematics in AI to drive the socio-economic advancement of our emerging intelligent society.

This Special Issue invites high-quality papers that provide comprehensive reviews, innovative implementations, and groundbreaking applications of applied mathematics in AI. We seek contributions that address engineering challenges and propose solutions relevant to real-world problems.

We welcome submissions in the following areas:

1. Mathematical foundations:

• Linear algebra;
• Calculus;
• Probability and statistics;
• Optimization techniques (gradient descent, convex optimization, combinatorial optimization).

2. Machine learning algorithms:

• Supervised learning;
• Unsupervised learning;
• Reinforcement learning.

3. Deep learning:

• Neural networks;
• Convolutional neural networks (CNNs);
• Recurrent neural networks (RNNs);
• Generative models.

4. Algorithmic techniques:

• Dimensionality reduction;
• Regularization techniques (L1, L2, dropout, data augmentation);
• Ensemble methods.

5. Computational methods:

• Numerical methods;
• Monte Carlo methods;
• Graph theory.

6. Applications:

• Computer vision;
• Natural language processing (NLP);
• Robotics;
• Healthcare;
• Finance;
• Autonomous systems.

7. Emerging areas:

• Quantum machine learning;
• Fairness and ethics in AI;
• Explainable AI.

We encourage researchers, practitioners, and academics to contribute their original research and insights to this Special Issue. Join us in advancing the frontiers of applied mathematics in artificial intelligence, fostering innovations that will shape the future of intelligent systems and their impact on society.

Submit your manuscripts for consideration and be part of this pivotal discourse in AI research. Submitted papers should not have been previously published nor be currently under consideration for publication elsewhere.

Dr. Ashutosh Mishra
Dr. Shodhan Rao
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. Mathematics is an international peer-reviewed open access semimonthly 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 2600 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.

• artificial intelligence applications
• application in computing
• artificial intelligence
• information management
• management science
• AI for science
• image classification, object detection, image segmentation, facial recognition
• NLP, text classification, sentiment analysis, machine translation, etc.
• robotics, path planning, control systems, sensor fusion
• healthcare, medical image analysis, predictive modeling, drug discovery
• finance, algorithmic trading, risk management, fraud detection
• autonomous systems, self-driving cars, drones
• automated decision making
• quantum machine learning
• fairness and ethics in AI
• explainable AI
• data management, data pre-processing techniques
• case studies and practical applications
• industry applications
• statistics applications
• optimization
• mathematical modeling
• reliability
• decision analysis