Dear Colleagues,

We are pleased to invite you to submit papers with original and high-quality ideas that are relevant to Mathematics for Artificial Intelligence and Big Data Analysis. The advent of Artificial Intelligence (AI) and Big Data Analysis has transformed various sectors, spanning from healthcare and finance to manufacturing, social media, and Physics-informed data mining. These transformative technologies have the potential to reshape the way we live, work, and interact with the world. To fully leverage the power of AI and Big Data, a strong foundation in mathematics is indispensable.

The main purpose of this Special Issue is to disclose the latest progress on the critical role of mathematics in enabling the capabilities of AI and extracting valuable insights from Big Data. This Special Issue targets an interdisciplinary audience of researchers, industry practitioners, and scholars to share and exchange new ideas on the use of AI and mathematical theories in Big Data mining.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

• Mathematical Foundations of Machine Learning: Exploring the underlying mathematical principles of machine learning.
• Deep Learning and Neural Networks: investigating advanced calculus in the context of deep neural networks, the topological aspects of neural network architectures, the application of non-Euclidean geometries in neural network training, and the application of deep learning and machine learning.
• Probability and Statistics for Data Analysis: delving into Bayesian statistics, the use of probabilistic graphical models for quantifying uncertainty, and statistical tests' role in data-driven decision-making.
• Computational Mathematics for AI: numerical methods for addressing AI-related problems, the role of high-performance and distributed computing in Big Data processing, and the application of algebraic and symbolic computation in AI research.
• Data Preprocessing and Feature Engineering: evaluating mathematical techniques for data cleaning, normalization, and feature selection and extraction, as well as exploring dimensionality reduction and its mathematical foundations.
• Big Data Analytics: Assessing scalable mathematical algorithms for processing and analyzing large datasets, examining time-series analysis and forecasting using mathematical models, and studying distributed mathematical frameworks for Big Data analytics.
• Optimization in AI: analyzing convex and non-convex optimization in machine learning, the relevance of stochastic optimization in AI model training, and the application of combinatorial optimization in decision-making and AI planning.
• Model and data compression: reducing the size of machine learning models and datasets while retaining useful information.
• Big data and explanability: critical applications such as healthcare, finance, and autonomous systems and understanding why an AI model made a particular decision.

I look forward to receiving your contributions.  

Dr. Marcel Atemkeng
Prof. Dr. Daniel-Ioan Curiac
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.

• data mining
• artificial intelligence
• big data analysis
• machine learning
• deep learning
• probability and statistics
• computational mathematics
• compression
• feature engineering
• optimization