Intelligent Data-Driven Computing: Advances in Machine Learning, Deep Learning, and Multimodal Models

Dear Colleagues,

The convergence of massive data availability, exponential growth in computational power, and breakthroughs in model architectures is driving a new era of intelligent data-driven computing. From traditional machine learning and data mining techniques to the recent emergence of multimodal large language models (MLLMs), data-driven methodologies are fundamentally transforming how we extract knowledge, generate content, and make decisions across virtually every domain of science, industry, and society.

This Special Issue, entitled “Intelligent Data-Driven Computing: Advances in Machine Learning, Deep Learning, and Multimodal Models” , aims to capture the breadth and depth of this rapidly evolving landscape. We seek to provide a comprehensive, interdisciplinary platform for showcasing cutting-edge research that leverages data-driven approaches—ranging from classical algorithms to state-of-the-art foundation models—to address complex real-world challenges.

We invite authors to submit original research articles and review articles, focusing on (but not limited to) the following topics:

• Multimodal Large Language Models (MLLMs) and Foundation Models: Vision–language models; multimodal understanding and generation; instruction tuning and alignment; efficient adaptation and deployment of large models; applications of MLLMs in domain-specific tasks.
• Generative AI and Creative Applications: Diffusion models for image, video, and 3D content generation; controllable generation; AI-generated content (AIGC) evaluation and applications.
• Computer Vision and Visual Intelligence: Object detection and scene understanding; video analysis and action recognition; vision transformers and self-supervised learning; visual data mining.
• Data Mining and Big Data Analytics: Pattern discovery and association rule mining in large-scale databases; anomaly and outlier detection; time-series forecasting; graph mining and network analysis.
• Industrial and Engineering Applications: Predictive maintenance and intelligent fault diagnosis; smart manufacturing and Industry 4.0; IoT data fusion and edge intelligence; supply chain optimization and digital twins.
• Computational Finance and Business Intelligence: Algorithmic trading and quantitative modeling; risk assessment and fraud detection; customer analytics and recommender systems.
• Interdisciplinary and Scientific Applications: AI for climate and environmental science; bioinformatics and computational biology; social network analysis and computational social science; physics-informed machine learning and scientific discovery.

Dr. Chao Tong
Dr. Mingming Liu
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 250 words) can be sent to the Editorial Office for assessment.

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.

• machine learning
• deep learning
• multimodal large language models (MLLMs)
• generative AI
• data mining
• artificial intelligence
• computer vision
• foundation models
• big data analytics