Advances in Intelligent Data Processing and Modeling

Dear Colleagues,

In the era of generative AI and large models, data has become a crucial element, especially during the modeling phase. The ability to process and analyze large-scale datasets is instrumental in developing highly effective and robust models. This capability underpins a wide array of applications, including, but not limited to, recommender systems, computer vision applications, and large language models. High-quality datasets enable the creation of models that are not only accurate but are also generalizable, capable of performing well across diverse scenarios. However, the challenges associated with handling big data are numerous and complex, as detailed below:

First, data processing: These models require meticulous preprocessing steps, including data cleaning, normalization, and augmentation, to ensure that the input data are suitable for training.

Second, selecting the proper model: The variety of data is another critical challenge. Large-scale datasets often comprise structured, semi-structured, and unstructured data, including text, images, audio, and videos. Integrating and analyzing these diverse data types require sophisticated preprocessing techniques to ensure that the data are in a usable format.

Third, understanding the data's latent structure: Different data types have distinct latent structures and characteristics, necessitating the selection of appropriate models to process and analyze each type effectively.

To address these challenges, we are announcing this Special Issue, “Advances in Intelligent Data Processing and Modeling,” and invite papers within the following topics:

• Data preprocessing: data imbalance, missing data, and data augmentation;
• Data modeling;
• Computer vision and image processing;
• Natural language processing and understanding;
• Recommender systems;
• Information retrieval and ranking systems;
• Large language models;
• Generative AI;
• Graph neural networks;
• Medical AI and clinical decision-making;
• Trustworthy AI: security and privacy;
• Large online platforms;
• Real-world ML applications;
• Machine learning systems.

Dr. Xiaohan Li
Dr. Zhiwei Liu
Dr. Zheng 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 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. Electronics 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 2400 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
• computer vision
• natural language processing
• recommender systems
• generative AI
• information retrieval
• trustworthy AI
• ranking systems
• graph neural networks
• machine learning