Trends in Artificial Intelligence and Data Mining

Dear Colleagues,

You are cordially invited to submit your original research or review papers to this Special Issue of Applied Sciences entitled “Trends in Artificial Intelligence and Data Mining”.

This Special Issue aims to meet the increasing demand for scientific enquiry on artificial intelligence trends related to data mining. The amount of data available every day is enormous and increasing at an exponential rate. Recently, there has been growing interest in using complex methods to analyze and visualize massive data generated from very different knowledge domains: social networks, smart cities, security, health sciences, medicine, business, education and multimedia entertainment. This Special Issue is aimed at encouraging researchers and developers to publish original, innovative and state-of-the-art machine complex methods, algorithms, resources and architectures that analyze and visualize large amounts of data and solve a range of problems.

We are particularly interested in candidates who have conducted research on the theoretical or practical aspects of data mining―in particular, text mining and knowledge discovery—which may be complemented by data that are heterogeneous (geolocation, categories, metadata, etc.) and multimodal (sound, image, video, etc.). These aspects can range from resources for improving or training machine learning algorithms to algorithms that use complex methods (i.e., deep learning, chaos algorithms, genetic algorithms, cellular automata, etc.) and statistical learning methods, applied to one or more domains, such as digital media data, bioinformatics, healthcare, multimedia entertainment, social networks, natural language processing and education.

Potential topics include but are not limited to the following:

• Soft computing for multimedia and heterogeneous data analysis (text data processing required);
• Deep learning (DL) in data mining (DM) and knowledge discovery (transfer learning is highly recommended);
• Auto machine learning algorithms (AutoML) for DM;
• Bias in machine learning (ML) and resources;
• Adversarial challenges of ML for DM;
• Democratization of resources and tool development for DM;
• Explainable text mining models and semantics into ML;
• Language generation from DM;
• Corpora for ML;
• Multimodal sentiment analysis and opinion mining using DL;
• DL for education data learning (text data processing required).

The Special Issue is an opportunity to disseminate the scientific and technological development related to intelligent management of big data. Research accompanied by standardized resources and source codes will be positively received.

Dr. Yoan Gutiérrez Vázquez
Dr. José Ignacio Abreu Salas
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. Applied Sciences 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.

• data mining
• artificial intelligence
• deep learning
• transfer learning
• auto machine learning
• knowledge discovery
• knowledge learning
• natural language processing
• heterogeneous data processing
• explainability of the machine learning
• language generation
• corpora for machine learning
• language understanding
• bias in machine learning
• adversarial challenges in machine learning
• democratic resource development
• semantic in machine learning
• bias in machine learning and resources

• Trends in Artificial Intelligence and Data Mining: 2021 and Beyond in Applied Sciences (8 articles)