Special Issue "Advances in Artificial Intelligence: Machine Learning, Data Mining and Data Sciences"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 31 March 2021.

Special Issue Editors

Prof. Sławomir Nowaczyk
Website
Guest Editor
Center for Applied Intelligent Systems Research, Halmstad University, Sweden
Interests: machine learning; autonomous knowledge creation; representation learning; aware intelligent systems; predictive maintenance
Dr. Mohamed-Rafik Bouguelia‬
Website
Guest Editor
Center for Applied Intelligent Systems Research, Halmstad University, Sweden
Interests: machine learning; anomaly and novelty detection; interactive learning; data stream mining; big data
Dr. Hadi Fanaee
Website
Guest Editor
Center for Applied Intelligent Systems Research, Halmstad University, Sweden
Interests: data mining; machine learning; tensor analysis; anomaly detection; time series analysis; spatiotemporal data mining

Special Issue Information

Dear Colleagues,

Machine learning (ML), data mining (DM), and data sciences in general are among the most exciting and rapidly growing research fields today. In recent years, ML and DM have been successfully used to solve practical problems in various domains, including engineering, healthcare, medicine, manufacturing, transportation, and finance.

In this era of big data, considerable research is being focused on designing efficient ML and DM methods. Nonetheless, practical applications of ML face several challenges, such as dealing with either too small or big data, missing and uncertain data, highly multidimensional data, and the need for interpretable ML models that can provide trustable evidence and explanations of the predictions they make. Moreover, in a time where the complexity of systems is continuously growing, it becomes not always feasible to collect clean and exhaustive datasets and produce high-quality labels. In addition, most systems generate data that are subject to change over time due to external conditions resulting in non-stationary data distributions. Therefore, there is a need to do more “knowledge creation”: to develop ML and DM methods that sift through large amounts of streaming data and extract useful high-level knowledge from there, without human supervision or with very little of it. In addition, learning and obtaining good generalization from fewer training examples, efficient data/knowledge representation schemes, knowledge transfer between tasks and domains, and learning to adapt to varying contexts are also examples of important research problems.

To address such problems, this Special Issue invites researchers to contribute new methods and to demonstrate the applicability of existing methods in various fields.

Topics of interest for this Special Issue include but are not limited to the following:

  • Novel methods and algorithms in machine learning, data mining, data science, including data cleaning, clustering, classification, feature selection and extraction, neural networks and deep learning, representation learning, knowledge discovery, anomaly detection, fault detection, transfer learning, and active learning;
  • Solutions improving the state-of-the-art regarding important challenges such as big data, streaming data, time series, interactive learning, concept drift and nonstationary data, change detection, and dimensionality reduction;
  • Applications in various domains, for example, activity and event recognition, computational biology and bioinformatics, computational social science, game playing, healthcare, information retrieval, natural language processing, predictive maintenance, recommender systems, signal processing, web applications, and internet data;
  • Societal challenges associated with AI, such as fairness, accountability, and transparency or privacy, anonymity, and security.

Prof. Sławomir Nowaczyk
Dr. Mohamed-Rafik Bouguelia‬
Dr. Hadi Fanaee
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 papers will be 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 1800 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.

Keywords

Active Learning;

Anomaly Detection;

Big Data;

Classification;         

Clustering; 

Causal Inference;

Concept Drift;

Data Mining;

Data Science;

Deep Learning;

Fairness, Accountability, and Transparency of AI;

Knowledge Discovery;

Machine Learning;

Medical Decision Support Systems;

Multitask Learning;  

Neural Networks;

Predictive Models;      

Representation Learning;     

Semi-Supervised Learning; 

Supervised Learning;

Transfer Learning;    

Unsupervised Learning;       

Predictive Maintenance;

Privacy, Anonymity, and Security of AI…

Published Papers

This special issue is now open for submission.
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