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Information
Special Issues
Application of Machine Learning in Data Science and Computational Intelligence
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Application of Machine Learning in Data Science and Computational Intelligence

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 31 May 2025 | Viewed by 532

Special Issue Editors

Dr. Elias Dritsas

E-Mail Website
Guest Editor
Industrial Systems Institute (ISI), Athena Research and Innovation Center, 26504 Patras, Greece
Interests: artificial intelligence; big data; data analysis; databases; data mining; data structures; machine learning; privacy; security; trust
Special Issues, Collections and Topics in MDPI journals
Dr. Maria Trigka

E-Mail Website
Guest Editor
Industrial Systems Institute (ISI), Athena Research and Innovation Center, 26504 Patras, Greece
Interests: 5G; 6G; artificial intelligence; deep learning; image processing; IoT; machine learning; MIMO; mmWave; signal processing; wireless communications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Data science is a field of study that focuses on the extraction of valuable information from noisy data and incorporates various disciplines, such as data engineering, data preprocessing, visualization, predictive analytics, data mining, machine learning and statistics. In recent years, there has been rapidly growing interest in the mathematical and theoretical aspects of data science. This manifests in deterministic and non-deterministic models (i.e., probabilistic and a family of probabilistic known as statistical) that provide guaranteed performance, robustness, and reusable and interpretable results. The digital transformation of information systems has made feasible the effective use of data science techniques such as artificial intelligence (AI) and machine learning (ML) for various applications. In addition, the application of sensor technology and AI/ML will inevitably lead to a more objective and enhanced performance, lower cost and more effective system management overall. The aim of this Special Issue is to present high-quality innovative ideas and research solutions (for both theoretical and practical challenges) that facilitate data analysis and modelling with the aid of artificial intelligence and machine learning in various domains and applications.

Dr. Elias Dritsas
Dr. Maria Trigka
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. Information is an international peer-reviewed open access monthly 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 1600 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

  • data science
  • data mining
  • artificial intelligence
  • machine learning
  • statistics
  • predictive modelling
  • monitoring
  • data analytics

Published Papers (1 paper)

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Research

24 pages, 667 KiB  
Article
Utilizing Multi-Class Classification Methods for Automated Sleep Disorder Prediction
by Elias Dritsas and Maria Trigka
Information 2024, 15(8), 426; https://doi.org/10.3390/info15080426 - 23 Jul 2024
Viewed by 280
Abstract
Even from infancy, a human’s day-life alternates from a period of wakefulness to a period of sleep at night, during the 24-hour cycle. Sleep is a normal process necessary for human physical and mental health. A lack of sleep makes it difficult to [...] Read more.
Even from infancy, a human’s day-life alternates from a period of wakefulness to a period of sleep at night, during the 24-hour cycle. Sleep is a normal process necessary for human physical and mental health. A lack of sleep makes it difficult to control emotions and behaviour, reduces productivity at work, and can even increase stress or depression. In addition, poor sleep affects health; when sleep is insufficient, the chances of developing serious diseases greatly increase. Researchers in sleep medicine have identified an extensive list of sleep disorders, and thus leveraged Artificial Intelligence (AI) to automate their analysis and gain a deeper understanding of sleep patterns and related disorders. In this research, we seek a Machine Learning (ML) solution that will allow for efficient classification of unlabeled instances as being Sleep Apnea, Insomnia or Normal (subjects without a specific sleep disorder) by assessing the performance of two well-established strategies for multi-class classification tasks: the One-Vs-All (OVA) and One-Vs-One (OVO). In the context of the specific strategies, two well-known binary classification models were assumed, Logistic Regression (LR) and Support Vector Machines (SVMs). Both strategies’ validity was verified upon a dataset of diverse information related to the profiles (anthropometric data, sleep metrics, lifestyle and cardiovascular health factors) of potential patients or individuals not exhibiting any specific sleep disorder. Performance evaluation was carried out by comparing the weighted average results in all involved classes that represent these two specific sleep disorders and no-disorder occurrence; accuracy, kappa score, precision, recall, f-measure, and Area Under the ROC curve (AUC) were recorded and compared to identify an effective and robust model and strategy, both class-wise and on average. The experimental evaluation unveiled that after feature selection, 2-degree polynomial SVM under both strategies was the least complex and most efficient, recording an accuracy of 91.44%, a kappa score of 84.97%, precision, recall and f-measure equal to 0.914, and an AUC of 0.927. Full article
(This article belongs to the Special Issue Application of Machine Learning in Data Science and Computational Intelligence)
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