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Applied Sciences
Special Issues
Machine Learning and Data Analysis: Bridging Theory and Real-World Solutions
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Machine Learning and Data Analysis: Bridging Theory and Real-World Solutions

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

Deadline for manuscript submissions: 15 July 2025 | Viewed by 961

Special Issue Editors

Prof. Dr. Sanja Seljan

E-Mail Website
Guest Editor
Department of Information and Communication Sciences, Faculty of Humanities and Social Sciences, University of Zagreb, 10000 Zagreb, Croatia
Interests: data science; machine learning; natural language processing; language technologies; machine translation; business analytics; open data
Dr. Ivan Dunđer

E-Mail Website
Guest Editor
Department of Information and Communication Sciences, Faculty of Humanities and Social Sciences, University of Zagreb, 10000 Zagreb, Croatia
Interests: machine learning; data science; machine translation; natural language processing; language technologies; information systems; databases
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Marija Brkić Bakarić

E-Mail Website
Guest Editor
Department of Informatics, University of Rijeka, Radmile Matejčić 2, 51000 Rijeka, Croatia
Interests: artificial intelligence; machine learning; interpretable machine learning; educational data mining; natural language processing; machine translation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The MDPI journal Applied Sciences invites submissions to a Special Issue on “Machine Learning and Data Analysis: Bridging Theory and Real-World Solutions”.

The goal of this Special Issue is to investigate how machine learning and data analysis can be utilized for solving real-world problems in a variety of domains. It presents research that transforms theoretical breakthroughs into practical applications. A wide range of subjects are covered in this issue, including the creation of machine learning algorithms and innovative methods for analyzing data, as well as their use in various fields.

In this Special Issue, original and unpublished works with results in any way related to machine learning, data science, natural language processing, and linked areas are welcome. We welcome various types of experimental and methodological aspects on novel solutions in machine learning and data analysis, including the following:

  • Research, analysis, or implementation approaches and innovations in machine learning and data analysis methods;
  • User studies on the application of machine learning and data science in various fields;
  • Emerging technologies and evaluation of integrative solutions for data analysis and predictive analytics;
  • Corpora and other digital resources that are essential to data science;
  • Research on natural language processing and language and speech technologies;
  • Strategies, challenges, and opportunities in data science. Nevertheless, submissions with a strong theoretical contribution are also desirable.

 Topics of interest include, but are not limited to, the following:

  • Innovations in machine learning and data analysis;
  • Analysis of machine learning methods and approaches;
  • Emerging technologies and evaluation of deep learning;
  • Integrative approaches to machine learning and data analysis;
  • Advanced data analysis and predictive data analytics;
  • Open data analytics and innovations;
  • Image recognition and generation;
  • Digital corpora and other resources for data science;
  • Research on natural language processing;
  • Innovative language technologies;
  • Novel approaches to speech technologies;
  • Real-world data-driven solutions and new strategies;
  • Data-driven decision making;
  • Challenges and opportunities in data science;
  • Ethical considerations and legal issues in artificial intelligence;
  • Application of machine learning and data science in various fields.

Prof. Dr. Sanja Seljan
Dr. Ivan Dunđer
Prof. Dr. Marija Brkić Bakarić
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.

Keywords

  • machine learning
  • deep learning
  • data analysis
  • data science
  • data mining
  • natural language procssing
  • language technologies
  • speech technologies
  • open data
  • image recognition
  • predictive analytics
  • digital resources

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  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
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  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

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18 pages, 2863 KiB  
Article
On the Optimum Linear Soft Fusion of Classifiers
by Luis Vergara and Addisson Salazar
Appl. Sci. 2025, 15(9), 5038; https://doi.org/10.3390/app15095038 - 1 May 2025
Viewed by 109
Abstract
We present new analytical developments that contribute to a better understanding of the (soft) fusion of classifiers. To this end, we propose an optimal linear combiner based on a minimum mean-square-error class estimation approach. This solution allows us to define a post-fusion mean-square-error [...] Read more.
We present new analytical developments that contribute to a better understanding of the (soft) fusion of classifiers. To this end, we propose an optimal linear combiner based on a minimum mean-square-error class estimation approach. This solution allows us to define a post-fusion mean-square-error improvement factor relative to the best fused classifier. Key elements for this improvement factor are the number of classifiers, their pairwise correlations, the imbalance between their performances, and the bias. Furthermore, we consider exponential models for the class-conditional probability densities to establish the relationship between the classifier’s error probability and the mean square error of the class estimate. This allows us to predict the reduction in the post-fusion error probability relative to that of the best classifier. These theoretical findings are contrasted in a biosignal application for the detection of arousals during sleep from EEG signals. The results obtained are reasonably consistent with the theoretical conclusions. Full article
(This article belongs to the Special Issue Machine Learning and Data Analysis: Bridging Theory and Real-World Solutions)
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Review

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30 pages, 843 KiB  
Review
Optimizing Internet of Things Honeypots with Machine Learning: A Review
by Stefanie Lanz, Sarah Lily-Rose Pignol, Patrick Schmitt, Haochen Wang, Maria Papaioannou, Gaurav Choudhary and Nicola Dragoni
Appl. Sci. 2025, 15(10), 5251; https://doi.org/10.3390/app15105251 - 8 May 2025
Viewed by 306
Abstract
The increasing use of Internet of Things (IoT) devices has led to growing security concerns, necessitating advanced solutions to address emerging threats. Honeypots enhance IoT security by attracting and analyzing attackers. However, traditional honeypots struggle with adaptability and efficiency. This paper examines how [...] Read more.
The increasing use of Internet of Things (IoT) devices has led to growing security concerns, necessitating advanced solutions to address emerging threats. Honeypots enhance IoT security by attracting and analyzing attackers. However, traditional honeypots struggle with adaptability and efficiency. This paper examines how machine learning enhances honeypot capabilities by improving threat detection and response mechanisms. A systematic literature review using the snowballing method explores the application of supervised, unsupervised, and reinforcement learning. Various classifiers for machine learning are analyzed to optimize honeypot architectures. This paper focuses on two types of honeypots: dynamic honeypots, which evolve to mislead attackers, and adaptive honeypots, which respond to threats in real time. By evaluating low-interaction, high-interaction, and hybrid honeypots, we determine how different machine learning techniques enhance detection and resource efficiency. Key findings include improved detection rates, with machine learning techniques, particularly supervised learning models like random forest, significantly enhancing detection accuracy, achieving up to 0.96 accuracy. Adaptive honeypots utilizing machine learning demonstrate better resource management, reducing false positives and optimizing computational resources. Despite these improvements, high computational demands and limited real-world testing hinder widespread adoption in IoT environments. This paper provides an overview of current trends, identifies research gaps, and offers insights for developing more intelligent IoT honeypots. There is no doubt that machine learning can help create more resilient and adaptive security solutions for IoT networks. Full article
(This article belongs to the Special Issue Machine Learning and Data Analysis: Bridging Theory and Real-World Solutions)
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