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New Advances in Applied Machine Learning
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New Advances in Applied Machine Learning

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

Deadline for manuscript submissions: 20 September 2025 | Viewed by 5008

Special Issue Editor

Dr. Mónica Vieira Martins

E-Mail Website
Guest Editor
Polytechnic Institute of Portalegre, 7300-110 Portalegre, Portugal
Interests: machine learning; deep learning; medical imaging; computational vision

Special Issue Information

Dear Colleagues,

The rapid pace of advancements in machine learning technologies has profoundly impacted research in sciences and technologies, and is continually driving innovation and revolutionizing how complex problems are tackled within various domains.

We are pleased to announce a Special Issue of Applied Sciences dedicated to showcasing the latest advancements in the field of applied machine learning (ML) across various scientific disciplines.

The scope of this Special Issue encompasses a broad range of topics related to applied machine learning, including but not limited to:

  • Novel algorithms and techniques;
  • Deep learning applications;
  • Natural language processing;
  • Automated machine learning;
  • Tiny machine learning;
  • Multi-modal machine learning;
  • Self-supervised machine learning;
  • Few-shot machine learning;
  • Human–AI collaboration

The specific domains of application include, but are not limited to, healthcare, industry, agriculture, material science, energy, climate science, smart infrastructures, remote sensing, robotics, automation and education.

In this Special Issue, researchers are encouraged to submit original research articles, reviews, and case studies that contribute to the advancement of applied machine learning.

We look forward to receiving your contributions.

Dr. Mónica Vieira Martins
Guest Editor

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
  • natural language processing
  • automated machine learning (AutoML)
  • tiny machine learning (TinyML)
  • multi-modal learning
  • few-shot learning
  • human-AI collaboration

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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.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

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Research

23 pages, 1336 KiB  
Article
A Multi-Agent Deep Reinforcement Learning System for Governmental Interoperability
by Azanu Mirolgn Mequanenit, Eyerusalem Alebachew Nibret, Pilar Herrero-Martín, María S. García-González and Rodrigo Martínez-Béjar
Appl. Sci. 2025, 15(6), 3146; https://doi.org/10.3390/app15063146 - 13 Mar 2025
Viewed by 1072
Abstract
This study explores the integration of the JADE (Java Agent Development Framework) platform with deep reinforcement learning (DRL) to enhance governmental interoperability and optimize administrative workflows in municipal settings. The proposed approach combines the JADE’s robust multi-agent system (MAS) capabilities with the adaptive [...] Read more.
This study explores the integration of the JADE (Java Agent Development Framework) platform with deep reinforcement learning (DRL) to enhance governmental interoperability and optimize administrative workflows in municipal settings. The proposed approach combines the JADE’s robust multi-agent system (MAS) capabilities with the adaptive decision-making power of DRL to address prevalent challenges faced by government agencies, such as fragmented operations, incompatible data formats, and rigid communication protocols. By enabling seamless communication between agents across departments such as the Treasury, the Event Management department, and the Public Safety department, the hybrid system fosters real-time collaboration and supports efficient, data-driven decision making. Agents leverage historical and real-time data to adapt to environmental changes and make optimized decisions that align with overarching governmental objectives, such as resource allocation and emergency response. The result is a system capable of managing intricate administrative duties using structured agent communication and the integration of DRL-driven learning models, improving governmental interoperability. Key performance indicators highlight the system’s effectiveness, achieving a task completion rate of 95%, decision accuracy of 96%, and a communication latency of just 120 ms. Additionally, the framework’s flexibility ensures seamless scalability, accommodating complex and large-scale tasks across multiple governmental units. This research presents a scalable, automated, and resilient framework for optimizing governmental processes, offering a pathway to more efficient, transparent, and adaptive public sector operations. Full article
(This article belongs to the Special Issue New Advances in Applied Machine Learning)
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22 pages, 2490 KiB  
Article
Machine Learning and Zombie Firms Classification
by Koutaroh Minami and Yukihiro Yasuda
Appl. Sci. 2024, 14(23), 11216; https://doi.org/10.3390/app142311216 - 2 Dec 2024
Viewed by 923
Abstract
We investigate whether the machine learning technique helps to identify zombie firms. We also analyze the differences in zombie indicators proposed by previous research.revious studies successfully classified firms as zombies by focusing on whether they receive subsidized credits. However, when the policy interest [...] Read more.
We investigate whether the machine learning technique helps to identify zombie firms. We also analyze the differences in zombie indicators proposed by previous research.revious studies successfully classified firms as zombies by focusing on whether they receive subsidized credits. However, when the policy interest rate is low, it becomes more challenging to identify zombies, because low-interest payments by firms can be caused by lenders’ support to zombies and by low policy interest rates. According to our machine learning approach, we show that we can predict zombie firms from financial information that is publicly available even when the policy interest rate is low. We also find that the financial accounts important for predicting zombie firms differ for every zombie indicator, suggesting that these indicators reflect different aspects of firms’ status. Full article
(This article belongs to the Special Issue New Advances in Applied Machine Learning)
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13 pages, 700 KiB  
Article
Novel PCA-Based Lower-Dimensional Remapping of the Solution Space for a Genetic Algorithm Optimization: Estimating the Director Distribution in LC-Based SLM Devices
by Jaume Colomina-Martínez, Joan Josep Sirvent-Verdú, Andrés P. Bernabeu, Tomás Lloret, Belén Nieto-Rodríguez, Cristian Neipp, Augusto Beléndez and Jorge Francés
Appl. Sci. 2024, 14(21), 9950; https://doi.org/10.3390/app14219950 - 31 Oct 2024
Viewed by 1057
Abstract
This work introduces a novel computational approach based on Principal Component Analysis (PCA) for dimensionality reduction of the solution space in optimisation problems with known linear interdependencies among solution variables. By creating synthetic datasets with deliberately engineered properties and applying PCA, the solution [...] Read more.
This work introduces a novel computational approach based on Principal Component Analysis (PCA) for dimensionality reduction of the solution space in optimisation problems with known linear interdependencies among solution variables. By creating synthetic datasets with deliberately engineered properties and applying PCA, the solution space’s remapping significantly reduces its dimensionality, leading to faster computation and more robust convergence in optimisation processes. We demonstrate this method by integrating it with a Genetic Algorithm (GA) for solving the optimal director distribution in liquid crystal (LC) devices, specifically addressing 2D and complex 3D spatial light modulator (SLM) structures such as twisted nematic liquid crystals (TN-LC) and parallel-aligned liquid crystal on silicon (PA-LCoS), respectively. The phase profiles obtained from the director vector distributions for horizontal and vertical high-frequency binary phase gratings closely match the theoretical values derived from minimising the traditional elastic Frank–Oseen functional via Euler–Lagrange equations. Beyond this specific application, our method offers a general framework for reducing computational complexity in optimisation problems by directly reducing the dimensionality of the solution space. This approach is applicable across various optimisation scenarios with well-known linear interdependencies among solution variables, enabling significant reductions in computational costs and improvements in robustness and convergence. Full article
(This article belongs to the Special Issue New Advances in Applied Machine Learning)
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19 pages, 2381 KiB  
Article
Application of Supervised Learning Methods and Information Gain Methods in the Determination of Asbestos–Cement Roofs’ Deterioration State
by Manuel Saba, David Valdelamar Martínez, Leydy K. Torres Gil, Gabriel E. Chanchí Golondrino and Manuel A. Ospina Alarcón
Appl. Sci. 2024, 14(18), 8441; https://doi.org/10.3390/app14188441 - 19 Sep 2024
Cited by 3 | Viewed by 1085
Abstract
This study introduces an innovative approach to evaluate the condition of asbestos–cement (AC) roofs by integrating field data with five distinct supervised learning models: decision trees, KNN, logistic regression, support vector machine, and random forest. A novel methodology for assessing the importance of [...] Read more.
This study introduces an innovative approach to evaluate the condition of asbestos–cement (AC) roofs by integrating field data with five distinct supervised learning models: decision trees, KNN, logistic regression, support vector machine, and random forest. A novel methodology for assessing the importance of 380 reflectance bands was employed, offering fresh insights into the key indicators of AC roof deterioration. The research systematically organized and prioritized reflectance bands based on their information gain, optimizing both the selection of relevant bands and the performance of the models in differentiating between low and high intervention priority (LIP and HIP) roofs. The decision tree model, when applied to the top 10 most relevant bands, achieved the highest cross-validation accuracy of 76.047%, making it the most effective tool for identifying AC roof conditions. Additionally, the random forest model demonstrated strong performance across various band groups, further validating its utility. Utilizing the open-source software Weka (version 3.8.6), this study adeptly executed relevance evaluation and model implementation, providing a practical and scalable solution for material characterization, especially in regions where resources for spectral and hyperspectral image analysis are limited. The findings of this study offer valuable tools for government and environmental authorities, particularly in developing countries, where efficient and cost-effective AC roof assessment is crucial for public health and safety. The methodology is adaptable to different urban environments and climatic conditions, supporting global efforts in asbestos management, especially in countries where asbestos regulations are newly implemented. Organized within the CRISP-DM framework, this paper details the methodological phases, presents compelling results on reflectance band relevance, evaluates machine learning models, and concludes with prospects for future research aimed at enhancing asbestos detection and removal strategies. Full article
(This article belongs to the Special Issue New Advances in Applied Machine Learning)
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