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AI
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Machine Learning in Action: Practical Applications and Emerging Trends
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Machine Learning in Action: Practical Applications and Emerging Trends

A special issue of AI (ISSN 2673-2688).

Deadline for manuscript submissions: 31 May 2026 | Viewed by 3937

Special Issue Editors

Dr. Alberto José Alvarellos González

E-Mail Website
Guest Editor
Software Engineering Laboratory, Computer Science Faculty, University of A Coruña, 15071 A Coruña, Spain
Interests: application of machine learning to civil engineering; application of IoT to civil engineering; machine learning and the environment
Special Issues, Collections and Topics in MDPI journals
Dr. Daniel Carreres-Prieto

E-Mail Website
Guest Editor
Department of Aerospace Technology and Science, Centro Universitario de la Defensa, Universidad Politécnica de Cartagena, C/Coronel López Peña S/N, Base Aérea de San Javier, 30720 Murcia, Spain
Interests: embedded electronics; machine learning; image processing; security and defense; indirect monitoring

Special Issue Information

Dear Colleagues,

We are pleased to invite you to contribute to this Special Issue, ‘Machine Learning in Action: Practical Applications and Emerging Trends’, which focuses on the deployment of machine learning systems in real-world settings. While recent years have seen remarkable advances in machine learning (ML) theory, a critical gap remains between academic research and operational deployment.

This Special Issue aims to bridge that gap by showcasing applied ML work across diverse domains, including, but not limited to, healthcare, finance, energy, transportation, industrial processes, and environmental monitoring. We seek contributions that detail the development, deployment, and performance of ML-based solutions in practice. Of particular interest are papers that highlight domain-specific constraints, implementation challenges, data limitations, or integration issues encountered in the field.

Although the academic literature is rich in algorithmic and theoretical advances, relatively few publications have systematically documented how these methods perform when embedded in real environments. This Special Issue will contribute a much-needed supplement to the existing body of scholarship by focusing on the transition from research prototypes to functioning systems, emphasizing the lessons learned, contextual adaptation, and long-term maintenance. It will serve as a valuable reference both for practitioners and researchers aiming to understand what it takes to transport ML from the lab to real-world settings.

We look forward to receiving your submissions.

Dr. Alberto José Alvarellos González
Dr. Daniel Carreres-Prieto
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 250 words) can be sent to the Editorial Office for assessment.

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. AI 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 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

  • applied machine learning
  • real-world deployment
  • domain-specific machine learning
  • machine learning systems in production
  • practical challenges in machine learning
  • data-driven decision making
  • case studies in machine learning
  • evaluation of machine learning applications
  • translational machine learning
  • machine learning for domain-centered applications

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

Published Papers (2 papers)

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31 pages, 9622 KB  
Article
View-Aware Pose Analysis: A Robust Pipeline for Multi-Person Joint Injury Prediction from Single Camera
by Basant Adel, Ahmad Salah, Mahmoud A. Mahdi and Heba Mohsen
AI 2026, 7(1), 7; https://doi.org/10.3390/ai7010007 - 27 Dec 2025
Viewed by 1242
Abstract
This paper presents a novel, accessible pipeline for the prediction and prevention of motion-related joint injuries in multiple individuals. Current methodologies for biomechanical analysis often rely on complex, restrictive setups such as multi-camera systems, wearable sensors, or markers, limiting their applicability in everyday [...] Read more.
This paper presents a novel, accessible pipeline for the prediction and prevention of motion-related joint injuries in multiple individuals. Current methodologies for biomechanical analysis often rely on complex, restrictive setups such as multi-camera systems, wearable sensors, or markers, limiting their applicability in everyday environments. To overcome these limitations, we propose a comprehensive solution that utilizes only single-camera 2D images. Our pipeline comprises four distinct stages: (1) extraction of 2D human pose keypoints for multiple persons using a pretrained Human Pose Estimation model; (2) a novel ensemble learning model for person-view classification—distinguishing between front, back, and side perspectives—which is critical for accurate subsequent analysis; (3) a view-specific module that calculates body-segment angles, robustly handling movement pairs (e.g., flexion–extension) and mirrored joints; and (4) a pose assessment module that evaluates calculated angles against established biomechanical Range of Motion (ROM) standards to detect potentially injurious movements. Evaluated on a custom dataset of high-risk poses and diverse images, the end-to-end pipeline demonstrated an 87% success rate in identifying dangerous postures. The view classification stage, a key contribution of this work, achieved a 90% overall accuracy. The system delivers individualized, joint-specific feedback, offering a scalable and deployable solution for enhancing human health and safety in various settings, from home environments to workplaces, without the need for specialized equipment. Full article
(This article belongs to the Special Issue Machine Learning in Action: Practical Applications and Emerging Trends)
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22 pages, 3339 KB  
Article
An AutoML Algorithm: Multiple-Steps Ahead Forecasting of Correlated Multivariate Time Series with Anomalies Using Gated Recurrent Unit Networks
by Ying Su and Morgan C. Wang
AI 2025, 6(10), 267; https://doi.org/10.3390/ai6100267 - 14 Oct 2025
Cited by 2 | Viewed by 1824
Abstract
Multiple time series forecasting is critical in domains such as energy management, economic analysis, web traffic prediction and air pollution monitoring to support effective resource planning. Traditional statistical learning methods, including Vector Autoregression (VAR) and Vector Autoregressive Integrated Moving Average (VARIMA), struggle with [...] Read more.
Multiple time series forecasting is critical in domains such as energy management, economic analysis, web traffic prediction and air pollution monitoring to support effective resource planning. Traditional statistical learning methods, including Vector Autoregression (VAR) and Vector Autoregressive Integrated Moving Average (VARIMA), struggle with nonstationarity, temporal dependencies, inter-series correlations, and data anomalies such as trend shifts, seasonal variations, and missing data. Furthermore, their effectiveness in multi-step ahead forecasting is often limited. This article presents an Automated Machine Learning (AutoML) framework that provides an end-to-end solution for researchers who lack in-depth knowledge of time series forecasting or advanced programming skills. This framework utilizes Gated Recurrent Unit (GRU) networks, a variant of Recurrent Neural Networks (RNNs), to tackle multiple correlated time series forecasting problems, even in the presence of anomalies. To reduce complexity and facilitate the AutoML process, many model parameters are pre-specified, thereby requiring minimal tuning. This design enables efficient and accurate multi-step forecasting while addressing issues including missing values and structural shifts. We also examine the advantages and limitations of GRU-based RNNs within the AutoML system for multivariate time series forecasting. Model performance is evaluated using multiple accuracy metrics across various forecast horizons. The empirical results confirm our proposed approach’s ability to capture inter-series dependencies and handle anomalies in long-range forecasts. Full article
(This article belongs to the Special Issue Machine Learning in Action: Practical Applications and Emerging Trends)
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