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Artificial Intelligence for Engineering Applications

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A special issue of Eng (ISSN 2673-4117).

Deadline for manuscript submissions: 31 March 2025 | Viewed by 8440

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Special Issue Editors

Prof. Dr. Juvenal Rodriguez-Resendiz

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Facultad de Ingeniería, Universidad Autónoma de Querétaro, Santiago de Querétaro 76010, Mexico
Interests: robotics; biomedical applications; instrumentation and measurement; signal processing
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Prof. Dr. Marco Antonio Aceves-Fernandez

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Facultad de Ingeniería, Universidad Autónoma de Querétaro, Cerro de las Campanas, Las Campanas, Queretaro 76010, Mexico
Interests: machine learning; neural networks and artificial intelligence; air pollution; particulate matter
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Dr. Akos Odry

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Department of Mechatronics and Automation, Faculty of Engineering, University of Szeged, 6275 Szeged, Hungary
Interests: intelligent control; sensor fusion; robotics; kalman filtering; industrial robotics; soft computing; localization; SLAM
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Dr. José Manuel Álvarez-Alvarado

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Facultad de Ingeniería, Universidad Autónoma de Queretaro, Queretaro 76010, Mexico
Interests: solar energy; power generation; waste heat recovery; control techniques; renewable energy technologies; solar radiation; energy engineering
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Special Issue Information

Dear Colleagues,

We are pleased to invite you to contribute to our Special Issue focused on the advances in artificial intelligence applied to comprehensive engineering solutions. These techniques range from machine learning models that enable accurate prediction and decision making to image processing that improves visual analysis and pattern detection.

In an increasingly technologically advanced world, integrating artificial intelligence into engineering has offered prominent results. This has motivated efforts to create comprehensive solutions by optimizing processes and improving the design and functionality of electronics to enhance different systems. From the health applications of engineering, such as biomedical technology, to the efficiency of energy systems, AI has been fundamental to revolutionizing these areas. This is why our SI aims to compile AI advances applied to innovative, technological, and scientific solutions in the engineering field.

The main areas of engineering that our Special Issue focuses on are as follows:

Automation;
Electronics;
Electric power;
Sustainability;
Biomedical;
Mechatronic;
Computer systems;
Multidisciplinary engineering.

Prof. Dr. Juvenal Rodriguez-Resendiz
Prof. Dr. Marco Antonio Aceves-Fernandez
Dr. Akos Odry
Dr. José Manuel Álvarez-Alvarado
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. Eng 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 1200 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

image processing
AI in embedded systems
optimization algorithms
autonomous robotics
system control
computational optimization
neural networks for engineering
IoT

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Published Papers (9 papers)

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Research

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24 pages, 3189 KiB

Open AccessArticle

Digital Twins and AI Decision Models: Advancing Cost Modelling in Off-Site Construction

by Joas Serugga

Eng 2025, 6(2), 22; https://doi.org/10.3390/eng6020022 - 22 Jan 2025

Viewed by 513

Abstract

The rising demand for housing continues to outpace traditional construction processes, highlighting the need for innovative, efficient, and sustainable delivery models. Off-site construction (OSC) has emerged as a promising alternative, offering faster project timelines and enhanced cost management. However, current research on cost models for OSC, particularly in automating material take-offs and optimising cost performance, remains limited. This study addresses this gap by proposing a new cost model integrating Digital Twin (DT) technology and AI-driven decision models for modular housing in the UK. The research explores the role of DTs in enhancing cost estimation and decision-making processes. By leveraging DTs and AI, the proposed model evaluates the impact of emergent technologies on cost performance, material efficiency, and sustainability across social, environmental, and economic dimensions. As proposed, this integrated approach enables a cost model tailored for OSC systems, providing a data-driven foundation for cost optimisation and material take-offs. The study’s findings highlight the potential of combining DTs and AI decision models to enhance cost modelling in modular construction, offering new capabilities to support sustainable and performance-driven housing delivery. The paper introduces a dynamic, data-driven cost model integrating real-time data acquisition through DTs and AI-powered predictive analytics. This dynamic approach enhances cost accuracy, reduces lifecycle cost variability, and supports adaptive decision-making throughout the OSC project lifecycle. Full article

(This article belongs to the Special Issue Artificial Intelligence for Engineering Applications)

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25 pages, 7148 KiB

Open AccessArticle

Tree-Based Algorithms and Incremental Feature Optimization for Fault Detection and Diagnosis in Photovoltaic Systems

by Khaled Chahine

Eng 2025, 6(1), 20; https://doi.org/10.3390/eng6010020 - 20 Jan 2025

Viewed by 540

Abstract

Despite their significant environmental benefits, solar photovoltaic (PV) systems are susceptible to malfunctions and performance degradation. This paper addresses detecting and diagnosing faults from a dataset representing a 250 kW PV power plant with three types of faults. A comprehensive dataset analysis is conducted to improve the dataset quality and uncover intricate relationships between features and the target variable. By introducing novel feature importance averaging techniques, a two-phase fault detection and diagnosis framework employing tree-based models is proposed to identify faults from normal cases and diagnose the fault type. An ensemble of six tree-based classifiers, including decision trees, random forest, Stochastic Gradient Boosting, LightGBM, CatBoost, and Extra Trees, is trained in both phases. The results show 100% accuracy in the first phase, particularly with the Extra Trees classifier. In the second phase, Extra Trees, XGBoost, LightGBM, and CatBoost achieve similar accuracy, with Extra Trees demonstrating superior training and convergence speed. This study then incorporates Explainable Artificial Intelligence (XAI), utilizing LIME and SHAP analyzers to validate the research findings. The results highlight the superiority of the proposed approach over others, solidifying its position as an innovative and effective solution for fault detection and diagnosis in PV systems. Full article

(This article belongs to the Special Issue Artificial Intelligence for Engineering Applications)

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16 pages, 5868 KiB

Open AccessArticle

A Deep Learning-Based Approach for Precise Emotion Recognition in Domestic Animals Using EfficientNetB5 Architecture

by Rashadul Islam Sumon, Haider Ali, Salma Akter, Shah Muhammad Imtiyaj Uddin, Md Ariful Islam Mozumder and Hee-Cheol Kim

Eng 2025, 6(1), 9; https://doi.org/10.3390/eng6010009 - 3 Jan 2025

Viewed by 805

Abstract

The perception of animal emotions is key to enhancing veterinary practice, human–animal interactions, and protecting domesticated species’ welfare. This study presents a unique emotion classification deep learning-based approach for pet animals. The actual and emotional status of dogs and cats have been classified using a modified EfficientNetB5 model. Utilizing a dataset of images classified into four different emotion categories—angry, sad, happy, and neutral—the model incorporates sophisticated feature extraction methods, such as Dense Residual Blocks and Squeeze-and-Excitation (SE) blocks, to improve the focus on important emotional indicators. The basis of the second strategy is EfficientNetB5, which is known for providing an optimal balance in terms of accuracy and processing capabilities. The model exhibited robust generalization abilities for the subtle identification of emotional states, achieving 98.2% accuracy in training and 91.24% during validation on a separate dataset. These encouraging outcomes support the model’s promise for real-time emotion detection applications and demonstrate its adaptability for wider application in ongoing pet monitoring systems. The dataset will be enlarged, model performance will be enhanced for more species, and real-time capabilities will be developed for real-world implementation. Full article

(This article belongs to the Special Issue Artificial Intelligence for Engineering Applications)

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19 pages, 5498 KiB

Open AccessArticle

Hybrid ML Algorithm for Fault Classification in Transmission Lines Using Multi-Target Ensemble Classifier with Limited Data

by Abdallah El Ghaly

Eng 2025, 6(1), 4; https://doi.org/10.3390/eng6010004 - 1 Jan 2025

Viewed by 516

Abstract

Fault detection and classification in transmission lines are critical for maintaining the reliability and stability of electrical power systems. Quick and accurate fault detection allows for timely intervention, minimizing equipment damage, and reducing downtime. This study addresses the challenge of effective fault classification, particularly when dealing with smaller, more practical datasets. Initially, the study examined the performance of conventional machine learning algorithms on a comprehensive dataset of 7681 samples, demonstrating high accuracy owing to the inherent symmetry of sinusoidal voltage and current signals. However, the true efficacy of these algorithms was evaluated by minimizing the dataset to 231 training samples, with the remainder being used for testing. A novel Multi-Target Ensemble Classifier was developed to improve classification accuracy. The proposed algorithm achieved an impressive overall accuracy of 0.829165, outperforming traditional methods, including the K-Nearest Neighbors Classifier, support vector classification, random forest classifier, decision tree classifier, AdaBoost classifier, gradient boosting classifier, and Gaussian NB. This research highlights the importance of efficient fault classification techniques in power systems and proposes a superior solution in the form of a multitarget ensemble classifier. Full article

(This article belongs to the Special Issue Artificial Intelligence for Engineering Applications)

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20 pages, 2708 KiB

Open AccessArticle

Benchmarking a Novel Particle Swarm Optimization Dynamic Model Versus HOMER in Optimally Sizing Grid-Integrated Hybrid PV–Hydrogen Energy Systems

by Ayatte I. Atteya and Dallia Ali

Eng 2024, 5(4), 3239-3258; https://doi.org/10.3390/eng5040170 - 9 Dec 2024

Viewed by 639

Abstract

This paper presents the development of an Artificial Intelligence (AI)-based integrated dynamic hybrid PV-H₂ energy system model together with a reflective comparative analysis of its performance versus that of the commercially available HOMER software. In this paper, a novel Particle Swarm Optimization (PSO) dynamic system model is developed by integrating a PSO algorithm with a precise dynamic hybrid PV-H₂ energy system model that is developed to accurately simulate the hybrid system by considering the dynamic behaviour of its individual system components. The developed novel model allows consideration of the dynamic behaviour of the hybrid PV-H₂ energy system while optimizing its sizing within grid-connected buildings to minimize the levelized cost of energy and maintain energy management across the hybrid system components and the grid in feeding the building load demands. The developed model was applied on a case-study grid-connected building to allow benchmarking of its results versus those from HOMER. Benchmarking showed that the developed model’s optimal sizing results as well as the corresponding levelized cost of energy closely match those from HOMER. In terms of energy management, the benchmarking results showed that the strategy implemented within the developed model allows maximization of the green energy supply to the building, thus aligning with the net-zero energy transition target, while the one implemented in HOMER is based on minimizing the levelized cost of energy regardless of the green energy supply to the building. Another privilege revealed by benchmarking is that the developed model allows a more realistic quantification of the hydrogen output from the electrolyser because it considers the dynamic behaviour of the electrolyser in response to the varying PV input, and also allows a more realistic quantification of the electricity output from the fuel cell because it considers the dynamic behaviour of the fuel cell in response to the varying hydrogen levels stored in the tank. Full article

(This article belongs to the Special Issue Artificial Intelligence for Engineering Applications)

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23 pages, 21043 KiB

Open AccessArticle

Advanced Cotton Boll Segmentation, Detection, and Counting Using Multi-Level Thresholding Optimized with an Anchor-Free Compact Central Attention Network Model

by Arathi Bairi and Uma N. Dulhare

Eng 2024, 5(4), 2839-2861; https://doi.org/10.3390/eng5040148 - 1 Nov 2024

Viewed by 657

Abstract

Nowadays, cotton boll detection techniques are becoming essential for weaving and textile industries based on the production of cotton. There are limited techniques developed to segment, detect, and count cotton bolls precisely. This analysis identified several limitations and issues with these techniques, including their complex structure, low performance, time complexity, poor quality data, and so on. A proposed technique was developed to overcome these issues and enhance the performance of the detection and counting of cotton bolls. Initially, data were gathered from the dataset, and a pre-processing stage was performed to enhance image quality. An adaptive Gaussian–Wiener filter (AGWF) was utilized to remove noise from the acquired images. Then, an improved Harris Hawks arithmetic optimization algorithm (IH²AOA) was used for segmentation. Finally, an anchor-free compact central attention cotton boll detection network (A-frC²AcbdN) was utilized for cotton boll detection and counting. The proposed technique utilized an annotated dataset extracted from weakly supervised cotton boll detection and counting, aiming to enhance the accuracy and efficiency in identifying and quantifying cotton bolls in the agricultural domain. The accuracy of the proposed technique was 94%, which is higher than that of other related techniques. Similarly, the precision, recall, F1-score, and specificity of the proposed technique were 93.8%, 92.99%, 93.48%, and 92.99%, respectively. Full article

(This article belongs to the Special Issue Artificial Intelligence for Engineering Applications)

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16 pages, 577 KiB

Open AccessArticle

Improved Quantum Particle Swarm Optimization of Optimal Diet for Diabetic Patients

by Abdellah Ahourag, Zakaria Bouhanch, Karim El Moutaouakil and Abdellah Touhafi

Eng 2024, 5(4), 2544-2559; https://doi.org/10.3390/eng5040133 - 10 Oct 2024

Viewed by 1124

Abstract

The dietary recommendations for individuals with diabetes focus on maintaining a balanced nutritional intake to manage blood sugar levels. This study suggests a nutritional strategy to improve glycemic control based on an analysis of a dietary optimization problem. The goal is to minimize the overall glycemic loads (GLs) of specific foods. Two variations of the particle swarm optimization (PSO) method, as well as random quantum process optimization (GQPSO), are introduced. The findings demonstrate that the quantum and random methods are more effective than the traditional techniques in reducing the glycemic loads of diets and addressing nutritional deficiencies while also aligning nutrient intake with the recommended levels. The resolution of this diet optimization model, executed multiple times with adjustments to the parameters of both methods, enables dynamic exploration and provides a wide range of diverse and effective food choices. Full article

(This article belongs to the Special Issue Artificial Intelligence for Engineering Applications)

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29 pages, 4864 KiB

Open AccessArticle

Comparative Analysis of Deep Learning Models for Optimal EEG-Based Real-Time Servo Motor Control

by Dimitris Angelakis, Errikos C. Ventouras, Spiros Kostopoulos and Pantelis Asvestas

Eng 2024, 5(3), 1708-1736; https://doi.org/10.3390/eng5030090 - 2 Aug 2024

Cited by 1 | Viewed by 1328

Abstract

This study harnesses EEG signals to enable the real-time control of servo motors, utilizing the OpenBCI Community Dataset to identify and assess brainwave patterns related to motor imagery tasks. Specifically, the dataset includes EEG data from 52 subjects, capturing electrical brain activity while participants imagined executing specific motor tasks. Each participant underwent multiple trials for each motor imagery task, ensuring a diverse and comprehensive dataset for model training and evaluation. A deep neural network model comprising convolutional and bidirectional long short-term memory (LSTM) layers was developed and trained using k-fold cross-validation, achieving a notable accuracy of 98%. The model’s performance was further compared against recurrent neural networks (RNNs), multilayer perceptrons (MLPs), and Τransformer algorithms, demonstrating that the CNN-LSTM model provided the best performance due to its effective capture of both spatial and temporal features. The model was deployed on a Python script interfacing with an Arduino board, enabling communication with two servo motors. The Python script predicts actions from preprocessed EEG data to control the servo motors in real-time. Real-time performance metrics, including classification reports and confusion matrices, demonstrate the seamless integration of the LSTM model with the Arduino board for precise and responsive control. An Arduino program was implemented to receive commands from the Python script via serial communication and control the servo motors, enabling accurate and responsive control based on EEG predictions. Overall, this study presents a comprehensive approach that combines machine learning, real-time implementation, and hardware interfacing to enable the precise and real-time control of servo motors using EEG signals, with potential applications in the human–robot interaction and assistive technology domains. Full article

(This article belongs to the Special Issue Artificial Intelligence for Engineering Applications)

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Review

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32 pages, 5286 KiB

Open AccessReview

A Review of Passenger Counting in Public Transport Concepts with Solution Proposal Based on Image Processing and Machine Learning

by Aleksander Radovan, Leo Mršić, Goran Đambić and Branko Mihaljević

Eng 2024, 5(4), 3284-3315; https://doi.org/10.3390/eng5040172 - 10 Dec 2024

Viewed by 1477

Abstract

The accurate counting of passengers in public transport systems is crucial for optimizing operations, improving service quality, and planning infrastructure. It can also contribute to reducing the number of public transport lines where a high number of vehicles is not needed in certain periods during the year, but also by increasing the number of lines where the need is increased. This paper provides a comprehensive review of current methodologies and technologies used for passenger counting, without the actual implementation of the automatic passenger counting system (APC), but with a proposal based on image processing and machine learning techniques and concepts, since it represents one of the most used approaches. The research explores various technologies and algorithms, like card swiping, infrared, weight and ultrasonic sensors, RFID, Wi-Fi, Bluetooth, LiDAR, thermos cameras, including CCTV cameras and traditional computer vision methods, and advanced deep learning approaches, highlighting their strengths and limitations. By analyzing recent advancements and case studies, this review aims to offer insights into the effectiveness, scalability, and practicality of different passenger counting solutions and offers a solution proposal. The research also analyzed the current General Data Protection Regulation (GDPR) that applies to the European Union and how it affects the use of systems like this. Future research directions and potential areas for technological innovation are also discussed to guide further developments in this field. Full article

(This article belongs to the Special Issue Artificial Intelligence for Engineering Applications)

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