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Eng
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Artificial Intelligence for Engineering Applications
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Artificial Intelligence for Engineering Applications

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

Deadline for manuscript submissions: 31 March 2025 | Viewed by 2410

Special Issue Editors

Prof. Dr. Juvenal Rodriguez-Resendiz

E-Mail Website
Guest Editor
Facultad de Ingeniería, Universidad Autónoma de Querétaro, Querétaro 76010, Mexico
Interests: robotics; biomedical applications; instrumentation and measurement; signal processing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Marco Antonio Aceves-Fernandez

E-Mail Website
Guest Editor
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
Special Issues, Collections and Topics in MDPI journals
Dr. Akos Odry

E-Mail Website
Guest Editor
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
Special Issues, Collections and Topics in MDPI journals
Dr. José Manuel Álvarez-Alvarado

E-Mail Website
Guest Editor
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
Special Issues, Collections and Topics in MDPI journals

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 quarterly 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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  • 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.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

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

Published Papers (3 papers)

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Research

23 pages, 21043 KiB  
Article
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 382
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 [...] Read more.
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  
Article
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 697
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 [...] Read more.
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  
Article
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 908
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 [...] Read more.
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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