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Engineering Applications of Hybrid Artificial Intelligence Tools

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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 April 2026 | Viewed by 9532

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Dr. Zbigniew Gomółka

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College of Natural Sciences, University of Rzeszow, Pigonia St. 1, 35-959 Rzeszow, Poland
Interests: eye tracking; image processing; neural networks with fractional derivative; pilot attention analysis; control; spacecraft formation; state estimation; scheduling of discrete production processes; control algorithms
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Dr. Damian Kordos

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The Faculty of Mechanical Engineering and Aeronautics, Rzeszow University of Technology, 35-959 Rzeszów, Poland
Interests: aircraft systems; vision system; flight simulator, eye tracking; HMI systems; image processing; neural networks; control
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Prof. Dr. Ewa Dudek-Dyduch

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Faculty of Electrical Engineering, Automatics, Computer Science, and Biomedical Engineering, AGH University of Science and Technology in Krakow, 30-059 Krakow, Poland
Interests: scheduling of discrete production processes; control algorithms; neural networks; control; knowledge base; multistage decision process; 3-D scenery analysis
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Dear Colleagues,

The interest in artificial intelligence leads to the consolidation of the activities of scientists and the education of the best experts from the broad activities in this field, so that the work of independent global centers can inspire their creators and find business applications faster. The development of simulation environments with a standardized API interface will allow for the collection of a large amount of data derived from interacting with the environment using AI methods in the branches of management, automation, robotics, autonomous vehicles, or energy consumption control. The use of fuzzy logic methods, evolutionary calculations, and neural networks in intelligent decision support and control systems (e.g., intelligent systems and machine learning methods for searching and processing information and supporting decision-making) allows for the optimal design of engineering systems. It seems important to use deep machine learning methods to recognize early symptoms of damage to physical objects based on the activity of their real processes and to automatically detect anomalies in multidimensional production systems. Research on machine learning, statistical inference, and information theory, including variable selection methods in high-dimensional classification problems, will allow for smooth communication and detailed data exchange in algorithmic AI systems.

What is common to the aforementioned areas of modern AI is the fact that they utilize the multidisciplinary nature of artificial intelligence, combining diverse achievements from “pure disciplines” such as computer science, mathematics, physics, automation, electronics, biology, genetics, medicine, aviation, and many others. The hybrid nature of the developed solutions gives them enormous commercial potential, encompassing the extremely important human component of the discoveries made, while simultaneously serving as a key element for the rapid development of technologies for Industry 5.0. Hence, the hybrid application of AI in engineering underscores the Special Issue to which we cordially invite all authors.

Dr. Zbigniew Gomółka
Dr. Damian Kordos
Prof. Dr. Ewa Dudek-Dyduch
Guest Editors

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Keywords

learning strategy
distributed optimization algorithms design and analysis
data-based modeling and control for optimization complex system
intelligent technologies for optimizing discrete processes
AI technologies for human–computer interaction
eyetracking technologies
multi-task and multi-objective optimization
AI applications for software engineering
neural networks and deep learning
hybrid and hierarchical intelligent systems
hybrid artificial intelligence tools
multi-agent systems
knowledge representation and management
preprocessing of industry processes data for DNN
AI for eyetracking technology
intelligent scheduling for discrete processes
intelligent technologies for UAV fleets including monitoring and management
AI applications in aviation systems

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Published Papers (8 papers)

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Research

27 pages, 3449 KB

Open AccessArticle

Possibilities of Reflecting the Mechanical Properties of Non-Absordable Surgical Meshes in an AI-Based Model in the Context of Industry 4.0/5.0

by Marek Andryszczyk, Izabela Rojek, Tomasz Bednarek and Dariusz Mikołajewski

Appl. Sci. 2025, 15(24), 12894; https://doi.org/10.3390/app152412894 (registering DOI) - 6 Dec 2025

Abstract

Non-absorbable surgical meshes are key biomedical materials used for tissue reinforcement, designed for durability, biocompatibility, and mechanical stability in clinical applications. The mechanical properties of these meshes, such as tensile strength, elasticity, and porosity, are crucial for their long-term performance and integration with host tissue. In the context of Industry 4.0/5.0, emphasis is placed on integrating intelligent technologies, such as real-time data acquisition and advanced computational modeling, to improve the design and production of surgical meshes. Computational models simulate the mechanical behavior of meshes under physiological conditions, enabling precise optimization of their material properties and design. In this article, we propose potential artificial intelligence (AI)-based approaches for future research, such as machine learning (ML), for analyzing large datasets from computational and experimental studies to identify optimal mesh configurations. The direction of tensile loading significantly influences the mechanical response of the mesh. Transversely stretched specimens demonstrated higher maximum failure forces and greater fatigue resistance than longitudinally stretched specimens, both in sutured and unsutured conditions. Suturing the mesh to biological tissue significantly reduced its mechanical strength and stiffness, demonstrating a weakening effect at the mesh-tissue interface. Cyclic loading revealed a gradual decrease in strength in all specimens, suggesting fatigue, but transversely stretched meshes maintained higher forces for >1000 cycles than longitudinally stretched meshes. The observed differences in mechanical behavior can be attributed to the anisotropic mesh structure and mechanical suturing effects, which introduce stress concentrations and structural discontinuities. These results emphasize the importance of considering both directionality and surgical technique when selecting and implementing mesh implants. Both AI-based models achieved scores above 80%, demonstrating their clinical utility and the potential for development toward prediction accuracy above 85–90% in clinical settings. Future research should incorporate AI-based computational models to improve predictive capabilities, ultimately leading to the development of more effective, patient-specific surgical meshes. Full article

(This article belongs to the Special Issue Engineering Applications of Hybrid Artificial Intelligence Tools)

37 pages, 6079 KB

Open AccessArticle

ARQ: A Cohesive Optimization Design for Stable Performance on Noisy Landscapes

by Vasileios Charilogis, Ioannis G. Tsoulos, Anna Maria Gianni and Dimitrios Tsalikakis

Appl. Sci. 2025, 15(22), 12180; https://doi.org/10.3390/app152212180 - 17 Nov 2025

Viewed by 207

Abstract

The proposed Adaptive RTR with Quarantine (ARQ) method integrates, within a single evolutionary scheme for continuous optimization, three mature ideas of pbest differential evolution with an archive, success-history parameter adaptation, and restricted tournament replacement (RTR) and extends them with a novel outlier quarantine mechanism. At the heart of ARQ is a combination of the following complementary mechanisms: (1) an event-driven outlier-quarantine loop that triggers on robustly detected tail behavior, (2) a robust center from the best half of the population to which quarantined candidates are gently repaired under feasibility projections, (3) local RTR-based replacement that preserves spatial diversity and avoids premature collapse, (4) archive-guided trial generation that blends current and archived differences while steering toward strong exemplars, and (5) success-history adaptation that self-regulates search from recent successes and reduces manual fine-tuning. Together, these parts sustain focused progress while periodically renewing diversity. Search pressure remains focused yet diversity is steadily replenished through micro-restarts when progress stalls, producing smooth and reliable improvement on noisy or rugged landscapes. In a comprehensive benchmark campaign spanning separable, ill-conditioned, multimodal, hybrid, and composition problems, ARQ was compared against leading state-of-the-art baselines, including top entrants and winners from CEC competitions under identical evaluation budgets and rigorous protocols. Across these settings, ARQ delivered competitive peak results while maintaining favorable average behaviour, thereby narrowing the gap between best and typical outcomes. Overall, this design positions ARQ as a robust choice for practical performance and consistency, providing a dependable tool that can meaningfully strengthen the methodological repertoire of the research community. Full article

(This article belongs to the Special Issue Engineering Applications of Hybrid Artificial Intelligence Tools)

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21 pages, 567 KB

Open AccessArticle

Identifying and Predicting Changes in Behavioral Patterns for Temporal Data in Treatment of Neonatal Respiratory Failure

by Adam Szczur, Jan G. Bazan, Urszula Bentkowska, Piotr Kruczek and Stanislawa Bazan-Socha

Appl. Sci. 2025, 15(22), 12133; https://doi.org/10.3390/app152212133 - 15 Nov 2025

Viewed by 188

Abstract

In this paper, we present the findings of a study focused on discovering process models and tracking their evolution over time. The research specifically targets a distinct category of these models known as behavioral patterns. Consequently, the challenges and techniques addressed here involve temporal data. To demonstrate the issues and methodologies associated with identifying process patterns and their changes, we use illustrative data from the treatment of respiratory failure in premature infants. The main achievement of the paper is to successfully model behavioral patterns using machine learning models and predict changes in a neonate’s state. That was justified by comparing the classifier’s sensitivity for cases of deterioration, improvement, and stability. Classification quality is best when the pattern remains constant. However, in the proposed model, when the pattern deteriorates, the classification quality decreases only slightly. Full article

(This article belongs to the Special Issue Engineering Applications of Hybrid Artificial Intelligence Tools)

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35 pages, 6369 KB

Open AccessArticle

Feature Importance Ranking Using Interval-Valued Methods and Aggregation Functions for Machine Learning Applications

by Aleksander Wojtowicz, Wiesław Paja and Urszula Bentkowska

Appl. Sci. 2025, 15(22), 12130; https://doi.org/10.3390/app152212130 - 15 Nov 2025

Viewed by 383

Abstract

Feature selection is one of the key stages in the process of creating machine learning models and conducting data analysis. This paper presents the results of research related to the implementation of a new algorithm for feature selection and ranking based on weighted interval aggregations. It took into account interval importance values obtained from dividing the dataset into subsets. The algorithm was highly effective in identifying relevant features. The results of comparative studies with nine known methods of feature importance assessment are presented. Ten synthetic datasets and five real datasets were used for the experiments. The calculations also included tests of the relevance of the results obtained. In most experiments, the IVWFR algorithm proved to be the best, achieving the best classification results after identifying subsets of relevant features. Full article

(This article belongs to the Special Issue Engineering Applications of Hybrid Artificial Intelligence Tools)

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23 pages, 16159 KB

Open AccessArticle

Adaptive Multi-Scale Feature Learning Module for Pediatric Pneumonia Recognition in Chest X-Rays

by Petra Radočaj, Goran Martinović and Dorijan Radočaj

Appl. Sci. 2025, 15(21), 11824; https://doi.org/10.3390/app152111824 - 6 Nov 2025

Viewed by 407

Abstract

Pneumonia remains a major global health concern, particularly among pediatric populations in low-resource settings where radiological expertise is limited. This study investigates the enhancement of deep convolutional neural networks (CNNs) for automated pneumonia diagnosis from chest X-ray images through the integration of a novel module combining Inception blocks, Mish activation, and Batch Normalization (IncMB). Four state-of-the-art transfer learning models—InceptionV3, InceptionResNetV2, MobileNetV2, and DenseNet201—were evaluated in their base form and with the proposed IncMB extension. Comparative analysis based on standardized classification metrics reveals consistent performance improvements across all models with the addition of the IncMB module. The most notable improvement was observed in InceptionResNetV2, where the IncMB-enhanced model achieved the highest accuracy of 0.9812, F1-score of 0.9761, precision of 0.9781, recall of 0.9742, and strong specificity of 0.9590. Other models also demonstrated similar trends, confirming that the IncMB module contributes to better generalization and discriminative capability. These enhancements were achieved while reducing the total number of parameters, indicating improved computational efficiency. In conclusion, the integration of IncMB significantly boosts the performance of CNN-based pneumonia classifiers, offering a promising direction for the development of lightweight, high-performing diagnostic tools suitable for real-world clinical application, particularly in underserved healthcare environments. Full article

(This article belongs to the Special Issue Engineering Applications of Hybrid Artificial Intelligence Tools)

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15 pages, 3012 KB

Open AccessArticle

Deep Learning-Based Layout Analysis Method for Complex Layout Image Elements

by Yunfei Zhong, Yumei Pu, Xiaoxuan Li, Wenxuan Zhou, Hongjian He, Yuyang Chen, Lang Zhong and Danfei Liu

Appl. Sci. 2025, 15(14), 7797; https://doi.org/10.3390/app15147797 - 11 Jul 2025

Viewed by 1393

Abstract

The layout analysis of elements is indispensable in graphic design, as effective layout design not only facilitates the delivery of visual information but also enhances the overall esthetic appeal to the audience. The combination of deep learning and graphic design has gradually turned into a popular research direction in graphic design in recent years. However, in the era of rapid development of artificial intelligence, the analysis of layout still requires manual participation. To address this problem, this paper proposes a method for analyzing the layout of complex layout image elements based on the improved DeepLabv3++ model. The method reduces the number of model parameters and training time by replacing the backbone network. To improve the effect of multi-scale semantic feature extraction, the null rate of ASPP is fine-tuned, and the model is trained by self-constructed movie poster dataset. The experimental results show that the improved DeepLabv3+ model achieves a better segmentation effect on the self-constructed poster dataset, with MIoU reaching 75.60%. Compared with the classical models such as FCN, PSPNet, and DeepLabv3, the improved model in this paper effectively reduces the number of model parameters and training time while also ensuring the accuracy of the model. Full article

(This article belongs to the Special Issue Engineering Applications of Hybrid Artificial Intelligence Tools)

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23 pages, 54884 KB

Open AccessArticle

Using Hybrid LSTM Neural Networks to Detect Anomalies in the Fiber Tube Manufacturing Process

by Zbigniew Gomolka, Ewa Zeslawska and Lukasz Olbrot

Appl. Sci. 2025, 15(3), 1383; https://doi.org/10.3390/app15031383 - 29 Jan 2025

Cited by 3 | Viewed by 1718

Abstract

The production process of tubes for fiber optic cables is a complex process, where proper execution is crucial to the quality of the final product. This process has a complex state vector whose structure and coordinates dynamically change during the tube extrusion process. Small fluctuations in process parameters, such as temperature, extrusion pressure, production speed, and optical fiber tension, affect the optical attenuation of the final product. Such defects necessitate the withdrawal of the product. Due to the high number of process coordinates and the technological inability to automatically label those segments of the production process that cause anomalies in the final product, the authors used data clustering methods to create a training set that enabled the use of neural tools for anomaly detection. The system proposed in the main part of the paper includes a hybrid Long short-term memory (LSTM) network model, which is fed with data streams recorded on the tube extrusion production line. The input module, which performs preprocessing of input data, conducts multiresolution analysis of recorded process parameters, and recommends the process state’s belonging to a set of classes describing individual production anomalies to appropriate LSTM network modules. The learning process of the three–channel network allowed effective recognition of five classes of the monitored tube production process. The fit level of the proposed network model reached R2 values of ≥0.85. Full article

(This article belongs to the Special Issue Engineering Applications of Hybrid Artificial Intelligence Tools)

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32 pages, 6636 KB

Open AccessArticle

Explainable AI (XAI) Techniques for Convolutional Neural Network-Based Classification of Drilled Holes in Melamine Faced Chipboard

by Alexander Sieradzki, Jakub Bednarek, Albina Jegorowa and Jarosław Kurek

Appl. Sci. 2024, 14(17), 7462; https://doi.org/10.3390/app14177462 - 23 Aug 2024

Cited by 3 | Viewed by 3971

Abstract

The furniture manufacturing sector faces significant challenges in machining composite materials, where quality issues such as delamination can lead to substandard products. This study aims to improve the classification of drilled holes in melamine-faced chipboard using Explainable AI (XAI) techniques to better understand and interpret Convolutional Neural Network (CNN) models’ decisions. We evaluated three CNN architectures (VGG16, VGG19, and ResNet101) pretrained on the ImageNet dataset and fine-tuned on our dataset of drilled holes. The data consisted of 8526 images, divided into three categories (Green, Yellow, Red) based on the drill’s condition. We used 5-fold cross-validation for model evaluation and applied LIME and Grad-CAM as XAI techniques to interpret the model decisions. The VGG19 model achieved the highest accuracy of 67.03% and the lowest critical error rate among the evaluated models. LIME and Grad-CAM provided complementary insights into the decision-making process of the model, emphasizing the significance of certain features and regions in the images that influenced the classifications. The integration of XAI techniques with CNN models significantly enhances the interpretability and reliability of automated systems for tool condition monitoring in the wood industry. The VGG19 model, combined with LIME and Grad-CAM, offers a robust solution for classifying drilled holes, ensuring better quality control in manufacturing processes. Full article

(This article belongs to the Special Issue Engineering Applications of Hybrid Artificial Intelligence Tools)

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