Engineering Applications of Artificial Intelligence最新文献

{"title":"An innovative contrastive learning approach to improve image recognition robustness and interpretability via simulated environmental perturbations","authors":"Leijun Cheng ,&nbsp;Xihe Qiu ,&nbsp;Xiaoyu Tan ,&nbsp;Haoyu Wang ,&nbsp;Yujie Xiong","doi":"10.1016/j.engappai.2025.111619","DOIUrl":"10.1016/j.engappai.2025.111619","url":null,"abstract":"<div><div>In the field of pattern recognition, the noise inherent in real-world images poses a significant challenge to traditional image processing methodologies. While existing approaches have made progress in addressing this issue, they often struggle with limited model generalization, data distribution shifts, and domain adaptability discrepancies between simulated environments and real-world contexts, compromising efficiency and robustness. In this paper, we propose a novel contrastive learning strategy for Enhancing Robustness and Interpretability in Image Recognition through Environmental Perturbations (ERIEP) of clear-featured image data. ERIEP meticulously identifies a set of core visual features, termed “invariant features”, which can offer optimal explanations for image predictions. Concurrently, it emphasizes learning noise-resistant strategies to amplify the model’s interpretability. Through ERIEP’s contrastive learning approach, we address complex images, enabling the model to progressively refine its understanding of both the invariant features and noise mitigation technique. Our extensive experiments on CIFAR-10, CIFAR-100, and ImageNet-1K demonstrate that ERIEP significantly outperforms several state-of-the-art image-processing baselines, showing robust performance under various noise intensities and environmental perturbations.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"159 ","pages":"Article 111619"},"PeriodicalIF":7.5,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144571402","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning in forensic Analysis: Optical coherence tomography image classification in methamphetamine detection","authors":"Nilifer Gurbuzer ,&nbsp;Alev Lazoglu Ozkaya ,&nbsp;Elif Topdagi Yaylali ,&nbsp;Elif Ozcan Tozoglu ,&nbsp;Mehmet Baygin ,&nbsp;Burak Tasci ,&nbsp;Sengul Dogan ,&nbsp;Turker Tuncer","doi":"10.1016/j.engappai.2025.111682","DOIUrl":"10.1016/j.engappai.2025.111682","url":null,"abstract":"<div><div>Detecting drug addiction in forensic science traditionally relies on expensive and time-consuming laboratory tests. This study proposes a rapid, non-invasive approach that uses optical coherence tomography images combined with deep learning techniques to identify methamphetamine users. A novel convolutional neural network was developed, incorporating depthwise and pointwise convolutions, patchify-based downsampling, and inception blocks to improve feature extraction and classification accuracy. To further enhance model performance, we introduced a grid-based deep feature engineering model that extracts and selects discriminative features using iterative neighborhood component analysis. The proposed model achieved 91.02 % accuracy, surpassing the 88.57 % accuracy of Mobile Network version 2 on the same dataset. By integrating the grid-based feature engineering model, classification accuracy was further improved to 93.27 %, demonstrating a significant enhancement over traditional deep learning approaches. The dataset consisted of 2172 optical coherence tomography images collected from 54 methamphetamine users and 60 control subjects, ensuring a diverse and representative sample. This research marks the first application of optical coherence tomography imaging in drug addiction detection, bridging biomedical imaging and forensic science. By employing gradient-weighted class activation mapping visualization, we identified key retinal features that distinguish methamphetamine users from non-users, thereby making the model more interpretable and clinically relevant. Given its high accuracy, lightweight architecture, and non-invasive nature, the proposed method offers a promising forensic tool for rapid, artificial intelligence-driven drug addiction screening with potential real-world applicability in forensic investigations and healthcare.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"159 ","pages":"Article 111682"},"PeriodicalIF":7.5,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144580193","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A model predictive trajectory tracking control strategy for heavy-duty unmanned tracked vehicle using deep Koopman operator","authors":"Yinchu Zuo ,&nbsp;Chao Yang ,&nbsp;Shengfei Li ,&nbsp;Weida Wang ,&nbsp;Changle Xiang ,&nbsp;Tianqi Qie","doi":"10.1016/j.engappai.2025.111698","DOIUrl":"10.1016/j.engappai.2025.111698","url":null,"abstract":"<div><div>Among the numerous technologies for the heavy-duty unmanned tracked vehicle (HDUTV), trajectory tracking is the key function to support the maneuverability. Unlike Ackermann steering vehicles, HDUTVs are easily affected by disturbances during the steering process, leading to different steering characteristics. The variable steering characteristics pose challenges for precise tracking control. Motivated by this challenge, a high accuracy model predictive trajectory tracking method is proposed to improve the tracking performance of HDUTVs. First, a deep Koopman operator-based tracked vehicle model is established. The proposed learning-based model provides an accurate description of the complex nonlinear dynamics of HDUTVs while maintaining the model linearity. Utilizing the model, the real-time performance of the trajectory tracking process is guaranteed. Second, a trajectory tracking control strategy is established considering the steering characteristic of the HDUTV to improve the tracking performance. Third, the deep Koopman operator-based model is integrated into the model predictive control framework to enhance predictive accuracy while ensuring the real-time performance of the trajectory tracking controller. Finally, the proposed method is validated through simulations and experiments with a full-sized HDUTV. Experiment results indicate that the proposed model enhances predictive ability for vehicle states, with a 59.51 % improvement in the accuracy of the sideslip angle. And the proposed trajectory tracking strategy improves the tracking accuracy by 57.93 %.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"159 ","pages":"Article 111698"},"PeriodicalIF":7.5,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144580194","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Density cluster-based feature selection: An information theory approach","authors":"Jingya Dong ,&nbsp;Shuai Tao ,&nbsp;Chunhe Song ,&nbsp;Peiming Ning ,&nbsp;Tao Zhang","doi":"10.1016/j.engappai.2025.111694","DOIUrl":"10.1016/j.engappai.2025.111694","url":null,"abstract":"<div><div>Feature selection plays a crucial role in data mining and machine learning. However, evident challenges exist: (1) current methods cannot autonomously identify the optimal feature set, requiring manual parameter adjustment based on the learning algorithm; (2) heuristic methods, which are widely used, often struggle to ensure the maximization of the objective function. To address these challenges, this paper proposes a density cluster-based feature selection (DCFS) method leveraging information theory, which involves the application of artificial intelligence (AI) in the clustering process. First, a novel initial feature selection method that maximizes feature-relevance and feature-difference is introduced to automate the selection of an initial feature subset. Second, a new density-centric automatic clustering (DAC) algorithm, an AI-based clustering approach, is proposed. This algorithm synthesizes non-parametric density estimation, decision graph-based density center selection strategies, and adaptive domain search clustering methods to enhance the precision and robustness of clustering outcomes. Third, a feature space selection method based on maximizing feature relevance is established to construct a comprehensive feature subspace. This feature space selection method converts the maximization of the objective function into an automated density clustering process, facilitating the automatic selection of the most optimal features. Extensive experiments conducted across 14 datasets have demonstrated the superior performance of the proposed DCFS in terms of effectiveness and robustness. To the best of our knowledge, this paper is the first work attempting automatic feature selection through clustering, thus pushing the frontier of feature selection algorithm development.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"159 ","pages":"Article 111694"},"PeriodicalIF":7.5,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144580196","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Detection of error in static and dynamic visual stimulation via electroencephalogram and eye-tracking systems","authors":"Hyowon Lee ,&nbsp;Ning Jiang ,&nbsp;Siby Samuel","doi":"10.1016/j.engappai.2025.111688","DOIUrl":"10.1016/j.engappai.2025.111688","url":null,"abstract":"<div><div>Human responses such as electroencephalogram (EEG), eye-tracking, and heart rate have been studied for error detection during visual stimulation, often in controlled settings with single-target fixation. This study delves into constructing machine learning (ML) models for binary error classification across diverse visual stimulation conditions, including static, dynamic, and single or multiple targets, using EEG and eye-tracking data. When constructing these models, using gaze fixation data for epoch extraction can enhance the ability to extract salient, stimulus-induced responses from EEG and eye-tracking data. These features can be strongly associated with changes in visual stimulation. Among 30 ML models tested, the best-performing ML models built on a personalized approach consistently achieved over 90 % accuracy across conditions. For feature importance, we integrate a repetition approach with the Boruta SHapley Additive exPlanations (BorutaSHAP) algorithm to enhance the legitimacy of key feature selection. Feature analysis revealed distinct patterns, e.g., eye-tracking features like log energy entropy being particularly prominent under dynamic conditions, EEG features from the delta, and theta bands being significant across all conditions. Interestingly, an increase in the number of visual targets led to a reduction in the importance of EEG features, especially during dynamic stimulations. These insights have the potential to enhance the ML models through tailored feature selection. While this study acknowledges certain limitations concerning real-time applicability, generalizability, etc, the novelty of our models p[presents opportunities for various applications in human-computer/robot interaction (HCI/HRI), monitoring systems, rehabilitation systems, assistive technologies for individuals with limited mobility and driving, etc.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"159 ","pages":"Article 111688"},"PeriodicalIF":7.5,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144580197","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automatic clustering by automatically generated algorithms","authors":"Moisés Silva-Muñoz ,&nbsp;Jonnatan Oyarzún ,&nbsp;Gustavo Semaan ,&nbsp;Carlos Contreras-Bolton ,&nbsp;Carlos Rey ,&nbsp;Victor Parada","doi":"10.1016/j.engappai.2025.111596","DOIUrl":"10.1016/j.engappai.2025.111596","url":null,"abstract":"<div><div>Clustering data based on similarity becomes particularly challenging when the number of clusters is not known in advance. This case, known as the automatic clustering problem (ACP), corresponds to an optimization problem that aims to identify the best possible clustering among the many existing options. Although several effective ACP methods have been proposed, identifying optimal clusterings remains a difficult task, and the space of algorithmic solutions has yet to be thoroughly explored. Existing approaches suggest that better results can be achieved by appropriately combining and assembling different techniques. While some combinations have been explored, many others remain unexamined and could be evaluated through a more exhaustive exploration, such as the automatic generation of algorithms (AGA). This article considers the combinations arising from the automatic construction of algorithms for the ACP. To this end, an optimization meta-problem is defined to construct algorithms with the best computational performance. The search for the optimal solution to the meta-problem allows a computational exploration of the space defined by all possible combinations of elementary algorithmic components. We specifically explore the potential of AGA to generate ACP-specialized algorithms tailored to each dataset. Through extensive computational experiments, we evaluate the effectiveness of these specialized algorithms with general-purpose algorithms generated by AGA and six state-of-the-art ACP algorithms across well-established datasets. The results demonstrate that both AGA-generated algorithms outperform the state-of-the-art ACP algorithms, with statistically significant differences. Furthermore, the specialized algorithms exhibit superior effectiveness, highlighting their advantage over their general-purpose counterparts.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"159 ","pages":"Article 111596"},"PeriodicalIF":7.5,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144580192","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An intelligent fuzzy logic based-trust system in underwater acoustic sensor networks","authors":"Parisa Khoshvaght ,&nbsp;Musaed Alhussein ,&nbsp;Khursheed Aurangzeb ,&nbsp;Mohammad Sadegh Yousefpoor ,&nbsp;Jan Lansky ,&nbsp;Mehdi Hosseinzadeh","doi":"10.1016/j.engappai.2025.111558","DOIUrl":"10.1016/j.engappai.2025.111558","url":null,"abstract":"<div><div>Due to the ongoing progress in ocean exploration, underwater acoustic sensor networks (UASNs) have become a significant focus of research. However, the inherent openness and lack of supervision in these networks expose them to various security threats. Thus, efficient and reliable security systems are very necessary to keep the normal performance of these networks. Nonetheless, in the trust evaluation process, dishonest nodes likely broadcast incorrect recommendations in the network. This decreases the accuracy of the trust value and affects the normal operation of the trust process. To solve this challenge, this paper presents an intelligent fuzzy logic-based trust system (IFTS) in UASNs. The proposed scheme employs a fuzzy trust mechanism to assess direct trust. To design this mechanism, energy evidence, data evidence, and communication evidence are considered as inputs in this fuzzy system, and direct trust is extracted as the fuzzy output. Energy evidence is obtained from the remaining energy and the energy change rate. Data evidence is obtained from the packet loss rate and data consistency, and communication evidence is calculated based on three link-related parameters, namely link reliability, link delay, and link stability. Likewise, recommendation trust depends on the recommendations offered by the recommenders. The trustor node evaluates each recommender and calculates its merit by using the root mean square (RMS) error and the trust value of the trustor relative to the recommender. Furthermore, IFTS computes indirect trust based on the trust chain, i.e., a set of recommender nodes. This trust chain is built using the greedy strategy based on the closest and most reliable recommender nodes. Further, IFTS uses a sliding time window for refreshing trust values. Finally, the simulation and evaluation process of IFTS is carried out in comparison with a recommendation management trust mechanism based on collaborative filtering and variable weight fuzzy algorithm (CFFTM), an adaptive trust model based on long short-term memory (LTrust), and a trust model based on cloud theory (TMC) under three attacks, namely bad/good mouthing attack, collusion attack, and hybrid attack, and its results are compared in terms of two criteria, i.e., diagnosis accuracy rate and false diagnosis rate. Hence, in the bad/good mouthing attack, IFTS improves the indirect trust level of honest nodes, accuracy, and the false diagnosis rate by 2.24%, 1.97%, and 12.68%, respectively. In the collusion attack, IFTS upgrades the indirect trust level of abnormal nodes, accuracy, and the false diagnosis rate by 7.2%, 1.17%, and 0.69%, respectively. In a hybrid attack, IFTS optimizes accuracy and the false diagnosis rate by 2.30% and 29.27%, respectively.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"159 ","pages":"Article 111558"},"PeriodicalIF":7.5,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144580503","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adversarial environment design for crowd navigation based on deep reinforcement learning","authors":"Jeongeun Kim ,&nbsp;Hyo-Seok Hwang ,&nbsp;Junhee Seok","doi":"10.1016/j.engappai.2025.111621","DOIUrl":"10.1016/j.engappai.2025.111621","url":null,"abstract":"<div><div>The widespread use of mobile robots has increased the shared space between humans and robots, necessitating advanced solutions for crowd navigation. Recent studies have proposed approaches based on deep reinforcement learning to safely and efficiently achieve this goal. However, these approaches face challenges such as difficulty in presenting diverse pedestrian patterns and limited generalization performance. This study proposes a framework called Simultaneous training Process with Adversarial Crowd Environment (SPACE), which is an implemented artificial intelligence that generates crowd navigation environments. This framework competitively trains a crowd navigation agent and an adversarial crowd environment. In the adversarial crowd environment, the adversarial agent places pedestrians to induce collisions with the crowd navigation agent. By applying artificial intelligence within the episode-generation, this framework addresses vulnerabilities of previous approaches and allows the training of robust crowd navigation agents with high generalization performance. Experimental results demonstrate up to a 24.62% increase in navigation success rate and a 41.6% improvement in minimum distance from pedestrians compared to agents trained in non-adversarial environments, ensuring safer crowd navigation. Furthermore, SPACE exhibits more stable navigation performance in evaluation environment settings that are significantly more complex than the training scenarios. These findings highlight the promise of SPACE for training crowd navigation agents capable of operating effectively under diverse environmental conditions.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"159 ","pages":"Article 111621"},"PeriodicalIF":7.5,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144570223","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An oscillation based simulated annealing algorithm for the single row facility layout problem","authors":"Baiyu Chen,&nbsp;Zhipeng Lü,&nbsp;Zhouxing Su,&nbsp;Junwen Ding","doi":"10.1016/j.engappai.2025.111551","DOIUrl":"10.1016/j.engappai.2025.111551","url":null,"abstract":"<div><div>The single row facility layout problem aims to position a set of facilities of given lengths on a single line so as to minimize the weighted sum of the distances between all the pairs of facilities, which has wide real-world applications in planning areas. In this paper, we propose an oscillation based simulated annealing algorithm for solving the single row facility layout problem. Our algorithm dynamically oscillates between two simulated annealing algorithms. One employs an exponential descent insertion strategy to capture effective movements, which increases the search efficiency, while the other adopts a radius-constrained neighborhood structure to reduce search space, which significantly enhances the intensification of the search. Besides, a new fast incremental evaluation method based on decomposition and recombination is adopted to speed up the search. Experiments on 110 instances demonstrate the competitiveness of our algorithm. In specific, for all the 110 instances, our algorithm discovers new upper bounds in 32 cases and matches the best known results for other 75 instances, only remaining 3 worse results.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"159 ","pages":"Article 111551"},"PeriodicalIF":7.5,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144570220","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Parameterized-action based deep reinforcement learning for intelligent traffic signal control","authors":"Salah Bouktif ,&nbsp;Abderraouf Cheniki ,&nbsp;Ali Ouni ,&nbsp;Hesham El-Sayed","doi":"10.1016/j.engappai.2025.111422","DOIUrl":"10.1016/j.engappai.2025.111422","url":null,"abstract":"<div><div>Traffic Signal Control (TSC) is a crucial component in Intelligent Transportation Systems (ITS) for optimizing traffic flow. Deep Reinforcement Learning (DRL) techniques have emerged as leading approaches for TSC due to their promising performance. Most existing DRL-based approaches typically use discrete action spaces to predict the next action phase, without specifying the signal duration. In contrast, some studies employ continuous action spaces to determine signal phase timing within a fixed light cycle. To address the limitations of both approaches, we propose a flexible framework that predicts both the appropriate traffic light phase along with its associated duration. Our approach utilizes a Parameterized-action based deep reinforcement learning architecture to handle the combination of discrete-continuous actions. We evaluate our method using the Simulation of Urban MObility (SUMO) environment, comparing its efficiency against state-of-the-art techniques. Results demonstrate that our approach significantly outperforms traditional and learning-based methods.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"159 ","pages":"Article 111422"},"PeriodicalIF":7.5,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144570226","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}