Intelligent Systems with Applications最新文献

{"title":"Optimizing the distribution of tasks in Internet of Things using edge processing-based reinforcement learning","authors":"Mohsen Latifi,&nbsp;Nahideh Derakhshanfard,&nbsp;Hossein Heydari","doi":"10.1016/j.iswa.2025.200585","DOIUrl":"10.1016/j.iswa.2025.200585","url":null,"abstract":"<div><div>As the Internet of Things expands, managing intelligent tasks in dynamic and heterogeneous environments has emerged as a primary challenge for processing-based systems at the network’s edge. A critical issue in this domain is the optimal allocation of tasks. A review of prior studies indicates that many existing approaches either focus on a single objective or suffer from instability and overestimation of decision values during the learning phase. This paper aims to bridge this by proposing an approach that utilizes reinforcement learning with a double Q-learning algorithm and a multi-objective reward function. Furthermore, the designed reward function facilitates intelligent decision-making under more realistic conditions by incorporating three essential factors: task execution delay, energy consumption of edge nodes, and computational load balancing across the nodes. The inputs for the proposed method encompass information such as task sizes, deadlines for each task, remaining energy in the nodes, computational power of the nodes, proximity to the edge nodes, and the current workload of each node. The method's output at any given moment is the decision regarding assigning any task to the most suitable node. Simulation results in a dynamic environment demonstrate that the proposed method outperforms traditional reinforcement learning algorithms. Specifically, the average task execution delay has been reduced by up to 23%, the energy consumption of the nodes has decreased by up to 18%, and load balancing among nodes has improved by up to 27%.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"28 ","pages":"Article 200585"},"PeriodicalIF":4.3,"publicationDate":"2025-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145097473","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mimicking human attention in driving scenarios for enhanced Visual Question Answering: Insights from eye-tracking and the human attention filter","authors":"Kaavya Rekanar ,&nbsp;Martin J. Hayes ,&nbsp;Ciarán Eising","doi":"10.1016/j.iswa.2025.200578","DOIUrl":"10.1016/j.iswa.2025.200578","url":null,"abstract":"<div><div>Visual Question Answering (VQA) models serve a critical role in interpreting visual data and responding to textual queries, particularly within the domain of autonomous driving. These models enhance situational awareness and enable naturalistic interaction between passengers and vehicle systems. However, existing VQA architectures often underperform in driving contexts due to their generic design and lack of alignment with domain-specific perceptual cues. This study introduces a targeted enhancement strategy based on the integration of human visual attention patterns into VQA systems. The proposed approach investigates visual subjectivity by analysing human responses and gaze behaviours captured through an eye-tracking experiment conducted in a realistic driving scenario. This method enables the direct observation of authentic attention patterns and mitigates the limitations introduced by subjective self-reporting. From these findings, a Human Attention Filter (HAF) is constructed to selectively preserve task-relevant features while suppressing visually distracting but semantically irrelevant content. Three VQA models – LXMERT, ViLBERT, and ViLT – are evaluated to demonstrate the adaptability and impact of HAF across different visual representation strategies, including region-based and patch-based architectures. Case studies involving LXMERT and ViLBERT are conducted to assess the integration of the HAF within region-based multimodal pipelines, showing measurable improvements in performance and alignment with human-like attention. Quantitative analysis reveals statistically significant performance trends correlated with driving experience, highlighting cognitive variability among human participants and informing model interpretability. In addition, failure cases are examined to identify potential limitations introduced by attention filtering, offering critical insight into the boundaries of gaze-guided model alignment.The findings validate the effectiveness of human-informed filtering for improving both accuracy and transparency in autonomous VQA systems, and present HAF as a sustainable, cognitively aligned strategy for advancing trustworthy AI in real-world environments.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"28 ","pages":"Article 200578"},"PeriodicalIF":4.3,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145061040","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving long-term prediction in industrial processes using neural networks with noise-added training data","authors":"Mohammadhossein Ghadimi Mahanipoor ,&nbsp;Amirhossein Fathi","doi":"10.1016/j.iswa.2025.200579","DOIUrl":"10.1016/j.iswa.2025.200579","url":null,"abstract":"<div><div>Accurate long-term prediction in industrial processes is essential for efficient control and operation. This study investigates the use of artificial neural networks (ANNs) for forecasting temperature in complex thermal systems, with a focus on enhancing model robustness under real-world conditions. A key innovation in this work is the intentional introduction of Gaussian noise into the training data to emulate sensor inaccuracies and environmental uncertainties, thereby improving the network's generalization capability. The target application is the prediction of water temperature in a non-stirred reservoir heated by two electric heaters, where phase change, thermal gradients, and sensor placement introduce significant modeling challenges. The proposed feedforward neural network architecture, comprising 90 neurons across three hidden layers, demonstrated a substantial reduction in long-term prediction error from 11.23 % to 2.02 % when trained with noise-augmented data. This result highlights the effectiveness of noise injection as a regularization strategy for improving performance in forecasting tasks. The study further contrasts this approach with Random Forest model and confirms the superior generalization and stability of the noise-trained ANN. These findings establish a scalable methodology for improving predictive accuracy in industrial systems characterized by limited data, strong nonlinearities, and uncertain measurements.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"28 ","pages":"Article 200579"},"PeriodicalIF":4.3,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145050158","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards efficient wafer visual inspection: Exploring novel lightweight approaches for anomaly detection and defect segmentation","authors":"Ivo Façoco,&nbsp;Rafaela Carvalho,&nbsp;Luís Rosado","doi":"10.1016/j.iswa.2025.200576","DOIUrl":"10.1016/j.iswa.2025.200576","url":null,"abstract":"<div><div>The rapid advancement of both wafer manufacturing and AI technologies is reshaping the semiconductor industry. As chip features become smaller and more intricate, the variety and complexity of defects continue to grow, making defect detection increasingly challenging. Meanwhile, AI has made significant strides in unsupervised anomaly detection and supervised defect segmentation, yet its application to wafer inspection remains underexplored. This work bridges these fields by investigating cutting-edge lightweight AI techniques for automated inspection of current generation of silicon wafers. Our study leverages a newly curated dataset comprising 1,055 images of 300 mm wafers, annotated with 6,861 defect labels across seven distinct types, along with PASS/FAIL decisions. From a data-centric perspective, we introduce a novel unsupervised dataset-splitting approach to ensure balanced representation of defect classes and image features. Using the DINO-ViT-S/8 model for feature extraction, our method achieves 96% coverage while maintaining the target 20% test ratio for both individual defects and PASS/FAIL classification. From a model-centric perspective, we benchmark several recent methods for unsupervised anomaly detection and supervised defect segmentation. For unsupervised anomaly detection, EfficientAD obtains the best performance for both pixel-level and image-wise metrics, with F1-scores of 75.14% and 82.35%, respectively. For supervised defect segmentation, UPerNet-Swin achieves the highest performance, with a pixel-level mDice of 47.90 and a mask-level F1-score of 57.45. To facilitate deployment in high-throughput conditions, we conduct a comparative analysis of computational efficiency. Finally, we explore a dual-stage output fusion approach that integrates the best-performing unsupervised anomaly detection and supervised segmentation models to refine PASS/FAIL decisions by incorporating defect severity.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"28 ","pages":"Article 200576"},"PeriodicalIF":4.3,"publicationDate":"2025-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145021018","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Liver cirrhosis prediction: The employment of the machine learning-based approaches","authors":"Genjuan Ma,&nbsp;Yan Li","doi":"10.1016/j.iswa.2025.200573","DOIUrl":"10.1016/j.iswa.2025.200573","url":null,"abstract":"<div><div>Early detection of liver cirrhosis remains problematic due to its asymptomatic onset and the inherent class imbalance in clinical data. This study conducts a comprehensive evaluation of machine learning models for predicting cirrhosis stages, with a focus on addressing these challenges. An approach employing Quadratic Discriminant Analysis (QDA) is benchmarked against seven other models, including powerful ensembles like Stacking and HistGradientBoosting, on a clinical dataset. Methodologies such as SMOTE oversampling, stratified data splitting, and class-specific covariance estimation were implemented to manage data complexity. The results demonstrate that a Stacking ensemble achieves the highest overall predictive performance with a micro-AUC of 0.80. The proposed QDA method also proves to be a highly effective and competitive model, achieving a robust AUC of 0.76 and outperforming several specialized imbalance-learning algorithms. Crucially, QDA offers this strong performance with exceptional computational efficiency. These findings show that while complex ensembles can yield top-tier accuracy, QDA’s capacity to model non-linear feature associations makes it a powerful and practical choice for the diagnosis of cirrhosis.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"28 ","pages":"Article 200573"},"PeriodicalIF":4.3,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145097472","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Neural Koopman forecasting for critical transitions in infrastructure networks","authors":"Ramen Ghosh","doi":"10.1016/j.iswa.2025.200575","DOIUrl":"10.1016/j.iswa.2025.200575","url":null,"abstract":"<div><div>We develop a data-driven framework for long-term forecasting of stochastic dynamics on evolving networked infrastructure systems using neural approximations of Koopman operators. In real-world nonlinear systems, the exact Koopman operator is infinite-dimensional and generally unavailable in closed form, necessitating learned finite-dimensional surrogates. Focusing on applications such as traffic flow and power grid oscillations, we model the underlying dynamics as random graph-driven nonlinear processes and introduce a graph-informed neural architecture that learns approximate Koopman eigenfunctions to capture system evolution over time. Our key contribution is the joint treatment of stochastic network evolution, Koopman operator learning, and phase-transition-induced breakdowns in forecasting. We identify critical regimes—arising from graph connectivity shifts or load-induced bifurcations—where the effective forecasting horizon collapses due to spectral degeneracy in the learned Koopman operator. We establish sufficient conditions under which this collapse occurs and propose regularization techniques to mitigate representational breakdown. Numerical experiments on traffic and power networks validate the proposed method and confirm the emergence of critical behavior. These results not only highlight the challenges of forecasting near structural transitions, but also suggest that spectral collapse may serve as a diagnostic signal for detecting phase transitions in dynamic networks. Our contributions unify spectral operator theory, random dynamical systems, and neural forecasting into a control-theoretic framework for real-time intelligent infrastructure. To our knowledge, this is the first work to jointly study Koopman operator learning, stochastic network evolution, and forecasting collapse induced by graph-theoretic phase transitions.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"27 ","pages":"Article 200575"},"PeriodicalIF":4.3,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144921887","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Formal concept views for explainable boosting: A lattice-theoretic framework for Extreme Gradient Boosting and Gradient Boosting Models","authors":"Sherif Eneye Shuaib ,&nbsp;Pakwan Riyapan ,&nbsp;Jirapond Muangprathub","doi":"10.1016/j.iswa.2025.200569","DOIUrl":"10.1016/j.iswa.2025.200569","url":null,"abstract":"<div><div>Tree-based ensemble methods, such as Extreme Gradient Boosting (XGBoost) and Gradient Boosting models (GBM), are widely used for supervised learning due to their strong predictive capabilities. However, their complex architectures often hinder interpretability. This paper extends a lattice-theoretic framework originally developed for Random Forests to boosting algorithms, enabling a structured analysis of their internal logic via formal concept analysis (FCA).</div><div>We formally adapt four conceptual views: leaf, tree, tree predicate, and interordinal predicate to account for the sequential learning and optimization processes unique to boosting. Using the binary-class version of the car evaluation dataset from the OpenML CC18 benchmark suite, we conduct a systematic parameter study to examine how hyperparameters, such as tree depth and the number of trees, affect both model performance and conceptual complexity. Random Forest results from prior literature are used as a comparative baseline.</div><div>The results show that XGBoost yields the highest test accuracy, while GBM demonstrates greater stability in generalization error. Conceptually, boosting methods generate more compact and interpretable leaf views but preserve rich structural information in higher-level views. In contrast, Random Forests tend to produce denser and more redundant concept lattices. These trade-offs highlight how boosting methods, when interpreted through FCA, can strike a balance between performance and transparency.</div><div>Overall, this work contributes to explainable AI by demonstrating how lattice-based conceptual views can be systematically extended to complex boosting models, offering interpretable insights without sacrificing predictive power.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"27 ","pages":"Article 200569"},"PeriodicalIF":4.3,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144907137","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advancing emergency vehicle systems with deep learning: A comprehensive review of computer vision techniques","authors":"Ali Omari Alaoui,&nbsp;Othmane Farhaoui,&nbsp;Mohamed Rida Fethi,&nbsp;Ahmed El Youssefi,&nbsp;Yousef Farhaoui,&nbsp;Ahmad El Allaoui","doi":"10.1016/j.iswa.2025.200574","DOIUrl":"10.1016/j.iswa.2025.200574","url":null,"abstract":"<div><div>Managing emergency vehicles efficiently is critical in urban areas where traffic jams and unpredictable road conditions can delay response times and put lives at risk. Over the years, machine learning methods like k-Nearest Neighbors (k-NN) and Support Vector Machines (SVM), combined with features like HOG and SIFT, paved the way for early image classification and object detection breakthroughs. Tools like Genetic Algorithms (GA) helped refine feature selection, while methods like AdaBoost and Random Forests improved decision-making reliability. The introduction of deep learning has transformed these systems. Convolutional Neural Networks (CNNs) now drive accurate emergency vehicle detection, while Siamese networks support precise identification, such as distinguishing between types of emergency vehicles. Attention mechanisms and Vision Transformers (ViTs) have enhanced the ability to understand context and handle complex scenarios, making them ideal for busy urban environments. Generative Adversarial Networks (GANs) tackle one of the biggest challenges in this field—limited training data—by creating realistic synthetic datasets. This review highlights how these advancements shape emergency response systems, from detecting emergency vehicles in real time to optimizing fleet management. It also explores the challenges of scaling these solutions and achieving faster processing speeds, providing a roadmap for researchers aiming to advance emergency vehicle technologies.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"28 ","pages":"Article 200574"},"PeriodicalIF":4.3,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145007598","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimized CNN-RNN architecture for rapid and accurate identification of hazardous bacteria in water samples","authors":"Ahmad Ihsan ,&nbsp;Khairul Muttaqin ,&nbsp;Nurul Fadillah ,&nbsp;Rahmatul Fajri ,&nbsp;Mursyidah Mursyidah","doi":"10.1016/j.iswa.2025.200577","DOIUrl":"10.1016/j.iswa.2025.200577","url":null,"abstract":"<div><div>Drinking water safety is a critical global issue, as pathogenic bacteria in water can cause various severe diseases, including diarrhea and systemic infections. Rapid and accurate detection of hazardous bacteria is key to ensuring water quality, especially in regions with limited access to water treatment facilities. Conventional detection methods, such as bacterial culture, are often time-consuming and may not detect bacteria in the \"viable but non-culturable\" (VBNC) state. To address these limitations, this study proposes the development of an optimized Convolutional Neural Network-Recurrent Neural Network (CNN-RNN) model for identifying harmful bacteria in drinking water samples. The CNN is used to extract spatial features from microscopic bacterial images, while the RNN handles temporal patterns in bacterial growth, enabling the system to detect bacteria more accurately. Experimental results show that the model, when using bacterial image staining, achieved 97.51% accuracy, 98.57% sensitivity, and 94.89% specificity. Even without image staining, the model still performed well, with 96.23% accuracy and 98.89% specificity. These findings indicate that the optimized CNN-RNN model can provide an efficient and rapid solution for detecting hazardous bacteria in drinking water. This research paves the way for further development, including the integration of IoT for real-time water quality monitoring.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"27 ","pages":"Article 200577"},"PeriodicalIF":4.3,"publicationDate":"2025-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144907131","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Application of LSTM and GRU neural networks to improve peristaltic pump dosing accuracy","authors":"Davide Privitera ,&nbsp;Stefano Bellissima ,&nbsp;Sandro Bartolini","doi":"10.1016/j.iswa.2025.200571","DOIUrl":"10.1016/j.iswa.2025.200571","url":null,"abstract":"<div><div>Peristaltic pumps (PP), widely acknowledged for their benefits in pharmaceutical contexts, face challenges in achieving optimal dosing accuracy. This investigation contributes novel insights for the improvement of dosing precision, identifying how to apply AI models, specifically focusing on Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) neural networks over a realistic span of target volumes. To provide a more accurate representation of real-world performance, we consider a modified root mean square error metric (<span><math><mrow><mi>R</mi><mi>M</mi><mi>S</mi><msub><mrow><mi>E</mi></mrow><mrow><mi>P</mi><mi>P</mi></mrow></msub></mrow></math></span>) that directly compares dispensed volumes to target volumes. Based on this the study delves into two main methodologies: an iterative retraining method, called Online Training, and Pre-trained approach. Online Training shows best results, especially for volumes below 1.0 ml, achieving 38.4% improvement in <span><math><mrow><mi>R</mi><mi>M</mi><mi>S</mi><msub><mrow><mi>E</mi></mrow><mrow><mi>P</mi><mi>P</mi></mrow></msub></mrow></math></span> and 31.6% in standard deviation (<span><math><mrow><mi>S</mi><mi>T</mi><mi>D</mi></mrow></math></span>). Pre-trained models are faster and exhibit promising outcomes especially for volumes above 1.0 ml, with a three-features approach delivering the best performance (13.8% and 4.6% improvements in <span><math><mrow><mi>R</mi><mi>M</mi><mi>S</mi><msub><mrow><mi>E</mi></mrow><mrow><mi>P</mi><mi>P</mi></mrow></msub></mrow></math></span> and <span><math><mrow><mi>S</mi><mi>T</mi><mi>D</mi></mrow></math></span>, respectively). Overall, the findings highlight the effectiveness of iterative learning techniques, particularly for smaller dosage amounts, which complements the good performance of non-AI approaches for larger ones.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"27 ","pages":"Article 200571"},"PeriodicalIF":4.3,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144895628","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}