Intelligent Systems with Applications最新文献

{"title":"Generative artificial intelligence for heart sound analysis: From model architectures to clinical applications","authors":"Mahmoud Fakhry ,&nbsp;Abeer FathAllah Brery","doi":"10.1016/j.iswa.2026.200637","DOIUrl":"10.1016/j.iswa.2026.200637","url":null,"abstract":"<div><div>Cardiac auscultation involves listening to heart sounds to diagnose cardiac conditions. Accurate interpretation of these sounds requires considerable experience, which limits consistency among clinicians and across settings. Although deep learning offers a path toward automation, data scarcity, class imbalance, and signal complexity pose challenges for its application to heart sound analysis. This study presents a pioneering comprehensive examination of how generative artificial intelligence (generative AI) models, including generative adversarial networks, variational autoencoders, diffusion models, and transformer-based architectures, can address key challenges in heart sound analysis. Specifically, these models mitigate core limitations by synthesizing high-fidelity heart sound signals for data augmentation, improving signal quality through advanced denoising, and enabling semi-supervised and unsupervised detection of rare pathological conditions. In addition, we discuss their roles in advanced feature learning and cross-modal translation. Despite notable progress, we highlight future challenges, including ensuring physiological fidelity, enhancing model interpretability, and validating clinical utility. In conclusion, generative AI addresses a key translational challenge by enabling accessible, accurate, and scalable expert cardiac evaluation, thereby supporting the implementation of precision cardiology.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"29 ","pages":"Article 200637"},"PeriodicalIF":4.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147394845","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":"Multi-modal document classification in AEC asset management","authors":"Floor Rademaker ,&nbsp;Faizan Ahmed ,&nbsp;Marcos R. Machado","doi":"10.1016/j.iswa.2025.200609","DOIUrl":"10.1016/j.iswa.2025.200609","url":null,"abstract":"<div><div>The digitalization of asset management within the architecture, engineering and construction (AEC) sector is in need of effective methods for the automatic classification of documents. This study focuses on the development and evaluation of multimodal document classification models, utilizing visual, textual, and layout-related document information. We examine various state-of-the-art machine learning models and combine them through an iterative development process. The performance of these models is evaluated on two different AEC-document datasets. The results demonstrate that each of the modalities is useful in classifying the documents, as well as the integration of the different information types. This study contributes by applying AI techniques, specifically document classification in the AEC sector, setting the initial step to automating information extraction and processing for Intelligent Asset Management, and lastly, by combining and comparing multimodal state-of-the-art classification models on real-life datasets.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"29 ","pages":"Article 200609"},"PeriodicalIF":4.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145555492","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":"UAV exploration for indoor navigation based on deep reinforcement learning and intrinsic curiosity","authors":"Huei-Yung Lin ,&nbsp;Xi-Sheng Zhang ,&nbsp;Syahrul Munir","doi":"10.1016/j.iswa.2025.200618","DOIUrl":"10.1016/j.iswa.2025.200618","url":null,"abstract":"<div><div>The operational versatility of Unmanned Aerial Vehicles (UAVs) continues to drive rapid development in the field of UAV. However, a critical challenge for diverse applications — such as search and rescue or warehouse inspection — is exploring the environment autonomously. Traditional exploration approaches are often hindered in practical deployments because they require precise navigation path planning and pre-defined obstacle avoidance rules for each of the testing environments. This paper presents a UAV indoor exploration technique based on deep reinforcement learning (DRL) and intrinsic curiosity. By integrating the reward function based on the extrinsic DRL reward and the intrinsic reward, the UAV is able to autonomously establish exploration strategies and actively encourage the exploration of unknown areas. In addition, NoisyNet is introduced to assess the value of different actions during the early stages of exploration. This proposed method will significantly improve the coverage of the exploration while relying solely on visual input. The effectiveness of our proposed technique is validated through experimental comparisons with several state-of-the-art algorithms. It achieves around at least 15% more exploration coverage at the same flight time compared to others, while achieving at least 20% less exploration distance at the same exploration coverage.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"29 ","pages":"Article 200618"},"PeriodicalIF":4.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145790058","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":"FireBoost: A new bio-inspired approach for feature selection based on firefly algorithm and optimized XGBoost","authors":"Nafaa Jabeur","doi":"10.1016/j.iswa.2025.200613","DOIUrl":"10.1016/j.iswa.2025.200613","url":null,"abstract":"<div><div>High-dimensional data often reduce model efficiency and interpretability by introducing redundant or irrelevant features. This challenge is especially critical in domains like healthcare and cybersecurity, where both accuracy and explainability are essential. To address this, we introduce FireBoost, a novel hybrid framework that enhances classification performance through effective feature selection and optimized model training. FireBoost integrates the Firefly Algorithm (FFA) for selecting the most informative features with a customized version of XGBoost. The customized learner includes dynamic learning-rate decay, feature-specific binning, and mini-batch gradient updates. Unlike existing hybrid models, FireBoost tightly couples the selection and learning phases, enabling informed, performance-driven feature prioritization. Experiments on the METABRIC and KDD datasets demonstrate that FireBoost consistently reduces feature dimensionality while maintaining or improving classification accuracy and training speed. It outperforms standard ensemble models and shows robustness across different parameter settings. FireBoost thus provides a scalable and interpretable solution for real-world binary classification tasks involving high-dimensional data.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"29 ","pages":"Article 200613"},"PeriodicalIF":4.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145924621","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":"Scalable and Adaptive Large-Scale Group Decision Making in Dynamic Social Networks via Graph Convolutional Neural Networks#","authors":"Elaheh Golzardi ,&nbsp;Alireza Abdollahpouri","doi":"10.1016/j.iswa.2025.200620","DOIUrl":"10.1016/j.iswa.2025.200620","url":null,"abstract":"<div><div>As social networks are constantly changing, decision-making in large groups becomes much more challenging. People form new connections, lose old ones, shift their preferences, and change how much they trust others (<span><span>Qin, Li, Liang &amp; Pedrycz, 2026</span></span>). Methods that work well in stable settings often fail to keep pace here, especially when both quick adaptation and the ability to handle scale are essential (<span><span>Ding et al., 2025</span></span>). Our approach, called GCD-GNN (Group Consensus Decision using Graph Neural Networks), builds on graph neural networks to track these ongoing changes in structure and preferences. It processes live updates on trust levels, social ties, and preference similarities, then adjusts influence weights in real time to keep the consensus process stable. In experiments using both synthetic and real-world datasets, GCD-GNN delivered higher agreement levels, improved accuracy and precision, and faster execution compared with leading alternatives. These results point to a framework that is not only scalable, but also able to adapt effectiveness in complex, large-scale decision-making environments.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"29 ","pages":"Article 200620"},"PeriodicalIF":4.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145924623","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":"AL-ViT: Label-efficient Robusta coffee-bean defect detection in Thailand using active learning vision transformers","authors":"Sirawich Vachmanus ,&nbsp;Wimolsiri Pridasawas ,&nbsp;Worapan Kusakunniran ,&nbsp;Kitti Thamrongaphichartkul ,&nbsp;Noppanan Phinklao","doi":"10.1016/j.iswa.2025.200612","DOIUrl":"10.1016/j.iswa.2025.200612","url":null,"abstract":"<div><div>In major training and export markets, the coffee bean grading process still relies heavily on manual labor to sort individual beans from large harvest volumes. This labor-intensive task is time-consuming, costly, and prone to human error, especially within Thailand’s rapidly expanding Robusta coffee sector. This study introduces AL–ViT, an end-to-end Active-Learning Vision Transformer framework that operationalizes active learning and transformer-based feature extraction within a single, production-oriented pipeline. The framework integrates a ViT-Base/16 backbone with seven active learning (AL) query strategies, random sampling, entropy-based selection, Bayesian Active Learning by Disagreement (BALD), Batch Active Learning by Diverse Gradient Embeddings (BADGE), Core-Set diversity sampling, ensemble disagreement, and a novel hybrid uncertainty–diversity strategy designed to balance informativeness and representativeness during sample acquisition. A high-resolution dataset of 2098 Robusta coffee bean images was collected under controlled-lighting conditions aligned with grading-machine setups, with only 5 % initially labeled and the remainder forming the AL pool. Across five random seeds, the hybrid strategy without MixUp augmentation achieved 97.1 % accuracy and an F1bad of 0.956 using just 850 labels (41 % of the dataset), within 0.3 percentage points of full supervision. Operational reliability, defined as 95 % accuracy, consistent with prior inspection benchmarks, was reached with only 407 labels, reflecting a 75 % reduction in annotation. Entropy sampling showed the fastest early-stage gains, whereas BADGE lagged by &gt;1 pp; Core-Set and Ensemble provided moderate but stable results. Augmentation and calibration analyses indicated that explicit methods (MixUp, CutMix, RandAugment) offered no further benefit, with the hybrid pipeline already achieving well-calibrated probabilities. Statistical validation via paired <em>t</em>-tests, effect sizes, and bootstrap CIs confirmed consistent improvements of uncertainty-driven strategies over random sampling. Overall, the proposed AL–ViT framework establishes a label-efficient and practically deployable approach for agricultural quality control, achieving near-supervised accuracy at a fraction of the labeling cost.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"29 ","pages":"Article 200612"},"PeriodicalIF":4.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145684888","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":"Novel quantum tunneling and fractional calculus-based metaheuristic for robust global data optimization and its applications in engineering design","authors":"Hussam Fakhouri ,&nbsp;Riyad Alrousan ,&nbsp;Niveen Halalsheh ,&nbsp;Najem Sirhan ,&nbsp;Jamal Zraqou ,&nbsp;Khalil Omar","doi":"10.1016/j.iswa.2025.200616","DOIUrl":"10.1016/j.iswa.2025.200616","url":null,"abstract":"<div><h3>Background:</h3><div>Bound-constrained single-objective optimization and constrained engineering design often feature heterogeneous landscapes and barrier-like structures, motivating search procedures that are scale-aware, robust near constraints, and economical in tuning.</div></div><div><h3>Contributions:</h3><div>We introduce Quantum Tunneling and Fractional Calculus-Based Metaheuristic (QTFM), a physics-inspired metaheuristic that is parameter-lean and employs bounded, range-aware operators to reduce sensitivity to tuning and to prevent erratic steps close to constraints.</div></div><div><h3>Methodology:</h3><div>QTFM couples fractional-step dynamics for scale-aware exploitation with a quantum-tunneling jump for barrier crossing, and augments these with a wavefunction-collapse local search that averages a small neighborhood and applies minimal perturbations to accelerate refinement without sacrificing diversity.</div></div><div><h3>Results:</h3><div>On the IEEE Congress on Evolutionary Computation CEC 2022 single-objective bound-constrained suite, QTFM ranked first on ten of twelve functions; it reached the best optimum on F1 and achieved the best mean values on F2–F8 and F10–F11 with stable standard deviations. In three constrained engineering problems, QTFM produced the lowest mean and the best-found solution for the robotic gripper design, and the lowest mean for the planetary gear train and three-bar truss design.</div></div><div><h3>Findings:</h3><div>The proposed fractional–quantum approach delivers fast, accurate, and robust search across heterogeneous landscapes and real-world design problems.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"29 ","pages":"Article 200616"},"PeriodicalIF":4.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145976712","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":"IndiSegNet: Real-time semantic segmentation for unstructured road scenes in intelligent transportation systems","authors":"Pritam Chakraborty ,&nbsp;Anjan Bandyopadhyay ,&nbsp;Kushagra Agrawal ,&nbsp;Jin Zhang ,&nbsp;Man-Fai Leung","doi":"10.1016/j.iswa.2026.200629","DOIUrl":"10.1016/j.iswa.2026.200629","url":null,"abstract":"<div><div>Autonomous driving in developing regions demands perception systems that can operate reliably in unstructured road environments marked by heterogeneous traffic, weak or missing lane geometry, frequent occlusions, and strong appearance variability. Existing semantic segmentation models, although successful in structured Western datasets, exhibit poor generalization to such chaotic conditions and are often too computationally heavy for real-time deployment on low-power edge hardware. To address these gaps, this paper focuses on the challenge of achieving fast, accurate, and resource-efficient segmentation tailored to complex Indian road scenes. We propose IndiSegNet, a lightweight architecture designed explicitly for this setting. The model introduces two novel components—Multi-Scale Contextual Features (MSCF) for capturing irregular object scales and Encoded Features Refining (EFR) for enhancing thin-structure and boundary detail, resulting in a more stable representation for unstructured environments. IndiSegNet achieves 67.2% mIoU on IDD, 78.9% on Cityscapes, and 74.6% on CamVid, while sustaining 112 FPS on Jetson Nano, outperforming standard baselines by 12%–18% IoU on safety-critical classes such as pedestrians, riders, and vehicles. Real-world evaluation across urban, monsoonal, rural, and mountainous regions shows less than 2.5% variance in mIoU with consistent inference speeds above 108 FPS. These results demonstrate that IndiSegNet offers a practical and hardware-efficient solution for high-speed autonomous navigation in the challenging traffic conditions of developing regions.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"29 ","pages":"Article 200629"},"PeriodicalIF":4.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146026198","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":"Buyer–Seller-Deception-Game Dataset: A new comprehensive dataset for facial expression based deception detection in economic contexts","authors":"Laslo Dinges ,&nbsp;Marc-André Fiedler ,&nbsp;Ayoub Al-Hamadi ,&nbsp;Dmitri Bershadskyy ,&nbsp;Joachim Weimann","doi":"10.1016/j.iswa.2026.200636","DOIUrl":"10.1016/j.iswa.2026.200636","url":null,"abstract":"<div><div>Automated deception detection using only video and audio modalities remains a challenging problem in computer vision. Deceptive behavior is highly context dependent, shaped by factors such as the specific scenario, cultural background, and the associated stakes. To explore the potential of future deception detection tools in online interactions, such as virtual sales meetings, we present a new high-quality low-stakes dataset specifically tailored for this task. It currently includes 500 annotated video samples, with a planned extension to around 1000 before public release. Participants engage in incentivized online interactions, in which sellers attempt to persuade buyers to choose a specific card, creating naturally motivated deceptive and truthful behavior. We evaluate a variety of visual and audio based feature sets, such as gaze, head pose, facial Action-Units (AUs), and prosodic features, on our dataset as well as on a high-stakes in-the-wild deception dataset. Our results show that OpenFace based AU features perform best on our clean and controlled recordings, while CNN based AU predictors outperform others in the more challenging dataset with lower video quality and unstable head pose. Multimodal approaches slightly outperform the best unimodal features in both cases. We will make the dataset freely available to support future research in automated deception detection.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"29 ","pages":"Article 200636"},"PeriodicalIF":4.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146173300","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":"Vision transformers in precision agriculture: A comprehensive survey","authors":"Saber Mehdipour ,&nbsp;Seyed Abolghasem Mirroshandel ,&nbsp;Seyed Amirhossein Tabatabaei","doi":"10.1016/j.iswa.2025.200617","DOIUrl":"10.1016/j.iswa.2025.200617","url":null,"abstract":"<div><div>Detecting plant diseases is a crucial aspect of modern agriculture, playing a key role in maintaining crop health and ensuring sustainable yields. Traditional approaches, though still valuable, often rely on manual inspection or conventional machine learning (ML) techniques, both of which face limitations in scalability and accuracy. The emergence of Vision Transformers (ViTs) marks a significant shift in this landscape by enabling superior modeling of long-range dependencies and offering improved scalability for complex visual tasks. This survey provides a rigorous and structured analysis of impactful studies that employ ViT-based models, along with a comprehensive categorization of existing research. It also offers a quantitative synthesis of reported performance — with accuracies ranging from 75.00% to 100.00% — highlighting clear trends in model effectiveness and identifying consistently high-performing architectures. In addition, this study examines the inductive biases of CNNs and ViTs, which is the first analysis of these architectural priors within an agricultural context. Further contributions include a comparative taxonomy of prior studies, an evaluation of dataset limitations and metric inconsistencies, and a statistical assessment of model efficiency across diverse crop-image sources. Collectively, these efforts clarify the current state of the field, identify critical research gaps, and outline key challenges — such as data diversity, interpretability, computational cost, and field adaptability — that must be addressed to advance the practical deployment of ViT technologies in precision agriculture.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"29 ","pages":"Article 200617"},"PeriodicalIF":4.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145790057","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}