Intelligent Systems with Applications最新文献

{"title":"Metaheuristics in automated machine learning: Strategies for optimization","authors":"Francesco Zito ,&nbsp;El-Ghazali Talbi ,&nbsp;Claudia Cavallaro ,&nbsp;Vincenzo Cutello ,&nbsp;Mario Pavone","doi":"10.1016/j.iswa.2025.200532","DOIUrl":"10.1016/j.iswa.2025.200532","url":null,"abstract":"<div><div>The present work explores the application of Automated Machine Learning techniques, particularly on the optimization of Artificial Neural Networks through hyperparameter tuning. Artificial Neural Networks are widely used across various fields, however building and optimizing them presents significant challenges. By employing an effective hyperparameter tuning, shallow neural networks might become competitive with their deeper counterparts, which in turn makes them more suitable for low-power consumption applications. In our work, we highlight the importance of Hyperparameter Optimization in enhancing neural network performance. We examine various metaheuristic algorithms employed and, in particular, their effectiveness in improving model performance across diverse applications. Despite significant advancements in this area, a comprehensive comparison of these algorithms across different deep learning architectures remains lacking. This work aims to fill this gap by systematically evaluating the performance of metaheuristic algorithms in optimizing hyperparameters and discussing advanced techniques such as parallel computing to adapt metaheuristic algorithms for use in hyperparameter optimization with high-dimensional hyperparameter search space.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"26 ","pages":"Article 200532"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144185956","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":"YOLO-SR: An optimized convolutional architecture for robust ship detection in SAR Imagery","authors":"Chi Kien Ha,&nbsp;Hoanh Nguyen,&nbsp;Vu Duc Van","doi":"10.1016/j.iswa.2025.200538","DOIUrl":"10.1016/j.iswa.2025.200538","url":null,"abstract":"<div><div>Accurate and efficient ship detection in synthetic aperture radar (SAR) imagery remains a challenging task due to speckle noise, scale variations, and the low contrast of small vessels. In this work, we present YOLO-SR, an enhanced version of YOLOv10 tailored for SAR ship detection, introducing four key innovations: Balanced Detail Fusion (BDF), C2f‐MSDR, DySample, and the Focaler-SIoU loss. Our BDF module adaptively merges shallow, fine‐grained features with deeper semantic features, preventing subtle ship signatures from being overshadowed by irrelevant clutter. Concurrently, C2f‐MSDR replaces standard bottleneck layers with multi-scale dilation residual blocks, expanding the receptive field to handle wide variations in ship size. To improve spatial resolution and retain boundary details, we incorporate DySample, a data-driven upsampling strategy that counteracts the artifacts of naive interpolation. Finally, Focaler-SIoU refines bounding-box regression by integrating distance, orientation, shape, and a focal-like reweighting, thereby emphasizing difficult, small, or partially occluded ships. Experimental results on SAR ship detection datasets confirm that YOLO-SR outperforms state-of-the-art methods in both precision and recall, while retaining competitive inference speeds. These advances offer a robust framework for real-time maritime surveillance, enhancing the detection of both small and large ships under challenging SAR conditions.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"26 ","pages":"Article 200538"},"PeriodicalIF":0.0,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144169914","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":"Pedestrian trajectory prediction model based on self-supervised spatiotemporal graph network","authors":"Shiji Yang,&nbsp;Xuezhong Xiao","doi":"10.1016/j.iswa.2025.200533","DOIUrl":"10.1016/j.iswa.2025.200533","url":null,"abstract":"<div><div>To improve the accuracy of pedestrian trajectory prediction, the graph - based pedestrian trajectory modeling method in the pedestrian trajectory prediction scenario is effective. Thus, a pedestrian trajectory prediction model based on a self - supervised spatiotemporal graph network is proposed. Firstly, in the process of spatiotemporal graph modeling, this model introduces hop interaction instead of node interaction to update node features, which greatly reduces the times of graph convolution operations, alleviates the problem of feature smoothing, and greatly improves the accuracy of prediction. Secondly, a unique self-supervised module is added to the model to mine commonalities between pedestrian’s multi-trajectories through self-supervised to further improve the accuracy of prediction. The experiment uses ETH and UCY public datasets to train and evaluate model performance. The experimental results demonstrate that this model exhibits enhancements in both ADE and FDE metrics when compared to the SOTA model, with an average prediction error reduction of 15 % and 10 %, respectively. In scenes with dense pedestrians such as the UNIV dataset, the prediction errors are reduced by 25 % and 22 %.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"26 ","pages":"Article 200533"},"PeriodicalIF":0.0,"publicationDate":"2025-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144124236","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":"From detection to intervention: An end-to-end system for recognizing the “signal for help” gesture in real-time","authors":"Federico Buccellato,&nbsp;Eleonora Vacca,&nbsp;Sarah Azimi,&nbsp;Corrado De Sio,&nbsp;Luca Sterpone","doi":"10.1016/j.iswa.2025.200536","DOIUrl":"10.1016/j.iswa.2025.200536","url":null,"abstract":"<div><div>The “Signal for Help” is a simple hand gesture, internationally recognized, that enables individuals experiencing domestic violence to discreetly signal their need for help without alerting their aggressors. Developed during the COVID-19 pandemic to address the growing isolation of victims, it serves as a powerful tool to facilitate silent communication in dangerous situations. Despite its potential, its effectiveness has been impeded by limited public awareness, the risk of misinterpretation, and the lack of reliable automated detection systems.</div><div>To address these challenges, this paper introduces a framework consisting of two interconnected components: a real-time detection system of the “Signal for Help” gesture using a machine learning-based recognition system and a custom mobile application that receives notifications from the detection system and alerts security personnel in real-time.</div><div>During the development process, we faced several challenges, including detecting the gesture in crowded environments and keeping the computational load low to ensure the system could run efficiently on edge devices.</div><div>We overcame these challenges by designing a system that combines hand tracking and feature extraction, using tools such as MediaPipe and DeepSORT, followed by a final classification step. After testing various classifiers, Random Forest achieved the best results, reaching an accuracy of 94 % with a very low rate of false positives. The system was carefully optimized to minimize computational cost while maintaining real-time performance. In fact, as shown by the tests conducted on Apple M3, NVIDIA Jetson Orin Nano, and NVIDIA Jetson AGX Orin, the system achieved inference times of 0.067 s, 0.471 s, and 0.343 s respectively. These outcomes demonstrate the system’s possibility for deployment in smart city environments, supporting both urban and non-urban areas. When a gesture is detected, the system immediately notifies the mobile application, which provides instant alerts, geolocation data, and a short video clip of the incident, enabling a rapid and informed response. Additionally, the app includes advanced features such as detailed notification history, real-time operator status monitoring, and an integrated team coordination chat, which optimize operations, enhance collaboration among security staff, and ensure timely and effective interventions in emergency situations. This research marks a step forward in real-time gesture recognition and intervention, setting a new benchmark for automated safety systems aimed at preventing domestic violence and other emergencies. By increasing awareness and ensuring a rapid response to the “Signal for Help” gesture, the system empowers individuals in distress and contributes to safeguarding those at risk.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"26 ","pages":"Article 200536"},"PeriodicalIF":0.0,"publicationDate":"2025-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144107387","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":"MNC: A multi-agent framework for complex network configuration","authors":"Cui Wang ,&nbsp;Huan Li","doi":"10.1016/j.iswa.2025.200531","DOIUrl":"10.1016/j.iswa.2025.200531","url":null,"abstract":"<div><div>Recent progress in large language models (LLMs) has led to substantial improvements in their ability to perform a wide range of natural language processing tasks, particularly in handling complex scenarios. These models exhibit strong generalization capabilities and reasoning skills, making them well-suited for tasks that require integrating external knowledge and logical reasoning. In this paper, we introduce <strong>M</strong>ulti-agent based <strong>N</strong>etwork <strong>C</strong>onfiguration (MNC), a novel multi-agent framework designed to leverage LLMs for complex network configuration tasks. Our framework consists of three core components: (1) the <strong>Requirement Analysis Module</strong>, which interprets user queries and retrieves relevant external network configuration knowledge; (2) the <strong>Configuration Generation Module</strong>, which uses an iterative Chain-of-Thought (COT) approach to produce and refine multiple analysis pathways; and (3) the <strong>Configuration Refinement Module</strong>, which evaluates and improves the final network configuration through a reflection-driven mechanism. We evaluate MNC on a network configuration dataset, where our proposed MNC outperforms existing baseline methods. Furthermore, an ablation study demonstrates the individual contributions of each module to the framework’s overall effectiveness. This research underscores the potential of LLM-based systems to advance complex network configuration tasks.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"26 ","pages":"Article 200531"},"PeriodicalIF":0.0,"publicationDate":"2025-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144107532","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":"Coffee plant disease identification with an attentive multi-image segmentation framework (MISF) with CycleGAN","authors":"Savitri Kulkarni,&nbsp;P. Deepa Shenoy,&nbsp;K.R. Venugopal","doi":"10.1016/j.iswa.2025.200534","DOIUrl":"10.1016/j.iswa.2025.200534","url":null,"abstract":"&lt;div&gt;&lt;div&gt;This research presents a novel approach to coffee plant disease identification by leveraging an attentive image to-image translation system based on CycleGAN. The proposed system enhances the diagnostic process by transforming images of healthy coffee plants into diverse representations of diseased states using the Multi-Image Segmentation Framework (MISF). Integrating an attention mechanism allows the model to focus selectively on pertinent regions, contributing to improved ac curacy in disease identification. Experimental evaluations on a comprehensive dataset demonstrate the approach’s effectiveness, showcasing its potential as a robust and efficient tool for automated coffee plant disease diagnosis. Hence, the proposed framework implements a strong and reliable synthetic image-to-image data generator based on CycleGAN, referred to as Extensified CycleGAN, along with the automated classification model ResNet101. The proposed work incorporates the benefits of pre-trained models in developing an architecture for synthetic data generation and disease identification by addressing the data imbalance problem effectively.&lt;/div&gt;&lt;div&gt;This research study introduces a novel attentive Multi-Image Segmentation Framework (MISF) combined with an enhanced CycleGAN architecture, tailored specifically for coffee plant disease diagnosis. The innovation lies in integrating region-focused segmentation prior to synthetic image generation, enabling the GAN to produce biologically meaningful and lesion-specific training data. Unlike conventional GAN-based augmentation approaches, this segmentation-driven pipeline emphasizes disease-affected regions, thus improving the discriminatory learning capability of CNN classifiers. Quantitatively, the ResNet101 model trained with the proposed augmented dataset achieved an overall classification accuracy of 98.38 %, significantly outperforming its counterparts trained without segmentation, which recorded only 91.93 % accuracy. Notably, precision and recall for individual diseases such as Rust and Phoma reached up to 98.9 % and 98.1 %, respectively. These results clearly validate the contribution of the segmentation-aware synthetic augmentation strategy, demonstrating its effectiveness in addressing data imbalance, enhancing classification accuracy, and ensuring better generalization in real-world agricultural scenarios.&lt;/div&gt;&lt;div&gt;The Multi-Image Segmentation Framework (MISF) is a composite segmentation technique designed to accurately extract the region of interest (ROI) from coffee leaf images. It combines multiple classical methods—thresholding, K-means clustering, Watershed, GrabCut, and Mean Shift—to isolate disease-specific lesions while eliminating irrelevant background elements. This targeted segmentation ensures that the subsequent synthetic image generation and classification processes focus only on meaningful disease features, thereby enhancing model accuracy and generalization. In comparison to existing sta","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"26 ","pages":"Article 200534"},"PeriodicalIF":0.0,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144124238","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":"Probability propagation for path planning in unknown environments","authors":"Giovanni Di Gennaro ,&nbsp;Amedeo Buonanno ,&nbsp;Giovanni Fioretti ,&nbsp;Francesco Verolla ,&nbsp;Francesco A.N. Palmieri ,&nbsp;Krishna R. Pattipati","doi":"10.1016/j.iswa.2025.200527","DOIUrl":"10.1016/j.iswa.2025.200527","url":null,"abstract":"<div><div>We propose a probability propagation framework for path planning on discrete grids where an agent can navigate in an unknown environment to discover new areas and goals. We introduce a technique in which the probabilistic backward flow provides guidance towards discovering multiple distributed goals and hidden regions. This is achieved using a maximum likelihood path estimation framework in which the hidden areas become constrained goals that “attract” the agent. Simulations on various grids are included in the paper. The results show how this idea, applied to a completely unknown environment and goal position, may provide a unifying and powerful method for distributed dynamic path planning.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"26 ","pages":"Article 200527"},"PeriodicalIF":0.0,"publicationDate":"2025-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143935990","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":"Efficient fragile watermarking for image tampering detection using adaptive matrix on chaotic sequencing","authors":"Prajanto Wahyu Adi ,&nbsp;Aris Sugiharto ,&nbsp;Muhammad Malik Hakim ,&nbsp;De Rosal Ignatius Moses Setiadi ,&nbsp;Edy Winarno","doi":"10.1016/j.iswa.2025.200530","DOIUrl":"10.1016/j.iswa.2025.200530","url":null,"abstract":"<div><div>This paper introduces a novel fragile watermarking technique for image forgery detection using adaptive matrices derived from Walsh and Hadamard transforms. The proposed method overcomes the limitations of traditional SVD, Hadamard, and Walsh methods by eliminating negative coefficients, simplifying the algorithm structure, and optimizing the computational complexity. The embedding process uses a two-stage authentication mechanism with a 16-bit validation scheme, ensuring precise forgery localization. This adaptive approach is intelligently designed to adapt the matrix pattern to the image characteristics. At the same time, the utilization of logistic sequencing enables the generation of non-periodic and non-convergent patterns, which significantly improves authentication efficiency and accuracy. Performance evaluation shows an average PSNR value of 55.90 dB and SSIM above 0.99, indicating a high degree of imperceptibility. In addition, this method achieves detection accuracy comparable to previous approaches, with an overall recall value of 1.00 and a TPR exceeding 0.96 across multiple forgery scenarios. This method offers the best efficiency compared to SVD, Hadamard, and Walsh methods and consistent authentication performance stability across multiple forgery levels. These advantages allow the proposed method to be developed in tampering detection applications that require speed and reliability.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"26 ","pages":"Article 200530"},"PeriodicalIF":0.0,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143935989","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":"Artificial neural networks applied to olive oil production and characterization: A systematic review","authors":"Francesca Lonetti ,&nbsp;Francesca Martelli ,&nbsp;Giovanni Resta","doi":"10.1016/j.iswa.2025.200525","DOIUrl":"10.1016/j.iswa.2025.200525","url":null,"abstract":"<div><div>In recent years, the olive oil sector has experienced growth due to the health benefits associated with olive oil and its increasing demand in international markets. Artificial Neural Networks (ANNs) have emerged as powerful tools in various scientific domains, enhancing both the efficiency and the accuracy of analyses in the olive oil sector. This paper aims to comprehensively review the adoption of ANNs in the assessment of olive oil across production and post-production stages. To achieve this goal, we followed the well-known guidelines of Kitchenham (2004) for performing <em>systematic reviews</em>. This up-to-date review examines literature from the last seven years, analyzing 628 publications and finally selecting 79 primary studies. Through a systematic and comprehensive analysis, this review seeks to provide insights into the current state of research, identify gaps in knowledge, and offer recommendations for future directions in harnessing ANNs to optimize the production and post-production analyses of olive oil.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"26 ","pages":"Article 200525"},"PeriodicalIF":0.0,"publicationDate":"2025-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143935988","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":"Disruptive attacks on artificial neural networks: A systematic review of attack techniques, detection methods, and protection strategies","authors":"Ahmad Alobaid ,&nbsp;Talal Bonny ,&nbsp;Maher Alrahhal","doi":"10.1016/j.iswa.2025.200529","DOIUrl":"10.1016/j.iswa.2025.200529","url":null,"abstract":"<div><div>This paper provides a systematic review of disruptive attacks on artificial neural networks (ANNs). As neural networks become increasingly integral to critical applications, their vulnerability to various forms of attack poses significant security challenges. This review categorizes and analyzes recent advancements in attack techniques, detection methods, and protection strategies for ANNs. It explores various attacks, including adversarial attacks, data poisoning, fault injections, membership inference, model inversion, timing, and watermarking attacks, examining their methodologies, limitations, impacts, and potential improvements. Key findings reveal that while detection and protection mechanisms such as adversarial training, noise injection, and hardware-based defenses have advanced significantly, many existing solutions remain vulnerable to adaptive attack strategies and scalability challenges. Additionally, fault injection attacks at the hardware level pose an emerging threat with limited countermeasures. The review identifies critical gaps in defense strategies, particularly in balancing robustness, computational efficiency, and real-world applicability. Future research should focus on scalable defense solutions to ensure effective deployment across diverse ANN architectures and critical applications, such as autonomous systems. Furthermore, integrating emerging technologies, including generative AI models and hybrid architectures, should be prioritized to better understand and mitigate their vulnerabilities.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"26 ","pages":"Article 200529"},"PeriodicalIF":0.0,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143931536","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}