International Journal of Intelligent Systems最新文献_第2页

TabNet-SFO: An Intrusion Detection Model for Smart Water Management in Smart Cities

IF 5 2区计算机科学

International Journal of Intelligent Systems Pub Date : 2025-03-14 DOI: 10.1155/int/6281847

Wahid Rajeh, Majed M. Aborokbah, Manimurugan S., Tawfiq Alashoor, Karthikeyan P.

{"title":"TabNet-SFO: An Intrusion Detection Model for Smart Water Management in Smart Cities","authors":"Wahid Rajeh, Majed M. Aborokbah, Manimurugan S., Tawfiq Alashoor, Karthikeyan P.","doi":"10.1155/int/6281847","DOIUrl":"https://doi.org/10.1155/int/6281847","url":null,"abstract":"<div>\u0000 <p>As Smart City (SC) infrastructures evolve rapidly, securing critical systems like smart water management (SWM) becomes paramount to protecting against cyber threats. Enhancing the security, sustainability and execution of conventional schemes is considered significant in developing smart environments. Intrusion detection systems (IDS) can be effectively leveraged to realise this security objective in an Internet of Things (IoT)-based smart environment. This research addresses this need by proposing a novel IDS model called TabNet architecture optimised using Sailfish Optimisation (SFO). The TabNet-SFO model was specifically developed for SWM in SC applications. The proposed IDS model includes data collection, preprocessing, feature selection and classification processes. For training the model, this research used the CIC-DDoS-2019 dataset, and for evaluation, real-time data collected using an IoT-based smart water metre are used. The preprocessing step eliminates unnecessary features, cleans the data, encodes labels and normalises the applied datasets. After preprocessing, the TabNet model selects significant features in the dataset. The TabNet architecture was optimised using the SFO algorithm, which allows hyperparameter tuning and model optimisation. The proposed model demonstrated improved detection accuracy and efficiency on both the simulated and real-time datasets. The model attained a 98.90% accuracy, a 98.85% recall, a 98.80% precision, a 98.82% specificity and a 98.78% f1 score on the CIC-DDoS dataset and a 99.21% accuracy, a 99.02% recall, a 99.05% precision, a 99.10% specificity and a 99.18% f1 score on real-time data. Compared to existing models, the TabNet-SFO model outperformed all existing models in terms of performance metrics and validated its efficiency in detecting attacks.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/6281847","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143622464","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Energy-Efficient Resource Allocation for Urban Traffic Flow Prediction in Edge-Cloud Computing

IF 5 2区计算机科学

International Journal of Intelligent Systems Pub Date : 2025-03-14 DOI: 10.1155/int/1863025

Ahmad Ali, Inam Ullah, Sushil Kumar Singh, Amin Sharafian, Weiwei Jiang, Hammad I. Sherazi, Xiaoshan Bai

{"title":"Energy-Efficient Resource Allocation for Urban Traffic Flow Prediction in Edge-Cloud Computing","authors":"Ahmad Ali, Inam Ullah, Sushil Kumar Singh, Amin Sharafian, Weiwei Jiang, Hammad I. Sherazi, Xiaoshan Bai","doi":"10.1155/int/1863025","DOIUrl":"https://doi.org/10.1155/int/1863025","url":null,"abstract":"<div>\u0000 <p>Understanding complex traffic patterns has become more challenging in the context of rapidly growing city road networks, especially with the rise of Internet of Vehicles (IoV) systems that add further dynamics to traffic flow management. This involves understanding spatial relationships and nonlinear temporal associations. Accurately predicting traffic in these scenarios, particularly for long-term sequences, is challenging due to the complexity of the data involved in smart city contexts. Traditional ways of predicting traffic flow use a single fixed graph structure based on the location. This structure does not consider possible correlations and cannot fully capture long-term temporal relationships among traffic flow data, making predictions less accurate. We propose a novel traffic prediction framework called Multi-scale Attention-Based Spatio-Temporal Graph Convolution Recurrent Network (MASTGCNet) to address this challenge. MASTGCNet records changing features of space and time by combining gated recurrent units (GRUs) and graph convolution networks (GCNs). Its design incorporates multiscale feature extraction and dual attention mechanisms, effectively capturing informative patterns at different levels of detail. Furthermore, MASTGCNet employs a resource allocation strategy within edge computing to reduce energy usage during prediction. The attention mechanism helps quickly decide which services are most important. Using this information, smart cities can assign tasks and allocate resources based on priority to ensure high-quality service. We have tested this method on two different real-world datasets and found that MASTGCNet predicts significantly better than other methods. This shows that MASTGCNet is a step forward in traffic prediction.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/1863025","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143622463","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Scalable and Generalised Deep Learning Framework for Anomaly Detection in Surveillance Videos

IF 5 2区计算机科学

International Journal of Intelligent Systems Pub Date : 2025-03-13 DOI: 10.1155/int/1947582

Sabah Abdulazeez Jebur, Laith Alzubaidi, Ahmed Saihood, Khalid A. Hussein, Haider Kadhim Hoomod, YuanTong Gu

{"title":"A Scalable and Generalised Deep Learning Framework for Anomaly Detection in Surveillance Videos","authors":"Sabah Abdulazeez Jebur, Laith Alzubaidi, Ahmed Saihood, Khalid A. Hussein, Haider Kadhim Hoomod, YuanTong Gu","doi":"10.1155/int/1947582","DOIUrl":"https://doi.org/10.1155/int/1947582","url":null,"abstract":"<div>\u0000 <p>Anomaly detection in videos is challenging due to the complexity, noise, and diverse nature of activities such as violence, shoplifting, and vandalism. While deep learning (DL) has shown excellent performance in this area, existing approaches have struggled to apply DL models across different anomaly tasks without extensive retraining. This repeated retraining is time-consuming, computationally intensive, and unfair. To address this limitation, a new DL framework is introduced in this study, consisting of three key components: transfer learning to enhance feature generalization, model fusion to improve feature representation, and multitask classification to generalize the classifier across multiple tasks without training from scratch when a new task is introduced. The framework’s main advantage is its ability to generalize without requiring retraining from scratch for each new task. Empirical evaluations demonstrate the framework’s effectiveness, achieving an accuracy of 97.99% on the RLVS (violence detection), 83.59% on the UCF dataset (shoplifting detection), and 88.37% across both datasets using a single classifier without retraining. Additionally, when tested on an unseen dataset, the framework achieved an accuracy of 87.25% and 79.39% on violence and shoplifting datasets, respectively. The study also utilises two explainability tools to identify potential biases, ensuring robustness and fairness. This research represents the first successful resolution of the generalization issue in anomaly detection, marking a significant advancement in the field.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/1947582","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143602701","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Joint Physical Layer Security and Information Freshness Analysis and Optimization for RIS-Assisted ISAC With Finite Blocklength

IF 5 2区计算机科学

International Journal of Intelligent Systems Pub Date : 2025-03-13 DOI: 10.1155/int/4075274

Wei Zhao, Jianxin Ni, Baogang Li, Shuai Hao

{"title":"Joint Physical Layer Security and Information Freshness Analysis and Optimization for RIS-Assisted ISAC With Finite Blocklength","authors":"Wei Zhao, Jianxin Ni, Baogang Li, Shuai Hao","doi":"10.1155/int/4075274","DOIUrl":"https://doi.org/10.1155/int/4075274","url":null,"abstract":"<div>\u0000 <p>Aiming to address the security and timeliness challenges in reconfigurable intelligent surface (RIS)-assisted integrated sensing and communication (ISAC) system with finite blocklength (FBL), this paper jointly investigates the communication security, sensing security, and information freshness performance of the system in the presence of communicating eavesdropper and sensing eavesdropper. Specifically, based on statistical channel state information (CSI), approximate closed-form expressions for secrecy throughput, average age of information (AoI), and channel parameter estimation errors are derived and analyzed to characterize the performance of communication security, information freshness, and sensing security. The asymptotic analyses between secrecy throughput and blocklength, number of antennas, and number of RIS reflecting elements are established. Furthermore, an optimization problem for maximizing sum secrecy throughput is established under the timeliness, sensing security, transmit power, and RIS unit modulus constraints. To handle the intractable stochastic nonconvex problem, a joint alternating optimization method based on noncooperative game and stochastic successive convex approximation (NCG-SSCA) is proposed by jointly designing RIS phase shift, transmit beamforming vector, sensing signal covariance, and blocklength. Simulation results validate our theoretical derivations and conclusions in the performance analysis. It is also shown that compared with SSCA and stochastic gradient descent (SGD) methods, the NCG-SSCA method proposed in this paper achieves an increase in sum secrecy throughput by 10.4% and 16.3% with faster convergence speed.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/4075274","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143602700","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Internet of Things in Healthcare Research: Trends, Innovations, Security Considerations, Challenges and Future Strategy

IF 5 2区计算机科学

International Journal of Intelligent Systems Pub Date : 2025-03-12 DOI: 10.1155/int/8546245

Attiq Ur Rehman, Songfeng Lu, Md Belal Bin Heyat, Muhammad Shahid Iqbal, Saba Parveen, Mohd Ammar Bin Hayat, Faijan Akhtar, Muhammad Awais Ashraf, Owais Khan, Dustin Pomary, Mohamad Sawan

{"title":"Internet of Things in Healthcare Research: Trends, Innovations, Security Considerations, Challenges and Future Strategy","authors":"Attiq Ur Rehman, Songfeng Lu, Md Belal Bin Heyat, Muhammad Shahid Iqbal, Saba Parveen, Mohd Ammar Bin Hayat, Faijan Akhtar, Muhammad Awais Ashraf, Owais Khan, Dustin Pomary, Mohamad Sawan","doi":"10.1155/int/8546245","DOIUrl":"https://doi.org/10.1155/int/8546245","url":null,"abstract":"<div>\u0000 <p>The Internet of Things (IoT) has become a transformative force across various sectors, including healthcare, offering new opportunities for automation and enhanced service delivery. The evolving architecture of the IoT presents significant challenges in establishing a comprehensive cyber-physical framework. This paper reviews recent advancements in IoT-driven healthcare automation, focussing on integrating technologies such as cloud computing, augmented reality and wearable devices. This work examines the IoT network architectures and platforms that support healthcare applications while addressing critical security and privacy issues, including specific threat models, attack classifications and security prerequisites relevant to the healthcare sector. This study highlights how emerging technologies like distributed intelligence, big data analytics and wearable devices are incorporated into healthcare to improve patient care and streamline medical operations. The findings reveal significant potential for IoT to transform healthcare practices, particularly in-patient monitoring, and clinical decision-making. However, security and privacy concerns continue to be a substantial barrier. The paper also explores the implications of global IoT and ehealth strategies and their influence on sustainable economic and community growth. It proposes an innovative cooperative security model to mitigate security risks in IoT-enabled healthcare systems. Finally, it identifies key unresolved challenges and opportunities for future research in IoT-based healthcare.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/8546245","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143602547","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Two-Stage CNN-Based Method for Enhanced Metastasis Segmentation in SPECT Bone Scans

IF 5 2区计算机科学

International Journal of Intelligent Systems Pub Date : 2025-03-11 DOI: 10.1155/int/3135835

Yang He, Qiang Lin, Zhengxing Man, Yongchun Cao, Xianwu Zeng, Xiaodi Huang

{"title":"A Two-Stage CNN-Based Method for Enhanced Metastasis Segmentation in SPECT Bone Scans","authors":"Yang He, Qiang Lin, Zhengxing Man, Yongchun Cao, Xianwu Zeng, Xiaodi Huang","doi":"10.1155/int/3135835","DOIUrl":"https://doi.org/10.1155/int/3135835","url":null,"abstract":"<div>\u0000 <p>Accurate segmentation of metastatic lesions is crucial for improving the quality of patient care, particularly in the context of bone scans. However, existing automated methods, which are predominantly data-driven, exhibit limited performance and lack interpretability. To address these challenges, we propose a novel two-stage framework that integrates human domain knowledge with data patterns to enhance CNN-based metastasis lesion segmentation in bone scans. The proposed method comprises two phases: Stage I detects hotspots in bone scans using a CNN-based model, while Stage II identifies actual metastases by leveraging clinical knowledge of uptake intensity asymmetry. Our approach incorporates a dual-sampling scheme inspired by diagnostic patterns and an enhanced feature extractor within the hotspot segmentation network, thus augmenting the detection capabilities of traditional data-driven CNN models. The assessment of symmetrical uptake intensity starts with the symmetry axis of the trunk in the image, followed by a composite similarity measure that considers both geometric symmetry and intensity consistency. Experimental evaluations on 302 clinical cases reveal that our proposed segmentation network improves the Dice similarity coefficient score by 4.34% compared to the baseline method. Furthermore, integrating clinical knowledge significantly reduces false positives, improving the class pixel accuracy score by 2.39% and demonstrating notable adaptability to other segmentation models. Comparative analysis with existing models for metastasis lesion segmentation demonstrates the superior performance of our approach. By incorporating domain knowledge into data patterns, our method enhances automated segmentation performance and bridges the gap between domain expertise and data-driven methodologies in the automated analysis of low-resolution bone scans.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/3135835","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143594984","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Software Defect Prediction Based on Fuzzy Cost Broad Learning System

IF 5 2区计算机科学

International Journal of Intelligent Systems Pub Date : 2025-03-11 DOI: 10.1155/int/6463038

Heling Cao, Zhiying Cui, Yonghe Chu, Lina Gong, Guangen Liu, Yun Wang, Fangchao Tian, Peng Li, Haoyang Ge

{"title":"Software Defect Prediction Based on Fuzzy Cost Broad Learning System","authors":"Heling Cao, Zhiying Cui, Yonghe Chu, Lina Gong, Guangen Liu, Yun Wang, Fangchao Tian, Peng Li, Haoyang Ge","doi":"10.1155/int/6463038","DOIUrl":"https://doi.org/10.1155/int/6463038","url":null,"abstract":"<div>\u0000 <p>Software defect prediction (SDP) is an effective approach to ensure software reliability. Machine learning models have been widely employed in SDP, but they ignore the impact of class imbalance, noise and outliers on the prediction performance. This study proposes a fuzzy cost broad learning system (FC-BLS). FC-BLS not only handles class imbalance problems but also considers the specific sample distribution to address noise and outliers in software defect datasets. Our approach draws fully on the idea of the cost matrix and fuzzy membership functions. It introduces them to BLS, where the cost matrix prioritises the training errors on the minority samples. Hence, the classification hyperplane position is more reasonable, and fuzzy membership functions calculate the membership degree of the sample in a feature mapping space to remove the prediction error caused by noise and outlier samples. Then, the optimisation problem is constructed based on the idea that the minority class and normal instances have relatively high costs. By contrast, the majority class and noise and outlier instances have relatively small costs. This study conducted experiments on nine NASA SDP datasets, and the experimental findings demonstrated the effectiveness of the proposed methodology on most datasets.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/6463038","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143594926","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Exploiting SWIPT–Enabled ARQ–Based Bidirectional Cellular IoV Spectrum Sharing Protocol and Its Performance Analysis

IF 5 2区计算机科学

International Journal of Intelligent Systems Pub Date : 2025-03-06 DOI: 10.1155/int/9933853

Suoping Li, Tongtong Jia, Qian Yang, Yin Ma, Jaafar Gaber

{"title":"Exploiting SWIPT–Enabled ARQ–Based Bidirectional Cellular IoV Spectrum Sharing Protocol and Its Performance Analysis","authors":"Suoping Li, Tongtong Jia, Qian Yang, Yin Ma, Jaafar Gaber","doi":"10.1155/int/9933853","DOIUrl":"https://doi.org/10.1155/int/9933853","url":null,"abstract":"<div>\u0000 <p>A new spectrum sharing protocol with simultaneous wireless information and power transfer (SWIPT) is proposed to cope with the increasingly prominent problem of spectrum and energy scarcity. It operates within a cognitive radio network (CRN) in the context of cellular IoV (C-IoV), enabling bidirectional communication between two vehicles parked within the base station coverage (VnBSs) while facilitating cooperation for a pair of primary users (PUs), i.e., VnBSs can act as relays to provide cooperation communication for the cell–edge vehicle user (eVU). Unlike most existing work, both VnBSs can use time switching (TS) to obtain energy from radio frequency (RF) signals emitted from the base station. In order to enhance the reliability of the network, this study incorporates the automatic repeat request (ARQ) technique in the CRN supported by the nonorthogonal multiple access (NOMA) and SWIPT, which has not been performed in other works. Based on this, the transmission is divided into one energy harvesting (EH) phase and three information processing (IP) phases. A new packet for PUs is transmitted in the first IP phase and is allowed to be retransmitted twice in the last two IP phases depending on the decoding. VnBSs act as relays to obtain energy in the EH phase to assist in retransmitting the PU’s packets and sending their own packets in the last two IP phases. The system states are analyzed by building a one-dimensional Markov chain, and the end-to-end outage probability (OP) is calculated for each state under the Nakagami-m fading channel. Using these two results, the OP of the primary and secondary networks, system throughput and energy efficiency (EE) are derived. Finally, the validity of the derived results is verified by Monte Carlo simulation using MATLAB and compared with the protocol without ARQ, and the protocol proposed shows a better performance.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/9933853","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143564806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Development and Validation of Explainable Artificial Intelligence System for Automatic Diagnosis of Cervical Lymphadenopathy From Ultrasound Images

IF 5 2区计算机科学

International Journal of Intelligent Systems Pub Date : 2025-03-06 DOI: 10.1155/int/5432766

Ming Xu, Yubiao Yue, Zhenzhang Li, Yinhong Li, Guoying Li, Haihua Liang, Di Liu, Xiaohong Xu

{"title":"Development and Validation of Explainable Artificial Intelligence System for Automatic Diagnosis of Cervical Lymphadenopathy From Ultrasound Images","authors":"Ming Xu, Yubiao Yue, Zhenzhang Li, Yinhong Li, Guoying Li, Haihua Liang, Di Liu, Xiaohong Xu","doi":"10.1155/int/5432766","DOIUrl":"https://doi.org/10.1155/int/5432766","url":null,"abstract":"<div>\u0000 <p>Clinical diagnosis of cervical lymphadenopathy (CLA) using ultrasound images is a time-consuming and laborious process that heavily relies on expert experience. This study aimed to develop an intelligent computer-aided diagnosis (CAD) system using deep learning models (DLMs) to enhance the efficiency of ultrasound screening and diagnostic accuracy of CLA. We retrospectively collected 4089 ultrasound images of cervical lymph nodes across four categories from two hospitals: normal, benign CLA, primary malignant CLA, and metastatic malignant CLA. We employed transfer learning, data augmentation, and five-fold cross-validation to evaluate the diagnostic performance of DLMs with different architectures. To boost the application potential of DLMs, we investigated the potential impact of various optimizers and machine learning classifiers on their diagnostic performance. Our findings revealed that EfficientNet-B1 with transfer learning and root-mean-square-propagation optimizer achieved state-of-the-art performance, with overall accuracies of 97.0% and 90.8% on the internal and external test sets, respectively. Additionally, human–machine comparison experiments and the implementation of explainable artificial intelligence technology further enhance the reliability and safety of DLMs and help clinicians easily understand the DLM results. Finally, we developed an application that can be implemented in systems running Microsoft Windows. However, additional prospective studies are required to validate the clinical utility of the developed application. All pretrained DLMs, codes, and application are available at https://github.com/YubiaoYue/DeepUS-CLN.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/5432766","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143564807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Improved A ∗ Algorithm Based on Simulated Annealing and Multidistance Heuristic Function

IF 5 2区计算机科学

International Journal of Intelligent Systems Pub Date : 2025-03-05 DOI: 10.1155/int/5979509

Yuandong Chen, Jinhao Pang, Zeyang Huang, Yuchen Gou, Zhen Jiang, Dewang Chen

{"title":"An Improved A ∗ Algorithm Based on Simulated Annealing and Multidistance Heuristic Function","authors":"Yuandong Chen, Jinhao Pang, Zeyang Huang, Yuchen Gou, Zhen Jiang, Dewang Chen","doi":"10.1155/int/5979509","DOIUrl":"https://doi.org/10.1155/int/5979509","url":null,"abstract":"<div>\u0000 <p>The traditional A <sup>∗</sup> algorithm has problems such as low search speed and huge expansion nodes, resulting in low algorithm efficiency. This article proposes a circular arc distance calculation method in the heuristic function, which combines the Euclidean distance and the Manhattan distance as radius, uses a deviation distance as the correction, and assignes dynamic weights to the combined distance to make the overall heuristic function cost close to reality. Furthermore, the repulsive potential field function and turning cost are introduced into the heuristic function, to consider the relative position of obstacles while minimizing turns in the path. In order to reduce the comparison of nodes with similar cost values, the bounded suboptimal method is used, and the idea of simulated annealing is introduced to overcome the local optima trapped by node expansion. Simulation experiments show that the average running time of the improved algorithm has decreased by about 70%, the number of extended nodes has decreased by 92%, and the path has also been shortened, proving the effectiveness of the algorithm improvement.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/5979509","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143554985","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0