International Journal of Intelligent Systems最新文献

{"title":"A Novel Correlation Coefficient for Spherical Fuzzy Sets and Its Application in Pattern Recognition, Medical Diagnosis, and Mega Project Selection","authors":"Mehboob Ali,&nbsp;Wajid Ali,&nbsp;Ishtiaq Hussain,&nbsp;Rasool Shah","doi":"10.1155/int/9164932","DOIUrl":"https://doi.org/10.1155/int/9164932","url":null,"abstract":"<div>\u0000 <p>The correlation coefficient (CC) is a statistical measure that is very useful to quantify the strength and direction of the relationship between two variables, processes, or sets. The primary objective of this paper is to propose a novel CC explicitly tailored for spherical fuzzy sets (SFSs), aiming to address the limitations and drawbacks associated with existing CCs. Our approach employs statistical concepts to quantify the correlation between variables and datasets within the context of SFSs. We formulate our proposed CC for SFSs by incorporating variance and covariance as fundamental components. This innovative approach not only accurately quantifies the degree of correlation between two SFSs but also characterizes the nature of their relationship, whether it is positive, neutral, or negative. As a result, our CC yields numerical values within the range of [−1, 1]. In contrast, existing methods focus solely on measuring the degree of association between two SFSs and are unable to differentiate the nature of the relationship, especially in cases of inverse correlation. We conduct a comparison to evaluate the efficiency of our proposed scheme in comparison to existing techniques, using numerical examples to showcase the dominance of our method. The comparative results indicate that our proposed approach effectively addresses the limitations of existing methods and produces more reliable and precise results. Furthermore, we applied our method to address three real-world challenges in pattern recognition, medical diagnosis, and mega-project selection, demonstrating its practicality, advantages, and usefulness.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/9164932","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143646129","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}

{"title":"ISAC-Assisted Defense Mechanisms for PUE Attacks in Cognitive Radio Networks","authors":"Junxian Li,&nbsp;Baogang Li,&nbsp;Guanfei You,&nbsp;Jingxi Zhang,&nbsp;Wei Zhao","doi":"10.1155/int/6618969","DOIUrl":"https://doi.org/10.1155/int/6618969","url":null,"abstract":"<div>\u0000 <p>With the evolution of communication systems toward the sixth-generation technology (6G), intelligent cognitive communication has gained considerable attention. As an important part of intelligent cognitive communication, cognitive radio (CR) offers promising prospects for efficient spectrum utilization. However, with the introduction of cognitive capabilities, CR networks (CRNs) face not only common security threats in wireless systems, but also unique security threats, including primary user emulation (PUE) attacks, endangering communication reliability and confidentiality. In order to enhance the defense ability of CRNs against PUE attacks, this paper proposes an integrated sensing and communication (ISAC)-assisted approach. Leveraging ISAC technology, our scheme enhances location detection precision. We introduce a high-resolution perception signal parameter estimation method and a position-based identity authentication scheme. Furthermore, deep reinforcement learning is used to dynamically optimize the authentication threshold to ensure the stability of authentication in dynamic scenarios. Simulation results show that the proposed scheme is effective in resisting PUE attacks and improves the security and reliability of CRNs.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/6618969","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143639148","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}

{"title":"Deep Convolutional Network-Based Probabilistic Selection Approach for Multiclassification of Brain Tumors Using Magnetic Resonance Imaging","authors":"Rajat Mehrotra,&nbsp;M. A. Ansari,&nbsp;Rajeev Agrawal,&nbsp;Md Belal Bin Heyat,&nbsp;Pragati Tripathi,&nbsp;Eram Sayeed,&nbsp;Saba Parveen,&nbsp;John Irish G. Lira,&nbsp;Hadaate Ullah","doi":"10.1155/int/6914757","DOIUrl":"https://doi.org/10.1155/int/6914757","url":null,"abstract":"<div>\u0000 <p>The human brain’s computer-assisted prognosis (CAP) system relies heavily on the self-regulating characterization of tumors. Despite being extensively researched, the classification of brain tumors into meningioma, glioma, and pituitary types using magnetic resonance (MR) images presents significant challenges. Although biopsies are currently the gold standard for evaluating tumors, the need for noninvasive and accurate methods to grade brain tumors is increasing due to the risks associated with invasive biopsies. The objective is to introduce a noninvasive brain tumor grading system based on MR imaging (MRI) and deep learning (DL) utilizing probabilistic selection techniques. In the proposed method, the best three of the seven state-of-the-art deep convolutional networks are chosen after extensive experimentation and combined with a probabilistic selection technique to enhance the overall performance of the proposed classification model. The results elucidate that the proposed model successfully classifies the tumor types into Glioma, Meningioma, and Pituitary achieving a sensitivity of 0.928, 0.939, and 0.992, respectively for each tumor type. Also, the precision in classifying the tumor classes is attained as 0.969, 0.932, and 0.957, respectively claiming an accuracy of 0.966, 0.956, and 0.983 for each of the three classes. The proposed model achieved an overall classification accuracy of 96.06%, surpassing the state-of-the-art advanced and sophisticated techniques. Extensive experiments were performed on brain MRI datasets to demonstrate the enhanced performance of the proposed approach. The suggested probabilistic selection technique yielded promising classification results for brain tumors and exhibited the potential to leverage the strengths of various models.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/6914757","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143632635","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}

{"title":"TabNet-SFO: An Intrusion Detection Model for Smart Water Management in Smart Cities","authors":"Wahid Rajeh,&nbsp;Majed M. Aborokbah,&nbsp;Manimurugan S.,&nbsp;Tawfiq Alashoor,&nbsp;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}

{"title":"Energy-Efficient Resource Allocation for Urban Traffic Flow Prediction in Edge-Cloud Computing","authors":"Ahmad Ali,&nbsp;Inam Ullah,&nbsp;Sushil Kumar Singh,&nbsp;Amin Sharafian,&nbsp;Weiwei Jiang,&nbsp;Hammad I. Sherazi,&nbsp;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}

{"title":"A Scalable and Generalised Deep Learning Framework for Anomaly Detection in Surveillance Videos","authors":"Sabah Abdulazeez Jebur,&nbsp;Laith Alzubaidi,&nbsp;Ahmed Saihood,&nbsp;Khalid A. Hussein,&nbsp;Haider Kadhim Hoomod,&nbsp;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}

{"title":"Joint Physical Layer Security and Information Freshness Analysis and Optimization for RIS-Assisted ISAC With Finite Blocklength","authors":"Wei Zhao,&nbsp;Jianxin Ni,&nbsp;Baogang Li,&nbsp;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}

{"title":"Internet of Things in Healthcare Research: Trends, Innovations, Security Considerations, Challenges and Future Strategy","authors":"Attiq Ur Rehman,&nbsp;Songfeng Lu,&nbsp;Md Belal Bin Heyat,&nbsp;Muhammad Shahid Iqbal,&nbsp;Saba Parveen,&nbsp;Mohd Ammar Bin Hayat,&nbsp;Faijan Akhtar,&nbsp;Muhammad Awais Ashraf,&nbsp;Owais Khan,&nbsp;Dustin Pomary,&nbsp;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}

{"title":"A Two-Stage CNN-Based Method for Enhanced Metastasis Segmentation in SPECT Bone Scans","authors":"Yang He,&nbsp;Qiang Lin,&nbsp;Zhengxing Man,&nbsp;Yongchun Cao,&nbsp;Xianwu Zeng,&nbsp;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}

{"title":"Software Defect Prediction Based on Fuzzy Cost Broad Learning System","authors":"Heling Cao,&nbsp;Zhiying Cui,&nbsp;Yonghe Chu,&nbsp;Lina Gong,&nbsp;Guangen Liu,&nbsp;Yun Wang,&nbsp;Fangchao Tian,&nbsp;Peng Li,&nbsp;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}