International Journal of Intelligent Systems最新文献

{"title":"X-SCSANet: Explainable Stack Convolutional Self-Attention Network for Brain Tumor Classification","authors":"Rahad Khan,&nbsp;Rafiqul Islam","doi":"10.1155/int/1444673","DOIUrl":"https://doi.org/10.1155/int/1444673","url":null,"abstract":"<div>\u0000 <p>Brain tumors are devastating and shorten the patient’s life. It has an impact on the physical, psychological, and financial well-being of both patients and family members. Early diagnosis and treatment can reduce patients’ chances of survival. Detecting and diagnosing brain cancers using MRI scans is time-consuming and requires expertise in that domain. Nowadays, instead of traditional approaches to brain tumor analysis, several deep learning models are used to assist professionals and mitigate time. This paper introduces a stack convolutional self-attention network that extracts important local and global features from a freely available MRI scan dataset. Since the medical domain is one of the most sensitive fields, end-users should put their trust in the deep learning model before automating tumor classification. Therefore, the Grad-CAM method has been updated to better explain the model’s output. Combining local and global features improves brain tumor classification performance, with the suggested model reaching an accuracy of 96.44% on the relevant dataset. The proposed model’s precision, specificity, sensitivity, and F1-score are reported as 96.5%, 98.83%, 96.44%, and 96.4%, respectively. Furthermore, the layers’ insights are examined to acquire a deeper knowledge of the decision-making process.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/1444673","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143741668","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":"Locality Sensitive Hashing-Based Deepfake Image Recognition for Athletic Celebrities","authors":"Bo Xiang,&nbsp;Qin Xie,&nbsp;Shuangzhou Bi,&nbsp;Edris Khezri","doi":"10.1155/int/1313970","DOIUrl":"https://doi.org/10.1155/int/1313970","url":null,"abstract":"<div>\u0000 <p>The rapid advancement of deepfake technology poses significant challenges to athletic celebrities, where altered or falsified media can impact athletes’ reputations, fan engagement, and the integrity of match broadcasting. This paper proposes a novel framework for deepfake image recognition for athletic celebrities using locality sensitive hashing (LSH). LSH, an efficient technique for high-dimensional nearest neighbor searches, is employed to detect and differentiate deepfake images from authentic media. By extracting high-dimensional features from images and videos using convolutional neural networks (CNNs), LSH is applied to hash similar content into clusters for quick and accurate deepfake detection. The proposed method is tested on real-world dataset, showing promising results in terms of accuracy and computational efficiency. This research highlights the importance of integrating advanced hashing techniques like LSH in safeguarding the authenticity of digital content and provides insights into future directions for deepfake detection mechanisms.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/1313970","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143698999","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 Question-Aware Few-Shot Text-to-SQL Neural Model for Industrial Databases","authors":"Ren Li,&nbsp;Yu Chen,&nbsp;Hongyi Zhang,&nbsp;Jianxi Yang,&nbsp;Qiao Xiao,&nbsp;Shixin Jiang","doi":"10.1155/int/8124797","DOIUrl":"https://doi.org/10.1155/int/8124797","url":null,"abstract":"<div>\u0000 <p>Intelligent question answering over industrial databases is a challenging task due to the multicolumn context and complex questions. The existing methods need to be improved in terms of SQL generation accuracy. In this paper, we propose a question-aware few-shot Text-to-SQL approach based on the SDCUP pretrained model. Specifically, an attention-based filtering approach is proposed to reduce the redundant information from multiple columns in the industrial database scenario. We further propose an operator semantics enhancement method to improve the ability of identifying complex conditions in queries. Experimental results on the industrial benchmarks in the fields of electric energy and structural inspection show that the proposed model outperforms the baseline models across all few-shot settings.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/8124797","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143707535","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 Lightweight Dynamic Hierarchical Neural Network Model and Learning Paradigm","authors":"Liping Liao,&nbsp;Junlong Lin,&nbsp;Wenjing Zhang,&nbsp;Jun Cai","doi":"10.1155/int/6833629","DOIUrl":"https://doi.org/10.1155/int/6833629","url":null,"abstract":"<div>\u0000 <p>In image analysis scenarios such as the Internet of Things and the metaverse, the introduction of federated learning (FL) is an effective solution to safeguard user data security and meet low-latency requirements during the machine learning process. However, due to the constrained computational power and memory of devices, facilitating the local training of complex models becomes challenging, thereby posing a significant obstacle to the application of FL. Consequently, a lightweight dynamic hierarchical neural network model and its learning paradigm are proposed in this study. Specifically, a lightweight compression method is designed based on enlarged receptive fields and separable convolutions to reduce redundancy in convolutional layer feature maps. A dynamic model partitioning method is devised, grounded in the Q-Learning reinforcement learning algorithm, to enable collaborative model training across multiple devices and enhance the utilization efficiency of device computing and storage resources. Furthermore, a hierarchical federated partition learning (HFSL) paradigm based on complete weight sharing is introduced to facilitate the compatibility of partitioned models with FL. Experimental results show that our lightweight model outperforms existing models in terms of accuracy, lightweight degree, and efficiency on image analysis tasks. Moreover, the proposed HFSL paradigm achieves performance comparable to centralized training.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/6833629","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143690199","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":"CSI Acquisition in Internet of Vehicle Network: Federated Edge Learning With Model Pruning and Vector Quantization","authors":"Yi Wang,&nbsp;Junlei Zhi,&nbsp;Linsheng Mei,&nbsp;Wei Huang","doi":"10.1155/int/5813659","DOIUrl":"https://doi.org/10.1155/int/5813659","url":null,"abstract":"<div>\u0000 <p>The conventional machine learning (ML)–based channel state information (CSI) acquisition has overlooked the potential privacy disclosure and estimation overhead problem caused by transmitting pilot datasets during the estimation stage. In this paper, we propose federated edge learning for CSI acquisition to protect the data privacy in the Internet of vehicle network with massive antenna array. To reduce the channel estimation overhead, the joint model pruning and vector quantization algorithm for network gradient parameters is presented to reduce the amount of exchange information between the centralized server and devices. This scheme allows for local fine-tuning to adapt the global model to the channel characteristics of each device. In addition, we also provide theoretical guarantees of convergence and quantization error bound in closed form, respectively. Simulation results demonstrate that the proposed FL-based CSI acquisition with model pruning and vector quantization scheme can efficiently improve the performance of channel estimation while reducing the communication overhead.</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/5813659","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143638991","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 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}