International Journal of Intelligent Systems最新文献

{"title":"Development and Applications of Penalty-Based Aggregation Operators in Multicriteria Decision Making","authors":"Shruti Rathod,&nbsp;Manoj Sahni,&nbsp;Jose M. Merigo","doi":"10.1155/int/6069158","DOIUrl":"https://doi.org/10.1155/int/6069158","url":null,"abstract":"<div>\u0000 <p>This article develops a new penalty-based aggregation operator known as the penalty-based induced ordered weighted averaging (P-IOWA) operator which is an extension of penalty-based ordered weighted averaging (P-OWA) operator. Our goal is to figure out how the induced variable realigns penalties when gathering information. We extend the P-OWA and P-IOWA operators with the different means such as generalized mean and quasi-arithmetic mean. This article also includes different families of P-OWA and P-IOWA operators. The value of these new operators is demonstrated through a case study centered on investment matters. This study evaluates the economic and governance performance of seven South Asian nations utilizing nine indicators from 2021 data. The research examines 5 economic indicators including GDP growth, exports and imports (% of GDP), inflation, and labor force metrics, alongside 4 governance indicators focusing on corruption control, government effectiveness, and political stability. We use min–max normalization to standardize the varied values, which originally ranged from 0.5% to 77.7% across various metrics. Following this, the normalized inverse penalty method is used to derive optimal weights for these indicators, tackling the task of combining multidimensional data. Subsequently, we implement and evaluate various penalty-based aggregation methodologies on the normalized data, each offering a distinct approach to penalizing outliers and balancing indicator weights. The study compares the results obtained from these operators to assess their impact on country rankings and overall performance evaluation. This approach allows for a comprehensive comparison of countries’ performances, integrating both economic and governance dimensions into a single, quantifiable framework.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/6069158","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143121484","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":"Improving the Generalization and Robustness of Computer-Generated Image Detection Based on Contrastive Learning","authors":"Yifang Chen,&nbsp;Weiwu Yin,&nbsp;Anwei Luo,&nbsp;Jianhua Yang,&nbsp;Jie Wang","doi":"10.1155/int/9939096","DOIUrl":"https://doi.org/10.1155/int/9939096","url":null,"abstract":"<div>\u0000 <p>With the rapid development of image generation techniques, it becomes much more difficult to distinguish high-quality computer-generated (CG) images from photographic (PG) images, challenging the authenticity and credibility of digital images. Therefore, distinguishing CG images from PG images has become an important research problem in image forensics, and it is crucial to develop reliable methods to detect CG images in practical scenarios. In this paper, we proposed a forensics contrastive learning (FCL) framework to adaptively learn intrinsic forensics features for the general and robust detection of CG images. The data augmentation module is specially designed for CG image forensics, which reduces the interference of forensic-irrelevant information and enhances discrimination features between CG and PG images in both the spatial and frequency domains. Instance-wise contrastive loss and patch-wise contrastive loss are simultaneously applied to capture critical discrepancies between CG and PG images from global and local views. Extensive experiments on different public datasets and common postprocessing operations demonstrate that our approach can achieve significantly better generalization and robustness than the state-of-the-art approaches. This manuscript was submitted as a pre-print in the following link https://papers.ssrn.com/-sol3/papers.cfm?abstract_id=4778441.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/9939096","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143121228","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":"Diabetes Prediction Using an Optimized Variational Quantum Classifier","authors":"Wajiha Rahim Khan,&nbsp;Muhammad Ahmad Kamran,&nbsp;Misha Urooj Khan,&nbsp;Malik Muhammad Ibrahim,&nbsp;Kwang Su Kim,&nbsp;Muhammad Umair Ali","doi":"10.1155/int/1351522","DOIUrl":"https://doi.org/10.1155/int/1351522","url":null,"abstract":"<div>\u0000 <p>Quantum information processing introduces novel approaches for classical data encoding to encompass the complex patterns of input data of practical computational challenges using basic principles of quantum mechanics. The classification of diabetes is an example of a problem that can be efficiently resolved by using quantum unitary operations and the variational quantum classifier (VQC). This study demonstrates the effects of the number of qubits, types of feature maps, optimizers’ class, and the number of layers in the parametrized circuit, and the number of learnable parameters in ansatz influences the effectiveness of the VQC. In total, 76 variants of VQC are analyzed for four and eight qubits’ cases and their results are compared with six classical machine learning models to predict diabetes. Three different types of feature maps (Pauli, Z, and ZZ) are implemented during analysis in addition to three different optimizers (COBYLA, SPSA and SLSQP). Experiments are performed using the PIMA Indian Diabetes Dataset (PIDD). The results conclude that VQC with six layers embedded with an error correction scaling factor of 0.01 and having ZZ feature map and COBYLA optimizer outperforms other quantum variants. The optimal proposed model attained the accuracy of 0.85 and 0.80 for eight and four qubits’ cases, respectively. In addition, the final quantum model among 76 variants was compared with six classical machine learning models. The results suggest that the proposed VQC model has outperformed four classical models including SVM, random forest (RF), decision tree (DT), and linear regression (LR).</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/1351522","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143120204","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":"Intelligent Joint Optimization of Deployment and Task Scheduling for Mobile Users in Multi-UAV-Assisted MEC System","authors":"Mohamed Abdel-Basset,&nbsp;Reda Mohamed,&nbsp;Amira Salam,&nbsp;Karam M. Sallam,&nbsp;Ibrahim M. Hezam,&nbsp;Ibrahim Radwan","doi":"10.1155/int/7224877","DOIUrl":"https://doi.org/10.1155/int/7224877","url":null,"abstract":"<div>\u0000 <p>Mobile edge computing (MEC) servers integrated with multi-unmanned aerial vehicles (multi-UAVs) present a new system the multi-UAV-assisted MEC system. This system relies on the mobility of the UAVs to reduce the transmission distance between the servers and mobile users, thereby enhancing service quality and minimizing the overall energy consumption. Achieving optimal UAV deployment and precise task scheduling is crucial for improved coverage and service quality in this system. This problem is framed as a nonconvex optimization problem known as joint task scheduling and deployment optimization. Recently, an optimization technique based on a dual-layer framework: Upper layer optimization and lower layer optimization have been proposed to tackle this problem and achieved superior performance compared to the alternative methods. In this framework, the lower layer was responsible for task scheduling optimization, while the upper layer was designed to assist in optimizing UAV deployment and thus achieving improved coverage and enhanced task scheduling for mobile users, thereby minimizing the total energy consumption. However, further refinement of upper layer optimization is needed to improve the deployment process. In this study, the upper layer undergoes enhancement through key modifications: First, random selection of the solutions is replaced with sequential selection to maintain the unique characteristics of each individual throughout the optimization process, fostering both exploration and exploitation. Second, a selection of recently reported metaheuristic algorithms, such as spider wasp optimizer (SWO), generalized normal distribution optimization (GNDO), and gradient-based optimizer (GBO), are adapted to optimize UAV deployments. Both improved upper layer and lower layer optimization led to the development of novel, more effective optimization approaches, including IToGBOTaS, IToGNDOTaS, and IToSWOTaS. These techniques are evaluated using nine instances with a variety of mobile tasks ranging from 100 to 900 to test their stability and then compared to different optimization techniques to measure their effectiveness. This comparison is based on several statistical information to determine the superiority and difference between their outcomes. The results reveal that IToGBOTaS and IToSWOTaS exhibit slightly superior performance compared to all other algorithms, showcasing their competitiveness and efficacy in addressing the optimization challenges of the multi-UAV-assisted MEC system.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/7224877","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143119853","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":"Enhancing Pharmacy Warehouse Management With Faster R-CNN for Accurate and Reliable Pharmaceutical Product Identification and Counting","authors":"Mohammad Javad Tavakoli,&nbsp;Fatemeh Fazl,&nbsp;Mahsa Sedighi,&nbsp;Kobra Naseri,&nbsp;Mohammad Ghavami,&nbsp;Mehran Taghipour-Gorjikolaie","doi":"10.1155/int/8883735","DOIUrl":"https://doi.org/10.1155/int/8883735","url":null,"abstract":"<div>\u0000 <p>The rise of digitalization and Industry 4.0 has led to significant changes in industrial warehouse management. However, managing warehouses remains challenging due to reliance on manual labor and limited automation. This article focuses on addressing issues in warehouse management, specifically in drug identification and counting. Although traditional methods such as barcode systems and RFID are common, artificial intelligence (AI) offers a promising solution. In this paper, an advanced visual recognition based on Faster R-CNN is introduced to accurately identify and count pharmaceutical items in pharmacies. The obtained results suggest that intelligent warehouse management in pharmacies can lead to cost savings and improved efficiency. The study also compares the proposed model with popular classification methods such as CNN, SVM, KNN, YOLOv5, and SSD, showing the effectiveness of the new approach.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/8883735","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143119854","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":"Intelligent Recognition for Operation States of Hydroelectric Generating Units Based on Data Fusion and Visualization Analysis","authors":"Yongfei Wang,&nbsp;Yu Liu,&nbsp;Xiaofei Li,&nbsp;Tong Wang,&nbsp;Zhuofei Xu,&nbsp;Pengcheng Guo,&nbsp;Bo Liao","doi":"10.1155/int/8850566","DOIUrl":"https://doi.org/10.1155/int/8850566","url":null,"abstract":"<div>\u0000 <p>This paper proposes a novel recognition approach for operation states of hydroelectric generating units based on data fusion and visualization analysis. First, the principal component analysis (PCA) is employed to simplify signals from multiple channels into a single fused signal, thereby reducing data computation for multiple-channel signals. To reflect the features of fused signals under different operation states, the Gramian angular field (GAF) method is applied to convert the fused signals into image formats, including Gramian angular differential field (GADF) images and Gramian angular summation field (GASF) images, then a depthwise separable convolution neural network (DSCNN) model is established to achieve the operation state recognition for the unit by GADF and GASF images. Based on the operation data from a Kaplan hydroelectric unit at a hydropower station in Southwest China, an experiment on operation recognition is conducted. The proposed PCA–GAF–DSCNN method achieves an accuracy rate of 95.21% with GADF images and 96.41% with GASF images, which were higher than the results obtained using original signals with the GAF–DSCNN method. The results indicate that the fused signal with PCA demonstrates superior performance in the operation recognition compared to the original signals, and PCA–GAF–DSCNN can be used for hydroelectric units effectively. This approach accurately identifies abnormal states in units, making it suitable for monitoring and fault diagnosis in the daily operations of hydroelectric generating units.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/8850566","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143119855","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":"AI-Enable Rice Image Classification Using Hybrid Convolutional Neural Network Models","authors":"Daya Shankar Verma,&nbsp;Mrinal Dafadar,&nbsp;Jitendra K. Mishra,&nbsp;Ankit Kumar,&nbsp;Shambhu Mahato","doi":"10.1155/int/5571940","DOIUrl":"https://doi.org/10.1155/int/5571940","url":null,"abstract":"<div>\u0000 <p>Rice is the most preferred grain worldwide, leading to the development of an automated method using convolutional neural networks (CNNs) for classifying rice types. This study evaluates the effectiveness of hybrid CNN models, including AlexNet, ResNet50, and EfficientNet-b1, in distinguishing five major rice varieties grown in Turkey: Arborio, Basmati, Ipsala, Jasmine, and Karacadag. It is estimated that there are 75,000 photographs of grains, with 15,000 images corresponding to each type. The training is improved by the use of preprocessing and optimization approaches. The performance of the model was assessed based on sensitivity, specificity, precision, <i>F</i>1 score, and confusion matrix analysis. The results show that EfficientNet-b1 achieved an accuracy of 99.87%, which is higher than the accuracy achieved by AlexNet (96.00%) and ResNet50 (99.00%). This study shows that EfficientNet-b1 is superior to other models that have emerged as state-of-the-art automated classification models for rice varieties. This indicates that there is a balance between the computational efficiency and the accuracy of EfficientNet-b1. These results exemplify the potential of CNN models for agriculture by reducing the restrictions associated with conventional classification approaches. These limitations include subjectivity and inconsistency regarding categorization.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/5571940","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143118902","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":"Medical Image Fusion Using Unified Image Fusion Convolutional Neural Network","authors":"Balasubramaniam S.,&nbsp;Vanajaroselin Chirchi,&nbsp;Sivakumar T. A.,&nbsp;Gururama Senthilvel P.,&nbsp;Duraimutharasan N.","doi":"10.1155/int/4296751","DOIUrl":"https://doi.org/10.1155/int/4296751","url":null,"abstract":"<div>\u0000 <p>Medical image fusion (IF) is a process of registering and combining numerous images from multiple- or single-imaging modalities to enhance image quality and lessen randomness as well as redundancy for increasing the clinical applicability of the medical images to diagnose and evaluate clinical issues. The information that is acquired additionally from fused images can be effectively employed for highly accurate positioning of abnormality. Since diverse kinds of images produce various information, IF becomes more complicated for conventional methods to generate fused images. Here, a unified image fusion convolutional neural network (UIFCNN) is designed for IF utilizing medical images. To execute the IF process, two input images, namely, native T1 and T2 fluid-attenuated inversion recovery (T2-FLAIR) are taken from a dataset. An input image-T1 is preprocessed employing bilateral filter (BF), and it is segmented by a recurrent prototypical network (RP-Net) to obtain segmented output-1. Simultaneously, input image-T2-FLAIR is also preprocessed by BF and then segmented using RP-Net to acquire segmented output-2. The two segmented outputs are fused utilizing the UIFCNN that is introduced by assimilating unified and unsupervised end-to-end IF network (U2Fusion) with IF framework based on the CNN (IFCNN). In addition, the UIFCNN obtained maximal Dice coefficient and Jaccard coefficient of 0.928 and 0.920 as well as minimal mean square error (MSE) of 0.221.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/4296751","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143115720","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":"Machine Learning Meets Encrypted Search: The Impact and Efficiency of OMKSA in Data Security","authors":"Zhongkai Wei,&nbsp;Ye Su,&nbsp;Xi Zhang,&nbsp;Haining Yang,&nbsp;Jing Qin,&nbsp;Jixin Ma","doi":"10.1155/int/2429577","DOIUrl":"https://doi.org/10.1155/int/2429577","url":null,"abstract":"<div>\u0000 <p>The convergence of machine learning and searchable encryption enhances the ability to protect the privacy and security of data and enhances the processing power of confidential data. To enable users to efficiently perform machine learning tasks on encrypted data domains, we delve into oblivious keyword search with authorization (OKSA). The OKSA scheme effectively maintains the privacy of the user’s query keywords and prevents the cloud server from inferring ciphertext information through the searching process. However, limitations arise because the traditional OKSA approach does not support multi-keyword searches. If a data file is associated with multiple keywords, each keyword and corresponding data must be encrypted one by one, resulting in inefficiency. We introduce an innovative approach aimed at enhancing the efficiency of search processes while addressing the limitation of current encryption and search systems that handle only a single keyword. This method, known as the oblivious multiple keyword search with authorization (OMKSA), is designed for more effective keyword retrieval. One of our important innovations is that it uses the arithmetic techniques of bilinear pairs to generate new tokens and new search methods to optimize communication efficiency. Moreover, we present a detailed and rigorous demonstration of the security for our proposed protocol, aligned with the predefined security model. We conducted a comparative experiment to determine which of the two schemes, OKSA and OMKSA, is more efficient when querying multiple keywords. Based on our experimental results, our OMKSA is very efficient for data searchers. As the number of query keywords increases, the computational overhead of connected keyword searches remains stable. Finally, as we move into the 5G era, the potential applications of OMKSA are huge, with clear implications for areas such as machine learning and artificial intelligence. Our findings pave the way for further exploration and deployment of these frontier areas.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/2429577","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143115394","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":"Learning-Based Progression Detection of Alzheimer’s Disease Using 3D MRI Images","authors":"Jacky Chung-Hao Wu,&nbsp;Tzu-Chi Chien,&nbsp;Chiung-Chih Chang,&nbsp;Hsin-I Chang,&nbsp;Hui-Ju Tsai,&nbsp;Min-Yu Lan,&nbsp;Nien-Chen Wu,&nbsp;Henry Horng-Shing Lu","doi":"10.1155/int/3981977","DOIUrl":"https://doi.org/10.1155/int/3981977","url":null,"abstract":"<div>\u0000 <p>Alzheimer’s disease (AD) is an irreversible brain disease. In addition to the functional deterioration of memory and cognition, patients with severe conditions lose their self-care ability. Patients exhibiting symptoms are often attributed to aging and thus lack proper medical care. If it can be diagnosed early, the doctor can provide adequate treatments to mitigate the symptoms. Magnetic resonance imaging (MRI) can reflect the characteristics of different human tissues and organs, and is a common tool implemented in clinical examinations. In this study, we tested learning-based approaches to detect disease progression in AD patients using MRI. Specifically, each patient is categorized as one of the following four classes: cognitive normal, early mild cognitive impairment, late mild cognitive impairment, and AD. To extract 3D information in MRI, we proposed a 3D convolutional neural network structure based on ResNet3D-18. We designed various multiclass classification frameworks. Moreover, we implemented ensemble classification combining these frameworks. Experiments demonstrated great potential for learning-based approaches on the Alzheimer’s Disease Neuroimaging Initiative dataset. The ensemble approach performed the best with an accuracy of 0.950, which is competitive with neurologists in diagnosing AD progression in clinical practice. With precise detection, patients can understand their conditions early and seek proper treatments.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/3981977","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143115308","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}