International Journal of Intelligent Systems最新文献

{"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}

{"title":"Veracity-Oriented Context-Aware Large Language Models–Based Prompting Optimization for Fake News Detection","authors":"Weiqiang Jin,&nbsp;Yang Gao,&nbsp;Tao Tao,&nbsp;Xiujun Wang,&nbsp;Ningwei Wang,&nbsp;Baohai Wu,&nbsp;Biao Zhao","doi":"10.1155/int/5920142","DOIUrl":"https://doi.org/10.1155/int/5920142","url":null,"abstract":"<div>\u0000 <p>Fake news detection (FND) is a critical task in natural language processing (NLP) focused on identifying and mitigating the spread of misinformation. Large language models (LLMs) have recently shown remarkable abilities in understanding semantics and performing logical inference. However, their tendency to generate hallucinations poses significant challenges in accurately detecting deceptive content, leading to suboptimal performance. In addition, existing FND methods often underutilize the extensive prior knowledge embedded within LLMs, resulting in less effective classification outcomes. To address these issues, we propose the CAPE–FND framework, context-aware prompt engineering, designed for enhancing FND tasks. This framework employs unique veracity-oriented context-aware constraints, background information, and analogical reasoning to mitigate LLM hallucinations and utilizes self-adaptive bootstrap prompting optimization to improve LLM predictions. It further refines initial LLM prompts through adaptive iterative optimization using a random search bootstrap algorithm, maximizing the efficacy of LLM prompting. Extensive zero-shot and few-shot experiments using GPT-3.5-turbo across multiple public datasets demonstrate the effectiveness and robustness of our CAPE–FND framework, even surpassing advanced GPT-4.0 and human performance in certain scenarios. To support further LLM–based FND, we have made our approach’s code publicly available on GitHub (our CAPE–FND code: https://github.com/albert-jin/CAPE-FND [Accessed on 2024.09]).</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/5920142","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143115307","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":"Exposing the Forgery Clues of DeepFakes via Exploring the Inconsistent Expression Cues","authors":"Jiatong Liu,&nbsp;Lina Wang,&nbsp;Run Wang,&nbsp;Jianpeng Ke,&nbsp;Xi Ye,&nbsp;Yadi Wu","doi":"10.1155/int/7945646","DOIUrl":"https://doi.org/10.1155/int/7945646","url":null,"abstract":"<div>\u0000 <p>The pervasive prevalence of DeepFakes poses a profound threat to individual privacy and the stability of society. Believing the synthetic videos of a celebrity and trumping up impersonated forgery videos as authentic are just a few consequences generated by DeepFakes. We investigate current detectors that blindly deploy deep learning techniques that are not effective in capturing subtle clues of forgery when generative models produce remarkably realistic faces. Inspired by the fact that synthetic operations inevitably modify the regions of eyes and mouth to match the target face with the identity or expression of the source face, we conjecture that the continuity of facial movement patterns representing expressions that existed in the veritable faces will be disrupted or completely broken in synthetic faces, making it a potentially formidable indicator for DeepFake detection. To prove this conjecture, we utilize a dual-branch network to capture the inconsistent patterns of facial movements within eyes and mouth regions separately. Extensive experiments on popular FaceForensics++, Celeb-DF-v1, Celeb-DF-v2, and DFDC-Preview datasets have demonstrated not only effectiveness but also the robust capability of our method to outperform the state-of-the-art baselines. Moreover, this work represents greater robustness against adversarial attacks, achieving ASR of 54.8% in the I-FGSM attack and 43.1% in the PGD attack on the DeepFakes dataset of FaceForensics++, respectively.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/7945646","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143114897","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}