{"title":"BeGuard: An LSTM–Fused Defense Model Against Deepfakes in Competitive Activities–Related Social Networks","authors":"Yujie Li, Guoxu Liu, Chunlei Chen, Sunkyoung Kang, Andia Foroughi","doi":"10.1155/int/1282012","DOIUrl":"https://doi.org/10.1155/int/1282012","url":null,"abstract":"<div>\u0000 <p>We propose a novel defense mechanism for protecting users from deepfakes by analyzing their behaviors in competitive activities and their social interactions. The model dynamically embeds user behaviors based on their participation in competitive activities, capturing these activities’ temporal dynamics through long short–term memory networks. This allows the model to effectively identify patterns and changes in user behaviors. BeGuard also considers users’ social relationships, embedding the behaviors of their social friends to account for the influence of these connections on their actions. This results in a richer and more contextually aware behavioral representation. To improve detection accuracy, the model uses an attention mechanism to evaluate abnormal values in user behaviors, particularly those indicating potential deepfake content. This attention-based evaluation enhances the model’s capacity to detect subtle anomalies, providing a more effective defense against deepfakes in competitive activities–related social networks.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/1282012","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144108898","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}
Yi Zhu, Chao Xi, Sen Wang, Lu Xu, Xiang Chen, Zhicheng Wang
{"title":"Knowledge-Driven and Low-Rank Tensor Regularized Multiview Fuzzy Clustering for Alzheimer’s Diagnosis","authors":"Yi Zhu, Chao Xi, Sen Wang, Lu Xu, Xiang Chen, Zhicheng Wang","doi":"10.1155/int/1458773","DOIUrl":"https://doi.org/10.1155/int/1458773","url":null,"abstract":"<div>\u0000 <p>Alzheimer’s disease (AD), as a complex neurodegenerative disorder, is the most common cause of dementia. In recent years, the emergence of multiview data has brought new possibilities for the diagnosis of AD. However, due to uneven density and uncertainty in the multiview data, existing algorithms still face challenges in extracting consistent and complementary information across views. To address this issue, a multiview fuzzy clustering algorithm, which integrates high-density knowledge point extraction and low-rank tensor regularization (K-LRT-MFC), is proposed in this paper. First, high-density knowledge point extraction is employed to tackle the issue of uneven density in high-dimensional data, enhancing the stability and accuracy of single-view clustering. Second, low-rank tensor regularization is applied to effectively capture high-order complementary information among multiview data, significantly improving the precision and computational efficiency of multiview clustering. Experimental results on several publicly available AD diagnostic datasets demonstrate that the proposed method outperforms existing approaches in terms of accuracy, sensitivity, and specificity, providing an efficient and accurate solution for early AD diagnosis.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/1458773","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144100925","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}
Yang Chen, Wenshan Li, Junjiang He, Tao Li, Wenbo Fang, Xiaolong Lan
{"title":"Weak Population–Empowered Large-Scale Multiobjective Immune Algorithm","authors":"Yang Chen, Wenshan Li, Junjiang He, Tao Li, Wenbo Fang, Xiaolong Lan","doi":"10.1155/int/6462697","DOIUrl":"https://doi.org/10.1155/int/6462697","url":null,"abstract":"<div>\u0000 <p>The multiobjective immune optimization algorithms (MOIAs) utilize the principle of clonal selection, iteratively evolving by replicating a small number of superior solutions to optimize decision vectors. However, this method often leads to a lack of diversity and is particularly ineffective when facing large-scale optimization problems. Moreover, an overemphasis on elite solutions may result in a large number of redundant offspring, reducing evolutionary efficiency. By delving into the causes of these issues, we find that a key factor is that existing algorithms overlook the role of weak solutions during the evolutionary process. With this in mind, we propose a weak population–empowered large-scale multiobjective immune algorithm (WP–MOIA). The core of this algorithm is to construct, in addition to the traditional elite population, a cooperative evolutionary population based on a portion of the remaining solutions, referred to as the weak population. During the evolution, both populations work together: the elite population maximizes its advantageous status for local searches, focusing on exploitation, while the weak population seeks greater variation to escape its disadvantaged position, engaging in broader exploration. At the same time, the sizes of both populations are dynamically adjusted to collaboratively maintain the balance of evolution. Through comparisons with nine state-of-the-art multiobjective evolutionary algorithms (MOEAs) and four powerful MOIAs on 30 benchmark problems, the proposed algorithm demonstrates superior performance in both small-scale and large-scale multiobjective optimization problems (MOPs), and exhibits better convergence efficiency. Especially in large-scale MOPs, the new algorithm’s performance nearly surpasses all 13 advanced algorithms being compared.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/6462697","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144085388","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 Review of Deepfake and Its Detection: From Generative Adversarial Networks to Diffusion Models","authors":"Baoping Liu, Bo Liu, Tianqing Zhu, Ming Ding","doi":"10.1155/int/9987535","DOIUrl":"https://doi.org/10.1155/int/9987535","url":null,"abstract":"<div>\u0000 <p>Deepfake technology, leveraging advanced artificial intelligence (AI) algorithms, has emerged as a powerful tool for generating hyper-realistic synthetic human faces, presenting both innovative opportunities and significant challenges. Meanwhile, the development of Deepfake detectors represents another branch of models striving to recognize AI-generated fake faces and protect people from the misinformation of Deepfake. This ongoing cat-and-mouse game between generation and detection has spurred a dynamic evolution in the landscape of Deepfake. This survey comprehensively studies recent advancements in Deepfake generation and detection techniques, focusing particularly on the utilization of generative adversarial networks (GANs) and diffusion models (DMs). For both GAN-based and DM-based Deepfake generators, we categorize them based on whether they synthesize new content or manipulate existing content. Correspondingly, we examine various strategies employed to identify synthetic and manipulated Deepfake, respectively. Finally, we summarize our findings by discussing the unique capabilities and limitations of GANs and DM in the context of Deepfake. We also identify promising future directions for research, including the development of hybrid approaches that leverage the strengths of both GANs and DM, the exploration of novel detection strategies utilizing advanced AI techniques, and the ethical considerations surrounding the development of Deepfake. This survey paper serves as a valuable resource for researchers, practitioners, and policymakers seeking to understand the state-of-the-art in Deepfake technology, its implications, and potential avenues for future research and development.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/9987535","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144085389","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":"Graph Learning of Semantic Relations (GLSR) for Cooperative Multiagent Reinforcement Learning","authors":"Pengting Duan, Chao Wen, Baoping Wang, Zhenni Wang, Zhifang Wei","doi":"10.1155/int/4810561","DOIUrl":"https://doi.org/10.1155/int/4810561","url":null,"abstract":"<div>\u0000 <p>Prominent achievements of multiagent reinforcement learning (MARL) have been recognized in the last few years, but effective cooperation among agents remains a challenge. Traditional methods neglect the modeling of action semantic relations in the learning process of joint action latent representations. In other words, the uncertain semantic relations might hinder the learning of sophisticated cooperative relationships among actions, which may lead to homogeneous behaviors across all agents and their limited exploration efficiency. Our aim is to learn the structure of the action semantic space to improve the cooperation-aware representation for policy optimization of MARL. To achieve this, a scheme called graph learning of semantic relations (GLSR) is proposed, where action semantic embeddings and joint action representations are learned in a collaborative way. GLSR incorporates an action semantic encoder for capturing semantic relations in the action semantic space. By leveraging the cross-attention mechanism with action semantic embeddings, GLSR prompts the action semantic relations to guide mining the cooperation-aware joint action representations, implicitly facilitating agent cooperation in the joint policy space for more diverse behaviors of cooperative agents. The experimental results on challenging tasks demonstrate that GLSR attains state-of-the-art outcomes and shows robust performance in multiagent cooperative tasks.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/4810561","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144085486","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":"Adversarial Transfer Learning-Based Hybrid Recurrent Network for Air Quality Prediction","authors":"Yanqi Hao, Chuan Luo, Tianrui Li, Junbo Zhang, Hongmei Chen","doi":"10.1155/int/6014262","DOIUrl":"https://doi.org/10.1155/int/6014262","url":null,"abstract":"<div>\u0000 <p>Air quality modeling and forecasting has become a key problem in environmental protection. The existing prediction models typically require large-scale and high-quality historical data to achieve better performance. However, insufficient data volume and significant differences between data distribution across different regions will definitely reduce the effectiveness of the model reuse. To address the above issues, we propose a novel hybrid recurrent network based on domain adversarial transfer to achieve a stronger generalization ability when training air quality data from multisource domains. The proposed model mainly consists of three fundamental modules, i.e., feature extractor, regression predictor, and domain classifier. One-dimensional convolutional neural networks (1D-CNNs) are used to extract temporal feature of data from source and target stations. Bi-directional gated recurrent unit (bi-GRU) and bi-directional long short-term memory (bi-LSTM) are utilized to learn temporal dependencies pattern of multivariate time series data. Two adversarial transfer strategies are employed to ensure that our model is capable of finding domain invariant representations automatically. Experiments with different number of source domains are conducted to demonstrate the effectiveness of the proposed domain transfer strategies. The experimental results also show that our composite model has superior performance for forecasting air quality in various regions. As further evidence, the adversarial training method could promote the positive transfer and alleviate the negative effect of irrelevant source data. Besides, our model exhibits preferable generalization capability as more robust prediction results are achieved on both unseen target domains and original source domains.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/6014262","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144091834","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":"ARIMA-Based Virtual Data Generation Using Deepfake for Robust Physique Test","authors":"Bo Fan, Kangrong Luo, Peng Wang, Andia Foroughi","doi":"10.1155/int/5533092","DOIUrl":"https://doi.org/10.1155/int/5533092","url":null,"abstract":"<div>\u0000 <p>Physique testing plays a crucial role in health monitoring and fitness assessment, with wearable devices becoming an essential tool to collect real-time data. However, incomplete or missing data from wearable devices often hamper the accuracy and reliability of such tests. Existing methods struggle to address this challenge effectively, leading to gaps in the analysis of physical conditions. To overcome this limitation, we propose a novel framework that combines ARIMA-based virtual data generation with deepfake technology. ARIMA is used to predict and reconstruct missing physique data from historical records, while deepfake technology synthesizes virtual data that mimic the physical attributes of the test subjects. This hybrid approach enhances the robustness and accuracy of physique tests, especially in scenarios where data are incomplete. The experimental results demonstrate significant improvements in the accuracy and reliability of data prediction and test reliability, offering a new avenue to advance the monitoring of health and fitness.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/5533092","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143950050","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 Protecting-Privacy Path Query Supporting Semantic-Based Multikeyword Search Over Ciphertext Graph Data in Cloud Computing","authors":"Bin Wu, Zhuolin Mei, Jiaoli Shi, Zongmin Cui, Zhiqiang Zhao, Jinzhou Huang","doi":"10.1155/int/8819489","DOIUrl":"https://doi.org/10.1155/int/8819489","url":null,"abstract":"<div>\u0000 <p>With the rapid development of information technology and intelligence, there are more and more usage scenarios of graph data. Path queries have always been a hot topic of research for scholars. There are already many mature methods and ideas in the study of the path query on the plaintext graph. For the path query on graph data in the case of cloud outsourcing, it is necessary to consider both the construction of query algorithms and the protection of privacy information. The processing of graph privacy information through encryption, and then outsourcing to the cloud platform, is a common measure. The semantic-based path query supporting multikeyword search is an extended path query method, which can improve the query function. For users, it is very troublesome to access and process the encrypted graph data. In this article, we propose a protecting-privacy semantic-based multikeyword path query scheme on the ciphertext graph (PSMP). Firstly, based on the principle of searchable encryption and vector operation, a secure index is constructed, and then the cloud server uses the secure index to implement path queries. This article demonstrates its security through formal analysis and verifies its effectiveness through experimental comparison and analysis. The work of this article has a certain promoting effect on the query processing and analysis of ciphertext graph data.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/8819489","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143949974","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}
Ali Ahmed Al-awamy, Nagi Al-shaibany, Axel Sikora, Dominik Welte
{"title":"Hybrid Consensus Mechanisms in Blockchain: A Comprehensive Review","authors":"Ali Ahmed Al-awamy, Nagi Al-shaibany, Axel Sikora, Dominik Welte","doi":"10.1155/int/5821997","DOIUrl":"https://doi.org/10.1155/int/5821997","url":null,"abstract":"<div>\u0000 <p>Blockchain technology, renowned for its foundational attributes of decentralization, security, and immutability, offers substantial potential for diverse applications. At the heart of blockchain functionality are consensus mechanisms, crucial for preserving the decentralized integrity of the network. However, traditional consensus algorithms like Proof of Work (PoW), Proof of Stake (PoS), and Byzantine Fault Tolerance (BFT) typically require significant computational and communication resources, which may not be feasible for resource-limited environments. The purpose of this paper is to explore hybrid consensus algorithms that integrate conventional consensus mechanisms with advanced nonlinear data structures. We comprehensively analyze a wide range of hybrid consensus mechanisms, emphasizing their architectural design, operational efficiencies, and ability to address both consensus-specific vulnerabilities and network-level threats, such as Sybil attacks, double-spending, and partitioning attacks. To achieve this, we employ a set of comprehensive evaluation criteria for blockchain technologies, namely, validation, IoT, real-time processing, application suitability, security, and implementation. These criteria help assess the adaptability and efficacy of each mechanism in diverse operational contexts. Through this examination, the paper seeks to illuminate the significant contributions and implications of hybrid consensus mechanisms, guiding stakeholders, researchers, and developers toward making informed decisions about optimizing blockchain technology for their specific needs and inspiring the development of innovative solutions.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/5821997","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143930291","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":"Collaborative Integration of Vehicle and Roadside Infrastructure Sensor for Temporal Dependency-Aware Task Offloading in the Internet of Vehicles","authors":"Kaiyue Luo, Yumei Wang, Yu Liu, Konglin Zhu","doi":"10.1155/int/8064086","DOIUrl":"https://doi.org/10.1155/int/8064086","url":null,"abstract":"<div>\u0000 <p>With advancements of in-vehicle computing and Multi-access Edge Computing (MEC), the Internet of Vehicles (IoV) is increasingly capable of supporting Vehicle-oriented Edge Intelligence (VEI) applications, such as autonomous driving and Intelligent Transportation Systems (ITSs). However, IoV systems that rely solely on vehicular sensors often encounter limitations in forecasting events beyond current roadways, which are critical for regional transportation management. Moreover, the inherent temporal dependency in VEI application data poses risks of interruptions, impeding the seamless tracking of incremental information. To address these challenges, this paper introduces a joint task offloading and resource allocation strategy within an MEC environment that collaboratively integrates vehicles and Roadside Infrastructure Sensors (RISs). The strategy carefully considers the Doppler shift from vehicle mobility and the Tolerance for Interruptions of Incremental Information (T3I) in VEI applications. We establish a decision-making framework that actively balances delay, energy consumption, and the T3I metric by formulating the task offloading as a stochastic network optimization problem. Utilizing Lyapunov optimization, we dissect this complex problem into three targeted subproblems that include optimizing local computational capacity, MEC computational capacity and comprehensive offloading decisions. To tackle the efficient offloading, we develop algorithms that separately optimize offloading scheduling, channel allocation and transmission power control. Notably, we incorporate a Potential Minimum Point (PMP) algorithm to boost parallel processing and simplify computational scale through matrix decomposition. Evaluations of our algorithm show that it excels in both complexity and accuracy, with accuracy improvements ranging from 74.3% to 114.0% in asymmetric resource environments. Simulation and experimental studies on offloading performance validate the effectiveness of our framework, which significantly balances network performance, reduces latency, and improves system stability.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/8064086","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143930294","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}