International Journal of Intelligent Systems最新文献

{"title":"Deep Convolutional Neural Networks for Plant Disease Detection: A Mobile Application Approach (Agri Bot)","authors":"Hegazi Ibrahim,&nbsp;Abdelmoty M. Ahmed,&nbsp;Belgacem Bouallegue,&nbsp;Mahmoud M. Khattab,&nbsp;Mohab Abd El-Fattah,&nbsp;Nesma Abd El-Mawla","doi":"10.1155/int/7644407","DOIUrl":"https://doi.org/10.1155/int/7644407","url":null,"abstract":"<div>\u0000 <p>Plant diseases imperil global food security, decimating crop yields and endangering farmers’ livelihoods. Rapid, accurate detection remains a challenge, particularly in resource-constrained environments lacking portable tools. Our contribution, Agri Bot, introduces a pioneering deep convolutional neural network (CNN) model, uniquely optimized for mobile deployment, transforming plant disease diagnosis. This novel model integrates a lightweight architecture with advanced feature extraction, achieving an exceptional 97.30% accuracy and 98.76% area under the curve (AUC). Unlike computationally intensive traditional CNNs, Agri Bot’s innovative design—featuring a hybrid convolutional autoencoder, max pooling, and dropout layers—ensures high-speed, real-time performance on mobile devices. Comparative studies reveal Agri Bot’s superiority, surpassing state-of-the-art models like VGG16 (71.48%) and ResNet50 (96.46%), while rivaling InceptionV3 (99.07%) with significantly lower computational demands. By delivering precise, accessible diagnostics to remote regions, Agri Bot revolutionizes agricultural disease management, enhancing crop resilience and global food security.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/7644407","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144171237","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":"Multidomain Secure Communication and Intelligent Traffic Detection Model in VANETs","authors":"Qikun Zhang,&nbsp;Mengqi Liu,&nbsp;Ping Li,&nbsp;Junling Yuan,&nbsp;Hongfei Zhu","doi":"10.1155/int/2539516","DOIUrl":"https://doi.org/10.1155/int/2539516","url":null,"abstract":"<div>\u0000 <p>Vehicular ad-hoc network (VANET) plays a vital role in the intelligent transportation system. It is crucial to ensure secure communication among entities in the VANET for realizing an efficient transportation system. In this scenario, the current communication scheme is vulnerable to the leakage of private information from entities. The research primarily centers on single-domain vehicular networks, with only a limited number of researchers exploring cross-domain authentication among vehicle entities. Cross-domain communication schemes have received little attention from scholars. Furthermore, there are issues, including the susceptibility of in-vehicle conversations to eavesdropping, the vulnerability of long-distance transmissions to interruptions, and the exposure of wireless networks to traffic attacks. To address these issues, a multidomain secure communication and intelligent traffic detection model in VANET is proposed. This model offers several notable advantages as follows: (1) It employs a key self-verification algorithm for local computation and authentication of entity keys. This approach mitigates the risks of identity impersonation attacks and key leakage results from third-party key escrow. (2) A multidomain communication scheme is devised to categorize vehicle-to-vehicle (V2V) scenarios into intradomain and interdomain, which correspond to situations where the communicating parties are within the same domain and across different domains, respectively. (3) We propose the implementation of new session message encryption algorithms for V2V communication. This involves generating dynamic random keys to ensure secure data sharing and facilitates long-distance cross-domain communication among vehicles. (4) An intelligent two-layer traffic detection paradigm is proposed to improve the efficiency of detecting attack traffic in vehicular networks. This paper provides security proofs and performance analysis of the proposed scheme. The experimental results demonstrate that within the communication module, the comparative scheme exhibits high computational demands and significant delays, whereas our approach provides superior security and better computational performance. Compared to the traditional detection model, our two-layer detection paradigm reduces model training time by 69–4477 ms and testing time by 9–1469 ms.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/2539516","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144171607","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 Fire Detection and Suppression System for the Surveillance of a Wind Turbine Nacelle","authors":"Minsoo Lee,&nbsp;Eun Chan Do,&nbsp;Moon-Woo Park,&nbsp;Ki-Yong Oh","doi":"10.1155/int/6278987","DOIUrl":"https://doi.org/10.1155/int/6278987","url":null,"abstract":"<div>\u0000 <p>This paper proposes a novel fire detection and suppression system (FDSS) designed to detect and extinguish fires in the nacelle of a wind turbine. The FDSS incorporates three sensors: an infrared camera, an optical camera, and a 3D LiDAR, as well as a fire suppression system mounted on a pan and tilt control system. The FDSS features three key characteristics. First, an ensemble learning network simultaneously classifies and detects fire/smoke regions by integrating a classification neural network, an object detection neural network, and a cumulative alarm. This novel architecture significantly improves fire detection accuracy and reduces false alarm rates. Second, multimodal information precisely localizes overheat and fire/smoke regions, enabling the FDSS to automatically aim and extinguish fires by controlling the pan and tilt system. Third, a graph-based neural network accurately classifies the affected components in the nacelle using point cloud data from the 3D LiDAR. This novel neural network for object classification provides sufficient information for the location of a fire accident. Field and virtual experiments conducted in a fire test room and virtual nacelle environments demonstrate the FDSS’s effectiveness. Quantitative comparisons of three deep learning networks further highlight that these neural networks outperform other state-of-the-art deep learning models. Consequently, the FDSS provides a cost-effective and autonomous surveillance solution, enhancing the safe operation of wind turbines with advanced technologies in the fourth industrial revolution.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/6278987","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144148614","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":"BeGuard: An LSTM–Fused Defense Model Against Deepfakes in Competitive Activities–Related Social Networks","authors":"Yujie Li,&nbsp;Guoxu Liu,&nbsp;Chunlei Chen,&nbsp;Sunkyoung Kang,&nbsp;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}

{"title":"Knowledge-Driven and Low-Rank Tensor Regularized Multiview Fuzzy Clustering for Alzheimer’s Diagnosis","authors":"Yi Zhu,&nbsp;Chao Xi,&nbsp;Sen Wang,&nbsp;Lu Xu,&nbsp;Xiang Chen,&nbsp;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}

{"title":"Weak Population–Empowered Large-Scale Multiobjective Immune Algorithm","authors":"Yang Chen,&nbsp;Wenshan Li,&nbsp;Junjiang He,&nbsp;Tao Li,&nbsp;Wenbo Fang,&nbsp;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,&nbsp;Bo Liu,&nbsp;Tianqing Zhu,&nbsp;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,&nbsp;Chao Wen,&nbsp;Baoping Wang,&nbsp;Zhenni Wang,&nbsp;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,&nbsp;Chuan Luo,&nbsp;Tianrui Li,&nbsp;Junbo Zhang,&nbsp;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,&nbsp;Kangrong Luo,&nbsp;Peng Wang,&nbsp;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}