International Journal of Intelligent Systems最新文献

{"title":"An Efficient Integrated Radio Detection and Identification Deep Learning Architecture","authors":"Zhiyong Luo,&nbsp;Yanru Wang,&nbsp;Xiti Wang","doi":"10.1155/int/4477742","DOIUrl":"https://doi.org/10.1155/int/4477742","url":null,"abstract":"<div>\u0000 <p>The detection and identification of radio signals play a crucial role in cognitive radio, electronic reconnaissance, noncooperative communication, etc. Deep neural networks have emerged as a promising approach for electromagnetic signal detection and identification, outperforming traditional methods. Nevertheless, the present deep neural networks not only overlook the characteristics of electromagnetic signals but also treat these two tasks as independent components, similar to conventional methods. These issues limit overall performance and unnecessarily increase computational consumption. In this paper, we have designed a novel and universally applicable integrated radio detection and identification deep architecture and corresponding training method, which organically combines detection and identification networks. Furthermore, we extract signal features using only one-dimensional horizontal convolution based on the characteristics of the impact of wireless channels on time-domain signals. Experiments show that the proposed methods perform signal detection and identification more efficiently, which can not only reduce unnecessary computational consumption but also improve the accuracy and robustness of both detection and identification simultaneously. More specifically, the ability to distinguish different modulated signal categories tends to increase with the rise in SNRs, and the upper limit of detection accuracy can exceed 95% at SNRs above 0 dB. The proposed method can improve both signal detection and identification accuracy from 83.44% to 83.56% and from 61.27% to 62.32%, respectively.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/4477742","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143856695","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 Resilience Recovery Method for Complex Traffic Network Security Based on Trend Forecasting","authors":"Sheng Hong,&nbsp;Tianyu Yue,&nbsp;Yang You,&nbsp;Zhengnan Lv,&nbsp;Xu Tang,&nbsp;Jing Hu,&nbsp;Hongwei Yin","doi":"10.1155/int/3715086","DOIUrl":"https://doi.org/10.1155/int/3715086","url":null,"abstract":"<div>\u0000 <p>Due to the rapid development of information technology, a huge and complex traffic network has been established across various sectors including aviation, aerospace, vehicles, ships, electric power, and industry. However, because of the complexity and diversity of its structure, the complex traffic network is vulnerable to be attacked and faces serious security challenges. Therefore, this paper innovatively proposes a traffic network resilience recovery method based on resilience trend forecasting. In this paper, the risk value is introduced into the analysis of network fault propagation process, and the Susceptible, Infectious, Recovered, Dead-Risk (SIRD-R) fault propagation model is established. The resilience model of traffic network, which encompasses real-time resilience and overall resilience, is constructed through the integration of network resilience bearing capacity and resilience recovery capacity. Then, the resilience of complex traffic network is forecasted by using long short-term memory network, and the resilience recovery strategy of complex traffic network based on forecasting is proposed. Finally, the effectiveness and scalability of the proposed method are demonstrated through experimental analysis conducted on a diverse range of complex traffic networks, affirming its applicability in real-world scenarios.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/3715086","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143852712","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":"Fine-Grained Dance Style Classification Using an Optimized Hybrid Convolutional Neural Network Architecture for Video Processing Over Multimedia Networks","authors":"Na Guo,&nbsp;Ahong Yang,&nbsp;Yan Wang,&nbsp;Elaheh Dastbaravardeh","doi":"10.1155/int/6434673","DOIUrl":"https://doi.org/10.1155/int/6434673","url":null,"abstract":"<div>\u0000 <p>Dance style recognition through video analysis during university training can significantly benefit both instructors and novice dancers. Employing video analysis in training offers substantial advantages, including the potential to train future dancers using innovative technologies. Over time, intricate dance gestures can be honed, reducing the burden on instructors who would, otherwise, need to provide repetitive demonstrations. Recognizing dancers’ movements, evaluating and adjusting their gestures, and extracting cognitive functions for efficient evaluation and classification are pivotal aspects of our model. Deep learning currently stands as one of the most effective approaches for achieving these objectives, particularly with short video clips. However, limited research has focused on automated analysis of dance videos for training purposes and assisting instructors. In addition, assessing the quality and accuracy of performance video recordings presents a complex challenge, especially when judges cannot fully focus on the on-stage performance. This paper proposes an alternative to manual evaluation through a video-based approach for dance assessment. By utilizing short video clips, we conduct dance analysis employing techniques such as fine-grained dance style classification in video frames, convolutional neural networks (CNNs) with channel attention mechanisms (CAMs), and autoencoders (AEs). These methods enable accurate evaluation and data gathering, leading to precise conclusions. Furthermore, utilizing cloud space for real-time processing of video frames is essential for timely analysis of dance styles, enhancing the efficiency of information processing. Experimental results demonstrate the effectiveness of our evaluation method in terms of accuracy and F1-score calculation, with accuracy exceeding 97.24% and the F1-score reaching 97.30%. These findings corroborate the efficacy and precision of our approach in dance evaluation analysis.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/6434673","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143856991","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":"MultiResFF-Net: Multilevel Residual Block-Based Lightweight Feature Fused Network With Attention for Gastrointestinal Disease Diagnosis","authors":"Sohaib Asif,&nbsp;Yajun Ying,&nbsp;Tingting Qian,&nbsp;Jun Yao,&nbsp;Jinjie Qu,&nbsp;Vicky Yang Wang,&nbsp;Rongbiao Ying,&nbsp;Dong Xu","doi":"10.1155/int/1902285","DOIUrl":"https://doi.org/10.1155/int/1902285","url":null,"abstract":"<div>\u0000 <p>Accurate detection of gastrointestinal (GI) diseases is crucial due to their high prevalence. Screening is often inefficient with existing methods, and the complexity of medical images challenges single-model approaches. Leveraging diverse model features can improve accuracy and simplify detection. In this study, we introduce a novel deep learning model tailored for the diagnosis of GI diseases through the analysis of endoscopy images. This innovative model, named MultiResFF-Net, employs a multilevel residual block-based feature fusion network. The key strategy involves the integration of features from truncated DenseNet121 and MobileNet architectures. This fusion not only optimizes the model’s diagnostic performance but also strategically minimizes complexity and computational demands, making MultiResFF-Net a valuable tool for efficient and accurate disease diagnosis in GI endoscopy images. A pivotal component enhancing the model’s performance is the introduction of the Modified MultiRes-Block (MMRes-Block) and the Convolutional Block Attention Module (CBAM). The MMRes-Block, a customized residual learning component, optimally handles fused features at the endpoint of both models, fostering richer feature sets without escalating parameters. Simultaneously, the CBAM ensures dynamic recalibration of feature maps, emphasizing relevant channels and spatial locations. This dual incorporation significantly reduces overfitting, augments precision, and refines the feature extraction process. Extensive evaluations on three diverse datasets—endoscopic images, GastroVision data, and histopathological images—demonstrate exceptional accuracy of 99.37%, 97.47%, and 99.80%, respectively. Notably, MultiResFF-Net achieves superior efficiency, requiring only 2.22 MFLOPS and 0.47 million parameters, outperforming state-of-the-art models in both accuracy and cost-effectiveness. These results establish MultiResFF-Net as a robust and practical diagnostic tool for GI disease detection.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/1902285","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143836091","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":"Fusion Network Model Based on Broad Learning System for Multidimensional Time-Series Forecasting","authors":"Yuting Bai,&nbsp;Xinyi Xue,&nbsp;Xuebo Jin,&nbsp;Zhiyao Zhao,&nbsp;Yulei Zhang","doi":"10.1155/int/1649220","DOIUrl":"https://doi.org/10.1155/int/1649220","url":null,"abstract":"<div>\u0000 <p>Multidimensional time-series prediction is significant in various fields, such as human production and life, weather forecasting, and artificial intelligence. However, a single model can only focus on specific features of time-series data, making it unable to consider both linear and nonlinear components simultaneously. In this study, we propose a fusion network that combines the advantages of deep and broad networks for multidimensional time-series prediction tasks. The complex multidimensional time-series data are divided into nonlinear and time-series data. Restricted Boltzmann machine and mapping functions are used for feature learning and generating mapping nodes at the mapping layer. The echo state network and gate recurrent unit are applied in the enhancement layer. The proposed model has been validated on PM2.5 and wind turbine power datasets, proving superior performance in multistep prediction tasks compared to the baseline models.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/1649220","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143824674","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":"LDSGAN: Unsupervised Image-to-Image Translation With Long-Domain Search GAN for Generating High-Quality Anime Images","authors":"Hao Wang,&nbsp;Chenbin Wang,&nbsp;Xin Cheng,&nbsp;Hao Wu,&nbsp;Jiawei Zhang,&nbsp;Jinwei Wang,&nbsp;Xiangyang Luo,&nbsp;Bin Ma","doi":"10.1155/int/4450460","DOIUrl":"https://doi.org/10.1155/int/4450460","url":null,"abstract":"<div>\u0000 <p>Image-to-image (<b>I2I</b>) translation has emerged as a valuable tool for privacy protection in the digital age, offering effective ways to safeguard portrait rights in cyberspace. In addition, I2I translation is applied in real-world tasks such as image synthesis, super-resolution, virtual fitting, and virtual live streaming. Traditional I2I translation models demonstrate strong performance when handling similar datasets. However, when the domain distance between two datasets is large, translation quality may degrade significantly due to notable differences in image shape and edges. To address this issue, we propose Long-Domain Search GAN (<b>LDSGAN</b>), an unsupervised I2I translation network that employs a GAN structure as its backbone, incorporating a novel Real-Time Routing Search (<b>RTRS</b>) module and Sketch Loss. Specifically, RTRS aids in expanding the search space within the target domain, aligning feature projection with images closest to the optimization target. Additionally, Sketch Loss retains human visual similarity during long-domain distance translation. Experimental results indicate that LDSGAN surpasses existing I2I translation models in both image quality and semantic similarity between input and generated images, as reflected by its mean FID and LPIPS scores of 31.509 and 0.581, respectively.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/4450460","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143822113","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 Interpretability: A Hierarchical Belief Rule-Based (HBRB) Method for Assessing Multimodal Social Media Credibility","authors":"Peng Wu,&nbsp;Jiahong Lin,&nbsp;Zhiyuan Ma,&nbsp;Huiwen Li","doi":"10.1155/int/7184626","DOIUrl":"https://doi.org/10.1155/int/7184626","url":null,"abstract":"<div>\u0000 <p>User and artificial intelligence generated contents, coupled with the multimodal nature of information, have made the identification of false news an arduous task. While models can assist users in improving their cognitive abilities, commonly used black-box models lack transparency, posing a significant challenge for interpretability. This study proposes a novel credibility assessment method of social media content, leveraging multimodal features by optimizing the hierarchical belief rule-based (HBRB) inference method. Compared to other popular feature engineering and deep learning models, our method integrates, analyses, and filters relevant features, improving the HBRB structure to make the model layered, independent, and interconnected, enhancing interpretability and controllability, thereby addressing the rule combination explosion problem. The results highlight the potential of our method to improve the integrity of the online information ecosystem, offering a promising solution for more transparent and reliable credibility assessment in social media.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/7184626","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143809593","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":"Generative Adversarial Networks With Noise Optimization and Pyramid Coordinate Attention for Robust Image Denoising","authors":"Min-Ling Zhu,&nbsp;Jia-Hua Yuan,&nbsp;En Kong,&nbsp;Liang-Liang Zhao,&nbsp;Li Xiao,&nbsp;Dong-Bing Gu","doi":"10.1155/int/1546016","DOIUrl":"https://doi.org/10.1155/int/1546016","url":null,"abstract":"<div>\u0000 <p>Image denoising is a significant challenge in computer vision. While many models perform well in low-noise environments, their denoising capabilities are relatively weak under high-noise conditions. In addition, these models often overlook the robustness issues under adversarial attacks, leading to a marked decrease in denoising stability when facing malicious attacks. To address the challenges of achieving consistently high-quality denoising in both high-noise and low-noise environments, adapting to various complex scenarios with high robustness, and enhancing the model’s resilience against attacks, we propose the NOP-GAN, a powerful image denoising model. This model modifies the GAN architecture by integrating a U-Net with a pyramid coordinate attention mechanism and a noise optimization algorithm into a generator of the GAN. Experimental results demonstrate that the NOP-GAN possesses superior performance in denoising tasks and robustness against adversarial attacks.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/1546016","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143809703","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 Massive MIMO Channel Estimation Method Based on Hybrid Deep Learning Model With Regularization Techniques","authors":"Xinyu Tian,&nbsp;Qinghe Zheng","doi":"10.1155/int/2597866","DOIUrl":"https://doi.org/10.1155/int/2597866","url":null,"abstract":"<div>\u0000 <p>The channel estimation technique is crucial for the development of wireless communication systems. By accurately estimating the channel state, transmission parameters such as power allocation, modulation schemes, and encoding strategies can be optimized to maximize system capacity and transmission rate. In this paper, we propose a hybrid deep learning model for channel estimation in multiple-input multiple-output (MIMO) wireless communication system. By combining the advantages of convolutions and gated recurrent units (GRUs), the generalization capability of deep learning models across various wireless communication scenarios can be fully utilized. Furthermore, a series of regularization techniques such as data augmentation and structural complexity constraints have been introduced to avoid overfitting problems. The stochastic gradient descent (SGD) based on error backpropagation is used to iteratively train the model to convergence. During the simulation process, we have validated the effectiveness of the hybrid deep learning model on two wireless channel conditions, including quasi-static block fading and time-varying fading condition. All the samples are generated offline with SNRs from 10 to 40 dB with a step size of 5 dB. The comparison results with a series of conventional methods and deep learning models have proven the effectiveness of the proposed method.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/2597866","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143793783","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":"Leveraging High-Dimensional Mapping for Effective JPEG Steganalysis","authors":"Meng Xu,&nbsp;Xiangyang Luo","doi":"10.1155/int/9674462","DOIUrl":"https://doi.org/10.1155/int/9674462","url":null,"abstract":"<div>\u0000 <p>Steganography is a critical information-hiding technique widely used for the covert transmission of secret information on social media. In contrast, steganalysis plays a key role in ensuring information security. Although various effective steganalysis algorithms have been proposed, existing studies typically treat color images as three independent channels and do not fully consider robust features suitable for JPEG images. To address this limitation, we propose a robust steganalysis algorithm based on high-dimensional mapping. By analyzing the changes in color images during the JPEG compression and decompression processes, we observe that the embedding of secret information causes shifts in the JPEG coefficients, which subsequently affects feature representation during decompression. Based on this observation, our method captures steganographic traces by utilizing the transformation errors produced during decompression. Additionally, due to the imbalance between luminance and chrominance, the feature weights of each channel are uneven. To ensure balanced analysis across the three channels, we adjust the distribution differences of each channel through high-dimensional mapping, thereby reducing intraclass feature variations. Experimental results demonstrate that the proposed method outperforms existing approaches in most cases.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/9674462","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143793319","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}