International Journal of Intelligent Systems最新文献

{"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}

{"title":"SEDNet: Real-Time Semantic Segmentation Algorithm Based on STDC","authors":"Sugang Ma,&nbsp;Ziyi Zhao,&nbsp;Zhiqiang Hou,&nbsp;Wangsheng Yu,&nbsp;Xiaobao Yang,&nbsp;Xiangmo Zhao","doi":"10.1155/int/8243407","DOIUrl":"https://doi.org/10.1155/int/8243407","url":null,"abstract":"<div>\u0000 <p>Recently, deep convolutional neural networks (DCNN) have been widely used in semantic segmentation tasks and have achieved high segmentation accuracy. However, most algorithms based on DCNN have high computational complexity, making them unsuitable for real-time segmentation. To solve this problem, this paper proposes a real-time semantic segmentation algorithm based on the STDC network. The algorithm adopts an “encoder–decoder” embedded in a U-shaped architecture to realize real-time segmentation while maintaining high accuracy. Following the encoder, a mixed pooling attention module is designed to expand the receptive field, enhancing the network model’s learning ability in complex scenarios. Then, a feature fusion module is used for combining features from different stages, and channel attention based on atrous convolution is employed to expand the receptive field and avoid dimensionality reduction learning. Finally, a Tversky-based detail loss function is used to encode more spatial details. The proposed algorithm was extensively tested on the challenging Cityscapes and CamVid datasets, and the experimental results showed that the proposed algorithm obtained 76.4% and 72.8% of mIoU, respectively. Meanwhile, our algorithm achieves 105.2 FPS and 165.6 FPS inference speed with a single NVIDIA GTX 1080Ti GPU, meeting the real-time segmentation requirements. The proposed algorithm can conduct real-time segmentation while maintaining high accuracy, achieving a good balance between accuracy and speed.</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/8243407","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143793781","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":"R-YOLOv5s: Improved YOLOv5s for Object Detection in Low-Light Environments","authors":"Yimeng Xia,&nbsp;Yuanmei Wang,&nbsp;Hao Luo,&nbsp;Shengzhe Liu,&nbsp;Tao Li","doi":"10.1155/int/8834271","DOIUrl":"https://doi.org/10.1155/int/8834271","url":null,"abstract":"<div>\u0000 <p>In response to the challenge of low detection accuracy exhibited by mainstream object detection models in low-light environments, this paper proposes a novel detection model named R-YOLOv5s. The model incorporates several key enhancements to address this issue. First, the SCI image enhancement algorithm is designed to preserve more target features and details. Next, a newly lightweight RepVIT backbone network is built to extract more image features; the global attention mechanism (GAM) is introduced to generate multiscale features that are more readily discernible, thereby enhancing the efficiency of feature capture. To significantly enhance the efficiency and precision of prediction box regression, a specialized loss function called SIoU loss is constructed. Results from experiments conducted on the ExDark dataset indicate notable improvements over the baseline model, with precision (P) increasing by 10%, recall (R) by 11%, and the mean average precision (mAP 0.5) by 13%. The newly devised R-YOLOv5s Model achieves higher detection accuracy in low-light environments, showcasing its effectiveness in addressing the challenges posed by such conditions.</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/8834271","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143793782","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":"Toward Diagnosis of Diseases Using Emerging Technologies: A Comprehensive Survey of the State of the Art in Metaverse","authors":"Nasim Aslani,&nbsp;Ali Garavand","doi":"10.1155/int/8820744","DOIUrl":"https://doi.org/10.1155/int/8820744","url":null,"abstract":"<div>\u0000 <p><b>Introduction:</b> The Metaverse, a rapidly growing technology in healthcare, is proving to be a game-changer in early disease detection and diagnosis. This study aimed to identify the latest scientific achievements in Metaverse, such as its effects, associated technologies, and obstacles for diagnosing diseases.</p>\u0000 <p><b>Methods:</b> In this review study, the scientific databases, including PubMed and Web of Science, were searched using related keywords. Related studies about using Metaverse in disease diagnosis were included according to inclusion and exclusion criteria. Data extraction was done using the data extraction form. The findings were summarized and reported in tables and figures according to the study objectives.</p>\u0000 <p><b>Results:</b> From 1706 retrieved articles, 28 studies were included according to inclusion and exclusion criteria. Most studies were conducted in 2023 (13 out of 28). 13 groups of specialists used Metaverse to diagnose diseases; oncologists and neurologists used it more than others. The most important technological aspects of the Metaverse were six main categories, including computer vision, artificial intelligence, virtual reality, blockchain, digital twin, and cloud computing. The Metaverse’s main effects in diagnostic interventions were 22 subcategories in five categories, including improving diagnosis, facilitating interactions, improving education, a better future, and uncertainty. The Metaverse’s role in improving diagnosis was particularly significant. The challenges of the Metaverse in diagnosis were seven subcategories: challenges related to the conducted studies, financial limitations, technological issues, structural issues, legal and ethical issues, its acceptance, and challenges about the nature of the Metaverse.</p>\u0000 <p><b>Conclusion:</b> Given the pivotal role of accurate diagnosis in patients treatment plans, the Metaverse’s potential in complex and challenging diagnoses is significant. However, it is important to note that this potential can only be fully realized through further research on utilizing the Metaverse in healthcare, specifically in disease diagnosis. This call for additional research is not just a suggestion but a necessity for the future of healthcare.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/8820744","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143770442","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":"Traffic Flow Prediction Framework That Can Appropriately Process the Noise, Volatility, and Nonlinearity in Traffic Flow Data","authors":"Yingping Tang,&nbsp;Qiang Shang,&nbsp;Longjiao Yin,&nbsp;Hu Zhang","doi":"10.1155/int/1789796","DOIUrl":"https://doi.org/10.1155/int/1789796","url":null,"abstract":"<div>\u0000 <p>Accurate traffic flow prediction is crucial for improving transportation efficiency. To improve the accuracy of traffic flow prediction, we developed a traffic flow prediction framework—namely, traffic flow multicomponent network—that appropriately processes the noise, volatility, and nonlinearity in traffic flow data. This framework comprises three components: a factor selection component, traffic flow decomposition component, and traffic flow prediction component. The factor selection component considers the dynamic effects of weather-related, environmental, and spatiotemporal factors on traffic flow; it then extracts and analyzes factors exhibiting strong correlations with traffic flow. The traffic flow decomposition component optimizes the parameters of variational mode decomposition on the basis of the envelope entropy by using the sparrow search algorithm; it then transforms traffic flow into multiple intrinsic mode functions to enable accurate traffic flow prediction. Finally, the traffic flow prediction component constructs dynamic feature matrices by using a bidirectional gated recurrent unit model to identify relationships within the data. Moreover, it uses an attention mechanism to assign different weights to different features on the basis of the importance of these features to traffic flow prediction, thereby enabling the efficient processing of a large volume of data. The performance of the proposed framework was examined in experiments conducted on large volumes of traffic flow data with different time granularities. The results indicated that the proposed framework achieved high prediction accuracy and stability for various time granularities, data samples, dataset sizes, and noise conditions. Moreover, it generally outperformed existing traffic flow prediction models under all experimental conditions.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/1789796","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143769971","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}