International Journal of Intelligent Systems最新文献

{"title":"PU-GNN: A Positive-Unlabeled Learning Method for Polypharmacy Side-Effects Detection Based on Graph Neural Networks","authors":"Abedin Keshavarz,&nbsp;Amir Lakizadeh","doi":"10.1155/2024/4749668","DOIUrl":"https://doi.org/10.1155/2024/4749668","url":null,"abstract":"<div>\u0000 <p>The simultaneous use of multiple drugs, known as polypharmacy, heightens the risks of harmful side effects due to drug-drug interactions. Predicting these interactions is crucial in drug research due to the rising prevalence of polypharmacy. Researchers employ a graphical structure to model these interactions, representing drugs and side effects as nodes and their interactions as edges. This creates a multipartite graph that encompasses various interactions such as protein-protein interactions, drug-target interactions, and side effects of polypharmacy. In this study, a method named PU-GNN, based on graph neural networks, is introduced to predict drug side effects. The proposed method involves three main steps: (1) drug features extraction using a novel biclustering algorithm, (2) reducing uncertainity in input data using a positive-unlabeled learning algorithm, and (3) prediction of drug’s polypharmacies by utilizing a graph neural network. Performance evaluation using 5-fold cross-validation reveals that PU-GNN surpasses other methods, achieving high scores of 0.977, 0.96, and 0.949 in the AUPR, AUC, and F1 measures, respectively.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2024 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/2024/4749668","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142077802","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":"Real-World Image Deraining Using Model-Free Unsupervised Learning","authors":"Rongwei Yu,&nbsp;Jingyi Xiang,&nbsp;Ni Shu,&nbsp;Peihao Zhang,&nbsp;Yizhan Li,&nbsp;Yiyang Shen,&nbsp;Weiming Wang,&nbsp;Lina Wang","doi":"10.1155/2024/7454928","DOIUrl":"https://doi.org/10.1155/2024/7454928","url":null,"abstract":"<div>\u0000 <p>We propose a novel model-free unsupervised learning paradigm to tackle the unfavorable prevailing problem of real-world image deraining, dubbed MUL-Derain. Beyond existing unsupervised deraining efforts, MUL-Derain leverages a model-free Multiscale Attentive Filtering (MSAF) to handle multiscale rain streaks. Therefore, formulation of any rain imaging is not necessary, and it requires neither iterative optimization nor progressive refinement operations. Meanwhile, MUL-Derain can efficiently compute spatial coherence and global interactions by modeling long-range dependencies, allowing MSAF to learn useful knowledge from a larger or even global rain region. Furthermore, we formulate a novel multiloss function to constrain MUL-Derain to preserve both color and structure information from the rainy images. Extensive experiments on both synthetic and real-world datasets demonstrate that our MUL-Derain obtains state-of-the-art performance over un/semisupervised methods and exhibits competitive advantages over the fully-supervised ones.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2024 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/2024/7454928","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142077882","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 Data and Knowledge Fusion-Driven Early Fault Warning Method for Traction Control Systems","authors":"Nanliang Shan,&nbsp;Xinghua Xu,&nbsp;Xianqiang Bao,&nbsp;Fei Cheng,&nbsp;Tao Liao,&nbsp;Shaohua Qiu","doi":"10.1155/2024/5115148","DOIUrl":"https://doi.org/10.1155/2024/5115148","url":null,"abstract":"<div>\u0000 <p>While high-speed maglev trains offer convenient travel options, they also pose challenging issues for fault detection and early warning in critical components. This study proposes a Temporal-Knowledge fusion Spatiotemporal Graph Convolutional Network (TK-STGCN) for early warning of faults in the traction control system (TCS). Compared with the existing literature that leverages the spatiotemporal characteristics of big data for fault feature discovery, TK-STGCN focuses on integrating prior knowledge to capture correlations between data and fault mechanisms, thereby improving data processing efficiency. This requires our method not only to extract spatiotemporal features from time series but also to efficiently integrate knowledge representations with time series as inputs to the model. Specifically, structural analysis (SA) is first employed to construct the predefined structural graph for the TK-STGCN backbone network. Subsequently, a knowledge fusion unit is used to integrate the knowledge graph representation with monitoring time series data as input for the TK-STGCN model. Finally, the TK-STGCN method is applied to provide early warnings for six common faults in TCS. Analysis based on 21,498 hardware-in-the-loop experiments reveals that this method can achieve a fault warning rate of over 90%. This demonstrates that the proposed method can effectively predict faults before they occur, preventing excessive equipment damage and even catastrophic consequences.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2024 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2024-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/2024/5115148","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142050553","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":"Complex Question Answering Method on Risk Management Knowledge Graph: Multi-Intent Information Retrieval Based on Knowledge Subgraphs","authors":"Yanjun Guo,&nbsp;Xinbo Ai,&nbsp;Guangsheng Liu","doi":"10.1155/2024/2907043","DOIUrl":"https://doi.org/10.1155/2024/2907043","url":null,"abstract":"<div>\u0000 <p>The critical aspects of risk management include hazard identification, risk assessment, and risk control. Timely risk management is critical to company decision-making, but the process of acquiring risk management knowledge is often time-consuming and labor-intensive. Knowledge graph question answering (KGQA) provides an effective solution by delivering knowledge through accurate reasoning. However, existing KGQA methods do not cover the critical risk management aspects and are difficult to retrieve quickly and accurately from large knowledge graphs. This study describes a complex question answering method for intelligently generating risk management knowledge, specifically through multi-intent information retrieval based on knowledge subgraphs. The proposed method comprises three main modules. First, in the question understanding module, we propose an intent recognition method that integrates topic entity extraction with convolutional neural networks (CNNs) to identify eleven different user intents. To enhance the retrieval efficiency, we propose a hierarchical knowledge-embedding subgraph constructed based on company and hazard descriptions. Once user intent is identified, the information retrieval module based on a novel approximate nearest neighbor (ANN) algorithm achieves deep semantic feature matching of company and hazard expressions from the knowledge embedding subgraph. After obtaining these two deep semantic features, in the answer generation module, we propose a rule-based knowledge subgraph reasoning method to answer complex questions including single-hop, multihop, constraints, and numerical calculations. On the real risk management dataset, the precision of the intent recognition module reaches 91.3% and the information retrieval module spends only 0.36 ms, verifying that the model outperforms the existing state-of-the-art models. Meanwhile, a question answering system based on the proposed method is developed to acquire risk management knowledge: Xiao An. Compared to the popular search engine and expert system for acquiring knowledge, Xiao An achieves the best results regarding ease of use, time spent, and overall performance.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2024 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2024-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/2024/2907043","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142050535","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":"Design of an Online Adaptive Fractional-Order Proportional-Integral-Derivative Controller to Reduce the Seismic Response of the 20-Story Benchmark Building Equipped with an Active Control System","authors":"Ommegolsoum Jafarzadeh,&nbsp;Seyyed Arash Mousavi Ghasemi,&nbsp;Seyed Mehdi Zahrai,&nbsp;Rasoul Sabetahd,&nbsp;Ardashir Mohammadzadeh,&nbsp;Ramin Vafaei Poursorkhabi","doi":"10.1155/2024/5648897","DOIUrl":"https://doi.org/10.1155/2024/5648897","url":null,"abstract":"<div>\u0000 <p>The objective of the present investigation is to introduce a novel adaptive fractional-order proportional-integral-derivative controller, which is characterized by the online tuning of its parameters by utilizing five distinct multilayer perceptron neural networks employing the extended Kalman filter. Utilizing the backpropagation algorithm in training a multilayer perceptron neural network is deemed effective in identifying the structural system and estimating the plant. The controller is applied using the Jacobian derived from the online estimated model. The utilization of adaptive interval type-2 fuzzy neural networks in conjunction with the extended Kalman filter tuning method and feedback error learning strategy results in enhanced stability and robustness of the controller in the face of estimation error, seismic disturbances, and unknown nonlinear functions. The study aims to validate the efficacy of the proposed controller by examining its performance on a 20-story nonlinear building. The numerical results show that including a compensator enhances the performance of the adaptive fractional-order proportional-integral-derivative controller. The results show that the proposed adaptive fractional-order proportional-integral-derivative controller has a better performance than other controllers and that the interstory drift ratio criterion under the El Centro earthquake with a magnitude of 1.5 times experienced an improvement of up to 65% compared to other controllers, and this amount in the Kobe earthquake reached more than 58%. Other criteria have also experienced significant improvement using the proposed controller.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2024 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/2024/5648897","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142013676","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 Multiantenna Spectrum Sensing Method Based on HFDE-CNN-GRU under Non-Gaussian Noise","authors":"Suoping Li,&nbsp;Yuzhou Han,&nbsp;Jaafar Gaber,&nbsp;Qian Yang","doi":"10.1155/2024/1085161","DOIUrl":"https://doi.org/10.1155/2024/1085161","url":null,"abstract":"<div>\u0000 <p>In many practical communication environments, traditional feature extraction methods in spectrum sensing fail to fully exploit the information of primary users. Additionally, conventional machine learning methods have weak learning capabilities, making it difficult to maintain efficient and stable spectrum sensing performance in complex noise environments. Furthermore, non-Gaussian noise can significantly affect the detection performance of spectrum sensing. To address these issues, this paper first proposes a feature extraction method based on Hierarchical Fuzzy Dispersion Entropy (HFDE) to better extract high-frequency and low-frequency information from signal samples, providing more comprehensive features for subsequent models to optimize feature extraction effectiveness. Then, a parallel model combining Convolutional Neural Networks (CNN) with Gated Recurrent Units (GRU) is constructed to enhance learning ability. While CNN extracts local features, GRU processes temporal relationships, and the features output by both are concatenated to achieve effective feature learning and temporal modeling of primary user signal data represented by HFDE. Finally, using the feature vectors output by the CNN-GRU model, detection statistics and detection thresholds for spectrum sensing are constructed for online detection. Simulation results validate the effectiveness and robustness of this method in spectrum sensing under non-Gaussian noise. In the presence of significant non-Gaussian noise intensity and a signal-to-noise ratio of −14 dB, the detection probability can reach 97.1%. Additionally, for the detection of unknown signals, the model can still maintain a detection probability of over 90%.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2024 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/2024/1085161","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142002583","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":"Contrastive Learning with Edge-Wise Augmentation for Rumor Detection","authors":"Nan Liu,&nbsp;Fengli Zhang,&nbsp;Qiang Gao,&nbsp;Xueqin Chen","doi":"10.1155/2024/3858526","DOIUrl":"https://doi.org/10.1155/2024/3858526","url":null,"abstract":"<div>\u0000 <p>Exploring and modeling the spreading process of rumors have shown great potential in improving rumor detection performance. However, existing propagation-based rumor detection models often overlook the uncertainty of the underlying propagation structure and typically require a large amount of labeled data for training. To address these challenges, we propose a novel rumor detection framework, namely, the Uncertainty-Inference Contrastive Learning (UICL) model. Specifically, UICL innovatively incorporates an edge-wise augmentation strategy into the general contrastive learning framework, including an edge-inference augmentation component and an EdgeDrop augmentation component, which primarily aim to capture the edge uncertainty of the propagation structure and alleviate the sparsity problem of the original dataset. A new negative sampling strategy is also introduced to enhance contrastive learning on rumor propagation graphs. Furthermore, we use labeled data to fine-tune the detection module. Our experiments, conducted on three real-world datasets, demonstrate that UICL can not only significantly improve detection accuracy but also reduce the dependency on labeled data compared to state-of-the-art baselines.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2024 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/2024/3858526","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141967839","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":"Enhancement of Infrared Imagery through Low-Light Image Guidance Leveraging Deep Learning Techniques","authors":"Yong Gan,&nbsp;Yuefeng Wang","doi":"10.1155/2024/8574836","DOIUrl":"https://doi.org/10.1155/2024/8574836","url":null,"abstract":"<div>\u0000 <p>Addressing challenges in infrared imaging, such as low contrast, blurriness, and detail scarcity due to environmental limitations and the target’s limited radiative capacity, this research introduces a novel infrared image enhancement approach using low-light image guidance. Initially, the Cbc-SwinIR model (coordinate-based convolution- image restoration using Swin Transformer) is applied for super-resolution reconstruction of both shimmer and infrared images, improving their resolution and clarity. Next, the MAXIM model (multiaxis MLP for image processing) enhances the visibility of low-light images under low illumination. Finally, the AILI (adaptive infrared and low-light)-fusion algorithm fuses the processed low-light image with the infrared image, achieving comprehensive visual enhancement. The enhanced infrared image exhibits significant improvements: a 0.08 increase in fractal dimension (FD), 0.094 rise in information entropy, 0.00512 elevation in mean square error (MSE), and a 12.206 reduction in peak signal-to-noise ratio (PSNR). These advancements in FD and information entropy highlight a substantial improvement in the complexity and diversity of the infrared image’s features. Despite a decrease in PSNR and an increase in MSE, this indicates that the newly introduced information enhances contrast and enriches texture details in the infrared images, resulting in pixel-level variations. This methodology demonstrates considerable improvements in visual content and analytical value, proving relevant, innovative, and efficient in infrared image enhancement with broad application prospects.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2024 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/2024/8574836","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141967096","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":"Channel Estimation Algorithm Based on Spatial Direction Acquisition and Dynamic-Window Expansion in Massive MIMO System","authors":"Shufeng Li,&nbsp;Baoxin Su,&nbsp;Yiming Liu,&nbsp;Junwei Zhang,&nbsp;Minglei You","doi":"10.1155/2024/7727469","DOIUrl":"https://doi.org/10.1155/2024/7727469","url":null,"abstract":"<div>\u0000 <p>Millimeter-wave (mmWave) and massive multiple-input multiple-output (MIMO) technologies are critical in current and future communication research. They play an essential role in meeting the demands for high-capacity, high-speed, and low-latency communication brought about by technological advancements. However, existing mmWave channel estimation schemes rely on idealized common sparse channel support assumptions, and their performance significantly degrades when encountering beam squint scenarios. To address this issue, this paper introduces a dynamic support detection window (DSDW) algorithm. This algorithm dynamically adjusts the position and size of the window based on the received signal strength, thereby better capturing signal strength variations and obtaining a more complete set of signal supports. The DSDW algorithm can better capture and utilize the sparsity of the channel, improving the efficiency and accuracy of the channel state information acquisition. By combining the beam-split pattern (BSP) algorithm with the DSDW algorithm, this paper designs an effective method to address the inherent beam-spreading problem in mmWave scenarios. Simulation results are proposed to demonstrate the effectiveness of the BSP-DSDW algorithm.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2024 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/2024/7727469","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141730284","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 Passenger Load Prediction inside Metro Network via Smart Card Data","authors":"Xiancai Tian,&nbsp;Chen Zhang,&nbsp;Baihua Zheng","doi":"10.1155/2024/6643018","DOIUrl":"https://doi.org/10.1155/2024/6643018","url":null,"abstract":"<div>\u0000 <p>Metro system serves as the backbone for urban public transportation. Accurate passenger load prediction for the metro system plays a crucial role in metro service quality improvement, such as helping operators schedule train timetables and passengers plan their trips. However, existing works can only predict low-grained passenger flows of origin-destination (O-D) paths or inflows/outflows of each station but cannot predict passenger load distribution over the whole metro network. To this end, this paper proposes an end-to-end inference framework, PIPE, for passenger load prediction of every metro segment between two adjacent stations, by only utilizing smart card data. In particular, PIPE includes two modules. The first is the core. It formulates the travel time distribution of each metro segment as a truncated Gaussian distribution. Since there might be several possible routes for certain O-D paths, the population-level travel time distribution of these O-D paths would be a mixture of travel times of different routes. Considering the route preference may change over time, a dynamic truncated Gaussian mixture model is proposed for parameter inference of each truncated Gaussian distribution of each metro segment. The second module serves as the supplement, which compiles a bunch of methods for predicting passenger flows of O-D paths. Built upon them, PIPE is able to predict the travel time that future passengers of each O-D path will take for passing each metro segment and consequently can predict the passenger load of each metro segment in the short future. Numerical studies from Singapore’s metro system demonstrate the efficacy of our method.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2024 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/2024/6643018","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141730210","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}