International Journal of Intelligent Systems最新文献

{"title":"Semantic Analysis of Vaccine and Online Shopping-Related Stock Forums During the COVID-19 Pandemic","authors":"Tsung-Sheng Chang,&nbsp;Shih-Chieh Wang","doi":"10.1155/2024/2834544","DOIUrl":"https://doi.org/10.1155/2024/2834544","url":null,"abstract":"<div>\u0000 <p>During the COVID-19 pandemic, the stay-at-home and biotechnology economies played a big part in economic development. The major internet forums have received more attention and discussions concerning stocks related to biotechnology and the stay-at-home economy. When the number of confirmed COVID-19 cases in a country rises, so does the positive or negative sentiment in stock commentaries. Whether stock forums can be a valuable source of information for investors has become a subject of academic research. This study used text mining and sentiment analysis to analyze stock forum articles, classify daily reports into emotion and investment orientation indicators, and correlate these indicators with the next-day 2019’s stock prices. The findings indicate a positive correlation between stock forum articles and stock prices. Additionally, this research enriched the case of sentiment analysis in the context of Chinese sentiment. This study contributes not only to academic reference and refinement but also to market investors’ judgment.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2024 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/2024/2834544","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142328562","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":"FLDATN: Black-Box Attack for Face Liveness Detection Based on Adversarial Transformation Network","authors":"Yali Peng,&nbsp;Jianbo Liu,&nbsp;Min Long,&nbsp;Fei Peng","doi":"10.1155/2024/8436216","DOIUrl":"https://doi.org/10.1155/2024/8436216","url":null,"abstract":"<div>\u0000 <p>Aiming at the shortcomings of the current face liveness detection attack methods in the low generation speed of adversarial examples and the implementation of white-box attacks, a novel black-box attack method for face liveness detection named as FLDATN is proposed based on adversarial transformation network (ATN). In FLDATN, a convolutional block attention module (CBAM) is used to improve the generalization ability of adversarial examples, and the misclassification loss function based on feature similarity is defined. Experiments and analysis on the Oulu-NPU dataset show that the adversarial examples generated by the FLDATN have a good black-box attack effect on the task of face liveness detection and can achieve better generalization performance than the traditional methods. In addition, since FLDATN does not need to perform multiple gradient calculations for each image, it can significantly improve the generation speed of the adversarial examples.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2024 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/2024/8436216","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142324605","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 Self-Attention Transfer Adaptive Learning Approach for Brain Tumor Categorization","authors":"Tawfeeq Shawly,&nbsp;Ahmed A. Alsheikhy","doi":"10.1155/2024/8873986","DOIUrl":"https://doi.org/10.1155/2024/8873986","url":null,"abstract":"<div>\u0000 <p>Brain tumors cause death to a lot of people globally. Brain tumor disease is seen as one of the most lethal diseases since its mortality rate is high. Nevertheless, this rate can be diminished if the disease is identified and treated early. Recently, healthcare providers have relied on computed tomography (CT) scans and magnetic resonance imaging (MRI) in their diagnosis. Currently, various artificial intelligence (AI)-based solutions have been implemented to diagnose this disease early to prepare suitable treatment plans. In this article, we propose a novel self-attention transfer adaptive learning approach (SATALA) to identify brain tumors. This approach is an automated AI-based model that contains two deep-learning technologies to determine the existence of brain tumors. In addition, the proposed approach categorizes the identified tumors into two groups, which are benign and malignant. The developed method incorporates two deep-learning technologies: a convolutional neural network (CNN), which is VGG-19, and a new UNET network architecture. This approach is trained and evaluated on six public datasets and attained exquisite results. It achieved an average of 95% accuracy and an <i>F</i>1-score of 96.61%. The proposed approach was compared with other state-of-the-art models that were reported in the related work. The conducted experiments show that the proposed approach generates exquisite outputs and exceeds other works in some scenarios. In conclusion, we can infer that the proposed approach provides trustworthy identifications of brain cancer and can be applied in healthcare facilities.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2024 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/2024/8873986","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142234924","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 Manifold-Guided Gravitational Search Algorithm for High-Dimensional Global Optimization Problems","authors":"Fang Su,&nbsp;Yance Wang,&nbsp;Shu Yang,&nbsp;Yuxing Yao","doi":"10.1155/2024/5806437","DOIUrl":"https://doi.org/10.1155/2024/5806437","url":null,"abstract":"<div>\u0000 <p>Gravitational Search Algorithm (GSA) is a well-known physics-based meta-heuristic algorithm inspired by Newton’s law of universal gravitation and performs well in solving optimization problems. However, when solving high-dimensional optimization problems, the performance of GSA may deteriorate dramatically due to severe interference of redundant dimensional information in the high-dimensional space. To solve this problem, this paper proposes a Manifold-Guided Gravitation Search Algorithm, called MGGSA. First, based on the Isomap, an effective dimension extraction method is designed. In this mechanism, the effective dimension is extracted by comparing the dimension differences of the particles located in the same sorting position both in the original space and the corresponding low-dimensional manifold space. Then, the gravitational adjustment coefficient is designed, so that the particles can be guided to move in a more appropriate direction by increasing the effect of effective dimension, reducing the interference of redundant dimension on particle motion. The performance of the proposed algorithm is tested on 35 high-dimensional (dimension is 1000) benchmark functions from CEC2010 and CEC2013, and compared with eleven state-of-art meta-heuristic algorithms, the original GSA and four latest GSA’s variants, as well as three well-known large-scale global optimization algorithms. The experimental results demonstrate that MGGSA not only has a fast convergence rate but also has high solution accuracy. Besides, MGGSA is applied to three real-world application problems, which verifies the effectiveness of MGGSA on practical applications.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2024 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/2024/5806437","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142130427","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":"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}