International Journal of Intelligent Systems最新文献

{"title":"An Evolutionary Game Model for Green Production Decisions of Supply Chain Enterprises Considering Supply Chain Break Risk","authors":"Fulei Shi,&nbsp;Chuansheng Wang,&nbsp;Zhenfang Qin","doi":"10.1155/int/4329215","DOIUrl":"https://doi.org/10.1155/int/4329215","url":null,"abstract":"<div>\u0000 <p>The problem of environmental pollution has been extensively discussed, especially in the production phase of the supply chain. Many enterprises seek innovation and strive to achieve a win-win situation of economic and environmental benefits. However, the cooperation and competition between enterprises are likely to cause the interruption of the supply chain. Therefore, by combining the green production strategy with supply chain risk management, this paper builds an evolutionary game model between suppliers and manufacturers, to deeply understand the impact of supply chain disruption on the choice of the green production strategy by suppliers and manufacturers and reveal the conditions under which the system evolves into different stability strategies. The results show that (1) under different conditions, the system will have an evolutionarily stable strategy. When the total revenue of green production alone by the supplier or manufacturer is greater than the expenditure and the cost of supply chain disruption is greater than the difference between the investment cost of green production and the total revenue of green production alone, the system will produce two different evolution results. (2) The cooperation willingness of the supplier and manufacturer, the investment cost of green production, and the risk coefficient of supply chain break will all affect the evolution trajectory of the system, and the greater the absolute difference between these factors and the threshold, the faster the system convergence speed. By formulating relevant policies, the system can meet the conditions of evolutionary stability strategy (1, 1), which can promote the upstream and downstream enterprises of the supply chain to realize cooperative green production faster. This paper contributes to the understanding of green supply chain management and evolutionary game theory, while providing insights into how companies along the supply chain can achieve cooperative green production for the benefit of society and the environment.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/4329215","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143897221","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":"Enhanced Multidimensional Nonlinear Correlation via Phase Reconstruction and Broad Learning for Distributed Fusion Detection of Weak Pulse Signals","authors":"Liyun Su,&nbsp;Xuelian Long","doi":"10.1155/int/8827255","DOIUrl":"https://doi.org/10.1155/int/8827255","url":null,"abstract":"<div>\u0000 <p>Due to the intricate chaotic environments encountered in distributed sensor applications, such as sea monitoring, machinery fault diagnosis, and EEG weak signal detection, neural networks often face insufficient data to effectively carry out detection tasks. In contrast to traditional machine learning models, a statistical approach employing multidimensional nonlinear correlation (MNC) exhibits an unparalleled signal pattern prediction capability and possesses a streamlined yet robust framework for signal processing. However, the direct application of MNC to weak pulse signal detection remains constrained. To surmount these challenges and achieve high-precision signal detection, we explore a novel MNC approach, integrating phase reconstruction and manifold broad learning, specifically tailored for distributed sensor fusion detection amidst chaotic noise. Initially, the distributed observational data undergoes phase space reconstruction, transforming it into fixed-size arrays. These reconstructed tuples are then processed through the high-dimensional sequence of manifold broad learning, serving as inputs for the nonlinear correlation module to extract spatiotemporal features. Subsequently, a MNC system augmented with a QRS detector layer is devised to predict and classify the presence of a weak pulse signal. This integrated MNC approach, combining phase reconstruction and broad learning, operates within an enhanced feature space of the source domain, realizing detection fusion across distributed sensors through a majority voting principle. Simulation studies and experiments conducted on sea clutter datasets demonstrate the efficacy and robustness of the proposed MNC method, leveraging phase reconstruction and manifold broad learning strategies, for distributed sensor weak pulse signal fusion detection within chaotic backgrounds.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/8827255","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143900955","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":"Interpretable Deep Learning for Classifying Skin Lesions","authors":"Mojeed Opeyemi Oyedeji,&nbsp;Emmanuel Okafor,&nbsp;Hussein Samma,&nbsp;Motaz Alfarraj","doi":"10.1155/int/2751767","DOIUrl":"https://doi.org/10.1155/int/2751767","url":null,"abstract":"<div>\u0000 <p>The global prevalence of skin cancer necessitates the development of AI-assisted technologies for accurate and interpretable diagnosis of skin lesions. This study presents a novel deep learning framework for enhancing the interpretability and reliability of skin lesion predictions from clinical images, which are more inclusive, accessible, and representative of real-world conditions than dermoscopic images. We comprehensively analyzed 13 deep learning models from four main convolutional neural network architecture classes: DenseNet, ResNet, MobileNet, and EfficientNet. Different data augmentation strategies and model optimization algorithms were explored to access the performances of the deep learning models in binary and multiclass classification scenarios. In binary classification, the DenseNet-161 model, initialized with random weights, obtained a top accuracy of 79.40%, while the EfficientNet-B7 model, initialized with pretrained weights from ImageNet, reached an accuracy of 85.80%. Furthermore, in the multiclass classification experiments, DenseNet121, initialized with random weights and trained with AdamW, obtained the best accuracy of 65.1%. Likewise, when initialized with pretrained weights, the DenseNet121 model attained a top accuracy of 75.07% in multiclass classification. Detailed interpretability analyses were carried out leveraging the SHAP and CAM algorithms to provide insights into the decision rationale of the investigated models. The SHAP algorithm was beneficial in understanding the feature attributions by visualizing how specific regions of the input image influenced the model predictions. Our study emphasizes using clinical images for developing AI algorithms for skin lesion diagnosis, highlighting the practicality and relevance in real-world applications, especially where dermoscopic tools are not readily accessible. Beyond accessibility, these developments also ensure that AI-assisted diagnostic tools are deployed in diverse clinical settings, thus promoting inclusiveness and ultimately improving early detection and treatment of skin cancers.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/2751767","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143884241","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":"Analyzing and Detecting Abnormal Behaviors of Drug Abuse and Addiction Users in School Environments Based on Deep Learning Approaches","authors":"Salma Kammoun Jarraya,&nbsp;Marwa Masmoudi,&nbsp;Fahad Abdullah Alqurashi,&nbsp;Sultanah M. Alshammari","doi":"10.1155/int/9722173","DOIUrl":"https://doi.org/10.1155/int/9722173","url":null,"abstract":"<div>\u0000 <p>Drug abuse and addiction problems are one of the most serious health, social, and psychological problems facing the world. Many international studies indicate that the start of drug abuse occurs mostly in adolescence, which is the period that young people spend in schools, institutes, and universities. Drugs in the student community have become a scourge that raises increasing concern, whether among families or educators, over the fate of school children and educational attainment. Regarding their behaviors, an addicted student often exhibits abnormal behaviors such as permanent lethargy, anxiety, tremors, and aggressive behavior toward others. Moreover, to obtain drugs, the addicted student becomes compelled to resort to various means and ways, and they gradually become criminal addicts. To this endeavor, a detector of abnormal behaviors in schools has become a necessity. In this paper, we built an automatic system able to analyze and detect abnormal behaviors of addicted students and inform the educational staff and parents to know how to manage and treat them. On a technical level, we used deep learning and the recent computer vision techniques in the suggested solution due to their contributions to human behavior and emotion recognition fields. The best-recorded result (97.5%) is obtained with fused handcrafted features based on skeleton joints and deep features extracted with the MobileNet pretrained model and forwarded to a deep proposed network based on two TimeDistributed layers, one BiLSTM layer, and several Dense layers.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/9722173","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143884038","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":"MalFSLDF: A Few-Shot Learning-Based Malware Family Detection Framework","authors":"Wenjie Guo,&nbsp;Jingfeng Xue,&nbsp;Yuxin Lin,&nbsp;Wenbiao Du,&nbsp;Jingjing Hu,&nbsp;Ning Shi,&nbsp;Weijie Han","doi":"10.1155/int/7390905","DOIUrl":"https://doi.org/10.1155/int/7390905","url":null,"abstract":"<div>\u0000 <p>The evolution of malware has led to the development of increasingly sophisticated evasion techniques, significantly escalating the challenges for researchers in obtaining and labeling new instances for analysis. Conventional deep learning detection approaches struggle to identify new malware variants with limited sample availability. Recently, researchers have proposed few-shot detection models to address the above issues. However, existing studies predominantly focus on model-level improvements, overlooking the potential of domain adaptation to leverage the unique characteristics of malware. Motivated by these challenges, we propose a few-shot learning-based malware family detection framework (MalFSLDF). We introduce a novel method for malware representation using structural features and a feature fusion strategy. Specifically, our framework employs contrastive learning to capture the unique textural features of malware families, enhancing the identification capability for novel malware variants. In addition, we integrate entropy graphs (EGs) and gray-level co-occurrence matrices (GLCMs) into the feature fusion strategy to enrich sample representations and mitigate information loss. Furthermore, a domain alignment strategy is proposed to adjust the feature distribution of samples from new classes, enhancing the model’s generalization performance. Finally, comprehensive evaluations of the MaleVis and BIG-2015 datasets show significant performance improvements in both 5-way 1-shot and 5-way 5-shot scenarios, demonstrating the effectiveness of the proposed framework.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/7390905","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143865895","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 Emotion Recognition System for Human–Robot Interaction (HRI) Using Deep Ensemble Classification","authors":"Khalid Zaman,&nbsp;Gan Zengkang,&nbsp;Sun Zhaoyun,&nbsp;Sayyed Mudassar Shah,&nbsp;Waqar Riaz,&nbsp;Jiancheng (Charles) Ji,&nbsp;Tariq Hussain,&nbsp;Razaz Waheeb Attar","doi":"10.1155/int/6611276","DOIUrl":"https://doi.org/10.1155/int/6611276","url":null,"abstract":"<div>\u0000 <p>Human emotion recognition (HER) has rapidly advanced, with applications in intelligent customer service, adaptive system training, human–robot interaction (HRI), and mental health monitoring. HER’s primary goal is to accurately recognize and classify emotions from digital inputs. Emotion recognition (ER) and feature extraction have long been core elements of HER, with deep neural networks (DNNs), particularly convolutional neural networks (CNNs), playing a critical role due to their superior visual feature extraction capabilities. This study proposes improving HER by integrating EfficientNet with transfer learning (TL) to train CNNs. Initially, an efficient R-CNN accurately recognizes faces in online and offline videos. The ensemble classification model is trained by combining features from four CNN models using feature pooling. The novel VGG-19 block is used to enhance the Faster R-CNN learning block, boosting face recognition efficiency and accuracy. The model benefits from fully connected mean pooling, dense pooling, and global dropout layers, solving the evanescent gradient issue. Tested on CK+, FER-2013, and the custom novel HER dataset (HERD), the approach shows significant accuracy improvements, reaching 89.23% (CK+), 94.36% (FER-2013), and 97.01% (HERD), proving its robustness and effectiveness.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/6611276","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143865625","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":"Feature Transformation Reconstruction (FTR) Network for Unsupervised Anomaly Detection","authors":"Linna Zhang,&nbsp;Lanyao Zhang,&nbsp;Qi Cao,&nbsp;Shichao Kan,&nbsp;Yigang Cen,&nbsp;Fugui Zhang,&nbsp;Yansen Huang","doi":"10.1155/int/1780499","DOIUrl":"https://doi.org/10.1155/int/1780499","url":null,"abstract":"<div>\u0000 <p>The goal of the feature reconstruction network based on an autoencoder in the training phase is to force the network to reconstruct the input features well. The network tends to learn shortcuts of “identity mapping,” which leads to the network outputting abnormal features as they are in the inference phase. As such, the abnormal features based on reconstruction error cannot be distinguished from normal features, significantly limiting the detection performance of such methods. To address this issue, we propose a feature transformation reconstruction (FTR) network, which can avoid the identity mapping problem. Specifically, we use a normalizing flow model as a feature transformation (FT) network to transform input features into other forms. The training goal of the feature reconstruction (FR) network is no longer to reconstruct the input features but to reconstruct the transformed features, effectively avoiding the shortcut of learning the “identity map.” Furthermore, this paper proposes a masked convolutional attention (MCA) module, which randomly masks the input features in the training phase and reconstructs the input features in a self-supervised manner. In the testing phase, the MCA can effectively suppress the excessive reconstruction of abnormal features and further improve anomaly detection performance. FTR achieves the scores of the area under the receiver operating characteristic curve (AUROC) at 99.5% and 97.8% on the MVTec AD and BTAD datasets, respectively, outperforming other state-of-the-art methods. Moreover, FTR is faster than the existing methods, with a high speed of 137 frames per second (FPS) on a 3080ti GPU.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/1780499","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143861884","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":"IntFedSV: A Novel Participants’ Contribution Evaluation Mechanism for Federated Learning","authors":"Tianxu Cui,&nbsp;Ying Shi,&nbsp;Wenge Li,&nbsp;Rijia Ding,&nbsp;Qing Wang","doi":"10.1155/int/3466867","DOIUrl":"https://doi.org/10.1155/int/3466867","url":null,"abstract":"<div>\u0000 <p>Federated learning (FL), which is a distributed privacy computing technology, has demonstrated strong capabilities in addressing potential privacy leakage for multisource data fusion and has been widely applied in various industries. Existing contribution evaluation mechanisms based on Shapley values uniquely allocate the total utility of a federation based on the marginal contributions of participants. However, in practical engineering applications, participants from different data sources typically exhibit significant differences and uncertainties in terms of their contributions to a federation, thus rendering it difficult to represent their contributions precisely. To evaluate the contribution of each participant to FL more effectively, we propose a novel interval federated Shapley value (IntFedSV) contribution evaluation mechanism. Second, to improve computational efficiency, we utilize a matrix semitensor product-based method to compute the IntFedSV. Finally, extensive experiments on four public datasets (MNIST, CIFAR10, AG_NEWS, and IMDB) demonstrate its potential in engineering applications. Our proposed mechanism can effectively evaluate the contribution levels of participants. Compared with the case of three advanced baseline methods, the minimum and maximum improvement rates of standard deviation for our proposed mechanism are 11.83% and 99.00%, respectively, thus demonstrating its greater stability and fault tolerance. This study contributes positively to promoting engineering applications of FL.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/3466867","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143857063","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":"Ethical Principles of Integrating ChatGPT Into IoT–Based Software Wearables: A Fuzzy-TOPSIS Ranking and Analysis Approach","authors":"Maseeh Ullah Khan,&nbsp;Muhammad Farhat Ullah,&nbsp;Sabeeh Ullah Khan,&nbsp;Weiqiang Kong","doi":"10.1155/int/6660868","DOIUrl":"https://doi.org/10.1155/int/6660868","url":null,"abstract":"<div>\u0000 <p>The rapid development of the internet of things (IoT) prompts organizations and developers to seek innovative approaches for future IoT device development and research. Leveraging advanced artificial intelligence (AI) models such as ChatGPT holds promise in reshaping the conceptualization, development, and commercialization of IoT devices. Through real-world data utilization, AI enhances the effectiveness, adaptability, and intelligence of IoT devices and wearables, expediting their production process from ideation to deployment and customer assistance. However, integrating ChatGPT into IoT–based devices and wearables poses ethical concerns including data ownership, security, privacy, accessibility, bias, accountability, cost, design, quality, storage, model training, explainability, consistency, fairness, safety, transparency, trust, and generalizability. Addressing these ethical principles necessitates a comprehensive review of the literature to identify and classify relevant principles. The author identified 14 ethical principles from the literature using a systematic literature review (SLR) with a criteria of frequency ≥ 50% based on similarities. Four categories emerge based on the identified ethical principles, culminating in the application of Fuzzy-TOPSIS for analyzing, categorizing, ranking, and prioritizing these ethical principles. From the Fuzzy-TOPSIS technique results, the principle of data security and privacy is the highly ranked ethical principle for IoT–based software wearable devices with the ranking value of “0.925” as a consistency coefficient index. This method, well-established in computer science, effectively navigates fuzzy and uncertain decision-making scenarios. The pioneer outcomes of this study provide a taxonomy-based valuable insight for software manufacturers, facilitating the analysis, ranking, categorization, and prioritization of ethical principles amid the integration of ChatGPT in IoT–based devices and wearables’ research and development.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/6660868","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143861674","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":"Facial Expression Recognition Method Based on Octonion Orthogonal Feature Extraction and Octonion Vision Transformer","authors":"Yuan Tian,&nbsp;Hang Cai,&nbsp;Huang Yao,&nbsp;Di Chen","doi":"10.1155/int/6388642","DOIUrl":"https://doi.org/10.1155/int/6388642","url":null,"abstract":"<div>\u0000 <p>In the field of artificial intelligence, facial expression recognition (FER) in natural scenes is a challenging topic. In recent years, vision transformer (ViT) models have been applied to FER tasks. The direct use of the original ViT structure consumes a lot of computational resources and longer training time. To overcome these problems, we propose a FER method based on octonion orthogonal feature extraction and octonion ViT. First, to reduce feature redundancy, we propose an orthogonal feature decomposition method to map the extracted features onto seven orthogonal sub-features. Then, an octonion orthogonal representation method is introduced to correlate the orthogonal features, maintain the intrinsic dependencies between different orthogonal features, and enhance the model’s ability to extract features. Finally, an octonion ViT is presented, which reduces the number of parameters to one-eighth of ViT while improving the accuracy of FER. Experimental results on three commonly used facial expression datasets show that the proposed method outperforms several state-of-the-art models with a significant reduction in the number of parameters.</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/6388642","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143852711","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}