Expert Systems最新文献

{"title":"Adversarial attack vulnerability for multi-biometric authentication system","authors":"MyeongHoe Lee,&nbsp;JunHo Yoon,&nbsp;Chang Choi","doi":"10.1111/exsy.13655","DOIUrl":"10.1111/exsy.13655","url":null,"abstract":"<p>Research on multi-biometric authentication systems using multiple biometric modalities to defend against adversarial attacks is actively being pursued. These systems authenticate users by combining two or more biometric modalities using score or feature-level fusion. However, research on adversarial attacks and defences against each biometric modality within these authentication systems has not been actively conducted. In this study, we constructed a multi-biometric authentication system using fingerprint, palmprint, and iris information from CASIA-BIT by employing score and feature-level fusion. We verified the system's vulnerability by deploying adversarial attacks on single and multiple biometric modalities based on the FGSM, with epsilon values ranging from 0 to 0.5. The experimental results show that when the epsilon value is 0.5, the accuracy of the multi-biometric authentication system against adversarial attacks on the palmprint and iris information decreases from 0.995 to 0.018 and 0.003, respectively, and the f1-score decreases from 0.995 to 0.007 and 0.000, respectively, demonstrating susceptibility to adversarial attacks. In the case of fingerprint data, however, the accuracy and f1-score decreased from 0.995 to 0.731 and from 0.995 to 0.741, respectively, indicating resilience against adversarial attacks.</p>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"41 10","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141530166","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Time series generative adversarial network for muscle force prognostication using statistical outlier detection","authors":"Hunish Bansal, Basavraj Chinagundi, Prashant Singh Rana, Neeraj Kumar","doi":"10.1111/exsy.13653","DOIUrl":"https://doi.org/10.1111/exsy.13653","url":null,"abstract":"Machine learning approaches, such as artificial neural networks (ANN), effectively perform various tasks and provide new predictive models for complicated physiological systems. Examples of Robotics applications involving direct human engagement, such as controlling prosthetic arms, athletic training, and investigating muscle physiology. It is now time for automated systems to take over modelling and monitoring tasks. However, there is a problem with the massive amount of time series data collected to build accurate forecasting systems. There may be inconsistencies in forecasting muscle forces due to the enormous amount of data. As a result, anomaly detection techniques play a significant role in detecting anomalous data. Detecting anomalies can help reduce redundancy and free up large storage space for storing relevant time‐series data. This paper employs several anomaly detection techniques, including Isolation Forest (iforest), K‐Nearest Neighbour (KNN), Open Support Vector Machine (OSVM), Histogram, and Local Outlier Factor (LOF). These techniques have been used by Long Short‐Term Memory (LSTM), Auto‐Regressive Integrated Moving Average (ARIMA), and Prophet models. The dataset used in this study contained raw measurements of body movements (kinematics) and the forces generated during walking (kinetics) of 57 healthy people (29 Female, 28 Male) without walking abnormalities or recent leg injuries. To increase the data samples, we used TimeGAN that generates synthetic time series data with temporal dependencies, aiding in training robust predictive models for muscle force prediction. The results are then compared with different evaluation metrics for five different samples. It is found that anomaly detection techniques with LSTM, ARIMA, and Prophet models provided better performance in forecasting muscle forces. The iforest method achieved the best Pearson's Correlation Coefficient (<jats:italic>r</jats:italic>) of 0.95, which is a competitive score with existing systems that perform between 0.7 and 0.9. The methodology provides a foundation for precision medicine, enhancing prognostic capability over relying solely on population averages.","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"58 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2024-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141522543","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A unique morpho‐feature extraction algorithm for medicinal plant identification","authors":"Ashwani Kumar Dubey, Jibi G. Thanikkal, Puneet Sharma, Manoj Kumar Shukla","doi":"10.1111/exsy.13663","DOIUrl":"https://doi.org/10.1111/exsy.13663","url":null,"abstract":"An image is a set of numbers arranged in matrix form. The image feature extraction algorithm converts the input image into different numerical forms to extract the useful information from the input image and the selection of appropriate feature extraction algorithm is crucial for medicinal plant identification. In medicinal plants, the leaves are an available important resource of morphological features. Botanists use these morphological features of leaf images for medicinal plant identification. The existing leaf‐based medicinal plant identification strategies include shape, colour and texture features. In these methods, environmental factors directly influence the features and hence, the impact can be observed in the accuracy of the result. To overcome these limitations, we have proposed a unique morpho‐feature extraction algorithm (UMFEA) for accurate identification of medicinal plants. The UMFEA includes three sub‐algorithms for shape, apex, base, and vein features extraction. The proposed UMFEA is tested over Flavia, Swedish, Leaf and our databases. The performance comparison of UMFEA is done on different databases and the results obtained were remarkably good.","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"241 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141522542","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Convolution-enhanced vision transformer method for lower limb exoskeleton locomotion mode recognition","authors":"Jianbin Zheng,&nbsp;Chaojie Wang,&nbsp;Liping Huang,&nbsp;Yifan Gao,&nbsp;Ruoxi Yan,&nbsp;Chunbo Yang,&nbsp;Yang Gao,&nbsp;Yu Wang","doi":"10.1111/exsy.13659","DOIUrl":"https://doi.org/10.1111/exsy.13659","url":null,"abstract":"<p>Providing the human body with smooth and natural assistance through lower limb exoskeletons is crucial. However, a significant challenge is identifying various locomotion modes to enable the exoskeleton to offer seamless support. In this study, we propose a method for locomotion mode recognition named Convolution-enhanced Vision Transformer (Conv-ViT). This method maximizes the benefits of convolution for feature extraction and fusion, as well as the self-attention mechanism of the Transformer, to efficiently capture and handle long-term dependencies among different positions within the input sequence. By equipping the exoskeleton with inertial measurement units, we collected motion data from 27 healthy subjects, using it as input to train the Conv-ViT model. To ensure the exoskeleton's stability and safety during transitions between various locomotion modes, we not only examined the typical five steady modes (involving walking on level ground [WL], stair ascent [SA], stair descent [SD], ramp ascent [RA], and ramp descent [RD]) but also extensively explored eight locomotion transitions (including WL-SA, WL-SD, WL-RA, WL-RD, SA-WL, SD-WL, RA-WL, RD-WL). In tasks involving the recognition of five steady locomotions and eight transitions, the recognition accuracy reached 98.87% and 96.74%, respectively. Compared with three popular algorithms, ViT, convolutional neural networks, and support vector machine, the results show that the proposed method has the best recognition performance, and there are highly significant differences in accuracy and F1 score compared to other methods. Finally, we also demonstrated the excellent performance of Conv-ViT in terms of generalization performance.</p>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"41 10","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142165643","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Arabic text classification based on analogical proportions","authors":"Myriam Bounhas,&nbsp;Bilel Elayeb,&nbsp;Amina Chouigui,&nbsp;Amir Hussain,&nbsp;Erik Cambria","doi":"10.1111/exsy.13609","DOIUrl":"https://doi.org/10.1111/exsy.13609","url":null,"abstract":"<p>Text classification is the process of labelling a given set of text documents with predefined classes or categories. Existing Arabic text classifiers are either applying classic Machine Learning algorithms such as <i>k</i>-NN and SVM or using modern deep learning techniques. The former are assessed using small text collections and their accuracy is still subject to improvement while the latter are efficient in classifying big data collections and show limited effectiveness in classifying small corpora with a large number of categories. This paper proposes a new approach to Arabic text classification to treat small and large data collections while improving the classification rates of existing classifiers. We first demonstrate the ability of analogical proportions (AP) (statements of the form ‘<i>x</i> is to <span></span><math>\u0000 <mrow>\u0000 <mi>y</mi>\u0000 </mrow></math> as <span></span><math>\u0000 <mrow>\u0000 <mi>z</mi>\u0000 </mrow></math> is to <span></span><math>\u0000 <mrow>\u0000 <mi>t</mi>\u0000 </mrow></math>’), which have recently been shown to be effective in classifying ‘structured’ data, to classify ‘unstructured’ text documents requiring preprocessing. We design an analogical model to express the relationship between text documents and their real categories. Next, based on this principle, we develop two new analogical Arabic text classifiers. These rely on the idea that the category of a new document can be predicted from the categories of three others, in the training set, in case the four documents build together a ‘valid’ analogical proportion on all or on a large number of components extracted from each of them. The two proposed classifiers (denoted AATC1 and AATC2) differ mainly in terms of the keywords extracted for classification. To evaluate the proposed classifiers, we perform an extensive experimental study using five benchmark Arabic text collections with small or large sizes, namely ANT (Arabic News Texts) v2.1 and v1.1, BBC-Arabic, CNN-Arabic and AlKhaleej-2004. We also compare analogical classifiers with both classical ML-based and Deep Learning-based classifiers. Results show that AATC2 has the best average accuracy (78.78%) over all other classifiers and the best average precision (0.77) ranked first followed by AATC1 (0.73), NB (0.73) and SVM (0.72) for the ANT corpus v2.1. Besides, AATC1 shows the best average precisions (0.88) and (0.92), respectively for the BBC-Arabic corpus and AlKhaleej-2004, and the best average accuracy (85.64%) for CNN-Arabic over all other classifiers. Results demonstrate the utility of analogical proportions for text classification. In particular, the proposed analogical classifiers are shown to significantly outperform a number of existing Arabic classifiers, and in many cases, compare  favourably to the robust SVM classifier.</p>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"41 10","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142165765","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Barzilai Borwein Incremental Grey Polynomial Regression for train delay prediction","authors":"Ajay Singh,&nbsp;Rajesh Kumar Dhanaraj,&nbsp;Seifedine Kadry","doi":"10.1111/exsy.13642","DOIUrl":"10.1111/exsy.13642","url":null,"abstract":"<p>The swift societal evolution and ceaseless advancement of human value of life have been set forth for reliability as well as rapidity of railway transportation. Latest advances in machine learning approaches as well as surging accessibility of numerous information sources is produced state-of-the-art probabilities for significant, precise train delay identification. In this method called, Barzilai Borwein Incremental Grey Polynomial Regression (BBI-GPR) is introduced for predicting train arrival/departure delays, which utilized for later delay management in an accurate manner with this method comprised into three sections such as, pre-processing, feature selection and classification. First, with the raw ETA train delay dataset as input, Barzilai–Borwein Feature Rescaling-based Pre-processing is applied to model computationally efficient feature rescaled and normalized values. Second with processed features as input, Incremental Maximum Relevance Minimum Redundant-based Feature Selection is applied to select error minimized optimal features. Finally, with optimal features selected as input, Grey Polynomial Regression-based Prediction algorithm is employed to analyse train delay. For confirming proposed BBI-GPR, as well as analyse its performance, compare standard train delay prediction method with existing machine learning-based regression method. Results show that new variants outperform existing train delay prediction method by minimizing train delay prediction time, error rate by 25% and 27% respectively, with improved accuracy rate of 7%, therefore paving ways for efficient train delay prediction.</p>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"41 10","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141351603","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automatic cross- and multi-lingual recognition of dysphonia by ensemble classification using deep speaker embedding models","authors":"Dosti Aziz,&nbsp;Dávid Sztahó","doi":"10.1111/exsy.13660","DOIUrl":"10.1111/exsy.13660","url":null,"abstract":"<p>Machine Learning (ML) algorithms have demonstrated remarkable performance in dysphonia detection using speech samples. However, their efficacy often diminishes when tested on languages different from the training data, raising questions about their suitability in clinical settings. This study aims to develop a robust method for cross- and multi-lingual dysphonia detection that overcomes the limitation of language dependency in existing ML methods. We propose an innovative approach that leverages speech embeddings from speaker verification models, especially ECAPA and x-vector and employs a majority voting ensemble classifier. We utilize speech features extracted from ECAPA and x-vector embeddings to train three distinct classifiers. The significant advantage of these embedding models lies in their capability to capture speaker characteristics in a language-independent manner, forming fixed-dimensional feature spaces. Additionally, we investigate the impact of generating synthetic data within the embedding feature space using the Synthetic Minority Oversampling Technique (SMOTE). Our experimental results unveil the effectiveness of the proposed method for dysphonia detection. Compared to results obtained from x-vector embeddings, ECAPA consistently demonstrates superior performance in distinguishing between healthy and dysphonic speech, achieving accuracy values of 93.33% and 96.55% in both cross-lingual and multi-lingual scenarios, respectively. This highlights the remarkable capabilities of speaker verification models, especially ECAPA, in capturing language-independent features that enhance overall detection performance. The proposed method effectively addresses the challenges of language dependency in dysphonia detection. ECAPA embeddings, combined with majority voting ensemble classifiers, show significant potential for improving the accuracy and reliability of dysphonia detection in cross- and multi-lingual scenarios.</p>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"41 10","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/exsy.13660","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141353066","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 supervised learning tool for heatwave predictions using daily high summer temperatures","authors":"Gazi Md Daud Iqbal,&nbsp;Jay Rosenberger,&nbsp;Matthew Rosenberger,&nbsp;Muhammad Shah Alam,&nbsp;Lidan Ha,&nbsp;Emmanuel Anoruo,&nbsp;Sadie Gregory,&nbsp;Tom Mazzone","doi":"10.1111/exsy.13656","DOIUrl":"10.1111/exsy.13656","url":null,"abstract":"<p>Global temperature is increasing at an alarming rate, which increases the number of heatwaves. Heatwaves have significant impacts, both directly and indirectly, on human and natural systems and can create considerable risk to public health. Predicting the occurrence of a heatwave can save lives, increase the production of crops, improve water quality, and reduce transportation restrictions. Because of its geographical location, Bangladesh is particularly vulnerable to cyclones, droughts, earthquakes, floods, and heatwaves. The Bangladesh Meteorological Department collects temperature data at multiple weather stations, and we use data from 10 weather stations in this research. Data show that most heatwaves occur in the summer months, namely, April, May, and June. In this research, we develop Classification and Regression Tree (CART) models that use daily temperature data for the months of March, April, May, and June to predict the likelihood of a heatwave within the next 7 days, the next 28 days, and on any particular day based on daily high temperatures from the previous 14 days. We also use different model parameters to evaluate the accuracy of the models. Finally, we develop treed Stepwise Logistic Regression models to predict the probability of heatwaves occurring. Even though this research uses data from Bangladesh Meteorological Department, the developed modeling approach can be used in other geographic regions.</p>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"41 10","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141359427","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"When geoscience meets generative AI and large language models: Foundations, trends, and future challenges","authors":"Abdenour Hadid,&nbsp;Tanujit Chakraborty,&nbsp;Daniel Busby","doi":"10.1111/exsy.13654","DOIUrl":"https://doi.org/10.1111/exsy.13654","url":null,"abstract":"<p>Generative Artificial Intelligence (GAI) represents an emerging field that promises the creation of synthetic data and outputs in different modalities. GAI has recently shown impressive results across a large spectrum of applications ranging from biology, medicine, education, legislation, computer science, and finance. As one strives for enhanced safety, efficiency, and sustainability, generative AI indeed emerges as a key differentiator and promises a paradigm shift in the field. This article explores the potential applications of generative AI and large language models in geoscience. The recent developments in the field of machine learning and deep learning have enabled the generative model's utility for tackling diverse prediction problems, simulation, and multi-criteria decision-making challenges related to geoscience and Earth system dynamics. This survey discusses several GAI models that have been used in geoscience comprising generative adversarial networks (GANs), physics-informed neural networks (PINNs), and generative pre-trained transformer (GPT)-based structures. These tools have helped the geoscience community in several applications, including (but not limited to) data generation/augmentation, super-resolution, panchromatic sharpening, haze removal, restoration, and land surface changing. Some challenges still remain, such as ensuring physical interpretation, nefarious use cases, and trustworthiness. Beyond that, GAI models show promises to the geoscience community, especially with the support to climate change, urban science, atmospheric science, marine science, and planetary science through their extraordinary ability to data-driven modelling and uncertainty quantification.</p>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"41 10","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/exsy.13654","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142165757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Multi-model deep learning system for screening human monkeypox using skin images","authors":"Kapil Gupta,&nbsp;Varun Bajaj,&nbsp;Deepak Kumar Jain,&nbsp;Amir Hussain","doi":"10.1111/exsy.13651","DOIUrl":"10.1111/exsy.13651","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>Human monkeypox (MPX) is a viral infection that transmits between individuals via direct contact with animals, bodily fluids, respiratory droplets, and contaminated objects like bedding. Traditional manual screening for the MPX infection is a time-consuming process prone to human error. Therefore, a computer-aided MPX screening approach utilizing skin lesion images to enhance clinical performance and alleviate the workload of healthcare providers is needed. The primary objective of this work is to devise an expert system that accurately classifies MPX images for the automatic detection of MPX subjects.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>This work presents a multi-modal deep learning system through the fusion of convolutional neural network (CNN) and machine learning algorithms, which effectively and autonomously detect MPX-infected subjects using skin lesion images. The proposed framework, termed MPXCN-Net is developed by fusing deep features of three pre-trained CNNs: MobileNetV2, DarkNet19, and ResNet18. Three classifiers—K-nearest neighbour, support vector machine (SVM), and ensemble classifier—with various kernel functions, are used to identify infected patients. To validate the efficacy of our proposed system, we employ a publicly accessible MPX skin lesion dataset.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>By amalgamating features extracted from all three CNNs and utilizing the medium Gaussian kernel of the SVM classifier, our proposed system achieves an outstanding average classification accuracy of 90.4%.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Developed MPXCN-Net is suitable for testing with a large diversified dataset before being used in clinical settings.</p>\u0000 </section>\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"41 10","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141367458","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}