Engineering Applications of Artificial Intelligence最新文献

{"title":"Prediction and evaluation of key parameters in coalbed methane pre-extraction based on transformer and inversion model","authors":"Li Yan,&nbsp;Hu Wen,&nbsp;Zhenping Wang,&nbsp;Yongfei Jin,&nbsp;Jun Guo,&nbsp;Yin Liu,&nbsp;Shixing Fan","doi":"10.1016/j.engappai.2024.109661","DOIUrl":"10.1016/j.engappai.2024.109661","url":null,"abstract":"<div><div>Accurate parameter prediction in the coalbed methane (CBM) pre-extraction process is crucial for formulating effective control measures and preventing CBM-related accidents. Traditional prediction methods rely on feature extraction or complex physical model parameter calculations, which require extensive manual intervention and have limited practical applicability. Additionally, simple neural network methods are prone to overfitting and gradient vanishing when handling parameters, and they lack the capability to dynamically monitor gas pressure during extraction, leading to inefficient and blind extraction operations. This study proposes a CBM pre-extraction parameter and completion time prediction method based on the Transformer model. By integrating autoregressive models and wavelet denoising techniques, the approach effectively captures temporal features and long-term dependencies in CBM data. Experimental results demonstrate that the proposed model outperforms traditional methods in short-, medium-, and long-term predictions, with a median R² value of 0.99072, and 76% of the training results exceeding 0.9. Furthermore, a CBM pressure inversion model was developed, combining dimensional analysis and physical similarity principles with the Transformer model, enabling the dynamic detection of high- and low-pressure regions in coal seams. In single borehole compliance time predictions, the median compliance time for the first stage is 4 days, with an average of 49 days and a maximum of 277 days, providing adjustment guidance for boreholes with extended compliance times. The proposed model significantly improves prediction accuracy and stability, offering critical support for developing scientifically sustainable pre-extraction plans and advancing intelligent CBM management.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"139 ","pages":"Article 109661"},"PeriodicalIF":7.5,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142659094","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Health evaluation of shield tunnel lining using combination weighting and finite interval cloud model","authors":"Yu-Wei Zhang ,&nbsp;De-Sai Guo ,&nbsp;Zhan-Ping Song ,&nbsp;Yi-Duo Zhang ,&nbsp;Lei Ruan ,&nbsp;Zhao-Bo Yan","doi":"10.1016/j.engappai.2024.109645","DOIUrl":"10.1016/j.engappai.2024.109645","url":null,"abstract":"<div><div>To solve the problem of inaccurate and unreasonable health evaluation of shield tunnel lining, a novel health evaluation model of shield tunnel lining based on the combination weighting method and finite interval cloud model is proposed. A health evaluation index system including 6 level-Ⅰ indexes and 15 level-II indexes and evaluation criteria are established for the shield tunnel lining. The weights of evaluation indexes are calculated by the game theory combination weighting method. The finite interval cloud model is used to evaluate the health of shield tunnel lining, which considers the uncertainty of various information within the interval. To verify the applicability of the proposed approach, it was applied to the shield construction section from Bei Chen Station to the Olympic Sports Center Station of Xi'an Metro Line 14. The results show that: (1) The health evaluation grade of shield tunnel lining in Samples 1–3 is level II. The result is in agreement with the actual situation which validates the practicality of the employed methodology. (2) The change in the evaluation index has little influence on the evaluation results, and the evaluation results are level II. The key risk factors were identified as <em>U</em>₃₂, <em>U</em>₃₁, and <em>U</em>₁₂ by sensitivity analysis. Corresponding measures should be taken to ensure the stability of these three indexes and to ensure the safety of shield tunnel operation. Therefore, the proposed approach maximizes the assurance of the rationality of the evaluation results, which can be feasibly used in various applications and can provide guidance for other similar projects.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"139 ","pages":"Article 109645"},"PeriodicalIF":7.5,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142659127","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 generative-adversarial-network-based temporal raw trace data augmentation framework for fault detection in semiconductor manufacturing","authors":"Shu-Kai S. Fan ,&nbsp;Wei-Yu Chen","doi":"10.1016/j.engappai.2024.109624","DOIUrl":"10.1016/j.engappai.2024.109624","url":null,"abstract":"<div><div>In modern semiconductor manufacturing, where sophisticated process control mechanisms are standard, processing tools are equipped with sensors that generate vast amounts of raw trace data for process monitoring and fault detection. However, one of the major challenges data scientists face is the scarcity of sufficient raw trace data for defective wafers, creating an imbalance that complicates the training of machine learning models for effective fault detection. To address this issue, this paper proposes novel data augmentation structures and strategies utilizing Cycle Generative Adversarial Networks (CycleGANs) as an artificial intelligence application to synthesize temporal raw trace data for defective wafers. The effectiveness of these methods is demonstrated using a real-world dataset from the thin-film process in semiconductor fabrication. Several machine learning classification models—Gaussian Naive Bayes, Adaptive Boosting, eXtreme Gradient Boosting, and Light Gradient Boosting Machine—are employed to evaluate the performance of the augmented data. The paper identifies the optimal augmentation structure and strategy to enhance classification performance within the CycleGAN-based framework. For the thin-film processing dataset under study, the best classification performance achieves an accuracy rate of up to 99.30%, with a notably low false negative rate of 6.45%.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"139 ","pages":"Article 109624"},"PeriodicalIF":7.5,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142659042","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"An end-to-end deep convolutional neural network-based data-driven fusion framework for identification of human induced pluripotent stem cell-derived endothelial cells in photomicrographs","authors":"Imran Iqbal ,&nbsp;Imran Ullah ,&nbsp;Tingying Peng ,&nbsp;Weiwei Wang ,&nbsp;Nan Ma","doi":"10.1016/j.engappai.2024.109573","DOIUrl":"10.1016/j.engappai.2024.109573","url":null,"abstract":"<div><div>Deep learning is a very powerful analytic tool to recognize the patterns in data to make appropriate predictions. It has tremendous potential in data analyses, particularly for cell biology domain, caused by the growing scale and inherent complexity of biological data. The core purpose of this research work is to design, implement, and calibrate an efficient deep convolutional neural network (DCNN) architecture in the context of binary-class classification problem. This diversified network is developed to precisely identify human induced pluripotent stem cell-derived endothelial cells (hiPSC-derived EC) based on photomicrograph. The proposed architecture is cerebrally developed with numerous convolutional modules, multiple kernel sizes, various pooling layers, activation functions and strides, nevertheless fewer trainable parameters to strengthen the network and enhance its performance. The proposed feature fusion framework is compared with the classifier fusion approach in terms of Matthews’s correlation coefficient (MCC), training time, inference time, number of layers, number of parameters, graphics processing unit (GPU) memory utilization, and floating-point operations (FLOPS). Specifically, it achieves 94.6% sensitivity, 94.5% specificity, and 94.7% precision. Induced pluripotent stem cell (iPS) dataset is also introduced in this research work that has 16278 images which are labelled by three independent and experienced human experts of cell biology domain to facilitate future research. Experimental results show that the proposed framework offers an innovative and attainable algorithm for accelerating and systematizing the classification task along with saving time and effort.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"139 ","pages":"Article 109573"},"PeriodicalIF":7.5,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142652633","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Rice leaf disease identification and classification using machine learning techniques: A comprehensive review","authors":"Rashmi Mukherjee ,&nbsp;Anushri Ghosh ,&nbsp;Chandan Chakraborty ,&nbsp;Jayanta Narayan De ,&nbsp;Debi Prasad Mishra","doi":"10.1016/j.engappai.2024.109639","DOIUrl":"10.1016/j.engappai.2024.109639","url":null,"abstract":"<div><div>In recent times, various researchers attempted to develop artificial intelligence (AI) assisted techniques in the field of agriculture for early detection, surveillance and treatment related to plant leaf, seed, root, and stem diseases. Rice leaf disease detection is one of such important areas, where the crop is frequently affected by various diseases. Farmer inspects usually at a later stage causing enormous damage. This manual inspection is subjective, time-consuming and error prone. Under such situation, AI-enabled tools and techniques play crucial role for early and more precise prediction of rice diseases.</div><div>This paper demonstrates a comprehensive review on application of AI-assisted rice leaf disease detection in the last two decades. Research studies were searched using relevant keywords through the online databases [<em>PubMed: 246; Science Direct: 100; Scopus: 56; Web of Science: 8; Willey online library:16; Cochrane:0; Cross references:20</em>]. A total of 446 titles and abstracts were identified as suitable for this study and finally, 48 most-appropriate state-of-art articles were considered. Furthermore, this study summarizes the visual characteristics of rice leaf diseases, imaging modalities and image acquisition techniques. Various image processing techniques for infected leaf area segmentation and feature extraction were also summarized. Finally, the reported machine learning (ML) algorithms were discussed and compared in respect to their advantages and limitations. In addition, AI-enabled mobile applications for rice disease detection have been discussed.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"139 ","pages":"Article 109639"},"PeriodicalIF":7.5,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142659089","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Cognitive Digital Twins of the natural environment: Framework and application","authors":"Jun Feng ,&nbsp;Hailin Tang ,&nbsp;Siyuan Zhou ,&nbsp;Yang Cai ,&nbsp;Jianxin Zhang","doi":"10.1016/j.engappai.2024.109587","DOIUrl":"10.1016/j.engappai.2024.109587","url":null,"abstract":"<div><div>Digital Twin (DT) technology offers a method of creating digital models of natural systems to enhance their ability to withstand natural disasters. Currently, DT of the natural environment is in its initial phases, lacking adaptive capabilities and relying on human-assisted modeling. The key to endowing DT of the natural environment with greater autonomy lies in the integration of expert knowledge. Knowledge graphs can efficiently arrange and structurally store expert knowledge, thereby supporting the autonomous functionality of DT. This paper introduces the concept of <em>Cognitive Digital Twin(CDT)</em> derived from the industrial domain and presents a framework for CDT of the natural environment. This framework is centered around knowledge graph technology, aiming to provide more insights and guidance for system development. This framework integrates human cognition by constructing knowledge graphs of objects, models, events, and scene modes. Moreover, these knowledge graphs support agents for the dynamic adjustment of processes, as well as the adaptation and parameter optimization of related models. As a use case, we utilize this framework to implement digital twin watersheds. We develop appropriate ontologies and agents to facilitate the construction of cognitive digital watersheds for various regions. Cognitive digital watersheds effectively fulfill the application needs of integrated flood forecasting and control scheduling. This application validates the framework’s effectiveness and provides a reference for constructing CDTs of other natural systems.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"139 ","pages":"Article 109587"},"PeriodicalIF":7.5,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142659090","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"MFLSCI: Multi-granularity fusion and label semantic correlation information for multi-label legal text classification","authors":"Chunyun Meng ,&nbsp;Yuki Todo ,&nbsp;Cheng Tang ,&nbsp;Li Luan ,&nbsp;Zheng Tang","doi":"10.1016/j.engappai.2024.109604","DOIUrl":"10.1016/j.engappai.2024.109604","url":null,"abstract":"<div><div>Multi-label text classification tasks face challenges such as sample diversity, complexity, and the need for effective utilization of label correlations. In this paper, we propose a model that integrates multi-granularity fusion of text sequence features and label semantic correlation information. Our model leverages graph convolutional networks to extract label semantic correlation, which enhances classification performance for samples with similar labels and addresses label omission issues. Additionally, text convolutional neural networks are employed to extract multi-granularity sense group features from text sequences, calculate their similarity with semantic correlation label distributions, and dynamically adjust the similarity between text context and label information. This approach tackles the limitations of feature extraction in short texts and label confusion. We replace the original multi-hot label encoding in model training with a label distribution that fuses text multi-granularity sense group features and label correlation information, using a more precise encoding method for soft alignment based on label probability distributions. This enhances the model’s resilience to noisy data, avoiding the issue of assigning high-confidence probabilities to incorrect categories due to hard-coded supervision. Our model’s performance improvement on noisy datasets significantly surpasses that achieved by label smoothing. Extensive experiments on three legal text datasets and two generalized multi-label datasets demonstrate the model’s excellent performance. Our approach is applicable in various real-world scenarios, such as legal judgment prediction, news categorization, and recommendation systems, where accurate multi-label classification is crucial. Ablation and experiments on noisy datasets validate the model’s effectiveness and robustness.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"139 ","pages":"Article 109604"},"PeriodicalIF":7.5,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142659125","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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-aware cross-domain point-of-interest recommendation in social networks","authors":"Malika Acharya,&nbsp;Krishna Kumar Mohbey","doi":"10.1016/j.engappai.2024.109630","DOIUrl":"10.1016/j.engappai.2024.109630","url":null,"abstract":"<div><div>Point-of-Interest recommendation within the single domain is quite easy compared to the cross-domain recommendation, as there is an acute dearth of check-in records for the target regions, aggravating the cold start problem. We propose a self-ensembled contextual Thompson sampling for cross-domain Point-of-Interest recommendation to solve this. This approach utilizes user preference transfer and user preference drift in the target domain for enhanced recommendation by deploying enhanced contextual sampling. As the user-context pairs of the target domain are not labeled for the given user, domain adaptation is highly sought. The approach has four major steps: i) Mining Point-of-Interests based on the long-term preferences of the target user and the user with a similar trajectory in the source domain, ii) Computing rewards for the Point-of-Interests in the source domain using multi-layer perceptron, iii) Estimate the rewards for unlabeled Point-of-Interests in the target domain and iv) Form the ensemble of rewards that are used to decide the final arm pulls. The rewards obtained for Point-of-Interests in the source and target domain are combined to form an ensemble of rewards with the help of self ensembling domain adaptation technique. Each Point-of-Interest in the ensemble rewards is termed an arm of action. We use this ensemble of rewards to control the diversity measure and the switching probability of the various arms, potential Point-of-Interests, in the contextual Thompson Sampling. Contextual Thompson sampling is modified to incorporate exploitation-exploration tradeoffs using this reward ensemble. The implicit weight measure of the different arms decides the probability of exploitation or exploration. The final arm pulls results in the final Point-of-Interest recommendation. For experimentation, we have used two real-world datasets, namely, Gowalla and Foursquare, and extracted the data for seven domains. We have obtained an accuracy of approximately 65% for Point-of-Interest recommendations on cold-start users.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"139 ","pages":"Article 109630"},"PeriodicalIF":7.5,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142659276","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 framework for robust glaucoma detection: A confidence-aware deep uncertainty quantification approach with a comprehensive assessment for enhanced clinical decision-making","authors":"Javad Zarean ,&nbsp;AmirReza Tajally ,&nbsp;Reza Tavakkoli-Moghaddam ,&nbsp;Seyed Mojtaba Sajadi ,&nbsp;Niaz Wassan","doi":"10.1016/j.engappai.2024.109651","DOIUrl":"10.1016/j.engappai.2024.109651","url":null,"abstract":"<div><div>Glaucoma poses a significant threat to public health worldwide, as it can result in irreversible vision loss. Timely identification is vital for halting the progression of visual field deterioration. In recent years, deep neural networks (DNNs) have become increasingly popular in medical imaging due to their ability to identify patterns. As a result, this study introduces a new computer-aided diagnosis (CAD) system based on deep learning (DL) algorithms for glaucoma detection that extracts meaningful features from retinal fundus images (RFIs) and employs uncertainty quantification (UQ) models, including Monte Carlo dropout (MCD), ensemble Bayesian, and ensemble Monte Carlo dropout (EMCD), to generate both point estimates and confidence values for the outputs, thereby capturing the uncertainty associated with the classifications. The proposed framework is validated using well-known clinical datasets, and the reliability of the outputs is evaluated using comprehensive performance metrics such as expected calibration error (ECE), entropy analysis, and a multi-criteria UQ assessment. Experimental results demonstrate the superiority of the ensemble model, with uncertainty accuracies registering at 97.64%, 97.26%, and 98.97% for the <em>“ACRIMA”</em>, <em>“RIM-ONE-DL”</em>, and <em>“ORIGA”</em> datasets, respectively. Moreover, the proposed algorithms can alert users to the majority of erroneous diagnoses by assigning uncertainty labels, providing valuable insights for clinicians in glaucoma detection. Such tools can assist healthcare professionals in reducing the probability of misdiagnosis and ensuring that patients receive timely and appropriate treatment.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"139 ","pages":"Article 109651"},"PeriodicalIF":7.5,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142659098","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"An intelligent model approach for leakage detection of modified atmosphere pillow bags","authors":"Xiangdong Guo ,&nbsp;Jingfa Yao ,&nbsp;Guoyu Yan ,&nbsp;Guifa Teng","doi":"10.1016/j.engappai.2024.109611","DOIUrl":"10.1016/j.engappai.2024.109611","url":null,"abstract":"<div><div>Modified atmosphere pillow bags have been widely used to package various food products due to their advantages for preservation and shipment. Sealing defects are statistically inevitable, although modern packaging machinery and manual inspection utilized by manufacturers continue reducing the leakage probability. Hence the bag contents may spoil if the seal is broken. Instead of manual inspection and various destructive methods utilized by factories, this study introduces non-destructive leakage detection using deep learning methods. Firstly, a squeezing method is developed to aggravate the feature difference between positive samples and negative samples without destroying the bag content, thus 2160 images of three different pillow bags are acquired to establish dataset. Secondly, the deep learning model Vision Transformer (ViT) is deployed and studied so that feasibility of computer vision method is verified. Then the Semantic segmentation and Contour Extraction model combining ViT (SCE-ViT) is proposed and improved to the Multi-dimensional Fusion model (SCE-MdF). The accuracies of SCE-MdF reached 97.5%, 97.5%, and 97.5%, respectively. The F1-scores of SCE-MdF reached 97.6%, 97.6%, and 97.4%, respectively. Compared to averaged accuracies of SCE-ViT, accuracies introduced in the ultimate model SCE-MdF improved by 19.17%, 5.84%, and 11.67%, respectively. Therefore, combination of unique squeezing method and Semantic segmentation Contour Extraction with Multi-dimensional Fused ViT, is eventually validated viable on leakage detection of modified atmosphere pillow bags. Hence a cost-effective, efficient and non-destructive leakage detection method for modified atmosphere pillow bags in relevant industry is introduced, filling a gap between artificial intelligence and food packaging industry.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"139 ","pages":"Article 109611"},"PeriodicalIF":7.5,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142659278","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}