PeerJ Computer Science最新文献_第8页

Enhancing rice yield prediction: a deep fusion model integrating ResNet50-LSTM with multi source data 加强水稻产量预测：ResNet50-LSTM 与多源数据的深度融合模型

IF 3.8 4区计算机科学

PeerJ Computer Science Pub Date : 2024-08-13 DOI: 10.7717/peerj-cs.2219

Aqsa Aslam, Saima Farhan

{"title":"Enhancing rice yield prediction: a deep fusion model integrating ResNet50-LSTM with multi source data","authors":"Aqsa Aslam, Saima Farhan","doi":"10.7717/peerj-cs.2219","DOIUrl":"https://doi.org/10.7717/peerj-cs.2219","url":null,"abstract":"Rice production is pivotal for ensuring global food security. In Pakistan, rice is not only the dominant Kharif crop but also a significant export commodity that significantly impacts the state’s economy. However, Pakistan faces challenges such as abrupt climate change and the COVID-19 pandemic, which affect rice production and underscore the need for predictive models for informed decisions aimed at improving productivity and ultimately the state’s economy. This article presents an innovative deep learning-based hybrid predictive model, ResNet50-LSTM, designed to forecast rice yields in the Gujranwala district, Pakistan, utilizing multi-modal data. The model incorporates MODIS satellite imagery capturing EVI, LAI, and FPAR indices along with meteorological and soil data. Google Earth Engine is used for the collection and preprocessing of satellite imagery, where the preprocessing steps involve data filtering, applying region geometry, interpolation, and aggregation. These preprocessing steps were applied manually on meteorological and soil data. Following feature extraction from the imagery data using ResNet50, the three LSTM model configurations are presented with distinct layer architectures. The findings of this study exhibit that the model configuration featuring two LSTM layers with interconnected cells outperforms other proposed configurations in terms of prediction performance. Analysis of various feature combinations reveals that the selected feature set (EVI, FPAR, climate, and soil variables) yields highly accurate results with an R2 = 0.9903, RMSE = 0.1854, MAPE = 0.62%, MAE = 0.1384, MRE = 0.0062, and Willmott’s index of agreement = 0.9536. Moreover, the combination of EVI and FPAR is identified as particularly effective. Our findings revealed the potential of our framework for globally estimating crop yields through the utilization of publicly available multi-source data.","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":null,"pages":null},"PeriodicalIF":3.8,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142203364","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}

引用次数: 0

Vehicle detection and classification using an ensemble of EfficientDet and YOLOv8 使用 EfficientDet 和 YOLOv8 的集合进行车辆检测和分类

IF 3.8 4区计算机科学

PeerJ Computer Science Pub Date : 2024-08-13 DOI: 10.7717/peerj-cs.2233

Caixia Lv, Usha Mittal, Vishu Madaan, Prateek Agrawal

{"title":"Vehicle detection and classification using an ensemble of EfficientDet and YOLOv8","authors":"Caixia Lv, Usha Mittal, Vishu Madaan, Prateek Agrawal","doi":"10.7717/peerj-cs.2233","DOIUrl":"https://doi.org/10.7717/peerj-cs.2233","url":null,"abstract":"With the rapid increase in vehicle numbers, efficient traffic management has become a critical challenge for society. Traditional methods of vehicle detection and classification often struggle with the diverse characteristics of vehicles, such as varying shapes, colors, edges, shadows, and textures. To address this, we proposed an innovative ensemble method that combines two state-of-the-art deep learning models i.e., EfficientDet and YOLOv8. The proposed work leverages data from the Forward-Looking Infrared (FLIR) dataset, which provides both thermal and RGB images. To enhance the model performance and to address the class imbalances, we applied several data augmentation techniques. Experimental results demonstrate that the proposed ensemble model achieves a mean average precision (mAP) of 95.5% on thermal images, outperforming the individual performances of EfficientDet and YOLOv8, which achieved mAPs of 92.6% and 89.4% respectively. Additionally, the ensemble model attained an average recall (AR) of 0.93 and an optimal localization recall precision (oLRP) of 0.08 on thermal images. For RGB images, the ensemble model achieved mAP of 93.1%, AR of 0.91, and oLRP of 0.10, consistently surpassing the performance of its constituent models. These findings highlight the effectiveness of proposed ensemble approach in improving vehicle detection and classification. The integration of thermal imaging further enhances detection capabilities under various lighting conditions, making the system robust for real-world applications in intelligent traffic management.","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":null,"pages":null},"PeriodicalIF":3.8,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142203359","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}

引用次数: 0

Enhancing geotechnical damage detection with deep learning: a convolutional neural network approach 利用深度学习加强岩土工程损害检测：卷积神经网络方法

IF 3.8 4区计算机科学

PeerJ Computer Science Pub Date : 2024-08-12 DOI: 10.7717/peerj-cs.2052

Thabatta Moreira Alves de Araujo, Carlos André de Mattos Teixeira, Carlos Renato Lisboa Francês

{"title":"Enhancing geotechnical damage detection with deep learning: a convolutional neural network approach","authors":"Thabatta Moreira Alves de Araujo, Carlos André de Mattos Teixeira, Carlos Renato Lisboa Francês","doi":"10.7717/peerj-cs.2052","DOIUrl":"https://doi.org/10.7717/peerj-cs.2052","url":null,"abstract":"Most natural disasters result from geodynamic events such as landslides and slope collapse. These failures cause catastrophes that directly impact the environment and cause financial and human losses. Visual inspection is the primary method for detecting failures in geotechnical structures, but on-site visits can be risky due to unstable soil. In addition, the body design and hostile and remote installation conditions make monitoring these structures inviable. When a fast and secure evaluation is required, analysis by computational methods becomes feasible. In this study, a convolutional neural network (CNN) approach to computer vision is applied to identify defects in the surface of geotechnical structures aided by unmanned aerial vehicle (UAV) and mobile devices, aiming to reduce the reliance on human-led on-site inspections. However, studies in computer vision algorithms still need to be explored in this field due to particularities of geotechnical engineering, such as limited public datasets and redundant images. Thus, this study obtained images of surface failure indicators from slopes near a Brazilian national road, assisted by UAV and mobile devices. We then proposed a custom CNN and low complexity model architecture to build a binary classifier image-aided to detect faults in geotechnical surfaces. The model achieved a satisfactory average accuracy rate of 94.26%. An AUC metric score of 0.99 from the receiver operator characteristic (ROC) curve and matrix confusion with a testing dataset show satisfactory results. The results suggest that the capability of the model to distinguish between the classes ‘damage’ and ‘intact’ is excellent. It enables the identification of failure indicators. Early failure indicator detection on the surface of slopes can facilitate proper maintenance and alarms and prevent disasters, as the integrity of the soil directly affects the structures built around and above it.","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":null,"pages":null},"PeriodicalIF":3.8,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141936307","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}

引用次数: 0

An efficient secure and energy resilient trust-based system for detection and mitigation of sybil attack detection (SAN) 基于信任的高效安全和能量弹性系统，用于检测和缓解sybil攻击检测（SAN）

IF 3.8 4区计算机科学

PeerJ Computer Science Pub Date : 2024-08-09 DOI: 10.7717/peerj-cs.2231

Muhammad Zunnurain Hussain, Zurina Mohd Hanapi, Azizol Abdullah, Masnida Hussin, Mohd Izuan Hafez Ninggal

{"title":"An efficient secure and energy resilient trust-based system for detection and mitigation of sybil attack detection (SAN)","authors":"Muhammad Zunnurain Hussain, Zurina Mohd Hanapi, Azizol Abdullah, Masnida Hussin, Mohd Izuan Hafez Ninggal","doi":"10.7717/peerj-cs.2231","DOIUrl":"https://doi.org/10.7717/peerj-cs.2231","url":null,"abstract":"In the modern digital market flooded by nearly endless cyber-security hazards, sophisticated IDS (intrusion detection systems) can become invaluable in defending against intricate security threats. Sybil-Free Metric-based routing protocol for low power and lossy network (RPL) Trustworthiness Scheme (SF-MRTS) captures the nature of the biggest threat to the routing protocol for low-power and lossy networks under the RPL module, known as the Sybil attack. Sybil attacks build a significant security challenge for RPL networks where an attacker can distort at least two hop paths and disrupt network processes. Using such a new way of calculating node reliability, we introduce a cutting-edge approach, evaluating parameters beyond routing metrics like energy conservation and actuality. SF-MRTS works precisely towards achieving a trusted network by introducing such trust metrics on secure paths. Therefore, this may be considered more likely to withstand the attacks because of these security improvements. The simulation function of SF-MRTS clearly shows its concordance with the security risk management features, which are also necessary for the network’s performance and stability maintenance. These mechanisms are based on the principles of game theory, and they allocate attractions to the nodes that cooperate while imposing penalties on the nodes that do not. This will be the way to avoid damage to the network, and it will lead to collaboration between the nodes. SF-MRTS is a security technology for emerging industrial Internet of Things (IoT) network attacks. It effectively guaranteed reliability and improved the networks’ resilience in different scenarios.","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":null,"pages":null},"PeriodicalIF":3.8,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141936309","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}

引用次数: 0

An optimized LSTM network for improving arbitrage spread forecasting using ant colony cross-searching in the K-fold hyperparameter space 利用 K 倍超参数空间中的蚁群交叉搜索改进套利价差预测的优化 LSTM 网络

IF 3.5 4区计算机科学

PeerJ Computer Science Pub Date : 2024-08-09 DOI: 10.7717/peerj-cs.2215

Zeliang Zeng, Panke Qin, Yue Zhang, Yongli Tang, Shenjie Cheng, Sensen Tu, Yongjie Ding, Zhenlun Gao, Yaxing Liu

{"title":"An optimized LSTM network for improving arbitrage spread forecasting using ant colony cross-searching in the K-fold hyperparameter space","authors":"Zeliang Zeng, Panke Qin, Yue Zhang, Yongli Tang, Shenjie Cheng, Sensen Tu, Yongjie Ding, Zhenlun Gao, Yaxing Liu","doi":"10.7717/peerj-cs.2215","DOIUrl":"https://doi.org/10.7717/peerj-cs.2215","url":null,"abstract":"Arbitrage spread prediction can provide valuable insights into the identification of arbitrage signals and assessing associated risks in algorithmic trading. However, achieving precise forecasts by increasing model complexity remains a challenging task. Moreover, uncertainty in the development and maintenance of model often results in extremely unstable returns. To address these challenges, we propose a K-fold cross-search algorithm-optimized LSTM (KCS-LSTM) network for arbitrage spread prediction. The KCS heuristic algorithm incorporates an iterative updating mechanism of the search space with intervals as the basic unit into the traditional ant colony optimization. It optimized the hyperparameters of the LSTM model with a modified fitness function to automatically adapt to various data sets, thereby simplified and enhanced the efficiency of model development. The KCS-LSTM network was validated using real spread data of rebar and hot-rolled coil from the past three years. The results demonstrate that the proposed model outperforms several common models on sMAPE by improving up to 12.6% to 72.4%. The KCS-LSTM network is shown to be competitive in predicting arbitrage spreads compared to complex neural network models.","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141921378","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}

引用次数: 0

A DoS attack detection method based on adversarial neural network 基于对抗神经网络的 DoS 攻击检测方法

IF 3.5 4区计算机科学

PeerJ Computer Science Pub Date : 2024-08-09 DOI: 10.7717/peerj-cs.2162

Yang Li, Haiyan Wu

{"title":"A DoS attack detection method based on adversarial neural network","authors":"Yang Li, Haiyan Wu","doi":"10.7717/peerj-cs.2162","DOIUrl":"https://doi.org/10.7717/peerj-cs.2162","url":null,"abstract":"In order to analyze the influence of deep learning model on detecting denial-of-service (DoS) attacks, this article first examines the concepts and attack strategies of DoS assaults before looking into the present detection methodologies for DoS attacks. A distributed DoS attack detection system based on deep learning is established in response to the investigation’s limitations. This system can quickly and accurately identify the traffic of distributed DoS attacks in the network that needs to be detected and then promptly send an alarm signal to the system. Then, a model called the Improved Conditional Wasserstein Generative Adversarial Network with Inverter (ICWGANInverter) is proposed in response to the characteristics of incomplete network traffic in DoS attacks. This model automatically learns the advanced abstract information of the original data and then employs the method of reconstruction error to identify the best classification label. It is then tested on the intrusion detection dataset NSL-KDD. The findings demonstrate that the mean square error of continuous feature reconstruction in the sub-datasets KDDTest+ and KDDTest-21 steadily increases as the noise factor increases. All of the receiver operating characteristic (ROC) curves are shown at the top of the diagonal, and the overall area under the ROC curve (AUC) values of the macro-average and micro-average are above 0.8, which demonstrates that the ICWGANInverter model has excellent detection performance in both single category attack detection and overall attack detection. This model has a greater detection accuracy than other models, reaching 87.79%. This demonstrates that the approach suggested in this article offers higher benefits for detecting DoS attacks.","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141921295","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}

引用次数: 0

SEF-UNet: advancing abdominal multi-organ segmentation with SEFormer and depthwise cascaded upsampling SEF-UNet：利用 SEFormer 和深度级联上采样推进腹部多器官分割

IF 3.5 4区计算机科学

PeerJ Computer Science Pub Date : 2024-08-09 DOI: 10.7717/peerj-cs.2238

Yaping Zhao, Yizhang Jiang, Li-Jun Huang, Kaijian Xia

{"title":"SEF-UNet: advancing abdominal multi-organ segmentation with SEFormer and depthwise cascaded upsampling","authors":"Yaping Zhao, Yizhang Jiang, Li-Jun Huang, Kaijian Xia","doi":"10.7717/peerj-cs.2238","DOIUrl":"https://doi.org/10.7717/peerj-cs.2238","url":null,"abstract":"The abdomen houses multiple vital organs, which are associated with various diseases posing significant risks to human health. Early detection of abdominal organ conditions allows for timely intervention and treatment, preventing deterioration of patients’ health. Segmenting abdominal organs aids physicians in more accurately diagnosing organ lesions. However, the anatomical structures of abdominal organs are relatively complex, with organs overlapping each other, sharing similar features, thereby presenting challenges for segmentation tasks. In real medical scenarios, models must demonstrate real-time and low-latency features, necessitating an improvement in segmentation accuracy while minimizing the number of parameters. Researchers have developed various methods for abdominal organ segmentation, ranging from convolutional neural networks (CNNs) to Transformers. However, these methods often encounter difficulties in accurately identifying organ segmentation boundaries. MetaFormer abstracts the framework of Transformers, excluding the multi-head Self-Attention, offering a new perspective for solving computer vision problems and overcoming the limitations of Vision Transformers and CNN backbone networks. To further enhance segmentation effectiveness, we propose a U-shaped network, integrating SEFormer and depthwise cascaded upsampling (dCUP) as the encoder and decoder, respectively, into the UNet structure, named SEF-UNet. SEFormer combines Squeeze-and-Excitation modules with depthwise separable convolutions, instantiating the MetaFormer framework, enhancing the capture of local details and texture information, thereby improving edge segmentation accuracy. dCUP further integrates shallow and deep information layers during the upsampling process. Our model significantly improves segmentation accuracy while reducing the parameter count and exhibits superior performance in segmenting organ edges that overlap each other, thereby offering potential deployment in real medical scenarios.","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141922050","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}

引用次数: 0

Predicting the satisfiability of Boolean formulas by incorporating gated recurrent unit (GRU) in the Transformer framework 通过将门控递归单元（GRU）纳入变换器框架来预测布尔公式的可满足性

IF 3.8 4区计算机科学

PeerJ Computer Science Pub Date : 2024-08-08 DOI: 10.7717/peerj-cs.2169

Wenjing Chang, Mengyu Guo, Junwei Luo

{"title":"Predicting the satisfiability of Boolean formulas by incorporating gated recurrent unit (GRU) in the Transformer framework","authors":"Wenjing Chang, Mengyu Guo, Junwei Luo","doi":"10.7717/peerj-cs.2169","DOIUrl":"https://doi.org/10.7717/peerj-cs.2169","url":null,"abstract":"The Boolean satisfiability (SAT) problem exhibits different structural features in various domains. Neural network models can be used as more generalized algorithms that can be learned to solve specific problems based on different domain data than traditional rule-based approaches. How to accurately identify these structural features is crucial for neural networks to solve the SAT problem. Currently, learning-based SAT solvers, whether they are end-to-end models or enhancements to traditional heuristic algorithms, have achieved significant progress. In this article, we propose TG-SAT, an end-to-end framework based on Transformer and gated recurrent neural network (GRU) for predicting the satisfiability of SAT problems. TG-SAT can learn the structural features of SAT problems in a weakly supervised environment. To capture the structural information of the SAT problem, we encodes a SAT problem as an undirected graph and integrates GRU into the Transformer structure to update the node embeddings. By computing cross-attention scores between literals and clauses, a weighted representation of nodes is obtained. The model is eventually trained as a classifier to predict the satisfiability of the SAT problem. Experimental results demonstrate that TG-SAT achieves a 2%–5% improvement in accuracy on random 3-SAT problems compared to NeuroSAT. It also outperforms in SR(N), especially in handling more complex SAT problems, where our model achieves higher prediction accuracy.","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":null,"pages":null},"PeriodicalIF":3.8,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141936311","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}

引用次数: 0

Image reconstruction in graphic design based on Global residual Network optimized compressed sensing model 基于全局残差网络优化压缩传感模型的平面设计图像重建

IF 3.5 4区计算机科学

PeerJ Computer Science Pub Date : 2024-08-08 DOI: 10.7717/peerj-cs.2227

Xinxin Fu, Lujing Tang, Yingjie Bai

引用次数: 0

Using machine learning to analyze mental health in distance education during the COVID-19 pandemic: an opinion study from university students in Mexico 利用机器学习分析 COVID-19 大流行期间远程教育中的心理健康：墨西哥大学生意见研究

IF 3.5 4区计算机科学

PeerJ Computer Science Pub Date : 2024-08-08 DOI: 10.7717/peerj-cs.2241

Roberto Angel Meléndez-Armenta, Giovanni Luna Chontal, Sandra Guadalupe Garcia Aburto

{"title":"Using machine learning to analyze mental health in distance education during the COVID-19 pandemic: an opinion study from university students in Mexico","authors":"Roberto Angel Meléndez-Armenta, Giovanni Luna Chontal, Sandra Guadalupe Garcia Aburto","doi":"10.7717/peerj-cs.2241","DOIUrl":"https://doi.org/10.7717/peerj-cs.2241","url":null,"abstract":"In times of lockdown due to the COVID-19 pandemic, it has been detected that some students are unable to dedicate enough time to their education. They present signs of frustration and even apathy towards dropping out of school. In addition, feelings of fear, anxiety, desperation, and depression are now present because society has not yet been able to adapt to the new way of living. Therefore, this article analyzes the feelings that university students of the Instituto Tecnológico Superior de Misantla present when using long distance education tools during COVID-19 pandemic in Mexico. The results suggest that isolation, because of the pandemic situation, generated high levels of anxiety and depression. Moreover, there are connections between feelings generated by lockdown and school performance while using e-learning platforms. The findings of this research reflect the students’ feelings, useful information that could lead to the development and implementation of pedagogical strategies that allow improving the students’ academic performance results.","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141929445","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}

引用次数: 0