Pub Date : 2020-10-24DOI: 10.1142/s1469026821500036
Erwin, Heranti Reza Damayanti
Retinal fundus is the inner surface of the eye associated with the lens. The identi¯cation of disease needs some parts of retinal fundus, such as blood vessel. Blood vessels are part of circulation system which functions to supply blood to retina area. This research proposed a method for segmentation of blood vessel in retinal image with Average Filter and Iterative SelfOrganizing Data Analysis (ISODATA) Technique. The ¯rst step with the input image changed to Gamma Correction, increasing contrast with Contrast Limited Adaptive Histogram Equalization (CLAHE), the ¯ltering process with Average Filter. The segmentation is used for ISODATA. Region of Interest was applied to take the center of a vessel object and remove the background. In the ¯nal stage, the process of noise reduction and removal of small pixel values with Median Filter and Closing Morphology. Datasets used in this research were DRIVE and STARE. The average result was obtained for STARE dataset with an accuracy of 94.41%, Sensitivity of 55.57%, Speci¯cation of 98.31%, F1 Score of 64.81% while for the DRIVE dataset with accuracy of 94.78%, Sensitivity of 43.46%, Speci¯cation of 99.81%, and F1 Score of 59.39%.
视网膜底是与晶状体相连的眼睛内表面。疾病的识别需要视网膜眼底的某些部位，如血管。血管是循环系统的一部分，其功能是向视网膜区域供血。提出了一种基于平均滤波和迭代自组织数据分析(ISODATA)技术的视网膜图像血管分割方法。第一步将输入图像更改为Gamma校正，使用对比度有限自适应直方图均衡化(CLAHE)增加对比度，使用平均过滤器进行过滤。分割用于ISODATA。利用感兴趣区域(Region of Interest)来取容器物体的中心并去除背景。在最后阶段，使用中值滤波和闭合形态学对小像素值进行降噪和去除的过程。本研究使用的数据集为DRIVE和STARE。STARE数据集的平均准确率为94.41%，灵敏度为55.57%，spec - cation为98.31%，F1 Score为64.81%;DRIVE数据集的平均准确率为94.78%，灵敏度为43.46%，spec - cation为99.81%，F1 Score为59.39%。
{"title":"Supervised Retinal Vessel Segmentation Based Average Filter and Iterative Self Organizing Data Analysis Technique","authors":"Erwin, Heranti Reza Damayanti","doi":"10.1142/s1469026821500036","DOIUrl":"https://doi.org/10.1142/s1469026821500036","url":null,"abstract":"Retinal fundus is the inner surface of the eye associated with the lens. The identi¯cation of disease \u0000needs some parts of retinal fundus, such as blood vessel. Blood vessels are part of circulation system \u0000which functions to supply blood to retina area. This research proposed a method for segmentation of \u0000blood vessel in retinal image with Average Filter and Iterative SelfOrganizing Data Analysis \u0000(ISODATA) Technique. The ¯rst step with the input image changed to Gamma Correction, increasing \u0000contrast with Contrast Limited Adaptive Histogram Equalization (CLAHE), the ¯ltering process with \u0000Average Filter. The segmentation is used for ISODATA. Region of Interest was applied to take the \u0000center of a vessel object and remove the background. In the ¯nal stage, the process of noise reduction \u0000and removal of small pixel values with Median Filter and Closing Morphology. Datasets used in this \u0000research were DRIVE and STARE. The average result was obtained for STARE dataset with an \u0000accuracy of 94.41%, Sensitivity of 55.57%, Speci¯cation of 98.31%, F1 Score of 64.81% while for \u0000the DRIVE dataset with accuracy of 94.78%, Sensitivity of 43.46%, Speci¯cation of 99.81%, and F1 \u0000Score of 59.39%.","PeriodicalId":422521,"journal":{"name":"Int. J. Comput. Intell. Appl.","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125981129","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-10-15DOI: 10.1142/s1469026820500285
Ramon Santos Correa, Patricia Teixeira Sampaio, R. Braga, Victor Alberto Lambertucci, G. M. Almeida, A. Braga
A bottleneck of laboratory analysis in process industries including steelmaking plants is the low sampling rate. Inference models using only variables measured online have then been used to made such information available in advance. This study develops predictive models for key mechanical properties of seamless steel tubes, by strength, ultimate tensile strength and hardness. A plant in Brazil was used as the case study. The sample sizes of some steel tube families given namely, yield a particular property are discrepant and sometimes very small. To overcome this sample imbalance and lack of representativeness, committees of predictive neural network models based on bagging predictors, a type of ensemble method, were adopted. As a result, all steel families for all properties have been satisfactorily described showing the correlations between targets and model estimates close to 99%. These results were compared to multiple linear regression, support vector machine and a simpler neural network. Such information available in advance favors corrective actions before complete tube production mitigating rework costs in general.
{"title":"Prediction of Mechanical Properties of Seamless Steel Tubes Using Artificial Neural Networks","authors":"Ramon Santos Correa, Patricia Teixeira Sampaio, R. Braga, Victor Alberto Lambertucci, G. M. Almeida, A. Braga","doi":"10.1142/s1469026820500285","DOIUrl":"https://doi.org/10.1142/s1469026820500285","url":null,"abstract":"A bottleneck of laboratory analysis in process industries including steelmaking plants is the low sampling rate. Inference models using only variables measured online have then been used to made such information available in advance. This study develops predictive models for key mechanical properties of seamless steel tubes, by strength, ultimate tensile strength and hardness. A plant in Brazil was used as the case study. The sample sizes of some steel tube families given namely, yield a particular property are discrepant and sometimes very small. To overcome this sample imbalance and lack of representativeness, committees of predictive neural network models based on bagging predictors, a type of ensemble method, were adopted. As a result, all steel families for all properties have been satisfactorily described showing the correlations between targets and model estimates close to 99%. These results were compared to multiple linear regression, support vector machine and a simpler neural network. Such information available in advance favors corrective actions before complete tube production mitigating rework costs in general.","PeriodicalId":422521,"journal":{"name":"Int. J. Comput. Intell. Appl.","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116040841","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-10-14DOI: 10.1142/s146902682050025x
Mason McCoy, S. Rahimi
Trading cryptocurrencies (digital currencies) are currently performed by applying methods similar to what is applied to the stock market or commodities; however, these algorithms are not necessaril...
{"title":"Prediction of Highly Volatile Cryptocurrency Prices Using Social Media","authors":"Mason McCoy, S. Rahimi","doi":"10.1142/s146902682050025x","DOIUrl":"https://doi.org/10.1142/s146902682050025x","url":null,"abstract":"Trading cryptocurrencies (digital currencies) are currently performed by applying methods similar to what is applied to the stock market or commodities; however, these algorithms are not necessaril...","PeriodicalId":422521,"journal":{"name":"Int. J. Comput. Intell. Appl.","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123925649","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-10-08DOI: 10.1142/s1469026821500024
S. Vidal-Beltrán, J. López-Bonilla, F. Martínez-Piñón, Jesús Yalja-Montiel
Recently, technologies based on neural networks (NNs) and deep learning have improved in different areas of Science such as wireless communications. This study demonstrates the applicability of NN-based receivers for detecting and decoding sparse code multiple access (SCMA) codewords. The simulation results reveal that the proposed receiver provides highly accurate predictions based on new data. Moreover, the performance analysis results of the primary optimization algorithms used in machine learning are presented in this study.
{"title":"Gradient Descent Optimization Algorithms for Decoding SCMA Signals","authors":"S. Vidal-Beltrán, J. López-Bonilla, F. Martínez-Piñón, Jesús Yalja-Montiel","doi":"10.1142/s1469026821500024","DOIUrl":"https://doi.org/10.1142/s1469026821500024","url":null,"abstract":"Recently, technologies based on neural networks (NNs) and deep learning have improved in different areas of Science such as wireless communications. This study demonstrates the applicability of NN-based receivers for detecting and decoding sparse code multiple access (SCMA) codewords. The simulation results reveal that the proposed receiver provides highly accurate predictions based on new data. Moreover, the performance analysis results of the primary optimization algorithms used in machine learning are presented in this study.","PeriodicalId":422521,"journal":{"name":"Int. J. Comput. Intell. Appl.","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123515428","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-10-07DOI: 10.1142/s1469026821500012
Wuke Li, Yin Guangluan, Xiaoxiao Chen
A new approach for one-class fault detection trained only by normal samples has been proposed in this paper. The approach contains multi-anterior-layers for feature extraction and one post-layer for one-class classification. The multi-anterior-layers are based on extreme learning machine-based auto-encoder (ELM-AE). Multi-ELM-AEs are stacked in the front hidden layers to extract abstract features from the raw input. The post-layer is based on the reconstruction error-based ELM-AE (Re-ELM-AE) to act as one-class classifier. As the extension of ELM-AE, the decision threshold and function are given in the Re-ELM-AE, which are utilized to identify whether the test sample is faulty. The efficacy of the presented algorithm is demonstrated on a mathematic example and fault dataset from motor bearing. The method has been compared with shallow learning methods such as one-class support vector machine (OCSVM), the Re-ELM-AE, and one multi-layer neural network named stacked auto-encoder (SAE). The experiment results show that the proposed method outperforms OCSVM and Re-ELM-AE in classification accuracy. Though the classification accuracy of the proposed method and SAE is similar, the training and testing time of the proposed method is much lower than SAE.
{"title":"One-Class Fault Detection Using Multi-Layer Elm-Based Auto-Encoder","authors":"Wuke Li, Yin Guangluan, Xiaoxiao Chen","doi":"10.1142/s1469026821500012","DOIUrl":"https://doi.org/10.1142/s1469026821500012","url":null,"abstract":"A new approach for one-class fault detection trained only by normal samples has been proposed in this paper. The approach contains multi-anterior-layers for feature extraction and one post-layer for one-class classification. The multi-anterior-layers are based on extreme learning machine-based auto-encoder (ELM-AE). Multi-ELM-AEs are stacked in the front hidden layers to extract abstract features from the raw input. The post-layer is based on the reconstruction error-based ELM-AE (Re-ELM-AE) to act as one-class classifier. As the extension of ELM-AE, the decision threshold and function are given in the Re-ELM-AE, which are utilized to identify whether the test sample is faulty. The efficacy of the presented algorithm is demonstrated on a mathematic example and fault dataset from motor bearing. The method has been compared with shallow learning methods such as one-class support vector machine (OCSVM), the Re-ELM-AE, and one multi-layer neural network named stacked auto-encoder (SAE). The experiment results show that the proposed method outperforms OCSVM and Re-ELM-AE in classification accuracy. Though the classification accuracy of the proposed method and SAE is similar, the training and testing time of the proposed method is much lower than SAE.","PeriodicalId":422521,"journal":{"name":"Int. J. Comput. Intell. Appl.","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114401246","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-09-24DOI: 10.1142/S1469026820500194
A. Mahani, Ebrahim Farahmand, Saeideh Sheikhpour, Nooshin Taheri-Chatrudi
Wireless sensor networks (WSNs) are beginning to be deployed at an accelerated pace, and they have attracted significant attention in a broad spectrum of applications. WSNs encompass a large number...
无线传感器网络(wsn)正开始以加速的速度部署，并在广泛的应用中引起了极大的关注。wsn包含大量…
{"title":"A Novel Energy-Efficient Clustering Protocol Using Two-Stage Genetic Algorithm for Improving the Lifetime of Wireless Sensor Networks","authors":"A. Mahani, Ebrahim Farahmand, Saeideh Sheikhpour, Nooshin Taheri-Chatrudi","doi":"10.1142/S1469026820500194","DOIUrl":"https://doi.org/10.1142/S1469026820500194","url":null,"abstract":"Wireless sensor networks (WSNs) are beginning to be deployed at an accelerated pace, and they have attracted significant attention in a broad spectrum of applications. WSNs encompass a large number...","PeriodicalId":422521,"journal":{"name":"Int. J. Comput. Intell. Appl.","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115058237","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-09-02DOI: 10.1142/s1469026820500248
Abdoul-Dalibou Abdou, N. Ngom, Oumar Niang
In biomedical signal processing, artificial intelligence techniques are used for identifying and extracting relevant information. However, it lacks effective solutions based on machine learning for...
在生物医学信号处理中，人工智能技术用于识别和提取相关信息。然而，它缺乏基于机器学习的有效解决方案…
{"title":"Arrhythmias Prediction Using an Hybrid Model Based on Convolutional Neural Network and Nonlinear Regression","authors":"Abdoul-Dalibou Abdou, N. Ngom, Oumar Niang","doi":"10.1142/s1469026820500248","DOIUrl":"https://doi.org/10.1142/s1469026820500248","url":null,"abstract":"In biomedical signal processing, artificial intelligence techniques are used for identifying and extracting relevant information. However, it lacks effective solutions based on machine learning for...","PeriodicalId":422521,"journal":{"name":"Int. J. Comput. Intell. Appl.","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115988481","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-09-01DOI: 10.1142/s1469026820500236
Ye Li, Xiaohu Shi
The study on the prediction of mine pressure, while exploiting in coal mine, is a critical and technical guarantee for coal mine safety and production. In this paper, primarily due to the actual demand for the prediction of mine pressure, a practical prediction model Mine Pressure Prediction (MPP) was proposed based on fuzzy cognitive maps (FCMs). The Real Coded Genetic Algorithm (RCGA) was proposed to solve the problem by introducing the weight regularization and dropout regularization. A numerical example involving in-situ monitoring data is studied. Mean Square Error (MSE) and fitness function were used to evaluate the applicability of MPP model which is trained by RCGA, Regularization Genetic Algorithm (RGA) and Weight and Dropout RGA optimization algorithms. The numerical results demonstrate that the proposed Weight and Dropout RGA is better than the other two algorithms, and realizing the requirement for prediction of mine pressure in the coal mine production.
煤矿开采过程中矿井压力预测的研究是煤矿安全生产的关键技术保障。本文主要针对矿山压力预测的实际需求，提出了一种基于模糊认知图(fcm)的实用矿山压力预测模型。通过引入权值正则化和dropout正则化，提出了实数编码遗传算法(RCGA)来解决这一问题。研究了一个现场监测数据的数值算例。采用均方误差(MSE)和适应度函数对RCGA、正则化遗传算法(RGA)和Weight and Dropout RGA优化算法训练的MPP模型的适用性进行了评价。数值计算结果表明，所提出的Weight and Dropout RGA算法优于其他两种算法，实现了煤矿生产中矿井压力预测的要求。
{"title":"Mine Pressure Prediction Study Based on Fuzzy Cognitive Maps","authors":"Ye Li, Xiaohu Shi","doi":"10.1142/s1469026820500236","DOIUrl":"https://doi.org/10.1142/s1469026820500236","url":null,"abstract":"The study on the prediction of mine pressure, while exploiting in coal mine, is a critical and technical guarantee for coal mine safety and production. In this paper, primarily due to the actual demand for the prediction of mine pressure, a practical prediction model Mine Pressure Prediction (MPP) was proposed based on fuzzy cognitive maps (FCMs). The Real Coded Genetic Algorithm (RCGA) was proposed to solve the problem by introducing the weight regularization and dropout regularization. A numerical example involving in-situ monitoring data is studied. Mean Square Error (MSE) and fitness function were used to evaluate the applicability of MPP model which is trained by RCGA, Regularization Genetic Algorithm (RGA) and Weight and Dropout RGA optimization algorithms. The numerical results demonstrate that the proposed Weight and Dropout RGA is better than the other two algorithms, and realizing the requirement for prediction of mine pressure in the coal mine production.","PeriodicalId":422521,"journal":{"name":"Int. J. Comput. Intell. Appl.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129062963","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-08-19DOI: 10.1142/S1469026820500200
Anuraganand Sharma
Single-objective bilevel optimization is a specialized form of constraint optimization problems where one of the constraints is an optimization problem itself. These problems are typically non-convex and strongly NP-Hard. Recently, there has been an increased interest from the evolutionary computation community to model bilevel problems due to its applicability in the real-world applications for decision-making problems. In this work, a partial nested evolutionary approach with a local heuristic search has been proposed to solve the benchmark problems and have outstanding results. This approach relies on the concept of intermarriage-crossover in search of feasible regions by exploiting information from the constraints. A new variant has also been proposed to the commonly used convergence approaches, i.e., optimistic and pessimistic. It is called extreme optimistic approach. The experimental results demonstrate the algorithm converges differently to known optimum solutions with the optimistic variants. Optimistic approach also outperforms pessimistic approach. Comparative statistical analysis of our approach with other recently published partial to complete evolutionary approaches demonstrates very competitive results.
{"title":"Optimistic variants of single-objective bilevel optimization for evolutionary algorithms","authors":"Anuraganand Sharma","doi":"10.1142/S1469026820500200","DOIUrl":"https://doi.org/10.1142/S1469026820500200","url":null,"abstract":"Single-objective bilevel optimization is a specialized form of constraint optimization problems where one of the constraints is an optimization problem itself. These problems are typically non-convex and strongly NP-Hard. Recently, there has been an increased interest from the evolutionary computation community to model bilevel problems due to its applicability in the real-world applications for decision-making problems. In this work, a partial nested evolutionary approach with a local heuristic search has been proposed to solve the benchmark problems and have outstanding results. This approach relies on the concept of intermarriage-crossover in search of feasible regions by exploiting information from the constraints. A new variant has also been proposed to the commonly used convergence approaches, i.e., optimistic and pessimistic. It is called extreme optimistic approach. The experimental results demonstrate the algorithm converges differently to known optimum solutions with the optimistic variants. Optimistic approach also outperforms pessimistic approach. Comparative statistical analysis of our approach with other recently published partial to complete evolutionary approaches demonstrates very competitive results.","PeriodicalId":422521,"journal":{"name":"Int. J. Comput. Intell. Appl.","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127403045","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-08-19DOI: 10.1142/s1469026820500182
Neha Singh, Deepali Virmani, Xiao-zhi Gao
Intrusion is one of the biggest problems in wireless sensor networks. Because of the evolution in wired and wireless mechanization, various archetypes are used for communication. But security is th...
入侵是无线传感器网络中最大的问题之一。由于有线和无线机械化的发展，各种原型被用于通信。但是安全是…
{"title":"A Fuzzy Logic-Based Method to Avert Intrusions in Wireless Sensor Networks Using WSN-DS Dataset","authors":"Neha Singh, Deepali Virmani, Xiao-zhi Gao","doi":"10.1142/s1469026820500182","DOIUrl":"https://doi.org/10.1142/s1469026820500182","url":null,"abstract":"Intrusion is one of the biggest problems in wireless sensor networks. Because of the evolution in wired and wireless mechanization, various archetypes are used for communication. But security is th...","PeriodicalId":422521,"journal":{"name":"Int. J. Comput. Intell. Appl.","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122591040","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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