Pub Date : 2022-01-01DOI: 10.32604/iasc.2022.018881
Ao Xiong, Meng Chen, Shaoyong Guo, Yongjie Li, Yujing Zhao, Q. Ou, Chuang Liu, Siwen Xu, Xiangang Liu
To solve the problem of energy consumption optimization of edge servers in the process of edge task unloading, we propose a task unloading algorithm based on reinforcement learning in this paper. The algorithm observes and analyzes the current environment state, selects the deployment location of edge tasks according to current states, and realizes the edge task unloading oriented to energy consumption optimization. To achieve the above goals, we first construct a network energy consumption model including servers’ energy consumption and link transmission energy consumption, which improves the accuracy of network energy consumption evaluation. Because of the complexity and variability of the edge environment, this paper designs a task unloading algorithm based on Proximal Policy Optimization (PPO), besides we use Dijkstra to determine the connection path between edge servers where adjacent tasks are deployed. Finally, lots of simulation experiments verify the effectiveness of the proposed method in the process of task unloading. Compared with contrast algorithms, the average energy saving of the proposed algorithm can reach 22.69%.
{"title":"An Energy Aware Algorithm for Edge Task Offloading","authors":"Ao Xiong, Meng Chen, Shaoyong Guo, Yongjie Li, Yujing Zhao, Q. Ou, Chuang Liu, Siwen Xu, Xiangang Liu","doi":"10.32604/iasc.2022.018881","DOIUrl":"https://doi.org/10.32604/iasc.2022.018881","url":null,"abstract":"To solve the problem of energy consumption optimization of edge servers in the process of edge task unloading, we propose a task unloading algorithm based on reinforcement learning in this paper. The algorithm observes and analyzes the current environment state, selects the deployment location of edge tasks according to current states, and realizes the edge task unloading oriented to energy consumption optimization. To achieve the above goals, we first construct a network energy consumption model including servers’ energy consumption and link transmission energy consumption, which improves the accuracy of network energy consumption evaluation. Because of the complexity and variability of the edge environment, this paper designs a task unloading algorithm based on Proximal Policy Optimization (PPO), besides we use Dijkstra to determine the connection path between edge servers where adjacent tasks are deployed. Finally, lots of simulation experiments verify the effectiveness of the proposed method in the process of task unloading. Compared with contrast algorithms, the average energy saving of the proposed algorithm can reach 22.69%.","PeriodicalId":50357,"journal":{"name":"Intelligent Automation and Soft Computing","volume":"30 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86732955","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}
Pub Date : 2022-01-01DOI: 10.32604/iasc.2022.023756
K. Babu, C. Kumar, C. Kannaiyaraju
Facial expression for different emotional feelings makes it interesting for researchers to develop recognition techniques. Facial expression is the outcome of emotions they feel, behavioral acts, and the physiological condition of one’s mind. In the world of computer visions and algorithms, precise facial recognition is tough. In predicting the expression of a face, machine learning/artificial intelligence plays a significant role. The deep learning techniques are widely used in more challenging real-world problems which are highly encouraged in facial emotional analysis. In this article, we use three phases for facial expression recognition techniques. The principal component analysis-based dimensionality reduction techniques are used with Eigen face value for edge detection. Then the feature extraction is performed using swarm intelligence-based grey wolf with particle swarm optimization techniques. The neural network is highly used in deep learning techniques for classification. Here we use a deep belief network (DBN) for classifying the recognized image. The proposed method’s results are assessed using the most comprehensive facial expression datasets, including RAF-DB, AffecteNet, and Cohn-Kanade (CK+). This developed approach improves existing methods with the maximum accuracy of 94.82%, 95.34%, 98.82%, and 97.82% on the test RAF-DB, AFfectNet, CK+, and FED-RO datasets respectively.
{"title":"Face Recognition System Using Deep Belief Network and Particle Swarm Optimization","authors":"K. Babu, C. Kumar, C. Kannaiyaraju","doi":"10.32604/iasc.2022.023756","DOIUrl":"https://doi.org/10.32604/iasc.2022.023756","url":null,"abstract":"Facial expression for different emotional feelings makes it interesting for researchers to develop recognition techniques. Facial expression is the outcome of emotions they feel, behavioral acts, and the physiological condition of one’s mind. In the world of computer visions and algorithms, precise facial recognition is tough. In predicting the expression of a face, machine learning/artificial intelligence plays a significant role. The deep learning techniques are widely used in more challenging real-world problems which are highly encouraged in facial emotional analysis. In this article, we use three phases for facial expression recognition techniques. The principal component analysis-based dimensionality reduction techniques are used with Eigen face value for edge detection. Then the feature extraction is performed using swarm intelligence-based grey wolf with particle swarm optimization techniques. The neural network is highly used in deep learning techniques for classification. Here we use a deep belief network (DBN) for classifying the recognized image. The proposed method’s results are assessed using the most comprehensive facial expression datasets, including RAF-DB, AffecteNet, and Cohn-Kanade (CK+). This developed approach improves existing methods with the maximum accuracy of 94.82%, 95.34%, 98.82%, and 97.82% on the test RAF-DB, AFfectNet, CK+, and FED-RO datasets respectively.","PeriodicalId":50357,"journal":{"name":"Intelligent Automation and Soft Computing","volume":"14 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86771215","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}
Pub Date : 2022-01-01DOI: 10.32604/iasc.2022.018654
Vinothini Arumugam, S. Baghavathi Priya
Nowadays, Chronic Kidney Disease (CKD) is one of the vigorous public health diseases. Hence, early detection of the disease may reduce the severity of its consequences. Besides, medical databases of any disease diagnosis may be collected from the blood test, urine test, and patient history. Nevertheless, medical information retrieved from various sources is diverse. Therefore, it is unadaptable to evaluate numerical and nominal features using the same feature selection algorithm, which may lead to fallacious analysis. Applying machine learning techniques over the medical database is a common way to help feature identification for CKD prediction. In this paper, a novel Mixed Data Feature Selection (MDFS) model is proposed to select and filter preeminent features from the medical dataset for earlier CKD prediction, where CKD clinical data with 12 numerical and 12 nominal features are fed to the MDFS model. For each feature in the mixed dataset, the model applies feature selection methods according to the data type of the feature. Point Biserial correlation and a Chi-square filter are applied to filter the numerical features and nominal features, respectively. Meanwhile, an SVM algorithm is employed to evaluate and select the best feature subset. In our experimental results, the proposed MDFS model performs superior to existing works in terms of accuracy and the number of reduced features. The identified feature subset is also demonstrated to preserve its original properties without discretization during feature selection.
{"title":"Selecting Dominant Features for the Prediction of Early-Stage Chronic Kidney Disease","authors":"Vinothini Arumugam, S. Baghavathi Priya","doi":"10.32604/iasc.2022.018654","DOIUrl":"https://doi.org/10.32604/iasc.2022.018654","url":null,"abstract":"Nowadays, Chronic Kidney Disease (CKD) is one of the vigorous public health diseases. Hence, early detection of the disease may reduce the severity of its consequences. Besides, medical databases of any disease diagnosis may be collected from the blood test, urine test, and patient history. Nevertheless, medical information retrieved from various sources is diverse. Therefore, it is unadaptable to evaluate numerical and nominal features using the same feature selection algorithm, which may lead to fallacious analysis. Applying machine learning techniques over the medical database is a common way to help feature identification for CKD prediction. In this paper, a novel Mixed Data Feature Selection (MDFS) model is proposed to select and filter preeminent features from the medical dataset for earlier CKD prediction, where CKD clinical data with 12 numerical and 12 nominal features are fed to the MDFS model. For each feature in the mixed dataset, the model applies feature selection methods according to the data type of the feature. Point Biserial correlation and a Chi-square filter are applied to filter the numerical features and nominal features, respectively. Meanwhile, an SVM algorithm is employed to evaluate and select the best feature subset. In our experimental results, the proposed MDFS model performs superior to existing works in terms of accuracy and the number of reduced features. The identified feature subset is also demonstrated to preserve its original properties without discretization during feature selection.","PeriodicalId":50357,"journal":{"name":"Intelligent Automation and Soft Computing","volume":"5 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91186982","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}
Pub Date : 2022-01-01DOI: 10.32604/iasc.2022.021310
Himanshu Padole, S. D. Joshi, Tapan K. Gandhi
{"title":"Early Detection of Alzheimer’s Disease Using Graph Signal Processing and Deep Learning","authors":"Himanshu Padole, S. D. Joshi, Tapan K. Gandhi","doi":"10.32604/iasc.2022.021310","DOIUrl":"https://doi.org/10.32604/iasc.2022.021310","url":null,"abstract":"","PeriodicalId":50357,"journal":{"name":"Intelligent Automation and Soft Computing","volume":"2 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74384428","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}
Pub Date : 2022-01-01DOI: 10.32604/iasc.2022.019669
N. Reda, A. Hamdy, E. Rashed
Regression testing is an essential quality test technique during the maintenance phase of the software. It is executed to ensure the validity of the software after any modification. As software evolves, the test suite expands and may become too large to be executed entirely within a limited testing budget and/or time. So, to reduce the cost of regression testing, it is mandatory to reduce the size of the test suite by discarding the redundant test cases and selecting the most representative ones that do not compromise the effectiveness of the test suite in terms of some predefined criteria such as its fault-detection capability. This problem is known as test suite reduction (TSR); and it is known to be as nondeterministic polynomial-time complete (NP-complete) problem. This paper formulated the TSR problem as a multi-objective optimization problem; and adapted the heuristic binary bat algorithm (BBA) to resolve it. The BBA algorithm was adapted in order to enhance its exploration capabilities during the search for Pareto-optimal solutions. The effectiveness of the proposed multiobjective adapted binary bat algorithm (MO-ABBA) was evaluated using 8 test suites of different sizes, in addition to twelve benchmark functions. Experimental results showed that, for the same fault discovery rate, the MO-ABBA is capable of reducing the test suite size more than each of the multi-objective original binary bat (MO-BBA) and the multi-objective binary particle swarm optimization (MOBPSO) algorithms. Moreover, MO-ABBA converges to the best solutions faster than each of the MO-BBA and the MO-BPSO.
{"title":"Multi-Objective Adapted Binary Bat for Test Suite Reduction","authors":"N. Reda, A. Hamdy, E. Rashed","doi":"10.32604/iasc.2022.019669","DOIUrl":"https://doi.org/10.32604/iasc.2022.019669","url":null,"abstract":"Regression testing is an essential quality test technique during the maintenance phase of the software. It is executed to ensure the validity of the software after any modification. As software evolves, the test suite expands and may become too large to be executed entirely within a limited testing budget and/or time. So, to reduce the cost of regression testing, it is mandatory to reduce the size of the test suite by discarding the redundant test cases and selecting the most representative ones that do not compromise the effectiveness of the test suite in terms of some predefined criteria such as its fault-detection capability. This problem is known as test suite reduction (TSR); and it is known to be as nondeterministic polynomial-time complete (NP-complete) problem. This paper formulated the TSR problem as a multi-objective optimization problem; and adapted the heuristic binary bat algorithm (BBA) to resolve it. The BBA algorithm was adapted in order to enhance its exploration capabilities during the search for Pareto-optimal solutions. The effectiveness of the proposed multiobjective adapted binary bat algorithm (MO-ABBA) was evaluated using 8 test suites of different sizes, in addition to twelve benchmark functions. Experimental results showed that, for the same fault discovery rate, the MO-ABBA is capable of reducing the test suite size more than each of the multi-objective original binary bat (MO-BBA) and the multi-objective binary particle swarm optimization (MOBPSO) algorithms. Moreover, MO-ABBA converges to the best solutions faster than each of the MO-BBA and the MO-BPSO.","PeriodicalId":50357,"journal":{"name":"Intelligent Automation and Soft Computing","volume":"84 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73559264","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}
Pub Date : 2022-01-01DOI: 10.32604/IASC.2022.019778
A. Dutta
{"title":"Detecting Lung Cancer Using Machine Learning Techniques","authors":"A. Dutta","doi":"10.32604/IASC.2022.019778","DOIUrl":"https://doi.org/10.32604/IASC.2022.019778","url":null,"abstract":"","PeriodicalId":50357,"journal":{"name":"Intelligent Automation and Soft Computing","volume":"31 1","pages":"1007-1023"},"PeriodicalIF":2.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69778414","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}
Pub Date : 2022-01-01DOI: 10.32604/IASC.2022.020662
H. Mahmoud, Amal H. Alharbi, N. Alghamdi
{"title":"Breast Cancer Detection Through Feature Clustering and Deep Learning","authors":"H. Mahmoud, Amal H. Alharbi, N. Alghamdi","doi":"10.32604/IASC.2022.020662","DOIUrl":"https://doi.org/10.32604/IASC.2022.020662","url":null,"abstract":"","PeriodicalId":50357,"journal":{"name":"Intelligent Automation and Soft Computing","volume":"31 1","pages":"1273-1286"},"PeriodicalIF":2.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69779510","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}
Pub Date : 2022-01-01DOI: 10.32604/iasc.2022.019555
W. Mao, Yu-Ying Chiu, Chao-Ting Chu, Binghuai Lin, Jian-Jie Hung
Electromagnets are commonly used as support for machine components and parts in magnetic bearing systems (MBSs). Compared with conventional mechanical bearings, the magnetic bearings have less noise, friction, and vibration, but the magnetic force has a highly nonlinear relationship with the control current and the air gap. This research presents a dynamic sliding mode backstepping control (DSMBC) designed to track the height position of modeless vertical MBS. Because MBS is nonlinear with model uncertainty, the design of estimator should be able to solve the lumped uncertainty. The proposed DSMBC controller can not only stabilize the nonlinear system under mismatched uncertainties, but also provide smooth control effort. The Lyapunov stability criterion and adaptive laws are derived to guarantee the convergence. The adaptive scheme that may be used to adjust the parameter vector is obtained, so the asymptotic stability of the developed system can be guaranteed. The backstepping algorithm is used to design the control system, and the stability and robustness of the MBS system are evaluated. Two position trajectories are considered to evaluate the proposed method. The experimental results show that the DSMBC method can improve the root mean square error (RMSE) by 29.94% compared with the traditional adaptive backstepping controller method under different position tracking conditions.
{"title":"Dynamic Sliding Mode Backstepping Control for Vertical Magnetic Bearing System","authors":"W. Mao, Yu-Ying Chiu, Chao-Ting Chu, Binghuai Lin, Jian-Jie Hung","doi":"10.32604/iasc.2022.019555","DOIUrl":"https://doi.org/10.32604/iasc.2022.019555","url":null,"abstract":"Electromagnets are commonly used as support for machine components and parts in magnetic bearing systems (MBSs). Compared with conventional mechanical bearings, the magnetic bearings have less noise, friction, and vibration, but the magnetic force has a highly nonlinear relationship with the control current and the air gap. This research presents a dynamic sliding mode backstepping control (DSMBC) designed to track the height position of modeless vertical MBS. Because MBS is nonlinear with model uncertainty, the design of estimator should be able to solve the lumped uncertainty. The proposed DSMBC controller can not only stabilize the nonlinear system under mismatched uncertainties, but also provide smooth control effort. The Lyapunov stability criterion and adaptive laws are derived to guarantee the convergence. The adaptive scheme that may be used to adjust the parameter vector is obtained, so the asymptotic stability of the developed system can be guaranteed. The backstepping algorithm is used to design the control system, and the stability and robustness of the MBS system are evaluated. Two position trajectories are considered to evaluate the proposed method. The experimental results show that the DSMBC method can improve the root mean square error (RMSE) by 29.94% compared with the traditional adaptive backstepping controller method under different position tracking conditions.","PeriodicalId":50357,"journal":{"name":"Intelligent Automation and Soft Computing","volume":"19 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74610495","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}
Pub Date : 2022-01-01DOI: 10.32604/iasc.2022.023750
Hatim M. Elhassan, Mohammed Abaker, Abdelzahir Abdelmaboud, Mohammed Burhanur Rehman
{"title":"Requirements Engineering: Conflict Detection Automation Using Machine Learning","authors":"Hatim M. Elhassan, Mohammed Abaker, Abdelzahir Abdelmaboud, Mohammed Burhanur Rehman","doi":"10.32604/iasc.2022.023750","DOIUrl":"https://doi.org/10.32604/iasc.2022.023750","url":null,"abstract":"","PeriodicalId":50357,"journal":{"name":"Intelligent Automation and Soft Computing","volume":"80 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80032706","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}
Pub Date : 2022-01-01DOI: 10.32604/iasc.2022.017622
V. R. Vimal, P. Anandan, N. Kumaratharan
Electrocardiogram (ECG) monitoring models are commonly employed for diagnosing heart diseases. Since ECG signals are normally acquired for a longer time duration with high resolution, there is a need to compress the ECG signals for transmission and storage. So, a novel compression technique is essential in transmitting the signals to the telemedicine center to monitor and analyse the data. In addition, the protection of ECG signals poses a challenging issue, which encryption techniques can resolve. The existing Encryption-Then-Compression (ETC) models for multimedia data fail to properly maintain the tradeoff between compression performance and signal quality. In this view, this study presents a new ETC with a diagnosis model for ECG data, called the ETC-ECG model. The proposed model involves four major processes, namely, pre-processing, encryption, compression, and classification. Once the ECG data of the patient are gathered, Discrete Wavelet Transform (DWT) with a Thresholding mechanism is used for noise removal. In addition, the chaotic map-based encryption technique is applied to encrypt the data. Moreover, the Burrows-Wheeler Transform (BWT) approach is employed for the compression of the encrypted data. Finally, a Deep Neural Network (DNN) is applied to the decrypted data to diagnose heart disease. The detailed experimental analysis takes place to ensure the effective performance of the presented model to assure data security, compression, and classification performance for ECG data.
{"title":"Heart Disease Diagnosis Using Electrocardiography (ECG) Signals","authors":"V. R. Vimal, P. Anandan, N. Kumaratharan","doi":"10.32604/iasc.2022.017622","DOIUrl":"https://doi.org/10.32604/iasc.2022.017622","url":null,"abstract":"Electrocardiogram (ECG) monitoring models are commonly employed for diagnosing heart diseases. Since ECG signals are normally acquired for a longer time duration with high resolution, there is a need to compress the ECG signals for transmission and storage. So, a novel compression technique is essential in transmitting the signals to the telemedicine center to monitor and analyse the data. In addition, the protection of ECG signals poses a challenging issue, which encryption techniques can resolve. The existing Encryption-Then-Compression (ETC) models for multimedia data fail to properly maintain the tradeoff between compression performance and signal quality. In this view, this study presents a new ETC with a diagnosis model for ECG data, called the ETC-ECG model. The proposed model involves four major processes, namely, pre-processing, encryption, compression, and classification. Once the ECG data of the patient are gathered, Discrete Wavelet Transform (DWT) with a Thresholding mechanism is used for noise removal. In addition, the chaotic map-based encryption technique is applied to encrypt the data. Moreover, the Burrows-Wheeler Transform (BWT) approach is employed for the compression of the encrypted data. Finally, a Deep Neural Network (DNN) is applied to the decrypted data to diagnose heart disease. The detailed experimental analysis takes place to ensure the effective performance of the presented model to assure data security, compression, and classification performance for ECG data.","PeriodicalId":50357,"journal":{"name":"Intelligent Automation and Soft Computing","volume":"72 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80449577","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}
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