Pub Date : 2021-07-01DOI: 10.4018/IJAIML.20210701.OA4
G. Khekare, Shahrukh Sheikh
For an autonomous robot to move safely in an environment where people are around and moving dynamically without knowing their goal position, it is required to set navigation rules and human behaviors. This problem is challenging with the highly stochastic behavior of people. Previous methods believe to provide features of human behavior, but these features vary from person to person. The method focuses on setting social norms that are telling the robot what not to do. With deep reinforcement learning, it has become possible to set a time-efficient navigation scheme that regulates social norms. The solution enables mobile robot full autonomy along with collision avoidance in people rich environment.
{"title":"Autonomous Navigation Using Deep Reinforcement Learning in ROS","authors":"G. Khekare, Shahrukh Sheikh","doi":"10.4018/IJAIML.20210701.OA4","DOIUrl":"https://doi.org/10.4018/IJAIML.20210701.OA4","url":null,"abstract":"For an autonomous robot to move safely in an environment where people are around and moving dynamically without knowing their goal position, it is required to set navigation rules and human behaviors. This problem is challenging with the highly stochastic behavior of people. Previous methods believe to provide features of human behavior, but these features vary from person to person. The method focuses on setting social norms that are telling the robot what not to do. With deep reinforcement learning, it has become possible to set a time-efficient navigation scheme that regulates social norms. The solution enables mobile robot full autonomy along with collision avoidance in people rich environment.","PeriodicalId":217541,"journal":{"name":"Int. J. Artif. Intell. Mach. Learn.","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114897741","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 : 2021-07-01DOI: 10.4018/IJAIML.20210701.OA8
C. PadmapriyaK., V. Leelavathy, Angelin Gladston
The human facial expressions convey a lot of information visually. Facial expression recognition plays a crucial role in the area of human-machine interaction. Automatic facial expression recognition system has many applications in human behavior understanding, detection of mental disorders and synthetic human expressions. Recognition of facial expression by computer with high recognition rate is still a challenging task. Most of the methods utilized in the literature for the automatic facial expression recognition systems are based on geometry and appearance. Facial expression recognition is usually performed in four stages consisting of pre-processing, face detection, feature extraction, and expression classification. In this paper we applied various deep learning methods to classify the seven key human emotions: anger, disgust, fear, happiness, sadness, surprise and neutrality. The facial expression recognition system developed is experimentally evaluated with FER dataset and has resulted with good accuracy.
{"title":"Automatic Multiface Expression Recognition Using Convolutional Neural Network","authors":"C. PadmapriyaK., V. Leelavathy, Angelin Gladston","doi":"10.4018/IJAIML.20210701.OA8","DOIUrl":"https://doi.org/10.4018/IJAIML.20210701.OA8","url":null,"abstract":"The human facial expressions convey a lot of information visually. Facial expression recognition plays a crucial role in the area of human-machine interaction. Automatic facial expression recognition system has many applications in human behavior understanding, detection of mental disorders and synthetic human expressions. Recognition of facial expression by computer with high recognition rate is still a challenging task. Most of the methods utilized in the literature for the automatic facial expression recognition systems are based on geometry and appearance. Facial expression recognition is usually performed in four stages consisting of pre-processing, face detection, feature extraction, and expression classification. In this paper we applied various deep learning methods to classify the seven key human emotions: anger, disgust, fear, happiness, sadness, surprise and neutrality. The facial expression recognition system developed is experimentally evaluated with FER dataset and has resulted with good accuracy.","PeriodicalId":217541,"journal":{"name":"Int. J. Artif. Intell. Mach. Learn.","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127153469","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 : 2021-07-01DOI: 10.4018/IJAIML.20210701.OA1
Amanda Lays Rodrigues da Silva, M. Santana, Clarisse Lins de Lima, José Filipe Silva de Andrade, Thifany Ketuli Silva de Souza, Maria Beatriz Jacinto de Almeida, W. W. A. D. Silva, R. C. Lima, W. D. Santos
{"title":"Features Selection Study for Breast Cancer Diagnosis Using Thermographic Images, Genetic Algorithms, and Particle Swarm Optimization","authors":"Amanda Lays Rodrigues da Silva, M. Santana, Clarisse Lins de Lima, José Filipe Silva de Andrade, Thifany Ketuli Silva de Souza, Maria Beatriz Jacinto de Almeida, W. W. A. D. Silva, R. C. Lima, W. D. Santos","doi":"10.4018/IJAIML.20210701.OA1","DOIUrl":"https://doi.org/10.4018/IJAIML.20210701.OA1","url":null,"abstract":"","PeriodicalId":217541,"journal":{"name":"Int. J. Artif. Intell. Mach. Learn.","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126983450","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 : 2021-07-01DOI: 10.4018/ijaiml.20210701.oa10
K. Kasthuriarachchi, S. Liyanage
The combination of different machine learning models to a single prediction model usually improves the performance of the data analysis. Stacking ensembles are one of such approaches to build a high performance classifier that can be applied to various contexts of data mining. This study proposes an enhanced stacking ensemble by collating a few machine learning algorithms with two layered meta classifications to address the limitations of existing stacking architecture to utilize Simulated Annealing Algorithm to optimize the classifier configuration in order to reach the best prediction accuracy. The proposed method significantly outperformed three general stacking ensembles of two layers that have been executed using the meta classifiers utilized in the proposed architecture. These assessments have been statistically proven at a 95% confidence level. The novel stacking ensemble has also outperformed the existing ensembles named; Adaboost algorithm, Gradient boosting algorithm, XGBoost classifier and bagging classifiers as well.
{"title":"Three-Layer Stacked Generalization Architecture With Simulated Annealing for Optimum Results in Data Mining","authors":"K. Kasthuriarachchi, S. Liyanage","doi":"10.4018/ijaiml.20210701.oa10","DOIUrl":"https://doi.org/10.4018/ijaiml.20210701.oa10","url":null,"abstract":"The combination of different machine learning models to a single prediction model usually improves the performance of the data analysis. Stacking ensembles are one of such approaches to build a high performance classifier that can be applied to various contexts of data mining. This study proposes an enhanced stacking ensemble by collating a few machine learning algorithms with two layered meta classifications to address the limitations of existing stacking architecture to utilize Simulated Annealing Algorithm to optimize the classifier configuration in order to reach the best prediction accuracy. The proposed method significantly outperformed three general stacking ensembles of two layers that have been executed using the meta classifiers utilized in the proposed architecture. These assessments have been statistically proven at a 95% confidence level. The novel stacking ensemble has also outperformed the existing ensembles named; Adaboost algorithm, Gradient boosting algorithm, XGBoost classifier and bagging classifiers as well.","PeriodicalId":217541,"journal":{"name":"Int. J. Artif. Intell. Mach. Learn.","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125887749","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 : 2021-07-01DOI: 10.4018/ijaiml.20210701oa12
S. Dash, Satyam Raj, Rahul Agarwal, Jibitesh Mishra
There are three types of maintenance management policy Run-tofailure (R2F), Preventive Maintenance (PvM) and Predictive Maintenance (PdM). In both R2F and PdM we have the data related to the maintenance cycle. In case of Preventive Maintenance (PvM) complete information about maintenance cycle is not available. Among these three maintenance policies, predictive Maintenance (PdM) is becoming a very important strategy as it can help us to minimize the repair time and the associated cost with it. In this paper we have proposed PdM, which allows the dynamic decision rules for the maintenance management. PdM is achieved by training the machine learning model with the datasets. It also helps in planning of maintenance schedules. We specially focused on two models that are Binary Classification and Recurrent Neural Network. In Binary Classification we classify whether our data belongs to the failure class or the non failure class. In Binary Classification the number of cycles is entered and classification model predicts whether it belongs to the failure/non failure class.
{"title":"Automobile Predictive Maintenance Using Deep Learning","authors":"S. Dash, Satyam Raj, Rahul Agarwal, Jibitesh Mishra","doi":"10.4018/ijaiml.20210701oa12","DOIUrl":"https://doi.org/10.4018/ijaiml.20210701oa12","url":null,"abstract":"There are three types of maintenance management policy Run-tofailure (R2F), Preventive Maintenance (PvM) and Predictive Maintenance (PdM). In both R2F and PdM we have the data related to the maintenance cycle. In case of Preventive Maintenance (PvM) complete information about maintenance cycle is not available. Among these three maintenance policies, predictive Maintenance (PdM) is becoming a very important strategy as it can help us to minimize the repair time and the associated cost with it. In this paper we have proposed PdM, which allows the dynamic decision rules for the maintenance management. PdM is achieved by training the machine learning model with the datasets. It also helps in planning of maintenance schedules. We specially focused on two models that are Binary Classification and Recurrent Neural Network. In Binary Classification we classify whether our data belongs to the failure class or the non failure class. In Binary Classification the number of cycles is entered and classification model predicts whether it belongs to the failure/non failure class.","PeriodicalId":217541,"journal":{"name":"Int. J. Artif. Intell. Mach. Learn.","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129883153","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 : 2021-07-01DOI: 10.4018/IJAIML.20210701.OA6
S. Nazir, Shushma Patel, D. Patel
Supervisory control and data acquisition (SCADA) systems are industrial control systems that are used to monitor critical infrastructures such as airports, transport, health, and public services of national importance. These are cyber physical systems, which are increasingly integrated with networks and internet of things devices. However, this results in a larger attack surface for cyber threats, making it important to identify and thwart cyber-attacks by detecting anomalous network traffic patterns. Compared to other techniques, as well as detecting known attack patterns, machine learning can also detect new and evolving threats. Autoencoders are a type of neural network that generates a compressed representation of its input data and through reconstruction loss of inputs can help identify anomalous data. This paper proposes the use of autoencoders for unsupervised anomaly-based intrusion detection using an appropriate differentiating threshold from the loss distribution and demonstrate improvements in results compared to other techniques for SCADA gas pipeline dataset.
{"title":"Autoencoder Based Anomaly Detection for SCADA Networks","authors":"S. Nazir, Shushma Patel, D. Patel","doi":"10.4018/IJAIML.20210701.OA6","DOIUrl":"https://doi.org/10.4018/IJAIML.20210701.OA6","url":null,"abstract":"Supervisory control and data acquisition (SCADA) systems are industrial control systems that are used to monitor critical infrastructures such as airports, transport, health, and public services of national importance. These are cyber physical systems, which are increasingly integrated with networks and internet of things devices. However, this results in a larger attack surface for cyber threats, making it important to identify and thwart cyber-attacks by detecting anomalous network traffic patterns. Compared to other techniques, as well as detecting known attack patterns, machine learning can also detect new and evolving threats. Autoencoders are a type of neural network that generates a compressed representation of its input data and through reconstruction loss of inputs can help identify anomalous data. This paper proposes the use of autoencoders for unsupervised anomaly-based intrusion detection using an appropriate differentiating threshold from the loss distribution and demonstrate improvements in results compared to other techniques for SCADA gas pipeline dataset.","PeriodicalId":217541,"journal":{"name":"Int. J. Artif. Intell. Mach. Learn.","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128952482","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 : 2021-07-01DOI: 10.4018/ijaiml.20210701.oa11
Safia Baali
The most challenging problem in human resources specially in the IT digital services company, is to assign the best collaborator’s in the adequate project , then ensure the delivery’s performance.in this paper we aim to develop à recommandation System using based-content and collaborative filtering in order to recommend potential profiles for a new job offer. The Principal parts of this recommandation is the matching between job offer of new project and collaborators profiles and the scoring using AHP method. In the first step we propose a model of criteria to measure collective skills , we validate by a survey realized in the IT service company , we analyze the data collected using PCA method (Principal Component Analysis).the results indicate six factors to measure collective skills of each collaborator (Technical skill, Proactivity ,Integrity, Cooperation, Communication and Benevolence/Interpersonal Relationship), these factors are used in AHP function to give score for each collaborator then allow the recommendation for the adequate project.
{"title":"Recommendation System: A New Approach to Recommend Potential Profile Using AHP Method","authors":"Safia Baali","doi":"10.4018/ijaiml.20210701.oa11","DOIUrl":"https://doi.org/10.4018/ijaiml.20210701.oa11","url":null,"abstract":"The most challenging problem in human resources specially in the IT digital services company, is to assign the best collaborator’s in the adequate project , then ensure the delivery’s performance.in this paper we aim to develop à recommandation System using based-content and collaborative filtering in order to recommend potential profiles for a new job offer. The Principal parts of this recommandation is the matching between job offer of new project and collaborators profiles and the scoring using AHP method. In the first step we propose a model of criteria to measure collective skills , we validate by a survey realized in the IT service company , we analyze the data collected using PCA method (Principal Component Analysis).the results indicate six factors to measure collective skills of each collaborator (Technical skill, Proactivity ,Integrity, Cooperation, Communication and Benevolence/Interpersonal Relationship), these factors are used in AHP function to give score for each collaborator then allow the recommendation for the adequate project.","PeriodicalId":217541,"journal":{"name":"Int. J. Artif. Intell. Mach. Learn.","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123155608","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 : 2021-01-01DOI: 10.4018/ijaiml.2021010102
P. Nagabushanam, S. George, D. J. Dolly, S. Radha
This paper has made a survey on motor imagery EEG signals and different classifiers to analyze them. Resolution for medical images like CT, MRI can be improved using deep sense CNN and improved resolution technology. Drowsiness of a student can be analyzed using deep CNN and it helps in teaching, assessment of the student. The authors have proposed 1D-CNN with 2 layers and 3 layers architecture to classify EEG signal for eyes open and eyes closed conditions. Various activation functions and combinations are tried for 2-layer 1D-CNN. Similarly, various loss models are applied in compile model to check the CNN performance. Simulation is carried out using Python 2.7 and 1D-CNN with 3 layers show better performance as it increases number of training parameters by increasing number of layers in the architecture. Accuracy and kappa coefficient increase whereas hamming loss and logloss decreases by increasing number of layers in CNN architecture.
{"title":"Convolution Neural Network Architectures for Motor Imagery EEG Signal Classification","authors":"P. Nagabushanam, S. George, D. J. Dolly, S. Radha","doi":"10.4018/ijaiml.2021010102","DOIUrl":"https://doi.org/10.4018/ijaiml.2021010102","url":null,"abstract":"This paper has made a survey on motor imagery EEG signals and different classifiers to analyze them. Resolution for medical images like CT, MRI can be improved using deep sense CNN and improved resolution technology. Drowsiness of a student can be analyzed using deep CNN and it helps in teaching, assessment of the student. The authors have proposed 1D-CNN with 2 layers and 3 layers architecture to classify EEG signal for eyes open and eyes closed conditions. Various activation functions and combinations are tried for 2-layer 1D-CNN. Similarly, various loss models are applied in compile model to check the CNN performance. Simulation is carried out using Python 2.7 and 1D-CNN with 3 layers show better performance as it increases number of training parameters by increasing number of layers in the architecture. Accuracy and kappa coefficient increase whereas hamming loss and logloss decreases by increasing number of layers in CNN architecture.","PeriodicalId":217541,"journal":{"name":"Int. J. Artif. Intell. Mach. Learn.","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123565565","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 : 2021-01-01DOI: 10.4018/ijaiml.2021010103
R. Priyanka, Prahanya Sriram, N. JayasreeL., Angelin Gladston
Gesture recognition is the most intuitive form of human-computer interface. Hand gestures provide a natural way for humans to interact with computers to perform a variety of different applications. However, factors such as complexity of hand gesture structures, differences in hand size, hand posture, and environmental illumination can influence the performance of hand gesture recognition algorithms. Considering the above factors, this paper aims to present a real time system for hand gesture recognition on the basis of detection of some meaningful shape-based features like orientation, center of mass, status of fingers, thumb in terms of raised or folded fingers of hand and their respective location in image. The internet is growing at a very fast pace. The use of web browser is also growing. Everyone has at least two or three most frequently visited website. Thus, in this paper, effectiveness of the gesture recognition and its ability to control the browser via the recognized hand gestures are experimented and the results are analyzed.
{"title":"Shape-Based Features for Optimized Hand Gesture Recognition","authors":"R. Priyanka, Prahanya Sriram, N. JayasreeL., Angelin Gladston","doi":"10.4018/ijaiml.2021010103","DOIUrl":"https://doi.org/10.4018/ijaiml.2021010103","url":null,"abstract":"Gesture recognition is the most intuitive form of human-computer interface. Hand gestures provide a natural way for humans to interact with computers to perform a variety of different applications. However, factors such as complexity of hand gesture structures, differences in hand size, hand posture, and environmental illumination can influence the performance of hand gesture recognition algorithms. Considering the above factors, this paper aims to present a real time system for hand gesture recognition on the basis of detection of some meaningful shape-based features like orientation, center of mass, status of fingers, thumb in terms of raised or folded fingers of hand and their respective location in image. The internet is growing at a very fast pace. The use of web browser is also growing. Everyone has at least two or three most frequently visited website. Thus, in this paper, effectiveness of the gesture recognition and its ability to control the browser via the recognized hand gestures are experimented and the results are analyzed.","PeriodicalId":217541,"journal":{"name":"Int. J. Artif. Intell. Mach. Learn.","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134123800","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}
In data mining, if a data set is new to the literature, the study is comparing the existing algorithms and determining the most suitable algorithm. This study is an example of this by including many quantitative analysis. Real data was obtained from a Pay-TV Company in Turkey to predict the churn behavior of the customers. The attributes such as membership period, payment method, education status, and city information of customers were used in order to predict the customers' churn status. By applying attributes selection algorithms, the most important attributes are obtained. As a result, two datasets are proposed. While one of the datasets consists of all attributes, the other one just includes the selected attributes. Many different data classification algorithms were applied to these datasets by using WEKA software. The best method and the best dataset which has the best accuracy rate was proposed to the company. The company can predict the customers' churn status and contact the right group of people for a specific campaign with a proposed user-friendly prediction methodology.
{"title":"Churn Prediction in a Pay-TV Company via Data Classification","authors":"Ilayda Ulku, Fadime Üney Yüksektepe, Oznur Yilmaz, M. Aktas, Nergiz Akbalik","doi":"10.4018/ijaiml.2021010104","DOIUrl":"https://doi.org/10.4018/ijaiml.2021010104","url":null,"abstract":"In data mining, if a data set is new to the literature, the study is comparing the existing algorithms and determining the most suitable algorithm. This study is an example of this by including many quantitative analysis. Real data was obtained from a Pay-TV Company in Turkey to predict the churn behavior of the customers. The attributes such as membership period, payment method, education status, and city information of customers were used in order to predict the customers' churn status. By applying attributes selection algorithms, the most important attributes are obtained. As a result, two datasets are proposed. While one of the datasets consists of all attributes, the other one just includes the selected attributes. Many different data classification algorithms were applied to these datasets by using WEKA software. The best method and the best dataset which has the best accuracy rate was proposed to the company. The company can predict the customers' churn status and contact the right group of people for a specific campaign with a proposed user-friendly prediction methodology.","PeriodicalId":217541,"journal":{"name":"Int. J. Artif. Intell. Mach. Learn.","volume":"82 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134256157","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：