Pub Date : 2020-10-01DOI: 10.23919/EECSI50503.2020.9251890
Mirza Turesinin, Abdullah Md Humayun Kabir, Tanzina Mollah, Sadvan Sarwar, M. S. Hosain
Water pollution is a major problem worldwide. In order to tackle the pollution and keeping the water resources clean, this paper presents an affordable and advanced floating garbage removing robot called “Aquatic Iguana”. The robot moves around the surface of the water and collects floating waste material such as plastic, packets, leaves, etc. Along with the waste-collecting system, the robot also includes water monitoring with pH, turbidity, temperature sensors, and a live streaming feature, increasing the capacity to a greater extent. We have developed this robot to ensure the cleaning of water resources and to create a strong data set of water quality for future predictions. The use of this technology will ensure the safety of all aquatic animals and plants.
{"title":"Aquatic Iguana: A Floating Waste Collecting Robot with IoT Based Water Monitoring System","authors":"Mirza Turesinin, Abdullah Md Humayun Kabir, Tanzina Mollah, Sadvan Sarwar, M. S. Hosain","doi":"10.23919/EECSI50503.2020.9251890","DOIUrl":"https://doi.org/10.23919/EECSI50503.2020.9251890","url":null,"abstract":"Water pollution is a major problem worldwide. In order to tackle the pollution and keeping the water resources clean, this paper presents an affordable and advanced floating garbage removing robot called “Aquatic Iguana”. The robot moves around the surface of the water and collects floating waste material such as plastic, packets, leaves, etc. Along with the waste-collecting system, the robot also includes water monitoring with pH, turbidity, temperature sensors, and a live streaming feature, increasing the capacity to a greater extent. We have developed this robot to ensure the cleaning of water resources and to create a strong data set of water quality for future predictions. The use of this technology will ensure the safety of all aquatic animals and plants.","PeriodicalId":6743,"journal":{"name":"2020 7th International Conference on Electrical Engineering, Computer Sciences and Informatics (EECSI)","volume":"99 1","pages":"21-25"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73213852","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 : 2020-10-01DOI: 10.23919/EECSI50503.2020.9251872
Kurniabudi, A. Harris, Albertus Edward Mintaria, Darmawijoyo, D. Stiawan, Mohd Yazid Bin Idris, R. Budiarto
The feature selection techniques are used to find the most important and relevant features in a dataset. Therefore, in this study feature selection technique was used to improve the performance of Anomaly Detection. Many feature selection techniques have been developed and implemented on the NSL-KDD dataset. However, with the rapid growth of traffic on a network where more applications, devices, and protocols participate, the traffic data is complex and heterogeneous contribute to security issues. This makes the NSL-KDD dataset no longer reliable for it. The detection model must also be able to recognize the type of novel attack on complex network datasets. So, a robust analysis technique for a more complex and larger dataset is required, to overcome the increase of security issues in a big data network. This study proposes particle swarm optimization (PSO) Search methods as a feature selection method. As contribute to feature analysis knowledge, In the experiment a combination of particle swarm optimization (PSO) Search methods with other search methods are examined. To overcome the limitation NSL-KDD dataset, in the experiments the CICIDS2017 dataset used. To validate the selected features from the proposed technique J48 classification algorithm used in this study. The detection performance of the combination PSO Search method with J48 examined and compare with other feature selection and previous study. The proposed technique successfully finds the important features of the dataset, which improve detection performance with 99.89% accuracy. Compared with the previous study the proposed technique has better accuracy, TPR, and FPR.
{"title":"Improving the Anomaly Detection by Combining PSO Search Methods and J48 Algorithm","authors":"Kurniabudi, A. Harris, Albertus Edward Mintaria, Darmawijoyo, D. Stiawan, Mohd Yazid Bin Idris, R. Budiarto","doi":"10.23919/EECSI50503.2020.9251872","DOIUrl":"https://doi.org/10.23919/EECSI50503.2020.9251872","url":null,"abstract":"The feature selection techniques are used to find the most important and relevant features in a dataset. Therefore, in this study feature selection technique was used to improve the performance of Anomaly Detection. Many feature selection techniques have been developed and implemented on the NSL-KDD dataset. However, with the rapid growth of traffic on a network where more applications, devices, and protocols participate, the traffic data is complex and heterogeneous contribute to security issues. This makes the NSL-KDD dataset no longer reliable for it. The detection model must also be able to recognize the type of novel attack on complex network datasets. So, a robust analysis technique for a more complex and larger dataset is required, to overcome the increase of security issues in a big data network. This study proposes particle swarm optimization (PSO) Search methods as a feature selection method. As contribute to feature analysis knowledge, In the experiment a combination of particle swarm optimization (PSO) Search methods with other search methods are examined. To overcome the limitation NSL-KDD dataset, in the experiments the CICIDS2017 dataset used. To validate the selected features from the proposed technique J48 classification algorithm used in this study. The detection performance of the combination PSO Search method with J48 examined and compare with other feature selection and previous study. The proposed technique successfully finds the important features of the dataset, which improve detection performance with 99.89% accuracy. Compared with the previous study the proposed technique has better accuracy, TPR, and FPR.","PeriodicalId":6743,"journal":{"name":"2020 7th International Conference on Electrical Engineering, Computer Sciences and Informatics (EECSI)","volume":"44 1","pages":"119-126"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73389994","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 : 2020-10-01DOI: 10.23919/EECSI50503.2020.9251903
S. M. Gueye, A. Diop, Amadou Dahirou Gueye
Nowadays, a new form of learning has emerged in higher education. This is e-Learning. Lessons are taught on a Learning Content Management Systems (LCMS). These platforms generate a large variety of data at very high speed. This massive data comes from the interactions between the system and the users and between the users themselves (Learners, Tutors, Teachers, administrative Agents). Since 2013, UVS (Virtual University of Senegal), a digital university that offers distance learning through Moodle and Blackboard Collaborate platforms, has emerged. In terms of statistics, it has 29340 students, more than 400 active Tutors and 1000 courses. As a result, a large volume of data is generated on its learning platforms. In this article, we have set up an architecture allowing us to execute all types of queries on all data from platforms (historical data and real-time data) in order to set up intelligent systems capable of improving learning in this university. We then set up a machine learning model as a use case which is based on multiple regression in order to predict the most influential learning objects on the learners' final mark according to his learning activities.
{"title":"A Machine Learning Model on Virtual University of Senegal's Educational Data Based On Lambda Architecture","authors":"S. M. Gueye, A. Diop, Amadou Dahirou Gueye","doi":"10.23919/EECSI50503.2020.9251903","DOIUrl":"https://doi.org/10.23919/EECSI50503.2020.9251903","url":null,"abstract":"Nowadays, a new form of learning has emerged in higher education. This is e-Learning. Lessons are taught on a Learning Content Management Systems (LCMS). These platforms generate a large variety of data at very high speed. This massive data comes from the interactions between the system and the users and between the users themselves (Learners, Tutors, Teachers, administrative Agents). Since 2013, UVS (Virtual University of Senegal), a digital university that offers distance learning through Moodle and Blackboard Collaborate platforms, has emerged. In terms of statistics, it has 29340 students, more than 400 active Tutors and 1000 courses. As a result, a large volume of data is generated on its learning platforms. In this article, we have set up an architecture allowing us to execute all types of queries on all data from platforms (historical data and real-time data) in order to set up intelligent systems capable of improving learning in this university. We then set up a machine learning model as a use case which is based on multiple regression in order to predict the most influential learning objects on the learners' final mark according to his learning activities.","PeriodicalId":6743,"journal":{"name":"2020 7th International Conference on Electrical Engineering, Computer Sciences and Informatics (EECSI)","volume":"88 1","pages":"270-275"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80291753","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 : 2020-10-01DOI: 10.23919/EECSI50503.2020.9251894
Rawaa Majid Obaise, M. A. Salman, H. A. Lafta
This paper investigates a data processing model for a real experimental environment in which data is collected from several IoT devices on an edge server where a clustering-based data reduction model is implemented. Then, only representative data is transmitted to a cloud-hosted service to avoid high bandwidth consumption and the storage space at the cloud. In our model, the subtractive clustering algorithm is employed for the first time for streamed IoT data with high efficiency. Developed services show the real impact of data reduction technique at the fog node on enhancing overall system performance. High accuracy and reduction rate have been obtained through visualizing data before and after reduction.
{"title":"Data Reduction Approach Based on Fog Computing in IoT Environment","authors":"Rawaa Majid Obaise, M. A. Salman, H. A. Lafta","doi":"10.23919/EECSI50503.2020.9251894","DOIUrl":"https://doi.org/10.23919/EECSI50503.2020.9251894","url":null,"abstract":"This paper investigates a data processing model for a real experimental environment in which data is collected from several IoT devices on an edge server where a clustering-based data reduction model is implemented. Then, only representative data is transmitted to a cloud-hosted service to avoid high bandwidth consumption and the storage space at the cloud. In our model, the subtractive clustering algorithm is employed for the first time for streamed IoT data with high efficiency. Developed services show the real impact of data reduction technique at the fog node on enhancing overall system performance. High accuracy and reduction rate have been obtained through visualizing data before and after reduction.","PeriodicalId":6743,"journal":{"name":"2020 7th International Conference on Electrical Engineering, Computer Sciences and Informatics (EECSI)","volume":"19 1","pages":"65-70"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85121921","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 : 2020-10-01DOI: 10.23919/EECSI50503.2020.9251873
Ariesta Satryoko, A. Runturambi
The ultimate goal of this paper is to ensure the proper functioning of the Monitoring Forest Temperature program in Indonesia using the IoT Narrow-Band Low earth orbit Satellite. As a new technology for monitoring the temperature continue to expand, its implementation in developing countries particularly in Indonesia requires strategic guidance of how the whole process will be executed. Nevertheless, due to this, cross-sectoral partnership in technology, policy, budget, industry is essential to be addressed. The World Bank has recorded the loss from forest fire where 28 million people directly affected including 19 people who died and over 500 thousand people suffered from respiratory problems. Smokes from forest and land fires have also struck Malaysia, Singapore, and Brunei Darussalam respectively. To respond to this, the IoT (Internet of Things) now comes with an extensive feature, using the capability of satellite reach. The Narrow Band Low Earth Orbit Satellite has released a feature for IoT connect to Low Orbit Satellite and transmit the data from the sensor directly. Therefore, we argue that this technology is crucial and needs to be functioned immediately to monitor forest temperature in Indonesia.
{"title":"Method Using IOT Low Earth Orbit Satellite to Monitor Forest Temperature in Indonesia","authors":"Ariesta Satryoko, A. Runturambi","doi":"10.23919/EECSI50503.2020.9251873","DOIUrl":"https://doi.org/10.23919/EECSI50503.2020.9251873","url":null,"abstract":"The ultimate goal of this paper is to ensure the proper functioning of the Monitoring Forest Temperature program in Indonesia using the IoT Narrow-Band Low earth orbit Satellite. As a new technology for monitoring the temperature continue to expand, its implementation in developing countries particularly in Indonesia requires strategic guidance of how the whole process will be executed. Nevertheless, due to this, cross-sectoral partnership in technology, policy, budget, industry is essential to be addressed. The World Bank has recorded the loss from forest fire where 28 million people directly affected including 19 people who died and over 500 thousand people suffered from respiratory problems. Smokes from forest and land fires have also struck Malaysia, Singapore, and Brunei Darussalam respectively. To respond to this, the IoT (Internet of Things) now comes with an extensive feature, using the capability of satellite reach. The Narrow Band Low Earth Orbit Satellite has released a feature for IoT connect to Low Orbit Satellite and transmit the data from the sensor directly. Therefore, we argue that this technology is crucial and needs to be functioned immediately to monitor forest temperature in Indonesia.","PeriodicalId":6743,"journal":{"name":"2020 7th International Conference on Electrical Engineering, Computer Sciences and Informatics (EECSI)","volume":"6 1","pages":"240-243"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91015065","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 : 2020-10-01DOI: 10.23919/EECSI50503.2020.9251874
Rizal Broer Bahaweres, Fajar Agustian, I. Hermadi, A. Suroso, Y. Arkeman
Software defect prediction is a practical approach to improve the quality and efficiency of time and costs for software testing by focusing on defect modules. The dataset of software defect prediction naturally has a class imbalance problem with very few defective modules compared to non-defective modules. This situation has a negative impact on the Neural Network, which can lead to overfitting and poor accuracy. Synthetic Minority Over-sampling Technique (SMOTE) is one of the popular techniques that can solve the problem of class imbalance. However, Neural Network and SMOTE both have hyperparameters which must be determined by the user before the modelling process. In this study, we applied the Neural Networks Based SMOTE, a combination of Neural Network and SMOTE with each hyperparameter of SMOTE and Neural Network that are optimized using random search to solve the class imbalance problem in the six NASA datasets. The results use a 5*5 cross-validation show that increases Bal by 25.48% and Recall by 45.99% compared to the original Neural Network. We also compare the performance of Neural Network-based SMOTE with “Traditional” Machine Learning-based SMOTE. The Neural Network-based SMOTE takes first place in the average rank.
{"title":"Software Defect Prediction Using Neural Network Based SMOTE","authors":"Rizal Broer Bahaweres, Fajar Agustian, I. Hermadi, A. Suroso, Y. Arkeman","doi":"10.23919/EECSI50503.2020.9251874","DOIUrl":"https://doi.org/10.23919/EECSI50503.2020.9251874","url":null,"abstract":"Software defect prediction is a practical approach to improve the quality and efficiency of time and costs for software testing by focusing on defect modules. The dataset of software defect prediction naturally has a class imbalance problem with very few defective modules compared to non-defective modules. This situation has a negative impact on the Neural Network, which can lead to overfitting and poor accuracy. Synthetic Minority Over-sampling Technique (SMOTE) is one of the popular techniques that can solve the problem of class imbalance. However, Neural Network and SMOTE both have hyperparameters which must be determined by the user before the modelling process. In this study, we applied the Neural Networks Based SMOTE, a combination of Neural Network and SMOTE with each hyperparameter of SMOTE and Neural Network that are optimized using random search to solve the class imbalance problem in the six NASA datasets. The results use a 5*5 cross-validation show that increases Bal by 25.48% and Recall by 45.99% compared to the original Neural Network. We also compare the performance of Neural Network-based SMOTE with “Traditional” Machine Learning-based SMOTE. The Neural Network-based SMOTE takes first place in the average rank.","PeriodicalId":6743,"journal":{"name":"2020 7th International Conference on Electrical Engineering, Computer Sciences and Informatics (EECSI)","volume":"5 1","pages":"71-76"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82662590","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 : 2020-10-01DOI: 10.23919/EECSI50503.2020.9251302
I. Al-Barazanchi, Zahraa A. Jaaz, H. Abbas, Haider Rasheed Abdulshaheed
The data management from end-to-end level is done by cloud-assisted IOT for its users and they keep a goal in increasing their number of users with the course of time. From saving the infiltration of data from both internal and external threats to the system, IOT is the best-proposed method used for securing the database. Connecting objects/individuals with the Internet via safe interaction is the main objective of IOT. It can assemble all the hardware devices that are designed to store data for an individual or an organization. The associated applications and the way in which it can be deployed in the present organization in order to optimize the current working system. This paper focuses on providing an overall systematic secured data sharing portal that is devoid of threats from internal as well as external entities. By using CIBPRE data encryption a major security reform is introduced by IOT in storing and sharing of data on a regular basis.
{"title":"Practical application of IOT and its implications on the existing software","authors":"I. Al-Barazanchi, Zahraa A. Jaaz, H. Abbas, Haider Rasheed Abdulshaheed","doi":"10.23919/EECSI50503.2020.9251302","DOIUrl":"https://doi.org/10.23919/EECSI50503.2020.9251302","url":null,"abstract":"The data management from end-to-end level is done by cloud-assisted IOT for its users and they keep a goal in increasing their number of users with the course of time. From saving the infiltration of data from both internal and external threats to the system, IOT is the best-proposed method used for securing the database. Connecting objects/individuals with the Internet via safe interaction is the main objective of IOT. It can assemble all the hardware devices that are designed to store data for an individual or an organization. The associated applications and the way in which it can be deployed in the present organization in order to optimize the current working system. This paper focuses on providing an overall systematic secured data sharing portal that is devoid of threats from internal as well as external entities. By using CIBPRE data encryption a major security reform is introduced by IOT in storing and sharing of data on a regular basis.","PeriodicalId":6743,"journal":{"name":"2020 7th International Conference on Electrical Engineering, Computer Sciences and Informatics (EECSI)","volume":"17 1","pages":"10-14"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76824372","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 : 2020-10-01DOI: 10.23919/EECSI50503.2020.9251891
D. Nurnaningsih, A. Permana, Salsabila Ramadhina, A. Rodoni
Indonesia is a country with Muslim majority. Muslims implement fasting as one of the essential Islamic pillars. Information regarding fasting is substantial for Muslims, especially warnings of imsak, sahur and iftar times. The integration of information related to fasting schedules and provisions in mobile devices with Android is a promising solution for Muslims. Designing the Shiyam application as the fasting reminder is great to perform. This application had been developed using the Waterfall model, emphasizing on the development of systematic and sequential information systems. The implementation of the Shiyam application that focuses on the aspect of fasting can provide detailed fasting-related information and provides warnings at the time of imsak, iftar, and sahur, which can help Muslims in carrying out their worship.
{"title":"Designing Shiyam Application: An Android-based Fasting Reminder","authors":"D. Nurnaningsih, A. Permana, Salsabila Ramadhina, A. Rodoni","doi":"10.23919/EECSI50503.2020.9251891","DOIUrl":"https://doi.org/10.23919/EECSI50503.2020.9251891","url":null,"abstract":"Indonesia is a country with Muslim majority. Muslims implement fasting as one of the essential Islamic pillars. Information regarding fasting is substantial for Muslims, especially warnings of imsak, sahur and iftar times. The integration of information related to fasting schedules and provisions in mobile devices with Android is a promising solution for Muslims. Designing the Shiyam application as the fasting reminder is great to perform. This application had been developed using the Waterfall model, emphasizing on the development of systematic and sequential information systems. The implementation of the Shiyam application that focuses on the aspect of fasting can provide detailed fasting-related information and provides warnings at the time of imsak, iftar, and sahur, which can help Muslims in carrying out their worship.","PeriodicalId":6743,"journal":{"name":"2020 7th International Conference on Electrical Engineering, Computer Sciences and Informatics (EECSI)","volume":"19 1","pages":"60-64"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83573094","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 : 2020-10-01DOI: 10.23919/EECSI50503.2020.9251905
Rakha Asyrofi, Yoni Azhar Winata
Image data augmentation is a way that makes it possible to increase the diversity of available data without actually collecting new data. In this study, researchers have evaluated the application of image manipulation with the Thatcher effect, double illusion, and inversion on the performance of face detection for data augmentation needs where the data obtained has a weakness that is the limited amount of data to create a training model. The purpose of this research is to increase the diversity of the data so that it can make predictions correctly if given other similar datasets. To perform face detection on images, it is done using YOLOv3 then comparing the accuracy results from the dataset after and before adding data augmentation.
{"title":"The Improvement Impact Performance of Face Detection Using YOLO Algorithm","authors":"Rakha Asyrofi, Yoni Azhar Winata","doi":"10.23919/EECSI50503.2020.9251905","DOIUrl":"https://doi.org/10.23919/EECSI50503.2020.9251905","url":null,"abstract":"Image data augmentation is a way that makes it possible to increase the diversity of available data without actually collecting new data. In this study, researchers have evaluated the application of image manipulation with the Thatcher effect, double illusion, and inversion on the performance of face detection for data augmentation needs where the data obtained has a weakness that is the limited amount of data to create a training model. The purpose of this research is to increase the diversity of the data so that it can make predictions correctly if given other similar datasets. To perform face detection on images, it is done using YOLOv3 then comparing the accuracy results from the dataset after and before adding data augmentation.","PeriodicalId":6743,"journal":{"name":"2020 7th International Conference on Electrical Engineering, Computer Sciences and Informatics (EECSI)","volume":"67 1","pages":"177-180"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80919077","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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