Pub Date : 2020-10-13DOI: 10.1109/ICCIS49240.2020.9257667
Farhat Afza, M. A. Khan, M. Sharif, T. Saba, A. Rehman, M. Javed
Melanoma is the most common and deadly kind of malignancy among all the existing types of cancers, worldwide. Globally, the incidence rate of melanoma rising in recent decades. Responses on a survey, in USA about 192,310 new cases are diagnosed while 7,230 deaths have been occurred due to melanoma in 2019. This ratio can be decreased if it is detected at an early stage. A novel systematic approach for skin cancer detection based on optimal feature selection is proposed in this work. In the normalization step, it differentiates the lesion region from the surrounding skin region by using a linear contrast stretching technique. Later, various type features are computed and put to optimal feature selection approach name higher entropy value features (HEVF). Optimized and best features are selected and classified using SVM classifier and evaluated on ISBI 2017 dataset. As a result, the proposed systems get a performance of 96.2% which is improved as compared to existing techniques.
{"title":"Skin Lesion Classification: An Optimized Framework of Optimal Color Features Selection","authors":"Farhat Afza, M. A. Khan, M. Sharif, T. Saba, A. Rehman, M. Javed","doi":"10.1109/ICCIS49240.2020.9257667","DOIUrl":"https://doi.org/10.1109/ICCIS49240.2020.9257667","url":null,"abstract":"Melanoma is the most common and deadly kind of malignancy among all the existing types of cancers, worldwide. Globally, the incidence rate of melanoma rising in recent decades. Responses on a survey, in USA about 192,310 new cases are diagnosed while 7,230 deaths have been occurred due to melanoma in 2019. This ratio can be decreased if it is detected at an early stage. A novel systematic approach for skin cancer detection based on optimal feature selection is proposed in this work. In the normalization step, it differentiates the lesion region from the surrounding skin region by using a linear contrast stretching technique. Later, various type features are computed and put to optimal feature selection approach name higher entropy value features (HEVF). Optimized and best features are selected and classified using SVM classifier and evaluated on ISBI 2017 dataset. As a result, the proposed systems get a performance of 96.2% which is improved as compared to existing techniques.","PeriodicalId":425637,"journal":{"name":"2020 2nd International Conference on Computer and Information Sciences (ICCIS)","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116889186","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 : 2020-10-13DOI: 10.1109/ICCIS49240.2020.9257719
N. Alnaghaimshi, S. A. Alneghaimshi
Productive families' projects and traditional handicrafts are a form of micro-enterprise in Saudi Arabia which can be considered as one of the main sources for generating employment opportunities, especially for low- and limited-income individuals and families. This type of project promotes self-employment among Saudis through manufacturing a variety of products at home. In line with Vision 2030 and seeking to empower the local women economically, this project is being conducted to develop a mobile application for promoting and marketing handmade products at lowest cost. The proposed application can be designed for both iOS and Android devices.
{"title":"Proposed Arabic Mobile Application for Micro-enterprises: A Saudi Arabian Setting","authors":"N. Alnaghaimshi, S. A. Alneghaimshi","doi":"10.1109/ICCIS49240.2020.9257719","DOIUrl":"https://doi.org/10.1109/ICCIS49240.2020.9257719","url":null,"abstract":"Productive families' projects and traditional handicrafts are a form of micro-enterprise in Saudi Arabia which can be considered as one of the main sources for generating employment opportunities, especially for low- and limited-income individuals and families. This type of project promotes self-employment among Saudis through manufacturing a variety of products at home. In line with Vision 2030 and seeking to empower the local women economically, this project is being conducted to develop a mobile application for promoting and marketing handmade products at lowest cost. The proposed application can be designed for both iOS and Android devices.","PeriodicalId":425637,"journal":{"name":"2020 2nd International Conference on Computer and Information Sciences (ICCIS)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115077733","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 : 2020-10-13DOI: 10.1109/ICCIS49240.2020.9257670
Hoda El-Batrawy, A. Atwan, Hassan H. Soliman, Mohammed M Elmogy
Computer vision and deep learning have significant leverage on the retrieval of image ranking. The impressive advancements of deep learning techniques for computer vision and other applications conducted an excellent performance for semantically image ranking. The great challenge in image ranking task concentrates on extracting the deepest features of the image. This paper investigates a highly scalable and computationally efficient of deep relevance image ranking system for large scale images. The superior deep network model called RetinaNet is utilized as a feature extractor to learn deep semantic feature embedding of the imaging data. Besides, The effective transfer learning scheme is proposed to transfer the RetinaNet learning to deep relevance image ranking system. The experimental results manifest that our deep learning procedure enhancement the retrieval results efficiently and accurately and focuses on inhibit the learning time of a deep, relevant ranking task. As compared with other state-of-the-art object detectors, the RetinaNet detector accomplished more than a 97% mean average precision (MAP). These superior results pretend the effective impact of our proposed procedure learning that drives the more efficient and relevant result of the deep ranking task.
{"title":"Image Ranking Relevancy Based on Semantic Web Using Deep Learning Technique","authors":"Hoda El-Batrawy, A. Atwan, Hassan H. Soliman, Mohammed M Elmogy","doi":"10.1109/ICCIS49240.2020.9257670","DOIUrl":"https://doi.org/10.1109/ICCIS49240.2020.9257670","url":null,"abstract":"Computer vision and deep learning have significant leverage on the retrieval of image ranking. The impressive advancements of deep learning techniques for computer vision and other applications conducted an excellent performance for semantically image ranking. The great challenge in image ranking task concentrates on extracting the deepest features of the image. This paper investigates a highly scalable and computationally efficient of deep relevance image ranking system for large scale images. The superior deep network model called RetinaNet is utilized as a feature extractor to learn deep semantic feature embedding of the imaging data. Besides, The effective transfer learning scheme is proposed to transfer the RetinaNet learning to deep relevance image ranking system. The experimental results manifest that our deep learning procedure enhancement the retrieval results efficiently and accurately and focuses on inhibit the learning time of a deep, relevant ranking task. As compared with other state-of-the-art object detectors, the RetinaNet detector accomplished more than a 97% mean average precision (MAP). These superior results pretend the effective impact of our proposed procedure learning that drives the more efficient and relevant result of the deep ranking task.","PeriodicalId":425637,"journal":{"name":"2020 2nd International Conference on Computer and Information Sciences (ICCIS)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124686965","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 : 2020-10-13DOI: 10.1109/ICCIS49240.2020.9257628
M. El-Amin, A. Subasi
In this paper, machine learning techniques are utilized to predict the temperature distribution in a vertical buoyant turbulent jet. Experimental results for five cases with different flow rates are reported. The results show that temperature behaves linearly along the vertical axis of the jet. Also, the thermal stratification phenomenon has been observed. Different machine learning techniques have been used to predict the temperature distribution in the induced vertical buoyant turbulent jet. The used machine learning including k-nearest neighbor algorithm (k-NN), artificial neural networks (ANNs), Support Vector Regression (SVR), and random forest (RF). It was found both SVR and RF methods are the best machine learning techniques to predict the temperature distribution in a vertical buoyant turbulent jet.
{"title":"Predicting Turbulent Buoyant Jet Using Machine Learning Techniques","authors":"M. El-Amin, A. Subasi","doi":"10.1109/ICCIS49240.2020.9257628","DOIUrl":"https://doi.org/10.1109/ICCIS49240.2020.9257628","url":null,"abstract":"In this paper, machine learning techniques are utilized to predict the temperature distribution in a vertical buoyant turbulent jet. Experimental results for five cases with different flow rates are reported. The results show that temperature behaves linearly along the vertical axis of the jet. Also, the thermal stratification phenomenon has been observed. Different machine learning techniques have been used to predict the temperature distribution in the induced vertical buoyant turbulent jet. The used machine learning including k-nearest neighbor algorithm (k-NN), artificial neural networks (ANNs), Support Vector Regression (SVR), and random forest (RF). It was found both SVR and RF methods are the best machine learning techniques to predict the temperature distribution in a vertical buoyant turbulent jet.","PeriodicalId":425637,"journal":{"name":"2020 2nd International Conference on Computer and Information Sciences (ICCIS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129443064","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 : 2020-10-13DOI: 10.1109/ICCIS49240.2020.9257693
Amina Khan, S. Gupta, E. I. Assiri, M. Rashid, Y. T. Mohammed, Mohd Najim, Yousef Ruzayq Alharbi
Natural disasters like floods bring along negative consequences like the loss of life, economic losses, which cannot be prevented, but proper planning can reduce the disastrous aftermath. A flood warning system typically integrates information on telemetric precipitation and water level/flow, calculated at different places in the local area. Based on these observations, it is difficult to provide information about river conditions, flood types, etc. The absence of a real-time monitoring system makes it difficult to alert the authorities and provide protection programs in case of critical contingency. So there is a need for the installation and development of an improved flood forecasting system. Implementation of end to end flood forecasting, warning, and response system is required, which can predict more accurately and is reliable. It is proposed to design modern flood monitoring and warning system, which is a simple, cost-effective, low power system and is easy to deploy and use. To confront traditional problems, the heterogeneous sensor (Het-Sens) based embedded system is proposed for forecasting upcoming phenomena and sending a prompt warning.
{"title":"Flood Monitoring and Warning System: Het-Sens a Proposed Model","authors":"Amina Khan, S. Gupta, E. I. Assiri, M. Rashid, Y. T. Mohammed, Mohd Najim, Yousef Ruzayq Alharbi","doi":"10.1109/ICCIS49240.2020.9257693","DOIUrl":"https://doi.org/10.1109/ICCIS49240.2020.9257693","url":null,"abstract":"Natural disasters like floods bring along negative consequences like the loss of life, economic losses, which cannot be prevented, but proper planning can reduce the disastrous aftermath. A flood warning system typically integrates information on telemetric precipitation and water level/flow, calculated at different places in the local area. Based on these observations, it is difficult to provide information about river conditions, flood types, etc. The absence of a real-time monitoring system makes it difficult to alert the authorities and provide protection programs in case of critical contingency. So there is a need for the installation and development of an improved flood forecasting system. Implementation of end to end flood forecasting, warning, and response system is required, which can predict more accurately and is reliable. It is proposed to design modern flood monitoring and warning system, which is a simple, cost-effective, low power system and is easy to deploy and use. To confront traditional problems, the heterogeneous sensor (Het-Sens) based embedded system is proposed for forecasting upcoming phenomena and sending a prompt warning.","PeriodicalId":425637,"journal":{"name":"2020 2nd International Conference on Computer and Information Sciences (ICCIS)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127656684","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 : 2020-10-13DOI: 10.1109/ICCIS49240.2020.9257680
Manar Aljazaeri, Y. Bazi, Haidar A. Almubarak, N. Alajlan
Glaucoma is one of the main retinal diseases. Glaucoma affects older people more often, and it can lead to vision loss. Until now there is no medicament for Glaucoma, but early detection is important, wherein it can limit the increase of vision loss or blindness. In this paper, we propose a deep learning approach based on two steps for Glaucoma detection in retinal fundus images. In the first step, we use a faster region proposal neural network (RCNN) to detect the optical disc (OD). Then in a second step, we train a regression network to estimate the cup-to-disc ratio (CDR) by analyzing reign around the detected OD. Experimental results of this method are demonstrated on the MESSIDOR and Magrabi datasets.
{"title":"Faster R-CNN and DenseNet Regression for Glaucoma Detection in Retinal Fundus Images","authors":"Manar Aljazaeri, Y. Bazi, Haidar A. Almubarak, N. Alajlan","doi":"10.1109/ICCIS49240.2020.9257680","DOIUrl":"https://doi.org/10.1109/ICCIS49240.2020.9257680","url":null,"abstract":"Glaucoma is one of the main retinal diseases. Glaucoma affects older people more often, and it can lead to vision loss. Until now there is no medicament for Glaucoma, but early detection is important, wherein it can limit the increase of vision loss or blindness. In this paper, we propose a deep learning approach based on two steps for Glaucoma detection in retinal fundus images. In the first step, we use a faster region proposal neural network (RCNN) to detect the optical disc (OD). Then in a second step, we train a regression network to estimate the cup-to-disc ratio (CDR) by analyzing reign around the detected OD. Experimental results of this method are demonstrated on the MESSIDOR and Magrabi datasets.","PeriodicalId":425637,"journal":{"name":"2020 2nd International Conference on Computer and Information Sciences (ICCIS)","volume":"120 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132416025","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 : 2020-10-13DOI: 10.1109/ICCIS49240.2020.9257709
Falmata Modu, Yusuf Sani, F. Aliyu, A. Mabu
Biometric-based authentication systems are prone to spoofing attacks, errors due to noisy data, intra- and inter-class variations. Combining multiple biometric traits (multibiometric system) promises more accuracy. However, this leads to overhead due to an increase in complexity, form factor, energy and latency in the system. In this paper, a trust management system is used together with a decision level multibiometric system to improve the accuracy and lower the energy consumption of the proposed system. The proposed system is found to drop the false positive rate value by a factor of 4 and the energy consumption was reduced by a factor of 7.
{"title":"Multibiometric System for Internet of Things using Trust Management","authors":"Falmata Modu, Yusuf Sani, F. Aliyu, A. Mabu","doi":"10.1109/ICCIS49240.2020.9257709","DOIUrl":"https://doi.org/10.1109/ICCIS49240.2020.9257709","url":null,"abstract":"Biometric-based authentication systems are prone to spoofing attacks, errors due to noisy data, intra- and inter-class variations. Combining multiple biometric traits (multibiometric system) promises more accuracy. However, this leads to overhead due to an increase in complexity, form factor, energy and latency in the system. In this paper, a trust management system is used together with a decision level multibiometric system to improve the accuracy and lower the energy consumption of the proposed system. The proposed system is found to drop the false positive rate value by a factor of 4 and the energy consumption was reduced by a factor of 7.","PeriodicalId":425637,"journal":{"name":"2020 2nd International Conference on Computer and Information Sciences (ICCIS)","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127637012","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 : 2020-10-13DOI: 10.1109/ICCIS49240.2020.9257639
J. Ghanim, A. Shatnawi
In VLSI design the hardware is implemented with some objective and constrain functions (as lower number of hardware used). When the system contains a lot of processing elements (PEs) and memory registers, the cost of the interconnections becomes of great issue and must be minimized. The work in the field of determination of the interconnection for a hardware implementation is not very common. In high-level synthesis it is usually considered the time scheduling and processor assignment from a given DFG. However, the cost of interconnection is not widely discussed and is left to a hardware system to determine it. In this paper, a technique for determining the interconnection in a hardware design is proposed. The objective function is the minimum number if hardware used and the constrain is minimum iteration period bound. This interconnection is shown to accomplish cost optimality in terms of minimizing the number of multiplexers used.
{"title":"Determine the Interconnection of a Hardware Implementation for DSP Applications","authors":"J. Ghanim, A. Shatnawi","doi":"10.1109/ICCIS49240.2020.9257639","DOIUrl":"https://doi.org/10.1109/ICCIS49240.2020.9257639","url":null,"abstract":"In VLSI design the hardware is implemented with some objective and constrain functions (as lower number of hardware used). When the system contains a lot of processing elements (PEs) and memory registers, the cost of the interconnections becomes of great issue and must be minimized. The work in the field of determination of the interconnection for a hardware implementation is not very common. In high-level synthesis it is usually considered the time scheduling and processor assignment from a given DFG. However, the cost of interconnection is not widely discussed and is left to a hardware system to determine it. In this paper, a technique for determining the interconnection in a hardware design is proposed. The objective function is the minimum number if hardware used and the constrain is minimum iteration period bound. This interconnection is shown to accomplish cost optimality in terms of minimizing the number of multiplexers used.","PeriodicalId":425637,"journal":{"name":"2020 2nd International Conference on Computer and Information Sciences (ICCIS)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115875746","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 : 2020-10-13DOI: 10.1109/ICCIS49240.2020.9257690
Ebtesam H Alharbi, Maryam M. Alahrbi, Sahar S. Alkhamali
Green computing has gained the attention of academia, cloud providers, and governments. The advent of cloud computing has raised sustainability issues (e.g. high power consumption, carbon emissions). Sustainability is the concept of meeting our needs while reducing our impacts on the environment and the life of future generations. It has become a critical concern in the modern environmental life and cloud providers worldwide. Therefore, this paper considers the adaptation of green computing in Saudi Arabia. It proposes a framework that illustrates the factors that affect the adaptation of green computing for current and future cloud computing providers in Saudi Arabia. The framework can be also used as a guideline to ensure that green computing is achieved with best practices.
{"title":"A Proposed Framework for Adoption Green Cloud Computing in Saudi Arabia","authors":"Ebtesam H Alharbi, Maryam M. Alahrbi, Sahar S. Alkhamali","doi":"10.1109/ICCIS49240.2020.9257690","DOIUrl":"https://doi.org/10.1109/ICCIS49240.2020.9257690","url":null,"abstract":"Green computing has gained the attention of academia, cloud providers, and governments. The advent of cloud computing has raised sustainability issues (e.g. high power consumption, carbon emissions). Sustainability is the concept of meeting our needs while reducing our impacts on the environment and the life of future generations. It has become a critical concern in the modern environmental life and cloud providers worldwide. Therefore, this paper considers the adaptation of green computing in Saudi Arabia. It proposes a framework that illustrates the factors that affect the adaptation of green computing for current and future cloud computing providers in Saudi Arabia. The framework can be also used as a guideline to ensure that green computing is achieved with best practices.","PeriodicalId":425637,"journal":{"name":"2020 2nd International Conference on Computer and Information Sciences (ICCIS)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128540407","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 : 2020-10-13DOI: 10.1109/ICCIS49240.2020.9257665
H. E. Jubara
In vehicular communication the handover procedure process becomes a common problem causing several issues during vehicle communication. These issues mainly can be as handover delay or signal loss that leads to throughput degrading and may cut the communication. This paper discuss an optimization of handover procedure to reduce the problems take place during handover of the vehicle especially with higher speeds. The idea is designing a cross-layer between transport layer and the data link layer of the protocol through an algorithm. Therefore, the suggested design can adapt a vehicle speed and handover procedure to reduce the delay time. The result clearly shows that the optimal design can achieve a minimum delay time of HO in any value of vehicle speed.
{"title":"An efficient handover procedure in vehicular communication","authors":"H. E. Jubara","doi":"10.1109/ICCIS49240.2020.9257665","DOIUrl":"https://doi.org/10.1109/ICCIS49240.2020.9257665","url":null,"abstract":"In vehicular communication the handover procedure process becomes a common problem causing several issues during vehicle communication. These issues mainly can be as handover delay or signal loss that leads to throughput degrading and may cut the communication. This paper discuss an optimization of handover procedure to reduce the problems take place during handover of the vehicle especially with higher speeds. The idea is designing a cross-layer between transport layer and the data link layer of the protocol through an algorithm. Therefore, the suggested design can adapt a vehicle speed and handover procedure to reduce the delay time. The result clearly shows that the optimal design can achieve a minimum delay time of HO in any value of vehicle speed.","PeriodicalId":425637,"journal":{"name":"2020 2nd International Conference on Computer and Information Sciences (ICCIS)","volume":"130 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116206832","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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