Pub Date : 2020-09-01DOI: 10.1109/ATSIP49331.2020.9231797
A. B. Aicha
Larynx cancer is developed from precancerous state. Some precancerous lesions such as Keratosis, Leukoplakia, Ery-throlplakia, Papiloma virus, etc., can be transformed into a cancer if they are note treated in time. In this paper, we propose a non-intrusive technique to detect precancerous lesions at an earlier stage. Hence, these lesions can be treated as soon as possible. The idea is based on the analysis of the human voice in order to detect pertinent acoustic features able to discriminate pathological voices with precancerous lesions from normal ones. We have tested a large number of speech acoustic features. A feature engineering methodology leads us to choose the most pertinent features. To detect mentioned lesions, several classification techniques are tested. Experimental results show the validity of the idea.
{"title":"Conventional Machine Learning Techniques with Features Engineering for Preventive Larynx Cancer Detection","authors":"A. B. Aicha","doi":"10.1109/ATSIP49331.2020.9231797","DOIUrl":"https://doi.org/10.1109/ATSIP49331.2020.9231797","url":null,"abstract":"Larynx cancer is developed from precancerous state. Some precancerous lesions such as Keratosis, Leukoplakia, Ery-throlplakia, Papiloma virus, etc., can be transformed into a cancer if they are note treated in time. In this paper, we propose a non-intrusive technique to detect precancerous lesions at an earlier stage. Hence, these lesions can be treated as soon as possible. The idea is based on the analysis of the human voice in order to detect pertinent acoustic features able to discriminate pathological voices with precancerous lesions from normal ones. We have tested a large number of speech acoustic features. A feature engineering methodology leads us to choose the most pertinent features. To detect mentioned lesions, several classification techniques are tested. Experimental results show the validity of the idea.","PeriodicalId":384018,"journal":{"name":"2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125980697","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-09-01DOI: 10.1109/ATSIP49331.2020.9231791
Noussaiba Jaafar, Z. Lachiri
Predicting the intensity level of aggression is a challenging problem in surveillance applications. Since there are no trivial fusion rules or classifiers, we developed a fusion framework to accomplish this complex task using Deep Neural Networks. This framework used a low level that contains the audio-visual features, an intermediate level composed of a set of concepts (meta-features) and a high level which is a final evaluation of the multimodal aggression detection. In this paper, we study the prediction of multimodal level for aggression detection and both Context and Semantics meta-features. This prediction is based on the audio modality using sensor and semantic information. Using meta-features for the semantic part of speech, we show the added value of such extra-information on the fusion process when the situations are more complicated. We also propose to use different text-based features such as linguistic and word affect features that will provide significant results for predicting the two meta-features and the multimodal aggression level using Deep Neural Networks when they are fused with the acoustic features although the nature of spontaneous speech.
{"title":"Combining Speech Features for Aggression Detection Using Deep Neural Networks","authors":"Noussaiba Jaafar, Z. Lachiri","doi":"10.1109/ATSIP49331.2020.9231791","DOIUrl":"https://doi.org/10.1109/ATSIP49331.2020.9231791","url":null,"abstract":"Predicting the intensity level of aggression is a challenging problem in surveillance applications. Since there are no trivial fusion rules or classifiers, we developed a fusion framework to accomplish this complex task using Deep Neural Networks. This framework used a low level that contains the audio-visual features, an intermediate level composed of a set of concepts (meta-features) and a high level which is a final evaluation of the multimodal aggression detection. In this paper, we study the prediction of multimodal level for aggression detection and both Context and Semantics meta-features. This prediction is based on the audio modality using sensor and semantic information. Using meta-features for the semantic part of speech, we show the added value of such extra-information on the fusion process when the situations are more complicated. We also propose to use different text-based features such as linguistic and word affect features that will provide significant results for predicting the two meta-features and the multimodal aggression level using Deep Neural Networks when they are fused with the acoustic features although the nature of spontaneous speech.","PeriodicalId":384018,"journal":{"name":"2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129175982","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-09-01DOI: 10.1109/ATSIP49331.2020.9231731
Asmae Touil, Karim Kalti, Pierre-Henri Conze, B. Solaiman, M. Mahjoub
In this paper, we propose a new morphological-based method for automatic detection of microcalcifications in digitized mammograms. It uses various structuring elements to deal with the diversity of microcalcification characteristics. The obtained morphological maps are converted to a continuous suspicion map (SM) based on the structural similarity index (SSIM). This new semantic representation map is then locally analyzed, using superpixels, to automatically estimate adaptive threshold values and finally identify potential microcalcification areas. The proposed method was evaluated using the publicly-available INBreast database. Experimental results show the benefits gained in terms of improving microcalcification detection performances compared to some state-of-the-art methods.
{"title":"Morphological-based microcalcification detection using adaptive thresholding and structural similarity indices","authors":"Asmae Touil, Karim Kalti, Pierre-Henri Conze, B. Solaiman, M. Mahjoub","doi":"10.1109/ATSIP49331.2020.9231731","DOIUrl":"https://doi.org/10.1109/ATSIP49331.2020.9231731","url":null,"abstract":"In this paper, we propose a new morphological-based method for automatic detection of microcalcifications in digitized mammograms. It uses various structuring elements to deal with the diversity of microcalcification characteristics. The obtained morphological maps are converted to a continuous suspicion map (SM) based on the structural similarity index (SSIM). This new semantic representation map is then locally analyzed, using superpixels, to automatically estimate adaptive threshold values and finally identify potential microcalcification areas. The proposed method was evaluated using the publicly-available INBreast database. Experimental results show the benefits gained in terms of improving microcalcification detection performances compared to some state-of-the-art methods.","PeriodicalId":384018,"journal":{"name":"2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133050061","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-09-01DOI: 10.1109/ATSIP49331.2020.9231870
Salim Klibi, Kais Tounsi, Zouhaier Ben Rabah, B. Solaiman, I. Farah
A major environmental threat is soil salinity caused by natural and human-induced processes. Therefore, soil salinity status monitoring is required to ensure sustainable land use and management. Hyperspectral satellite images can make a significant contribution to the detection of soil salinity. The increase in production in semi-arid and arid regions such as Zaghouan in the northeast of Tunisia requires good soil management because this resource is a determining factor for agricultural production. This paper aims to predict soil salinity in this area using spectral signature and features vector of the Hyperion hyperspectral image. The AutoEncoder (AE) is one of neural network architectures that were adopted for feature representation. Support Vector Machines (SVM), K-Nearest-Neighbors (KNN) and Decision Tree (DT) were used for the classification. Results showed that the AE-SVM combination outperforms among the three other approaches used for soil salinity prediction.
{"title":"Soil salinity prediction using a machine learning approach through hyperspectral satellite image","authors":"Salim Klibi, Kais Tounsi, Zouhaier Ben Rabah, B. Solaiman, I. Farah","doi":"10.1109/ATSIP49331.2020.9231870","DOIUrl":"https://doi.org/10.1109/ATSIP49331.2020.9231870","url":null,"abstract":"A major environmental threat is soil salinity caused by natural and human-induced processes. Therefore, soil salinity status monitoring is required to ensure sustainable land use and management. Hyperspectral satellite images can make a significant contribution to the detection of soil salinity. The increase in production in semi-arid and arid regions such as Zaghouan in the northeast of Tunisia requires good soil management because this resource is a determining factor for agricultural production. This paper aims to predict soil salinity in this area using spectral signature and features vector of the Hyperion hyperspectral image. The AutoEncoder (AE) is one of neural network architectures that were adopted for feature representation. Support Vector Machines (SVM), K-Nearest-Neighbors (KNN) and Decision Tree (DT) were used for the classification. Results showed that the AE-SVM combination outperforms among the three other approaches used for soil salinity prediction.","PeriodicalId":384018,"journal":{"name":"2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116766651","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-09-01DOI: 10.1109/ATSIP49331.2020.9231712
Bilel Tarchoun, Imen Jegham, Anouar Ben Khalifa, Ihsen Alouani, M. Mahjoub
Autonomous driving systems and driver assistance systems are becoming the center of attention in transport technology. Given its safety criticality, pedestrian detection is a highly important task. Transport oriented intelligent systems use embedded sensors for the detection task. However, vehicle side detection is starting to show its limitations especially when dealing with certain challenges such as occlusions. In this paper, we propose an infrastructure side perception system that has a bird’s eye view. We introduce a new deep pedestrian detector that can use the detection results to warn nearby vehicles of the presence of pedestrians on the road. The results show that our proposed system is able to detect pedestrians in most conditions with 70.41% precision and 69.17% recall.
{"title":"Deep CNN-based Pedestrian Detection for Intelligent Infrastructure","authors":"Bilel Tarchoun, Imen Jegham, Anouar Ben Khalifa, Ihsen Alouani, M. Mahjoub","doi":"10.1109/ATSIP49331.2020.9231712","DOIUrl":"https://doi.org/10.1109/ATSIP49331.2020.9231712","url":null,"abstract":"Autonomous driving systems and driver assistance systems are becoming the center of attention in transport technology. Given its safety criticality, pedestrian detection is a highly important task. Transport oriented intelligent systems use embedded sensors for the detection task. However, vehicle side detection is starting to show its limitations especially when dealing with certain challenges such as occlusions. In this paper, we propose an infrastructure side perception system that has a bird’s eye view. We introduce a new deep pedestrian detector that can use the detection results to warn nearby vehicles of the presence of pedestrians on the road. The results show that our proposed system is able to detect pedestrians in most conditions with 70.41% precision and 69.17% recall.","PeriodicalId":384018,"journal":{"name":"2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115699804","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-09-01DOI: 10.1109/ATSIP49331.2020.9231706
A. Oueslati, Nadia Feddaoui, S. Belghith, K. Hamrouni
this paper presents one of our contributions in our palm vein recognition system, this contribution consists on region of interest extraction (ROI)in images obtained by near infrared. The proposed ROI extraction is find using a new process to find the candidate key points then a square based on perpendicular lines algorithme is used to detect the region of interest, our method is performed at 99% of correct segmentation rate.
{"title":"An Efficient palm vein Region of Interest extraction method","authors":"A. Oueslati, Nadia Feddaoui, S. Belghith, K. Hamrouni","doi":"10.1109/ATSIP49331.2020.9231706","DOIUrl":"https://doi.org/10.1109/ATSIP49331.2020.9231706","url":null,"abstract":"this paper presents one of our contributions in our palm vein recognition system, this contribution consists on region of interest extraction (ROI)in images obtained by near infrared. The proposed ROI extraction is find using a new process to find the candidate key points then a square based on perpendicular lines algorithme is used to detect the region of interest, our method is performed at 99% of correct segmentation rate.","PeriodicalId":384018,"journal":{"name":"2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115729457","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-09-01DOI: 10.1109/ATSIP49331.2020.9231862
I. Montassar, A. Benazza-Benyahia
This paper tackles the problem of estimating water turbidity by analyzing images. This computer-vision solution avoids to resort to use specific laboratory instruments and, hence facilitates the water characterization in situ. Our contribution consists in designing a whole image processing chain composed of pre-processing, segmentation, feature extraction and classification modules. The second originality of our work relies on comparing two dual approaches for the segmentation and feature extraction: handcrafted and deep neural network based approaches. Finally, the lack of a publicly available dataset has motivated the building of an appropriate dataset. Experimental results indicate satisfactory performances of the proposed approaches.
{"title":"Water turbidity estimation in water sampled images","authors":"I. Montassar, A. Benazza-Benyahia","doi":"10.1109/ATSIP49331.2020.9231862","DOIUrl":"https://doi.org/10.1109/ATSIP49331.2020.9231862","url":null,"abstract":"This paper tackles the problem of estimating water turbidity by analyzing images. This computer-vision solution avoids to resort to use specific laboratory instruments and, hence facilitates the water characterization in situ. Our contribution consists in designing a whole image processing chain composed of pre-processing, segmentation, feature extraction and classification modules. The second originality of our work relies on comparing two dual approaches for the segmentation and feature extraction: handcrafted and deep neural network based approaches. Finally, the lack of a publicly available dataset has motivated the building of an appropriate dataset. Experimental results indicate satisfactory performances of the proposed approaches.","PeriodicalId":384018,"journal":{"name":"2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127166932","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-09-01DOI: 10.1109/ATSIP49331.2020.9231782
Abderrazek Zeraii, I. Alaya, M. Mars, C. Drissi, T. Kraiem
Diffusion weighted imaging (DWI-MRI) is debatably the method of choice for characterizing brain microstructure non-invasively and in vivo in the chronic phase post-stroke. In fact, the degree of motor impairment after stroke is closely linked to the structural integrity of the corticospinal tract (CST). The aim of our study was to extract tract biophysical characteristics such as fractional anisotropy (FA) and Apparent Diffusion Coefficient (ADC) from the CST and to identify the optimal parameters of measuring CST integrity. We conduct experiments with ten healthy human subjects. For each subject, biophysical values were calculated for two Regions of Interest (the posterior limb of the internal capsule and the anterior pons) at two b-value (b=1000s/mm2 and b=3000 s/mm2). In this work, our results showed that the pons region more accurately predict CST integrity than the posterior limb of internal capsule using a b-value equal to 1000 s/mm2. FA and ADC are a promising metric for clinical applications.
{"title":"Optimal Parameters of Diffusion MRI measuring Corticospinal Tract Integrity in healthy subjects","authors":"Abderrazek Zeraii, I. Alaya, M. Mars, C. Drissi, T. Kraiem","doi":"10.1109/ATSIP49331.2020.9231782","DOIUrl":"https://doi.org/10.1109/ATSIP49331.2020.9231782","url":null,"abstract":"Diffusion weighted imaging (DWI-MRI) is debatably the method of choice for characterizing brain microstructure non-invasively and in vivo in the chronic phase post-stroke. In fact, the degree of motor impairment after stroke is closely linked to the structural integrity of the corticospinal tract (CST). The aim of our study was to extract tract biophysical characteristics such as fractional anisotropy (FA) and Apparent Diffusion Coefficient (ADC) from the CST and to identify the optimal parameters of measuring CST integrity. We conduct experiments with ten healthy human subjects. For each subject, biophysical values were calculated for two Regions of Interest (the posterior limb of the internal capsule and the anterior pons) at two b-value (b=1000s/mm2 and b=3000 s/mm2). In this work, our results showed that the pons region more accurately predict CST integrity than the posterior limb of internal capsule using a b-value equal to 1000 s/mm2. FA and ADC are a promising metric for clinical applications.","PeriodicalId":384018,"journal":{"name":"2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126604905","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-09-01DOI: 10.1109/ATSIP49331.2020.9231545
R. Baklouti, A. Hamida, M. Mansouri, H. Nounou, M. Nounou
Fault Detection (FD) is a fundamental step in process monitoring. Owning to its simplicity and effectiveness to deal with nonlinear and highly correlated process variables, kernel principal component analysis (KPCA) has been successfully used in process monitoring. However, the major drawback of this method-based kernel generalized likelihood ratio test (KGLRT) is the neglect of small faults. Inspired by the effectiveness of this detection metric and motivated by the advantages of the univariate exponentially weighted movng average (EWMA), we propose, in this paper, a KPCA-based EWMA-KGLRT FD algorithm. Hence, its performance is illustrated and compared to the conventional KPCA-based KGLRT method through continuously simulated tank reactor (CSTR). In fact, the experimental results confirmed the performance of the proposed algorithm in terms of missed detection (MD) and false alarm (FA) rates.
{"title":"EWMA Kernel Generalized Likelihood Ratio Test for Fault Detection of Chemical Processes","authors":"R. Baklouti, A. Hamida, M. Mansouri, H. Nounou, M. Nounou","doi":"10.1109/ATSIP49331.2020.9231545","DOIUrl":"https://doi.org/10.1109/ATSIP49331.2020.9231545","url":null,"abstract":"Fault Detection (FD) is a fundamental step in process monitoring. Owning to its simplicity and effectiveness to deal with nonlinear and highly correlated process variables, kernel principal component analysis (KPCA) has been successfully used in process monitoring. However, the major drawback of this method-based kernel generalized likelihood ratio test (KGLRT) is the neglect of small faults. Inspired by the effectiveness of this detection metric and motivated by the advantages of the univariate exponentially weighted movng average (EWMA), we propose, in this paper, a KPCA-based EWMA-KGLRT FD algorithm. Hence, its performance is illustrated and compared to the conventional KPCA-based KGLRT method through continuously simulated tank reactor (CSTR). In fact, the experimental results confirmed the performance of the proposed algorithm in terms of missed detection (MD) and false alarm (FA) rates.","PeriodicalId":384018,"journal":{"name":"2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127006409","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-09-01DOI: 10.1109/ATSIP49331.2020.9231701
Ibtissem Hadj Ali, M. Mahjoub
An essential issue for the recognition of handwritten mathematical formulas is the identification of the structural relationships between each pairs of adjacent symbols that compose the entire mathematical formula. The classification of the structural relationship is a key problem as this classification often determines the semantic interpretation of an expression. In this work, we propose a system for the identification of spatial relationships based on geometric features and a new descriptor named spatial histogram. After the combination of extracted features, we classify the relationship into six different classes using four different classifiers in order to determine the most efficient. In our proposed system, a support vector machine (SVM) classifier, Random Forest, Adaboost and KNN are employed. Experimental results show that our features give promising results.
{"title":"Structure relationship classification for the recognition of mathematical expression handwritten in Arabic","authors":"Ibtissem Hadj Ali, M. Mahjoub","doi":"10.1109/ATSIP49331.2020.9231701","DOIUrl":"https://doi.org/10.1109/ATSIP49331.2020.9231701","url":null,"abstract":"An essential issue for the recognition of handwritten mathematical formulas is the identification of the structural relationships between each pairs of adjacent symbols that compose the entire mathematical formula. The classification of the structural relationship is a key problem as this classification often determines the semantic interpretation of an expression. In this work, we propose a system for the identification of spatial relationships based on geometric features and a new descriptor named spatial histogram. After the combination of extracted features, we classify the relationship into six different classes using four different classifiers in order to determine the most efficient. In our proposed system, a support vector machine (SVM) classifier, Random Forest, Adaboost and KNN are employed. Experimental results show that our features give promising results.","PeriodicalId":384018,"journal":{"name":"2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)","volume":"186 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125838268","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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