Pub Date : 2021-10-21DOI: 10.1109/ISMSIT52890.2021.9604748
I. Cil, Fahri Arisoy, Hilal Kilinc, Ekrem Özgürbüz, A. Cil
The shipyard industry, like other industries, is struggling to implement the principles of Industry 4.0 to shipyards in order to keep up with the challenges, and to realize Shipyard 4.0 in their own industry. Establishing a comprehensive positioning system to determine the positions of all living and non-living things in the shipyards and to follow their movements forms the basis of these studies. An advanced indoor positioning and tracking system can increase shipyards' efficiency, productivity and safety. SEDEF Shipyard, one of the largest shipbuilders in Turkey, is in a transformation to implement Shipyard 4.0 and to keep up with the challenges. In this context, this article primarily provides a comprehensive analysis of the shipyard environment. From this analysis, the basic hardware and software technical requirements regarding which indoor positioning system would be more suitable for shipyards are determined. Next, an integrated evaluation model is developed with a fuzzy AHP and fuzzy TOPSIS for the selection of positioning technology that allows the delivery of advanced services at the SEDEF shipyard. With the model, it is aimed to determine the most suitable technology for SEDEF shipyard by evaluating different technology options.
{"title":"Fuzzy AHP-TOPSIS Hybrid Method for Indoor Positioning Technology Selection for Shipyards","authors":"I. Cil, Fahri Arisoy, Hilal Kilinc, Ekrem Özgürbüz, A. Cil","doi":"10.1109/ISMSIT52890.2021.9604748","DOIUrl":"https://doi.org/10.1109/ISMSIT52890.2021.9604748","url":null,"abstract":"The shipyard industry, like other industries, is struggling to implement the principles of Industry 4.0 to shipyards in order to keep up with the challenges, and to realize Shipyard 4.0 in their own industry. Establishing a comprehensive positioning system to determine the positions of all living and non-living things in the shipyards and to follow their movements forms the basis of these studies. An advanced indoor positioning and tracking system can increase shipyards' efficiency, productivity and safety. SEDEF Shipyard, one of the largest shipbuilders in Turkey, is in a transformation to implement Shipyard 4.0 and to keep up with the challenges. In this context, this article primarily provides a comprehensive analysis of the shipyard environment. From this analysis, the basic hardware and software technical requirements regarding which indoor positioning system would be more suitable for shipyards are determined. Next, an integrated evaluation model is developed with a fuzzy AHP and fuzzy TOPSIS for the selection of positioning technology that allows the delivery of advanced services at the SEDEF shipyard. With the model, it is aimed to determine the most suitable technology for SEDEF shipyard by evaluating different technology options.","PeriodicalId":120997,"journal":{"name":"2021 5th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT)","volume":"9 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114030315","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 : 2021-10-21DOI: 10.1109/ISMSIT52890.2021.9604673
Dzenan Selmanovic, A. Sayar, P. O. Durdu
In this research, the usability evaluation of the MOOC platform Udemy was performed with the participation of 10 undergraduate and postgraduate students from American, European and African countries coming from different cultures. Participants were assigned five tasks, which were decided according to website context, and the author observed and reported the difficulties they encountered under qualitative results. Quantitative results are given as success rate and time needed to perform each task. Participants were then asked to fill out a questionnaire on Web Site Analysis and Measurement Inventory (WAMMI). Based on the results, it was decided that usability measurements of the participants were above the average but author was not able to make any inference about cross cultural differences due to limited number or participants.
{"title":"Cross cultural usability testing of MOOC platform","authors":"Dzenan Selmanovic, A. Sayar, P. O. Durdu","doi":"10.1109/ISMSIT52890.2021.9604673","DOIUrl":"https://doi.org/10.1109/ISMSIT52890.2021.9604673","url":null,"abstract":"In this research, the usability evaluation of the MOOC platform Udemy was performed with the participation of 10 undergraduate and postgraduate students from American, European and African countries coming from different cultures. Participants were assigned five tasks, which were decided according to website context, and the author observed and reported the difficulties they encountered under qualitative results. Quantitative results are given as success rate and time needed to perform each task. Participants were then asked to fill out a questionnaire on Web Site Analysis and Measurement Inventory (WAMMI). Based on the results, it was decided that usability measurements of the participants were above the average but author was not able to make any inference about cross cultural differences due to limited number or participants.","PeriodicalId":120997,"journal":{"name":"2021 5th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123882599","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 : 2021-10-21DOI: 10.1109/ISMSIT52890.2021.9604611
Armagan Fidan, Rabia Ozge Bircan, S. Karamzadeh
Developing technology and innovations have led to the development in many areas, and the age estimation with the human voice is a research area that has increased its popularity recently. For security problems or in the advertising sector, age recognition applications with voice have been used. Sound is structurally complex, but it has been seen that it is possible to extract the characteristic features of the sound. The designed system was created without giving any gender information in order to estimate age from human speech. The most popular audio feature extraction methods are Mel-Frequency Cepstrum Coefficient (MFCC) and Perceptual Linear Prediction (PLP) which were used in this study. In addition, Chroma features were also used. This study, it is aimed to get the highest efficiency from the voice features by using different feature extractors and rearranging the dataset according to the feature importance priority. For this purpose, eight age groups were formed from a dataset containing different speakers and so, the MLP (Multi-Layer Perceptron) classification method was used. Mozilla Open-Source Dataset was used in our system, and the highest accuracy rate of age classification was observed as 94.34% being the highest score in the literature.
{"title":"A New Approach For Age Estimation System Based on Speech Signals","authors":"Armagan Fidan, Rabia Ozge Bircan, S. Karamzadeh","doi":"10.1109/ISMSIT52890.2021.9604611","DOIUrl":"https://doi.org/10.1109/ISMSIT52890.2021.9604611","url":null,"abstract":"Developing technology and innovations have led to the development in many areas, and the age estimation with the human voice is a research area that has increased its popularity recently. For security problems or in the advertising sector, age recognition applications with voice have been used. Sound is structurally complex, but it has been seen that it is possible to extract the characteristic features of the sound. The designed system was created without giving any gender information in order to estimate age from human speech. The most popular audio feature extraction methods are Mel-Frequency Cepstrum Coefficient (MFCC) and Perceptual Linear Prediction (PLP) which were used in this study. In addition, Chroma features were also used. This study, it is aimed to get the highest efficiency from the voice features by using different feature extractors and rearranging the dataset according to the feature importance priority. For this purpose, eight age groups were formed from a dataset containing different speakers and so, the MLP (Multi-Layer Perceptron) classification method was used. Mozilla Open-Source Dataset was used in our system, and the highest accuracy rate of age classification was observed as 94.34% being the highest score in the literature.","PeriodicalId":120997,"journal":{"name":"2021 5th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127997528","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 : 2021-10-21DOI: 10.1109/ISMSIT52890.2021.9604593
M. Yildirim, E. Dandıl
Multiple Sclerosis (MS) is a neurological disease with a remarkable incidence in young and middle-aged adults. When diagnosing MS on MR images, physicians often use computer-aided and automated secondary assistive tools in the decision-making process. Since the identification of MS lesions on MR images is a difficult and time-consuming process, performing MS lesions manually by experts can be prone to user error, variable and time consuming. In this study, a Mask R-CNN based deep learning method is proposed for automatic segmentation of MS lesions from MR scans. The MR image series used in the study are obtained from ISBI 2015 and MICCAI 2008 databases, which are publicly-available datasets. In the study, Detectron 2 framework is used as the infrastructure platform for architecture of Mask R-CNN. In experimental studies for automatic segmentation of MS lesions, Dice similarity scores of 86.30% and 81.32% are achieved on ISBI 2015 and MICCAI 2008 datasets, respectively. In conclusion, the Detectron 2-based Mask R-CNN deep learning method proposed in this study for automatic segmentation of MS lesions on MR slices is verified to be successful.
{"title":"Automated Multiple Sclerosis Lesion Segmentation on MR Images via Mask R-CNN","authors":"M. Yildirim, E. Dandıl","doi":"10.1109/ISMSIT52890.2021.9604593","DOIUrl":"https://doi.org/10.1109/ISMSIT52890.2021.9604593","url":null,"abstract":"Multiple Sclerosis (MS) is a neurological disease with a remarkable incidence in young and middle-aged adults. When diagnosing MS on MR images, physicians often use computer-aided and automated secondary assistive tools in the decision-making process. Since the identification of MS lesions on MR images is a difficult and time-consuming process, performing MS lesions manually by experts can be prone to user error, variable and time consuming. In this study, a Mask R-CNN based deep learning method is proposed for automatic segmentation of MS lesions from MR scans. The MR image series used in the study are obtained from ISBI 2015 and MICCAI 2008 databases, which are publicly-available datasets. In the study, Detectron 2 framework is used as the infrastructure platform for architecture of Mask R-CNN. In experimental studies for automatic segmentation of MS lesions, Dice similarity scores of 86.30% and 81.32% are achieved on ISBI 2015 and MICCAI 2008 datasets, respectively. In conclusion, the Detectron 2-based Mask R-CNN deep learning method proposed in this study for automatic segmentation of MS lesions on MR slices is verified to be successful.","PeriodicalId":120997,"journal":{"name":"2021 5th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121082930","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 : 2021-10-21DOI: 10.1109/ISMSIT52890.2021.9604556
M. F. Adak, Musa Balta
The increasing population in the world causes heavy traffic conditions, especially in metropolises. The traffic problem is the main challenge for the city. Considering the reasons for the traffic density in Turkey, the traffic lights schedule is shown in the first among when comparing with other parameters. Determining the green light durations according to the vehicle density will reduce the average waiting times of vehicles at intersections. This study performed different traffic scenarios based on VANET on SUMO for adaptive and non-adaptive intersections. The gathered traffic information data from vehicles are given to the developed fuzzy logic model to optimize green light durations. A Period of a scenario for analyzing took 10 minutes, and according to 10 minutes input, the fuzzy model optimizes the green light durations for the following period. Test results show that using a fuzzy model in traffic light optimization decreases the average waiting time of vehicles and average queue length.
{"title":"Fuzzy Logic-based Adaptive Traffic Light Control of an Intersection: A Case Study","authors":"M. F. Adak, Musa Balta","doi":"10.1109/ISMSIT52890.2021.9604556","DOIUrl":"https://doi.org/10.1109/ISMSIT52890.2021.9604556","url":null,"abstract":"The increasing population in the world causes heavy traffic conditions, especially in metropolises. The traffic problem is the main challenge for the city. Considering the reasons for the traffic density in Turkey, the traffic lights schedule is shown in the first among when comparing with other parameters. Determining the green light durations according to the vehicle density will reduce the average waiting times of vehicles at intersections. This study performed different traffic scenarios based on VANET on SUMO for adaptive and non-adaptive intersections. The gathered traffic information data from vehicles are given to the developed fuzzy logic model to optimize green light durations. A Period of a scenario for analyzing took 10 minutes, and according to 10 minutes input, the fuzzy model optimizes the green light durations for the following period. Test results show that using a fuzzy model in traffic light optimization decreases the average waiting time of vehicles and average queue length.","PeriodicalId":120997,"journal":{"name":"2021 5th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128601815","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 : 2021-10-21DOI: 10.1109/ISMSIT52890.2021.9604659
Ali Can Karaca
Synthetic aperture radar (SAR) is one of the most important active imaging systems used in remote sensing. Thanks to SAR and deep learning methods, ship detection can be performed with high performances in recent years. However, using the images of different satellites with changing ship sizes and detecting the ships under complex backgrounds are two challenging tasks that decrease ship detection performance. Since the dimensions of the satellite images are quite high, it is also important to use a fast and lightweight deep learning model. In this paper, we propose the usage of EfficientDet-D0 model to provide a robust and fast solution to the above problems. Experiments were carried out on the Ship-Detection-Dataset that includes nearly 40,000 image patches from Sentinel-1 and Gaofen-3 satellites. EfficientDet-D0 model was compared with Faster R-CNN, RetinaNet, and SSD-MobileNetv2 in terms of 13 different performance metrics, computation times, and visual comparison. The results demonstrate that EfficienDet-D0 model provides the most robust solution to the complex background and multiscale ship size problems.
{"title":"Robust and Fast Ship Detection In SAR Images With Complex Backgrounds Based on EfficientDet Model","authors":"Ali Can Karaca","doi":"10.1109/ISMSIT52890.2021.9604659","DOIUrl":"https://doi.org/10.1109/ISMSIT52890.2021.9604659","url":null,"abstract":"Synthetic aperture radar (SAR) is one of the most important active imaging systems used in remote sensing. Thanks to SAR and deep learning methods, ship detection can be performed with high performances in recent years. However, using the images of different satellites with changing ship sizes and detecting the ships under complex backgrounds are two challenging tasks that decrease ship detection performance. Since the dimensions of the satellite images are quite high, it is also important to use a fast and lightweight deep learning model. In this paper, we propose the usage of EfficientDet-D0 model to provide a robust and fast solution to the above problems. Experiments were carried out on the Ship-Detection-Dataset that includes nearly 40,000 image patches from Sentinel-1 and Gaofen-3 satellites. EfficientDet-D0 model was compared with Faster R-CNN, RetinaNet, and SSD-MobileNetv2 in terms of 13 different performance metrics, computation times, and visual comparison. The results demonstrate that EfficienDet-D0 model provides the most robust solution to the complex background and multiscale ship size problems.","PeriodicalId":120997,"journal":{"name":"2021 5th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT)","volume":"185 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115422605","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 : 2021-10-21DOI: 10.1109/ISMSIT52890.2021.9604744
Tsvetelina Mladenova
Machine learning is viewed as one of the most progressive and researched areas in recent years. While many businesses are adopting the technology there is quite a big percentage of organizations that are facing challenges when deciding on their machine learning strategy. This article is an overview of the term "business process" and the ways a machine learning algorithm can be implemented in it. Some challenges when designing and using machine learning algorithms for a real-time business environment are reviewed.
{"title":"Machine Learning in Business: A Short Overview","authors":"Tsvetelina Mladenova","doi":"10.1109/ISMSIT52890.2021.9604744","DOIUrl":"https://doi.org/10.1109/ISMSIT52890.2021.9604744","url":null,"abstract":"Machine learning is viewed as one of the most progressive and researched areas in recent years. While many businesses are adopting the technology there is quite a big percentage of organizations that are facing challenges when deciding on their machine learning strategy. This article is an overview of the term \"business process\" and the ways a machine learning algorithm can be implemented in it. Some challenges when designing and using machine learning algorithms for a real-time business environment are reviewed.","PeriodicalId":120997,"journal":{"name":"2021 5th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT)","volume":"52 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114462783","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 : 2021-10-21DOI: 10.1109/ISMSIT52890.2021.9604526
G. Bogdanova, Negoslav Sabev, Zhivko Tomov, M. Ekmekci
Accessibility is a multidisciplinary field and requires innovation. People with disabilities are often excluded from the cultural life, especially blind people. None of the 55 Bulgarian museums included in the study offers them adequate visualization and interaction. The research is conducted with blind and physically challenged people as participants and combines physical and online tests. The results are collected through online questionnaire
{"title":"Physical and Digital Accessibility in Museums in the New Reality","authors":"G. Bogdanova, Negoslav Sabev, Zhivko Tomov, M. Ekmekci","doi":"10.1109/ISMSIT52890.2021.9604526","DOIUrl":"https://doi.org/10.1109/ISMSIT52890.2021.9604526","url":null,"abstract":"Accessibility is a multidisciplinary field and requires innovation. People with disabilities are often excluded from the cultural life, especially blind people. None of the 55 Bulgarian museums included in the study offers them adequate visualization and interaction. The research is conducted with blind and physically challenged people as participants and combines physical and online tests. The results are collected through online questionnaire","PeriodicalId":120997,"journal":{"name":"2021 5th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114208921","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 : 2021-10-21DOI: 10.1109/ISMSIT52890.2021.9604603
Guyang Zhang, W. Abdulla
Honey is a nutritious natural food product with many health benefits and is thus widely utilized as a natural sweetener or consumed as a dietary ingredient. Different botanic origin honey types have various quality, flavor, or health benefits. Therefore their market values differ significantly. Many studies have been devoted to investigating honey quality with various chemically-based techniques. Nevertheless, these methods are expensive, laborious, and time-consuming. In addition, it is impossible to collect honey samples containing all the wide variety of botanical origins or adulteration methods. Thus, a more feasible approach is to develop a databank including authentic honey types of interest, whose data is also easy to process and collect, then designe a model to tell whether an unknown sample belongs to the same class of samples in the databank or not. This paper proposes a new approach using Siamese neural networks designated to learn similarities between hyperspectral imaging of honey samples. Siamese neural networks learning allows models to make correct predictions, given only a single example of a new class. With convolutional neural network architecture, the learned features acquired generalized the discriminating power to predict new unseen images correctly. The average validation accuracy rate we achieved is 95%. We interestingly found that the spectra properties of honey types collected from the same botanic origin produced by different producers vary significantly
{"title":"Hyperspectral Imaging for Honey Quality Detection using Siamese Neural Networks","authors":"Guyang Zhang, W. Abdulla","doi":"10.1109/ISMSIT52890.2021.9604603","DOIUrl":"https://doi.org/10.1109/ISMSIT52890.2021.9604603","url":null,"abstract":"Honey is a nutritious natural food product with many health benefits and is thus widely utilized as a natural sweetener or consumed as a dietary ingredient. Different botanic origin honey types have various quality, flavor, or health benefits. Therefore their market values differ significantly. Many studies have been devoted to investigating honey quality with various chemically-based techniques. Nevertheless, these methods are expensive, laborious, and time-consuming. In addition, it is impossible to collect honey samples containing all the wide variety of botanical origins or adulteration methods. Thus, a more feasible approach is to develop a databank including authentic honey types of interest, whose data is also easy to process and collect, then designe a model to tell whether an unknown sample belongs to the same class of samples in the databank or not. This paper proposes a new approach using Siamese neural networks designated to learn similarities between hyperspectral imaging of honey samples. Siamese neural networks learning allows models to make correct predictions, given only a single example of a new class. With convolutional neural network architecture, the learned features acquired generalized the discriminating power to predict new unseen images correctly. The average validation accuracy rate we achieved is 95%. We interestingly found that the spectra properties of honey types collected from the same botanic origin produced by different producers vary significantly","PeriodicalId":120997,"journal":{"name":"2021 5th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125470444","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 : 2021-10-21DOI: 10.1109/ismsit52890.2021.9604675
{"title":"[Copyright notice]","authors":"","doi":"10.1109/ismsit52890.2021.9604675","DOIUrl":"https://doi.org/10.1109/ismsit52890.2021.9604675","url":null,"abstract":"","PeriodicalId":120997,"journal":{"name":"2021 5th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126946512","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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