Pub Date : 2020-12-01DOI: 10.1109/ICRAIE51050.2020.9358326
Nor Haniza binti Mohd Khir, Marina Ismail, J. Ahmad
Segmentation plays an important aspect in persona modelling. Numerous researches directed to discover better choices to improve the accuracy of the segmentation process so that the narrative writing will produce the best persona model. Modelling persona that suit the different children behaviors, goals and attitudes towards the use of gamification in Children Computer Interaction (CCI) might be difficult. Interviews and observations conducted during the data collection phase before show that children have different expectations when playing games and their emotions and attitudes change when asked the same questions repeatedly and sometimes, they are not sure of the answer. Thus, this paper aims to discuss the relationship between cognitive and physical development with social/emotional development using correlation and regression analysis.
{"title":"Persona Modelling via Correlation and Regression Analysis in CCI Gamification","authors":"Nor Haniza binti Mohd Khir, Marina Ismail, J. Ahmad","doi":"10.1109/ICRAIE51050.2020.9358326","DOIUrl":"https://doi.org/10.1109/ICRAIE51050.2020.9358326","url":null,"abstract":"Segmentation plays an important aspect in persona modelling. Numerous researches directed to discover better choices to improve the accuracy of the segmentation process so that the narrative writing will produce the best persona model. Modelling persona that suit the different children behaviors, goals and attitudes towards the use of gamification in Children Computer Interaction (CCI) might be difficult. Interviews and observations conducted during the data collection phase before show that children have different expectations when playing games and their emotions and attitudes change when asked the same questions repeatedly and sometimes, they are not sure of the answer. Thus, this paper aims to discuss the relationship between cognitive and physical development with social/emotional development using correlation and regression analysis.","PeriodicalId":149717,"journal":{"name":"2020 5th IEEE International Conference on Recent Advances and Innovations in Engineering (ICRAIE)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133349944","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 : 2020-12-01DOI: 10.1109/ICRAIE51050.2020.9358374
Deepesh Sahoo, Abhishek Deshpande, M. Sunitha
This paper presents the comparison of Multiple 16 Bit Adders using Gate Diffusion Input (GDI) and CMOS logic. Performance enhancements using Swing Restoration has been studied. The paper focuses on three commonly used adders: Ripple Carry Adder, Carry Select Adder and the Carry Lookahead Adder. The simulation results reveal smaller area and better consumption for the GDI logic when compared with their CMOS designs at 180nm GPDK Technologies. The results have been evaluated using spectre in Cadence Virtuoso IC614. For Ripple carry adder and carry Select adder the GDI logic out performs the CMOS logic in terms of area and power. However, CMOS shows better performance in terms of delay and Rise/Fall time. For Carry lookahead adder, GDI performs better in terms of area, while CMOS performs better in terms of power, delay and Rise/Fall Time.)
{"title":"Study of Different Adders Using Full Swing Gate Diffusion Input","authors":"Deepesh Sahoo, Abhishek Deshpande, M. Sunitha","doi":"10.1109/ICRAIE51050.2020.9358374","DOIUrl":"https://doi.org/10.1109/ICRAIE51050.2020.9358374","url":null,"abstract":"This paper presents the comparison of Multiple 16 Bit Adders using Gate Diffusion Input (GDI) and CMOS logic. Performance enhancements using Swing Restoration has been studied. The paper focuses on three commonly used adders: Ripple Carry Adder, Carry Select Adder and the Carry Lookahead Adder. The simulation results reveal smaller area and better consumption for the GDI logic when compared with their CMOS designs at 180nm GPDK Technologies. The results have been evaluated using spectre in Cadence Virtuoso IC614. For Ripple carry adder and carry Select adder the GDI logic out performs the CMOS logic in terms of area and power. However, CMOS shows better performance in terms of delay and Rise/Fall time. For Carry lookahead adder, GDI performs better in terms of area, while CMOS performs better in terms of power, delay and Rise/Fall Time.)","PeriodicalId":149717,"journal":{"name":"2020 5th IEEE International Conference on Recent Advances and Innovations in Engineering (ICRAIE)","volume":"111 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132030130","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 : 2020-12-01DOI: 10.1109/ICRAIE51050.2020.9358335
Vaishali Shirsath, R. Agrawal
In this article analysis of wind power based on mechanical design parameters, reliability parameter and consideration electrical power in addition to Wind Canyon Model are presented. It is observed that, there is strong need to optimize design parameters in a wind farm to achieve quality power as output which can sustain and maintain its availability with maximum efficiency. This article provides critical review of previous researcher. Paper also presents a detailed direction for wind analysis considering most and important constraint. Mathematical modeling is an important task for the modeling. This article discusses Mathematical modeling for wind data. Overview of research on aerodynamic structure of Wind turbine reliability analysis during the 1999 to 2020 are presented. Since the last years many researchers achieved data based on reliability of wind turbines and published findings in different journals and articles. The issues in wind research is also addressed in this article. The review shows mechanical design analysis, reliability analysis and power optimization techniques. The critical findings provided here is helpful for other researcher. A combined objective function or cost function needs to develop subjected to constraints to achieve maximum profit through this business. A research work is required to improve the design variable in wind generator in addition to Wind power aspects such as power efficiency in addition to reliability.
{"title":"A Review of Wind Station Data Modeling For Wind Turbine Reliability Enhancement To Optimize Wind Energy Considering Turbine Design","authors":"Vaishali Shirsath, R. Agrawal","doi":"10.1109/ICRAIE51050.2020.9358335","DOIUrl":"https://doi.org/10.1109/ICRAIE51050.2020.9358335","url":null,"abstract":"In this article analysis of wind power based on mechanical design parameters, reliability parameter and consideration electrical power in addition to Wind Canyon Model are presented. It is observed that, there is strong need to optimize design parameters in a wind farm to achieve quality power as output which can sustain and maintain its availability with maximum efficiency. This article provides critical review of previous researcher. Paper also presents a detailed direction for wind analysis considering most and important constraint. Mathematical modeling is an important task for the modeling. This article discusses Mathematical modeling for wind data. Overview of research on aerodynamic structure of Wind turbine reliability analysis during the 1999 to 2020 are presented. Since the last years many researchers achieved data based on reliability of wind turbines and published findings in different journals and articles. The issues in wind research is also addressed in this article. The review shows mechanical design analysis, reliability analysis and power optimization techniques. The critical findings provided here is helpful for other researcher. A combined objective function or cost function needs to develop subjected to constraints to achieve maximum profit through this business. A research work is required to improve the design variable in wind generator in addition to Wind power aspects such as power efficiency in addition to reliability.","PeriodicalId":149717,"journal":{"name":"2020 5th IEEE International Conference on Recent Advances and Innovations in Engineering (ICRAIE)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129622580","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 : 2020-12-01DOI: 10.1109/ICRAIE51050.2020.9358286
Nurul Husna Mahadzir, Norhalizawaty Abdul Razak, Mohd Faizal Mohd Omar
Sentiment analysis is one of the most active research areas in Natural Language Processing since early 2000. To date, sentiment analysis has been applied to various domains such as product, movie, sport and political reviews. Previously, the research in sentiment analysis area only concentrated on mining a single language. Nevertheless, due to the growth of multiple language usage in the form of writing and speaking, sentiment analysis activities have become more challenging. Many opinion keywords carry different polarities when they are used in different context, posing huge challenges to this field of research. Furthermore, in a social media environment where users tend to mix up languages, a lot of ambiguous content is present which makes a post difficult to be classified. Thus, this paper is a foray into the sentiment analysis for the context of mixed language.
{"title":"A New Sentiment Analysis Model for Mixed Language using Contextual Lexicon","authors":"Nurul Husna Mahadzir, Norhalizawaty Abdul Razak, Mohd Faizal Mohd Omar","doi":"10.1109/ICRAIE51050.2020.9358286","DOIUrl":"https://doi.org/10.1109/ICRAIE51050.2020.9358286","url":null,"abstract":"Sentiment analysis is one of the most active research areas in Natural Language Processing since early 2000. To date, sentiment analysis has been applied to various domains such as product, movie, sport and political reviews. Previously, the research in sentiment analysis area only concentrated on mining a single language. Nevertheless, due to the growth of multiple language usage in the form of writing and speaking, sentiment analysis activities have become more challenging. Many opinion keywords carry different polarities when they are used in different context, posing huge challenges to this field of research. Furthermore, in a social media environment where users tend to mix up languages, a lot of ambiguous content is present which makes a post difficult to be classified. Thus, this paper is a foray into the sentiment analysis for the context of mixed language.","PeriodicalId":149717,"journal":{"name":"2020 5th IEEE International Conference on Recent Advances and Innovations in Engineering (ICRAIE)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133042994","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 : 2020-12-01DOI: 10.1109/ICRAIE51050.2020.9358375
Preeti Sahu, S. Agrahari
In the design of ICs, power dissipation is an important parameter that indicates the need of Low Power circuits in modern VLSI design. In IC chip design various techniques invented for low power design. In several techniques Clock gating is one of widely used technique, which provides very effective solutions for reduction of dynamic power dissipation. Many researchers are modified clock gating techniques in many different ways. This paper included comparative analysis of power in Clock Divider circuit using different clock gating techniques.
{"title":"Comparative Analysis of Different Clock Gating Techniques","authors":"Preeti Sahu, S. Agrahari","doi":"10.1109/ICRAIE51050.2020.9358375","DOIUrl":"https://doi.org/10.1109/ICRAIE51050.2020.9358375","url":null,"abstract":"In the design of ICs, power dissipation is an important parameter that indicates the need of Low Power circuits in modern VLSI design. In IC chip design various techniques invented for low power design. In several techniques Clock gating is one of widely used technique, which provides very effective solutions for reduction of dynamic power dissipation. Many researchers are modified clock gating techniques in many different ways. This paper included comparative analysis of power in Clock Divider circuit using different clock gating techniques.","PeriodicalId":149717,"journal":{"name":"2020 5th IEEE International Conference on Recent Advances and Innovations in Engineering (ICRAIE)","volume":"574 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116204526","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 : 2020-12-01DOI: 10.1109/ICRAIE51050.2020.9358309
Amrita Sharma, N. Chaudhary
Software cost estimation is always an essential task for the development management as it requires for estimating the effort and the time required for developing the software. A project manager requires software estimation for making a decision and predict the total budget. Success or failure of software development depends on the accurate estimation of cost and time. There are numerous tools and techniques have been developed for estimating the software cost. But all these techniques are best suitable for the traditional development methodology. From the past two decades, the agile methodology has been com for software development. So the traditional cost estimation techniques may not give the appropriate results for agile development. In this paper, the multiple linear regression models are proposed for comparing the best model for agile development. The correlation between the dependent and independent variables are also found out. The results showed that the proposed model outperforms from the decision tree, stochastic gradient boosting, and random forest.
{"title":"Linear Regression Model for Agile Software Development Effort Estimation","authors":"Amrita Sharma, N. Chaudhary","doi":"10.1109/ICRAIE51050.2020.9358309","DOIUrl":"https://doi.org/10.1109/ICRAIE51050.2020.9358309","url":null,"abstract":"Software cost estimation is always an essential task for the development management as it requires for estimating the effort and the time required for developing the software. A project manager requires software estimation for making a decision and predict the total budget. Success or failure of software development depends on the accurate estimation of cost and time. There are numerous tools and techniques have been developed for estimating the software cost. But all these techniques are best suitable for the traditional development methodology. From the past two decades, the agile methodology has been com for software development. So the traditional cost estimation techniques may not give the appropriate results for agile development. In this paper, the multiple linear regression models are proposed for comparing the best model for agile development. The correlation between the dependent and independent variables are also found out. The results showed that the proposed model outperforms from the decision tree, stochastic gradient boosting, and random forest.","PeriodicalId":149717,"journal":{"name":"2020 5th IEEE International Conference on Recent Advances and Innovations in Engineering (ICRAIE)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128252548","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 : 2020-12-01DOI: 10.1109/ICRAIE51050.2020.9358360
Sathwic Somarouthu, S. Manam, Arpitha Thakre
Orthogonal frequency division multiplexing is a multicarrier digital modulation technique that is extensively used in modern wireless communication systems. This technique is very sensitive to synchronization errors. Symbol timing offset is one of such synchronization errors. We here attempt to perform detection of symbols in presence of symbol timing offset using machine learning method. Symbol detection can be modeled as a classification problem. We use support vector machine method to classify the received symbols in one of many possible classes. We propose a special pilot data pattern that can be used to train multiple classifiers for different subcarriers and at different signal to noise ratios. We show that we incur lesser pilot overhead when we use this new machine learning based approach. A comparison between the traditional approach and our proposed technique has also been analyzed and presented.
{"title":"Symbol Detection in presence of Symbol Timing Offset using Machine Learning Technique","authors":"Sathwic Somarouthu, S. Manam, Arpitha Thakre","doi":"10.1109/ICRAIE51050.2020.9358360","DOIUrl":"https://doi.org/10.1109/ICRAIE51050.2020.9358360","url":null,"abstract":"Orthogonal frequency division multiplexing is a multicarrier digital modulation technique that is extensively used in modern wireless communication systems. This technique is very sensitive to synchronization errors. Symbol timing offset is one of such synchronization errors. We here attempt to perform detection of symbols in presence of symbol timing offset using machine learning method. Symbol detection can be modeled as a classification problem. We use support vector machine method to classify the received symbols in one of many possible classes. We propose a special pilot data pattern that can be used to train multiple classifiers for different subcarriers and at different signal to noise ratios. We show that we incur lesser pilot overhead when we use this new machine learning based approach. A comparison between the traditional approach and our proposed technique has also been analyzed and presented.","PeriodicalId":149717,"journal":{"name":"2020 5th IEEE International Conference on Recent Advances and Innovations in Engineering (ICRAIE)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128584439","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 : 2020-12-01DOI: 10.1109/ICRAIE51050.2020.9358278
Saffa Raihan Zainal Abidin, Siti Fadzilah Mat Noor, N. Sahari, Noor Hasnita Abdul Talib
Serious game is an alternative teaching aid that is beneficial to students, especially slow learner students or Halus students. This model is designed to meet the needs of Halus students in literacy. Game development using child centered design involves four phases namely analysis, design, development and evaluation. This game called “Mari Membaca” is a 2D educational environment game prototype that uses Unity software technology as a game application builder engine. This game is loaded with various interactive multimedia elements to enhance the impact of information delivery to Halus students. Brain based elements are also incorporated into the game to help the Halus students optimize their brain in learning. The result of this study is a serious game that uses brain-based strategies to help Halus students learn literacy effectively.
{"title":"Serious Game Development - A Miraculous Literacy Tool for Halus Students*","authors":"Saffa Raihan Zainal Abidin, Siti Fadzilah Mat Noor, N. Sahari, Noor Hasnita Abdul Talib","doi":"10.1109/ICRAIE51050.2020.9358278","DOIUrl":"https://doi.org/10.1109/ICRAIE51050.2020.9358278","url":null,"abstract":"Serious game is an alternative teaching aid that is beneficial to students, especially slow learner students or Halus students. This model is designed to meet the needs of Halus students in literacy. Game development using child centered design involves four phases namely analysis, design, development and evaluation. This game called “Mari Membaca” is a 2D educational environment game prototype that uses Unity software technology as a game application builder engine. This game is loaded with various interactive multimedia elements to enhance the impact of information delivery to Halus students. Brain based elements are also incorporated into the game to help the Halus students optimize their brain in learning. The result of this study is a serious game that uses brain-based strategies to help Halus students learn literacy effectively.","PeriodicalId":149717,"journal":{"name":"2020 5th IEEE International Conference on Recent Advances and Innovations in Engineering (ICRAIE)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128645649","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 : 2020-12-01DOI: 10.1109/ICRAIE51050.2020.9358364
P. Raj, M. Kumar, Priyanka Dwivedi
This paper presents a deep learning-based traffic advisory system relying on the count of vehicles on road at a certain time as the parameter advising people to take alternative routes as per requirement. There are some techniques that have led to a better inference time for a deep learning model but they are computationally expensive. Although we can afford to carry out the expensive computation on the cloud, this could hamper the performance of the real time traffic advisory system. In the proposed method implementation of two different deep learning frameworks - You only look once (YOLOv3) and Tiny YOLOv3 - to clock a quicker inference time while maintaining a significant level of accuracy and scalability of the system. Towards the end, we have presented our detection results on Indian driving dataset for vehicle detection. A comparative analysis of 4 deep learning techniques namely YoloV5, Ssd, Faster RCNN and EfficientDet has been performed in terms of performance.
{"title":"An Embedded Deep Learning Based Traffic Advisory System","authors":"P. Raj, M. Kumar, Priyanka Dwivedi","doi":"10.1109/ICRAIE51050.2020.9358364","DOIUrl":"https://doi.org/10.1109/ICRAIE51050.2020.9358364","url":null,"abstract":"This paper presents a deep learning-based traffic advisory system relying on the count of vehicles on road at a certain time as the parameter advising people to take alternative routes as per requirement. There are some techniques that have led to a better inference time for a deep learning model but they are computationally expensive. Although we can afford to carry out the expensive computation on the cloud, this could hamper the performance of the real time traffic advisory system. In the proposed method implementation of two different deep learning frameworks - You only look once (YOLOv3) and Tiny YOLOv3 - to clock a quicker inference time while maintaining a significant level of accuracy and scalability of the system. Towards the end, we have presented our detection results on Indian driving dataset for vehicle detection. A comparative analysis of 4 deep learning techniques namely YoloV5, Ssd, Faster RCNN and EfficientDet has been performed in terms of performance.","PeriodicalId":149717,"journal":{"name":"2020 5th IEEE International Conference on Recent Advances and Innovations in Engineering (ICRAIE)","volume":"121 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130543916","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 : 2020-12-01DOI: 10.1109/ICRAIE51050.2020.9358362
Bhawana Maurya, Saroj Hiranwal, M. Kumar
In this paper, a detailed review has been done on liver cancer detections and this paper provides details of different techniques that reveal how hybrid intelligent approaches are applied to different categories of cancer detections and treatments. The principle goal of this review is to highlight mostly used features, classifiers, methodologies, key concepts, and their accuracy. Under cancer detection techniques, various types of machine learning algorithms are used such as decision tree, SVM, neural networks, random forest, computer aided detection, genetic algorithms etc. These strategies exert significant effects on liver image characterization and having different accuracy levels. All the long short solutions talked about strategies are provided in this manuscript and it is explored up to various execution measurements.
{"title":"A Review on Liver Cancer Detection Techniques","authors":"Bhawana Maurya, Saroj Hiranwal, M. Kumar","doi":"10.1109/ICRAIE51050.2020.9358362","DOIUrl":"https://doi.org/10.1109/ICRAIE51050.2020.9358362","url":null,"abstract":"In this paper, a detailed review has been done on liver cancer detections and this paper provides details of different techniques that reveal how hybrid intelligent approaches are applied to different categories of cancer detections and treatments. The principle goal of this review is to highlight mostly used features, classifiers, methodologies, key concepts, and their accuracy. Under cancer detection techniques, various types of machine learning algorithms are used such as decision tree, SVM, neural networks, random forest, computer aided detection, genetic algorithms etc. These strategies exert significant effects on liver image characterization and having different accuracy levels. All the long short solutions talked about strategies are provided in this manuscript and it is explored up to various execution measurements.","PeriodicalId":149717,"journal":{"name":"2020 5th IEEE International Conference on Recent Advances and Innovations in Engineering (ICRAIE)","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124755072","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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