Pub Date : 2020-01-01DOI: 10.1109/Confluence47617.2020.9058106
Anmol Uppal, Vipul Sachdeva, Seema Sharma
Online news platforms greatly influence our society and culture in both positive and negative ways. As online media becomes more dependent for source of information, a lot of fake news is posted online, that widespread with people following it without any prior or complete information of event authenticity. Such misinformation has the potential to manipulate public opinions. The exponential growth of fake news propagation have become a great threat to public for news trustworthiness. It has become a compelling issue for which discovering, examining and dealing with fake news has increased in demand. However, with the limited availability of literature on the issue of uncovering fake news, a number of potential methodologies and techniques remains unexplored. The primary aim of this paper is to review existing methodologies, to propose and implement a method for automated deception detection. The proposed methodology uses deep learning in discourse-level structure analysis to formulate the structure that differentiates fake and real news. The baseline model achieved 74% accuracy.
{"title":"Fake news detection using discourse segment structure analysis","authors":"Anmol Uppal, Vipul Sachdeva, Seema Sharma","doi":"10.1109/Confluence47617.2020.9058106","DOIUrl":"https://doi.org/10.1109/Confluence47617.2020.9058106","url":null,"abstract":"Online news platforms greatly influence our society and culture in both positive and negative ways. As online media becomes more dependent for source of information, a lot of fake news is posted online, that widespread with people following it without any prior or complete information of event authenticity. Such misinformation has the potential to manipulate public opinions. The exponential growth of fake news propagation have become a great threat to public for news trustworthiness. It has become a compelling issue for which discovering, examining and dealing with fake news has increased in demand. However, with the limited availability of literature on the issue of uncovering fake news, a number of potential methodologies and techniques remains unexplored. The primary aim of this paper is to review existing methodologies, to propose and implement a method for automated deception detection. The proposed methodology uses deep learning in discourse-level structure analysis to formulate the structure that differentiates fake and real news. The baseline model achieved 74% accuracy.","PeriodicalId":180005,"journal":{"name":"2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125163297","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 : 2020-01-01DOI: 10.1109/Confluence47617.2020.9057867
Bedatri Moulik, Anupama Prakash, A. Ganguly
This contribution investigates two different power management optimization techniques to optimally split the power between the engine and accumulator of a parallel hybrid hydraulic vehicle (HHV). The goal is to operate the engine at its most efficient region, keep the accumulator charge within bounds, and reduce the fuel consumption while maintaining the vehicle performance. After deriving the mathematical model of the HHV, a local optimization technique is used to solve the problem in each time step for an urban European drive cycle. Then for the same cycle, the results are compared with a global optimization technique. The global optimization shows a distinct improvement in terms of fuel consumption.
{"title":"Improvement in fuel economy of hybrid hydraulic powertrain by conducting a comparative study of two different optimization strategies","authors":"Bedatri Moulik, Anupama Prakash, A. Ganguly","doi":"10.1109/Confluence47617.2020.9057867","DOIUrl":"https://doi.org/10.1109/Confluence47617.2020.9057867","url":null,"abstract":"This contribution investigates two different power management optimization techniques to optimally split the power between the engine and accumulator of a parallel hybrid hydraulic vehicle (HHV). The goal is to operate the engine at its most efficient region, keep the accumulator charge within bounds, and reduce the fuel consumption while maintaining the vehicle performance. After deriving the mathematical model of the HHV, a local optimization technique is used to solve the problem in each time step for an urban European drive cycle. Then for the same cycle, the results are compared with a global optimization technique. The global optimization shows a distinct improvement in terms of fuel consumption.","PeriodicalId":180005,"journal":{"name":"2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127021334","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 : 2020-01-01DOI: 10.1109/Confluence47617.2020.9058000
Vanshika Nehra, Renuka Nagpal, Rajni Sehgal
The term “Collective” is just not restricted to the human beings but can also be referred to the organisms such as flock of birds, swarm of bees, colony of bats etc. In computer environments, the term may also refer to groups of virtual artificially intelligent agents. Most generally it can applicable to the workings of the entire planet or universe as smart organization whose intelligence is supplied and manifested through the entities within it. Collective Intelligence is a no new terms infact it’s been used from several decades now but what’s new is the emergence of computer technology which makes it a new and one of the most promising application of it used in a variety of field. Machine learning and Artificial Intelligence are making an enormous buzz around the world. The plenty of utilizations in Artificial Intelligence have changed the substance of innovation. This paper would give an overview of the promising future aspects and researches in the field of Collective Intelligence in brief. We need to concentrate on the elements that guide collective intelligence if we really want to optimize our groups for excellent cooperation. We need to concentrate on personality characteristics that are not so simple to follow, yet they are critical to the long-term achievement of organizations, such as intellect, consciousness, compassion, empathy, and regard. In this paper along with the definition of the Collective Intelligence, it would be measured, compared with individual intelligence and its applications are studied in brief.
{"title":"Collective Intelligence: When, Where and Why","authors":"Vanshika Nehra, Renuka Nagpal, Rajni Sehgal","doi":"10.1109/Confluence47617.2020.9058000","DOIUrl":"https://doi.org/10.1109/Confluence47617.2020.9058000","url":null,"abstract":"The term “Collective” is just not restricted to the human beings but can also be referred to the organisms such as flock of birds, swarm of bees, colony of bats etc. In computer environments, the term may also refer to groups of virtual artificially intelligent agents. Most generally it can applicable to the workings of the entire planet or universe as smart organization whose intelligence is supplied and manifested through the entities within it. Collective Intelligence is a no new terms infact it’s been used from several decades now but what’s new is the emergence of computer technology which makes it a new and one of the most promising application of it used in a variety of field. Machine learning and Artificial Intelligence are making an enormous buzz around the world. The plenty of utilizations in Artificial Intelligence have changed the substance of innovation. This paper would give an overview of the promising future aspects and researches in the field of Collective Intelligence in brief. We need to concentrate on the elements that guide collective intelligence if we really want to optimize our groups for excellent cooperation. We need to concentrate on personality characteristics that are not so simple to follow, yet they are critical to the long-term achievement of organizations, such as intellect, consciousness, compassion, empathy, and regard. In this paper along with the definition of the Collective Intelligence, it would be measured, compared with individual intelligence and its applications are studied in brief.","PeriodicalId":180005,"journal":{"name":"2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133219786","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 : 2020-01-01DOI: 10.1109/Confluence47617.2020.9057939
H. S. Saraswathi, Mohammed Rafi, K. G. Manjunath, A. Shankar
malignant growth is an irregular development of cell tissue. Pancreatic disease is one of the observable reasons for death around the world. Pancreatic malignant growth starts in the tissues of pancreas. The pancreas secretes proteins that helps the processing and hormones that directs the breakdown of sugars. Pancreatic malignancy is usually detected in the later stages, spreads rapidly and has a poor prediction. In this paper we have made an attempt to discuss various artificial intelligence methods to detect pancreatic cancer and proposing new AI method to spot subtle patterns and provide accurate information to pathologist.
{"title":"Review on computer aided diagnosis of pancreatic cancer using Artificial Intelligence System","authors":"H. S. Saraswathi, Mohammed Rafi, K. G. Manjunath, A. Shankar","doi":"10.1109/Confluence47617.2020.9057939","DOIUrl":"https://doi.org/10.1109/Confluence47617.2020.9057939","url":null,"abstract":"malignant growth is an irregular development of cell tissue. Pancreatic disease is one of the observable reasons for death around the world. Pancreatic malignant growth starts in the tissues of pancreas. The pancreas secretes proteins that helps the processing and hormones that directs the breakdown of sugars. Pancreatic malignancy is usually detected in the later stages, spreads rapidly and has a poor prediction. In this paper we have made an attempt to discuss various artificial intelligence methods to detect pancreatic cancer and proposing new AI method to spot subtle patterns and provide accurate information to pathologist.","PeriodicalId":180005,"journal":{"name":"2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133653576","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 : 2020-01-01DOI: 10.1109/Confluence47617.2020.9058077
F. Siddiqui, Shubham Gupta, Shashwat Dubey, Shariq Murtuza, Arti Jain
In the past decades, researchers have demonstrated abilities to automate the process of detection and analysis of different kinds of cancers using Whole Slide Images (WSI) datasets. The breast cancer detection in histopathology images (one of the WSI dataset) using deep learning is one of the key research areas among the Computer AiDed (CAD) diagnostic systems. When it is done manually, it is a very tedious and challenging task for a pathologist as it involves thorough scanning of tissues to detect malignancy. This paper presents Convolutional Neural Network (CNN) classifier for breast cancer detection on the Breast Histopathology Images (BHI) dataset. A confusion matrix is computed for the BHI samples to analyze the prediction results of the CNN classifier. The CNN detects carcinoma tissues while labeling 55,505 image test samples as positive or negative; and achieves accuracy of 84.93%, recall of 84.70% and F-measure as 76.07% respectively.
{"title":"Classification and Diagnosis of Invasive Ductal Carcinoma Using Deep Learning","authors":"F. Siddiqui, Shubham Gupta, Shashwat Dubey, Shariq Murtuza, Arti Jain","doi":"10.1109/Confluence47617.2020.9058077","DOIUrl":"https://doi.org/10.1109/Confluence47617.2020.9058077","url":null,"abstract":"In the past decades, researchers have demonstrated abilities to automate the process of detection and analysis of different kinds of cancers using Whole Slide Images (WSI) datasets. The breast cancer detection in histopathology images (one of the WSI dataset) using deep learning is one of the key research areas among the Computer AiDed (CAD) diagnostic systems. When it is done manually, it is a very tedious and challenging task for a pathologist as it involves thorough scanning of tissues to detect malignancy. This paper presents Convolutional Neural Network (CNN) classifier for breast cancer detection on the Breast Histopathology Images (BHI) dataset. A confusion matrix is computed for the BHI samples to analyze the prediction results of the CNN classifier. The CNN detects carcinoma tissues while labeling 55,505 image test samples as positive or negative; and achieves accuracy of 84.93%, recall of 84.70% and F-measure as 76.07% respectively.","PeriodicalId":180005,"journal":{"name":"2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134522498","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 : 2020-01-01DOI: 10.1109/Confluence47617.2020.9058026
Rajiv Parwani, H. Al-Amoudi, Abdul Jhummarwala
Organizations have moved towards service based architectures and applications are hosted in the Clouds. An interruption in online delivery of such services is of grave concern to the organizations as it causes problems to a large number of users. The identification of the security vulnerability in these systems which can be exploited by cybercriminals is of utmost importance when developing an architecture for online hosting of applications. Defensive capabilities to thwart the cybercriminals must be deployed. A Distributed Denial of Service (DDoS) attack is commonly employed to create panic and prevent the delivery of services to legitimate users. This paper presents the use of network security simulation and modeling for understanding the effect of cyberattacks such as DDoS on a system. Experimentations conducted include deployment of services such as FTP, Email, and HTTP in a simulated environment. The main aim is to simulate network infrastructure and security policies before online deployment of the services. As these services will be used by a large number of users concurrently, it would be important to create a resilient system against modern DDoS attacks. The observations from the simulations will allow to share and expand the knowledge of the users for development of secure systems.
{"title":"Modeling and Simulating large scale Cyber Effects for Cybersecurity using Riverbed Modeler","authors":"Rajiv Parwani, H. Al-Amoudi, Abdul Jhummarwala","doi":"10.1109/Confluence47617.2020.9058026","DOIUrl":"https://doi.org/10.1109/Confluence47617.2020.9058026","url":null,"abstract":"Organizations have moved towards service based architectures and applications are hosted in the Clouds. An interruption in online delivery of such services is of grave concern to the organizations as it causes problems to a large number of users. The identification of the security vulnerability in these systems which can be exploited by cybercriminals is of utmost importance when developing an architecture for online hosting of applications. Defensive capabilities to thwart the cybercriminals must be deployed. A Distributed Denial of Service (DDoS) attack is commonly employed to create panic and prevent the delivery of services to legitimate users. This paper presents the use of network security simulation and modeling for understanding the effect of cyberattacks such as DDoS on a system. Experimentations conducted include deployment of services such as FTP, Email, and HTTP in a simulated environment. The main aim is to simulate network infrastructure and security policies before online deployment of the services. As these services will be used by a large number of users concurrently, it would be important to create a resilient system against modern DDoS attacks. The observations from the simulations will allow to share and expand the knowledge of the users for development of secure systems.","PeriodicalId":180005,"journal":{"name":"2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115530196","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 : 2020-01-01DOI: 10.1109/Confluence47617.2020.9058045
Sukanya, Gaurav Dubey
Road detection and segmentation is an important aspect in navigation system and is widely used to detect new roads and patterns in the region. These system has the main objective to help navigate the autonomous vehicle and robot on the ground. Road detection is very useful in finding valid road path where the vehicle can go for supportive vehicles preventing the collision with the obstacles, object detection on the road and other necessary information exchange. It has a variety of uses such as the disaster monitoring, traffic monitoring, crop monitoring, border surveillance, security and so on. There are several techniques used for detection and segmentation purpose of roads such as Artificial Neural Network, Support Vector Machine (SVM), Self-Organizing Map (SOM), Convolution Neural Network (CNN), and Deep learning techniques. In this paper, a new technique for road detection and segmentation is proposed which includes a combination algorithm of CNN and Random Field segmentation for road maps using aerial images. This road detection and segmentations give alternative solution for road classification and detection with a higher accuracy. In this system normally accuracy (ACC) have an average range of 97.7%.
{"title":"Segmentation and Detection of Road Region in Aerial Images using Hybrid CNN-Random Field Algorithm","authors":"Sukanya, Gaurav Dubey","doi":"10.1109/Confluence47617.2020.9058045","DOIUrl":"https://doi.org/10.1109/Confluence47617.2020.9058045","url":null,"abstract":"Road detection and segmentation is an important aspect in navigation system and is widely used to detect new roads and patterns in the region. These system has the main objective to help navigate the autonomous vehicle and robot on the ground. Road detection is very useful in finding valid road path where the vehicle can go for supportive vehicles preventing the collision with the obstacles, object detection on the road and other necessary information exchange. It has a variety of uses such as the disaster monitoring, traffic monitoring, crop monitoring, border surveillance, security and so on. There are several techniques used for detection and segmentation purpose of roads such as Artificial Neural Network, Support Vector Machine (SVM), Self-Organizing Map (SOM), Convolution Neural Network (CNN), and Deep learning techniques. In this paper, a new technique for road detection and segmentation is proposed which includes a combination algorithm of CNN and Random Field segmentation for road maps using aerial images. This road detection and segmentations give alternative solution for road classification and detection with a higher accuracy. In this system normally accuracy (ACC) have an average range of 97.7%.","PeriodicalId":180005,"journal":{"name":"2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114017974","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 : 2020-01-01DOI: 10.1109/Confluence47617.2020.9058278
Parita Jain, Anupam Sharma, P. Aggarwal
The innovative advancement of cell phones, the significance of the Internet in the present society and the blasting market of the mobile devices have upset the mobile software programming altogether known as the product quality of portable intuitive gadgets. The mobile software programming gets increasingly competent and complex, which enables designers to apply entrenched quality strategies and models, from the work area of software programming advancement to mobile software programming. But still, mobile software programming moreover still has its portable explicit qualities, comparing models and techniques that must be balanced for its use in the larger domain. In the following research, some of the key attributes that must be incorporated and taken care for developing a portable quality mobile applications are identified. The key attributes determined by investigating before developed quality models which allows enhancing knowledge that can be drifted in the near future.
{"title":"Key Attributes for a Quality Mobile Application","authors":"Parita Jain, Anupam Sharma, P. Aggarwal","doi":"10.1109/Confluence47617.2020.9058278","DOIUrl":"https://doi.org/10.1109/Confluence47617.2020.9058278","url":null,"abstract":"The innovative advancement of cell phones, the significance of the Internet in the present society and the blasting market of the mobile devices have upset the mobile software programming altogether known as the product quality of portable intuitive gadgets. The mobile software programming gets increasingly competent and complex, which enables designers to apply entrenched quality strategies and models, from the work area of software programming advancement to mobile software programming. But still, mobile software programming moreover still has its portable explicit qualities, comparing models and techniques that must be balanced for its use in the larger domain. In the following research, some of the key attributes that must be incorporated and taken care for developing a portable quality mobile applications are identified. The key attributes determined by investigating before developed quality models which allows enhancing knowledge that can be drifted in the near future.","PeriodicalId":180005,"journal":{"name":"2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115359020","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 : 2020-01-01DOI: 10.1109/Confluence47617.2020.9058084
A. Kumari, Chandan Chhabra, Saurabh Singh
The ability of modern smartphones to provide us with real time location-based data is one of its most important features. Being able to predict a person’s future location based on the real time location data would be the next step in utilizing this functionality. Using this functionality, combined with machine learning one’s probable destination can be predicted with a reasonable accuracy. People don’t always use map-based navigation for the places they visit every day, like their work place or school and there may be significant traffic on the regular route taken, however, if our device knows where we’re headed, it can warn us beforehand and help us reroute. This functionality can also be used by cops to determine the future location of a criminal fleeing a crime scene.These features and functionalities can be implemented through various machine learning algorithms which are compared to determine the most accurate one. The proposed system can predict a user’s future location using the current location and time, learning from the user’s previously visited locations.
{"title":"Future Location Prediction of a Mobile User Using Historic Visiting Patterns","authors":"A. Kumari, Chandan Chhabra, Saurabh Singh","doi":"10.1109/Confluence47617.2020.9058084","DOIUrl":"https://doi.org/10.1109/Confluence47617.2020.9058084","url":null,"abstract":"The ability of modern smartphones to provide us with real time location-based data is one of its most important features. Being able to predict a person’s future location based on the real time location data would be the next step in utilizing this functionality. Using this functionality, combined with machine learning one’s probable destination can be predicted with a reasonable accuracy. People don’t always use map-based navigation for the places they visit every day, like their work place or school and there may be significant traffic on the regular route taken, however, if our device knows where we’re headed, it can warn us beforehand and help us reroute. This functionality can also be used by cops to determine the future location of a criminal fleeing a crime scene.These features and functionalities can be implemented through various machine learning algorithms which are compared to determine the most accurate one. The proposed system can predict a user’s future location using the current location and time, learning from the user’s previously visited locations.","PeriodicalId":180005,"journal":{"name":"2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115667009","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 : 2020-01-01DOI: 10.1109/Confluence47617.2020.9057829
Prabhat Singh, Abhay Bansal, Sunil Kumar
Roads are the main infrastructure of every city, state or country to grow but in accordance with the present scenario in road conditions, they are not up to the mark even to be said well. Similarly, major road causing incidents like vehicle accidents, traffic congestion etc are just because of the worse conditions of roads and their improper maintenance. So, it’s a great need of today time to bring a revolutionary change in the field of it. Further, this paper will help in putting forward a methodology in this noble cause. This paper focuses on regular monitoring of the roads and proper feedback system for monitoring from centers. Furthermore, various Infrastructures based and Infrastructure less approaches used for the detection of quality of Indian Roads. This is all being discussed in this paper along with the technologies used by us, their benefits and their way of working in this field.
{"title":"Performance Analysis of various Information Platforms for recognizing the quality of Indian Roads","authors":"Prabhat Singh, Abhay Bansal, Sunil Kumar","doi":"10.1109/Confluence47617.2020.9057829","DOIUrl":"https://doi.org/10.1109/Confluence47617.2020.9057829","url":null,"abstract":"Roads are the main infrastructure of every city, state or country to grow but in accordance with the present scenario in road conditions, they are not up to the mark even to be said well. Similarly, major road causing incidents like vehicle accidents, traffic congestion etc are just because of the worse conditions of roads and their improper maintenance. So, it’s a great need of today time to bring a revolutionary change in the field of it. Further, this paper will help in putting forward a methodology in this noble cause. This paper focuses on regular monitoring of the roads and proper feedback system for monitoring from centers. Furthermore, various Infrastructures based and Infrastructure less approaches used for the detection of quality of Indian Roads. This is all being discussed in this paper along with the technologies used by us, their benefits and their way of working in this field.","PeriodicalId":180005,"journal":{"name":"2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114456834","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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