Pub Date : 2020-06-01DOI: 10.1109/incet49848.2020.9154174
Sreeram K L, Sundharam V M, Bharathwaj G
The main concept of this paper is to predict the natural disasters beforehand. With the help of deep learning one can apply statistical models to historical data to predict the future outcomes. With the help of GIS data of tectonic plates and occurred earthquakes we can train a model to predict the future earthquakes and tsunamis. The proposed system helps to predict disasters well in ahead of time which can give suitable time for evacuation and preparation for the disasters.
{"title":"Proactive Disaster Detection","authors":"Sreeram K L, Sundharam V M, Bharathwaj G","doi":"10.1109/incet49848.2020.9154174","DOIUrl":"https://doi.org/10.1109/incet49848.2020.9154174","url":null,"abstract":"The main concept of this paper is to predict the natural disasters beforehand. With the help of deep learning one can apply statistical models to historical data to predict the future outcomes. With the help of GIS data of tectonic plates and occurred earthquakes we can train a model to predict the future earthquakes and tsunamis. The proposed system helps to predict disasters well in ahead of time which can give suitable time for evacuation and preparation for the disasters.","PeriodicalId":174411,"journal":{"name":"2020 International Conference for Emerging Technology (INCET)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125022117","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 : 2020-06-01DOI: 10.1109/incet49848.2020.9154050
Abhishek L
This paper deals with retrieval of contents of any printed or handwritten document. Maximally Stable Extremal Regions (MSER) algorithm along with region-growing methods are used for the detection of printed regions. Histogram of Oriented Gradients (HOG features) are used for feature extraction. Various machine learning algorithms, namely Decision Trees, Random Forest, Extra Trees Classifier, MLP, and SVM along with ensemble method were used for classification, and the accuracies compared.
{"title":"Optical Character Recognition using Ensemble of SVM, MLP and Extra Trees Classifier","authors":"Abhishek L","doi":"10.1109/incet49848.2020.9154050","DOIUrl":"https://doi.org/10.1109/incet49848.2020.9154050","url":null,"abstract":"This paper deals with retrieval of contents of any printed or handwritten document. Maximally Stable Extremal Regions (MSER) algorithm along with region-growing methods are used for the detection of printed regions. Histogram of Oriented Gradients (HOG features) are used for feature extraction. Various machine learning algorithms, namely Decision Trees, Random Forest, Extra Trees Classifier, MLP, and SVM along with ensemble method were used for classification, and the accuracies compared.","PeriodicalId":174411,"journal":{"name":"2020 International Conference for Emerging Technology (INCET)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131263655","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 : 2020-06-01DOI: 10.1109/incet49848.2020.9153973
M. Saini, Mala Kalra
Sentiment analysis is an approach to analyse the opinion and views of the people from the text or images posted by them on social media like Facebook and Twitter. Sentiment analysis is a challenging task because it is not easy to analyse the exact views, opinions, and feelings of the text. The way of expressing feelings varies with people in different contexts and topics. This issue can be resolved by combining the text and prior knowledge. This research work proposes a deep convolutional neural network that uses the character to sentence-level information to perform sentiment analysis of tweets. A new approach for the initialization of the weights of the convolutional neural network is suggested which helps to train the network efficiently and helps to find effective features. The model is further tuned by a deep learning model which reduces the classification error. It uses word vector features with feature engineering by means of a convolution neural network. Further, the process involves learning by the soft-max classifier. The experiments are performed using three different datasets with 3K,10K and 100K tweets. The proposed approach represents a significant improvement in accuracy, precision, and recall in comparison to existing approaches.
{"title":"An Enhanced Convolution Neural Network Based Approach for Classification of Sentiments","authors":"M. Saini, Mala Kalra","doi":"10.1109/incet49848.2020.9153973","DOIUrl":"https://doi.org/10.1109/incet49848.2020.9153973","url":null,"abstract":"Sentiment analysis is an approach to analyse the opinion and views of the people from the text or images posted by them on social media like Facebook and Twitter. Sentiment analysis is a challenging task because it is not easy to analyse the exact views, opinions, and feelings of the text. The way of expressing feelings varies with people in different contexts and topics. This issue can be resolved by combining the text and prior knowledge. This research work proposes a deep convolutional neural network that uses the character to sentence-level information to perform sentiment analysis of tweets. A new approach for the initialization of the weights of the convolutional neural network is suggested which helps to train the network efficiently and helps to find effective features. The model is further tuned by a deep learning model which reduces the classification error. It uses word vector features with feature engineering by means of a convolution neural network. Further, the process involves learning by the soft-max classifier. The experiments are performed using three different datasets with 3K,10K and 100K tweets. The proposed approach represents a significant improvement in accuracy, precision, and recall in comparison to existing approaches.","PeriodicalId":174411,"journal":{"name":"2020 International Conference for Emerging Technology (INCET)","volume":"133 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133286964","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 : 2020-06-01DOI: 10.1109/incet49848.2020.9154179
R. P. Maurya, Nayanica Srivastava, S. Mitra
This paper proposed a bulk Si-FinFET for different digital applications. The proposed device has been analyzed for different gate dielectric material such as SiO2 as low-K dielectric and HfO2 as high-K dielectric. It is observed that by using high-K dielectric, ON Current is slightly enhanced. Further the comparative study of a device for silicon material and graphite material is also performed. It is observed that when body thickness is 12 nm, the ON Current of the device is high for Silicon at higher gate voltage as compared to graphite.
{"title":"Analysis of Graphite FinFET","authors":"R. P. Maurya, Nayanica Srivastava, S. Mitra","doi":"10.1109/incet49848.2020.9154179","DOIUrl":"https://doi.org/10.1109/incet49848.2020.9154179","url":null,"abstract":"This paper proposed a bulk Si-FinFET for different digital applications. The proposed device has been analyzed for different gate dielectric material such as SiO2 as low-K dielectric and HfO2 as high-K dielectric. It is observed that by using high-K dielectric, ON Current is slightly enhanced. Further the comparative study of a device for silicon material and graphite material is also performed. It is observed that when body thickness is 12 nm, the ON Current of the device is high for Silicon at higher gate voltage as compared to graphite.","PeriodicalId":174411,"journal":{"name":"2020 International Conference for Emerging Technology (INCET)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133866306","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 : 2020-06-01DOI: 10.1109/incet49848.2020.9154138
S. Akella, Harit Bandi, Siddhant Bhagat
Heart Rate is a health parameter that is a fair indicator of the functioning of the human cardiovascular system. It is one of the most basic parameters utilised to assess basic human health. This paper explicates the development and usability of a comprehensive heart rate monitoring and notification relay system, based on a Microcontroller Unit. The non-invasive technique of photoplethysmography (PPG) has been used for measuring the heart rate (coupled with dynamic algorithms to reinforce accuracy) and the readings are transmitted via cloud-based applications to pre-designated entities for effective monitoring. A rudimentary analysis of the measured data is made accessible to the user in real-time to ensure actionable insights.
{"title":"Notification-enabled Heart Rate Monitor using Photoplethysmography and Real-time Moving Averages","authors":"S. Akella, Harit Bandi, Siddhant Bhagat","doi":"10.1109/incet49848.2020.9154138","DOIUrl":"https://doi.org/10.1109/incet49848.2020.9154138","url":null,"abstract":"Heart Rate is a health parameter that is a fair indicator of the functioning of the human cardiovascular system. It is one of the most basic parameters utilised to assess basic human health. This paper explicates the development and usability of a comprehensive heart rate monitoring and notification relay system, based on a Microcontroller Unit. The non-invasive technique of photoplethysmography (PPG) has been used for measuring the heart rate (coupled with dynamic algorithms to reinforce accuracy) and the readings are transmitted via cloud-based applications to pre-designated entities for effective monitoring. A rudimentary analysis of the measured data is made accessible to the user in real-time to ensure actionable insights.","PeriodicalId":174411,"journal":{"name":"2020 International Conference for Emerging Technology (INCET)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133895802","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 : 2020-06-01DOI: 10.1109/incet49848.2020.9154032
Pratik D. Shah, R. Bichkar
Steganography is used to perform covert communication. The advantage of steganography over other secret communication techniques is its ability to conceal the presence of covert communication. In image steganography, the secret information is concealed in the cover image, in such a way that it produces very negligible change in the cover image. A vast amount of research is performed in image steganography but very limited studies have explored the possibility of choosing a cover image for steganography which provides better compatibility with the secret data. In this paper, we propose a genetic algorithm based technique for selecting a cover image from a database of images. The selected cover image is most compatible with the given secret data. We further explore the possibility of rearranging the secret data to increase the imperceptibility of the stego image.
{"title":"Genetic Algorithm based Approach to Select Suitable Cover Image for Image Steganography","authors":"Pratik D. Shah, R. Bichkar","doi":"10.1109/incet49848.2020.9154032","DOIUrl":"https://doi.org/10.1109/incet49848.2020.9154032","url":null,"abstract":"Steganography is used to perform covert communication. The advantage of steganography over other secret communication techniques is its ability to conceal the presence of covert communication. In image steganography, the secret information is concealed in the cover image, in such a way that it produces very negligible change in the cover image. A vast amount of research is performed in image steganography but very limited studies have explored the possibility of choosing a cover image for steganography which provides better compatibility with the secret data. In this paper, we propose a genetic algorithm based technique for selecting a cover image from a database of images. The selected cover image is most compatible with the given secret data. We further explore the possibility of rearranging the secret data to increase the imperceptibility of the stego image.","PeriodicalId":174411,"journal":{"name":"2020 International Conference for Emerging Technology (INCET)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117108611","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}
One-third of the total food produced gets wasted according to the Food And Agriculture Association of the United Nations. This wastage accounts for 1.3 billion tonnes and the scarcity of food is one of the major concerns globally. This paper presents comprehensive research on various factors that lead to the wastage of food in the retail sector. And a robust methodology is proposed which aims at reducing the waste to as minimal as possible in this sector. A method is proposed which integrates the inventory prediction and forecasting technique with smart dustbins which uses state of the art object detection technique to analyze the waste that gets thrown into bins in order to provide with insights to help optimize the use of raw materials that are used in preparing food and further redistribution and valorization of unpredictable waste. Thus producing minimal food waste.
{"title":"Minimization of Food Waste in Retail Sector using Time-Series Analysis and Object Detection Algorithm","authors":"Harsh Agarwal, Bhavya Ahir, Pramod J. Bide, Somil Jain, Harshit Barot","doi":"10.1109/incet49848.2020.9154156","DOIUrl":"https://doi.org/10.1109/incet49848.2020.9154156","url":null,"abstract":"One-third of the total food produced gets wasted according to the Food And Agriculture Association of the United Nations. This wastage accounts for 1.3 billion tonnes and the scarcity of food is one of the major concerns globally. This paper presents comprehensive research on various factors that lead to the wastage of food in the retail sector. And a robust methodology is proposed which aims at reducing the waste to as minimal as possible in this sector. A method is proposed which integrates the inventory prediction and forecasting technique with smart dustbins which uses state of the art object detection technique to analyze the waste that gets thrown into bins in order to provide with insights to help optimize the use of raw materials that are used in preparing food and further redistribution and valorization of unpredictable waste. Thus producing minimal food waste.","PeriodicalId":174411,"journal":{"name":"2020 International Conference for Emerging Technology (INCET)","volume":"154 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116225352","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 : 2020-06-01DOI: 10.1109/incet49848.2020.9154067
B. Sravani, M. M. Bala
This paper is about how the application of machine Learning have huge impact in teaching and learning for further improvement in learning environment in higher education. Due to the interest of students in online and digital courses increased rapidly websites such as Course Era, Udemy etc became very influential. We implement the new applications of machine learning in teaching and learning considering the students background, students past academic score and considering other attributes. As the sizes of classes are large, it would be difficult to assist each individual student in each open learning course, this can increase the bar of the dropout rate at the end of the course. In this paper we are implementing linear regression which is a machine learning algorithm to predict the student’s performance in academics
{"title":"Prediction of Student Performance Using Linear Regression","authors":"B. Sravani, M. M. Bala","doi":"10.1109/incet49848.2020.9154067","DOIUrl":"https://doi.org/10.1109/incet49848.2020.9154067","url":null,"abstract":"This paper is about how the application of machine Learning have huge impact in teaching and learning for further improvement in learning environment in higher education. Due to the interest of students in online and digital courses increased rapidly websites such as Course Era, Udemy etc became very influential. We implement the new applications of machine learning in teaching and learning considering the students background, students past academic score and considering other attributes. As the sizes of classes are large, it would be difficult to assist each individual student in each open learning course, this can increase the bar of the dropout rate at the end of the course. In this paper we are implementing linear regression which is a machine learning algorithm to predict the student’s performance in academics","PeriodicalId":174411,"journal":{"name":"2020 International Conference for Emerging Technology (INCET)","volume":"159 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116234570","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 : 2020-06-01DOI: 10.1109/incet49848.2020.9154178
Sayed Azain Jaffer, Siddharth Pandey, R. Mehta, P. Bhavathankar
Delivery of subsidies to deserving beneficiaries forms an essential part of government expenditure. In 2018-19 alone, the Government of India spent $60 Bn on welfare subsidies, majorly through the Public Distribution System(PDS). Of this amount, it is estimated that 40% was lost in the form of misuse, corruption and related inefficiencies in the system. Recognising this problem, the government began Direct Benefit Transfers in 2013 for a select few schemes, for instance, LPG subsidy. Using Aadhaar and biometric tokens for validation, the beneficiaries would receive the subsidy as direct cash transfers to their bank accounts. However, in reality, the DBT program has had the same efficiency as the PDS. According to the analysis of the DBT policy, the key drawbacks of this system are lack of auditability, inability to control the use of funds for intended purposes, and over-reliance on the banking infrastructure, which is underdeveloped in the rural areas. In order to plug loopholes in the DBT system, we propose a blockchain-based system. Blockchain consists of cryptographic hash secured distributed ledgers which maintain an immutable log of transactions between all participants of a blockchain network. They have the ability to execute Smart Contracts, which allow for automation of execution of real-world contracts given that certain specified conditions are met. Appropriating the Governments Aadhaar UID, we aim to develop a smart blockchain which automates the disbursement of subsidy which bypasses the need for banks in rural nodes while creating an auditable and transparent ecosystem to curb corruption and financial mismanagement.
{"title":"Blockchain Based Direct Benefit Transfer System For Subsidy Delivery","authors":"Sayed Azain Jaffer, Siddharth Pandey, R. Mehta, P. Bhavathankar","doi":"10.1109/incet49848.2020.9154178","DOIUrl":"https://doi.org/10.1109/incet49848.2020.9154178","url":null,"abstract":"Delivery of subsidies to deserving beneficiaries forms an essential part of government expenditure. In 2018-19 alone, the Government of India spent $60 Bn on welfare subsidies, majorly through the Public Distribution System(PDS). Of this amount, it is estimated that 40% was lost in the form of misuse, corruption and related inefficiencies in the system. Recognising this problem, the government began Direct Benefit Transfers in 2013 for a select few schemes, for instance, LPG subsidy. Using Aadhaar and biometric tokens for validation, the beneficiaries would receive the subsidy as direct cash transfers to their bank accounts. However, in reality, the DBT program has had the same efficiency as the PDS. According to the analysis of the DBT policy, the key drawbacks of this system are lack of auditability, inability to control the use of funds for intended purposes, and over-reliance on the banking infrastructure, which is underdeveloped in the rural areas. In order to plug loopholes in the DBT system, we propose a blockchain-based system. Blockchain consists of cryptographic hash secured distributed ledgers which maintain an immutable log of transactions between all participants of a blockchain network. They have the ability to execute Smart Contracts, which allow for automation of execution of real-world contracts given that certain specified conditions are met. Appropriating the Governments Aadhaar UID, we aim to develop a smart blockchain which automates the disbursement of subsidy which bypasses the need for banks in rural nodes while creating an auditable and transparent ecosystem to curb corruption and financial mismanagement.","PeriodicalId":174411,"journal":{"name":"2020 International Conference for Emerging Technology (INCET)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123848328","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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