Pub Date : 2020-01-01DOI: 10.1109/Confluence47617.2020.9057853
Ishika Dhall, Shubham Vashisth, Garima Aggarwal
The tremendous growth in the domain of deep learning has helped in achieving breakthroughs in computer vision applications especially after convolutional neural networks coming into the picture. The unique architecture of CNNs allows it to extract relevant information from the input images without any hand-tuning. Today, with such powerful models we have quite a flexibility build technology that may ameliorate human life. One such technique can be used for detecting and understanding various human gestures as it would make the human-machine communication effective. This could make the conventional input devices like touchscreens, mouse pad, and keyboards redundant. Also, it is considered as a highly secure tech compared to other devices. In this paper, hand gesture technology along with Convolutional Neural Networks has been discovered followed by the construction of a deep convolutional neural network to build a hand gesture recognition application.
{"title":"Automated Hand Gesture Recognition using a Deep Convolutional Neural Network model","authors":"Ishika Dhall, Shubham Vashisth, Garima Aggarwal","doi":"10.1109/Confluence47617.2020.9057853","DOIUrl":"https://doi.org/10.1109/Confluence47617.2020.9057853","url":null,"abstract":"The tremendous growth in the domain of deep learning has helped in achieving breakthroughs in computer vision applications especially after convolutional neural networks coming into the picture. The unique architecture of CNNs allows it to extract relevant information from the input images without any hand-tuning. Today, with such powerful models we have quite a flexibility build technology that may ameliorate human life. One such technique can be used for detecting and understanding various human gestures as it would make the human-machine communication effective. This could make the conventional input devices like touchscreens, mouse pad, and keyboards redundant. Also, it is considered as a highly secure tech compared to other devices. In this paper, hand gesture technology along with Convolutional Neural Networks has been discovered followed by the construction of a deep convolutional neural network to build a hand gesture recognition application.","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":"134435979","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/Confluence47617.2020.9058235
Archit Aggarwal, Garima Aggrawal
Caffeine is a stimulant which enables the prevention or delay of drowsiness or a feeling of sleepiness. Caffeine is an unregulated substance in most parts of the world and hence poses a threat of addiction. The symptoms of caffeine addiction and withdrawal are defined well but are large in number and sometimes inseparable from the same symptoms of other conditions. Fuzzy logic can be used to combine many such symptoms and arrive at a certain conclusion. This paper aims to implement fuzzy logic to predict the risk caffeine addiction in functioning adults based on certain predictors. The system takes into account four such predictors. The proposed model gives adequate results with an accuracy of eighty to hundred percent under different scenarios.
{"title":"A Fuzzy Interface System for the Prediction of Caffeine Addiction","authors":"Archit Aggarwal, Garima Aggrawal","doi":"10.1109/Confluence47617.2020.9058235","DOIUrl":"https://doi.org/10.1109/Confluence47617.2020.9058235","url":null,"abstract":"Caffeine is a stimulant which enables the prevention or delay of drowsiness or a feeling of sleepiness. Caffeine is an unregulated substance in most parts of the world and hence poses a threat of addiction. The symptoms of caffeine addiction and withdrawal are defined well but are large in number and sometimes inseparable from the same symptoms of other conditions. Fuzzy logic can be used to combine many such symptoms and arrive at a certain conclusion. This paper aims to implement fuzzy logic to predict the risk caffeine addiction in functioning adults based on certain predictors. The system takes into account four such predictors. The proposed model gives adequate results with an accuracy of eighty to hundred percent under different scenarios.","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":"121259695","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/Confluence47617.2020.9058058
Aastha Maheshwari, R. Yadav
In IoT (Internet of Things) network, a big amount of data is generated within a period of time. Hence it is required to critically consider and design a load balancing protocol. In this paper we survey different congestion control mechanisms designed for IoT based network, classified in two major categories i.e. protocol dependent and performing offloading. These classifications are based on technique used to balance load and avoid congestion respectively. Protocol dependent approach is further classified as application layer protocol (CoAP) or network layer (RPL) protocol. These techniques improvise CoAP and RPL protocols to handle congestion issues. Offloading dependent approach covers different methods to balance the load evenly within a network. This analysis also includes the major concerns and the focus of different techniques to achieve congestion control within an IoT network.
{"title":"Analysis of Congestion Control Mechanism for IOT","authors":"Aastha Maheshwari, R. Yadav","doi":"10.1109/Confluence47617.2020.9058058","DOIUrl":"https://doi.org/10.1109/Confluence47617.2020.9058058","url":null,"abstract":"In IoT (Internet of Things) network, a big amount of data is generated within a period of time. Hence it is required to critically consider and design a load balancing protocol. In this paper we survey different congestion control mechanisms designed for IoT based network, classified in two major categories i.e. protocol dependent and performing offloading. These classifications are based on technique used to balance load and avoid congestion respectively. Protocol dependent approach is further classified as application layer protocol (CoAP) or network layer (RPL) protocol. These techniques improvise CoAP and RPL protocols to handle congestion issues. Offloading dependent approach covers different methods to balance the load evenly within a network. This analysis also includes the major concerns and the focus of different techniques to achieve congestion control within an IoT network.","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":"121266861","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/Confluence47617.2020.9058303
J. K. Sethi, Mamta Mittal
Due to the major consequences of air pollution on human health, this problem is resulting in a major public crisis which requires immediate attention. Nowadays, the prediction of air quality has been a potential research area. There exist a number of methods in literature, but the focus of this work is based on the prediction of air quality using time series analysis. This analysis has been carried out using univariate and multivariate techniques namely Autoregressive Integrated Moving Average (ARIMA) and Vector Autoregression (VAR) models. To perform the experimental work, the dataset of Gurugram has been considered. Further, the performance of both the models has been evaluated based on a number of metrics and it has been observed that the ARIMA model produced better results in comparison to VAR model for the prediction of Air Quality Index (AQI).
{"title":"Analysis of Air Quality using Univariate and Multivariate Time Series Models","authors":"J. K. Sethi, Mamta Mittal","doi":"10.1109/Confluence47617.2020.9058303","DOIUrl":"https://doi.org/10.1109/Confluence47617.2020.9058303","url":null,"abstract":"Due to the major consequences of air pollution on human health, this problem is resulting in a major public crisis which requires immediate attention. Nowadays, the prediction of air quality has been a potential research area. There exist a number of methods in literature, but the focus of this work is based on the prediction of air quality using time series analysis. This analysis has been carried out using univariate and multivariate techniques namely Autoregressive Integrated Moving Average (ARIMA) and Vector Autoregression (VAR) models. To perform the experimental work, the dataset of Gurugram has been considered. Further, the performance of both the models has been evaluated based on a number of metrics and it has been observed that the ARIMA model produced better results in comparison to VAR model for the prediction of Air Quality Index (AQI).","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":"129378744","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/Confluence47617.2020.9058178
Shubham Vashisth, Ishika Dhall, Shipra Saraswat
Chronic Kidney Disease or CKD is one of the most widespread Kidney diseases that affect people on a larger scale. It gives rise to other biological problems like weak bones, anemia, nerve damage, high blood pressure and can even lead to complete kidney failure. Millions of deaths are caused each year because of CKD. The diagnosis of CKD is a problematic job as there is no major symptom that serves a classification feature in detecting this disease. This paper proposes a Multi-Layer Perceptron Classifier that uses a fully connected Deep Neural Network to predict whether a patient suffers from the problem of CKD or not. The model is trained on a dataset of around 400 patients and considers various symptoms like blood pressure, age, sugar level, red blood cell count, etc. that assist the model in performing accurate classification. Our experimental results show that the proposed model can perform classification with the testing accuracy of 92.5&, surpassing the scores achieved by SVM and Naïve Bayes Classifier.
{"title":"Chronic Kidney Disease (CKD) Diagnosis using Multi-Layer Perceptron Classifier","authors":"Shubham Vashisth, Ishika Dhall, Shipra Saraswat","doi":"10.1109/Confluence47617.2020.9058178","DOIUrl":"https://doi.org/10.1109/Confluence47617.2020.9058178","url":null,"abstract":"Chronic Kidney Disease or CKD is one of the most widespread Kidney diseases that affect people on a larger scale. It gives rise to other biological problems like weak bones, anemia, nerve damage, high blood pressure and can even lead to complete kidney failure. Millions of deaths are caused each year because of CKD. The diagnosis of CKD is a problematic job as there is no major symptom that serves a classification feature in detecting this disease. This paper proposes a Multi-Layer Perceptron Classifier that uses a fully connected Deep Neural Network to predict whether a patient suffers from the problem of CKD or not. The model is trained on a dataset of around 400 patients and considers various symptoms like blood pressure, age, sugar level, red blood cell count, etc. that assist the model in performing accurate classification. Our experimental results show that the proposed model can perform classification with the testing accuracy of 92.5&, surpassing the scores achieved by SVM and Naïve Bayes Classifier.","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":"128705899","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/Confluence47617.2020.9058334
A. Verma, Ankur Choudhary, S. Tiwari
Software cannot be release until unless it attains significant degree of confidence on quality parameters. In order to maintain the software quality, testing plays an important role. But this is a costly affair as it consumes almost 50 percent of the overall software development cost. The increasing competitiveness and ever updating technological change as well as customer requirements make regression testing a most important activity. So, regression testing is conducted before every release of the software which becomes expensive. Optimization of regression test suite is a way to reduce this higher cost. This paper proposes an efficient self adaptive butterfly optimization technique. The proposed approach is further utilized on regression test suite optimization problem to reduce the regression test suite size. Performance of proposed approach has been evaluated against Bat Search Optimization based approaches using fault detection as performance measures. Different tests are performed to analyze and validate the results. These results demonstrate the dominance of the proposed approach over the compared ones.
{"title":"Test Case Optimization using Butterfly Optimization Algorithm","authors":"A. Verma, Ankur Choudhary, S. Tiwari","doi":"10.1109/Confluence47617.2020.9058334","DOIUrl":"https://doi.org/10.1109/Confluence47617.2020.9058334","url":null,"abstract":"Software cannot be release until unless it attains significant degree of confidence on quality parameters. In order to maintain the software quality, testing plays an important role. But this is a costly affair as it consumes almost 50 percent of the overall software development cost. The increasing competitiveness and ever updating technological change as well as customer requirements make regression testing a most important activity. So, regression testing is conducted before every release of the software which becomes expensive. Optimization of regression test suite is a way to reduce this higher cost. This paper proposes an efficient self adaptive butterfly optimization technique. The proposed approach is further utilized on regression test suite optimization problem to reduce the regression test suite size. Performance of proposed approach has been evaluated against Bat Search Optimization based approaches using fault detection as performance measures. Different tests are performed to analyze and validate the results. These results demonstrate the dominance of the proposed approach over the compared ones.","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":"128829873","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/Confluence47617.2020.9057911
Hina Gupta, Zaheeruddin
The improper management of the traffic conditions has hampered the sustainable development in the urban areas. Various factors influence the characteristic of the traffic congestion. In order to conduct a microscopic analysis regarding the causes of congestion we need to establish a relationship between the traffic congestion patterns and the influencing factors. The work has been carried out on the basis of the previous studies and the discernment of the proficient involved in management of traffic. In this work, a methodology named Decision Making Trial and Evaluation Laboratory (DEMATEL) has been employed, for comprehending the contextual affiliation structure amongst the various key enablers.
{"title":"An Investigation of Barriers affecting the movement of Emergency Vehicles using the DEMATEL approach","authors":"Hina Gupta, Zaheeruddin","doi":"10.1109/Confluence47617.2020.9057911","DOIUrl":"https://doi.org/10.1109/Confluence47617.2020.9057911","url":null,"abstract":"The improper management of the traffic conditions has hampered the sustainable development in the urban areas. Various factors influence the characteristic of the traffic congestion. In order to conduct a microscopic analysis regarding the causes of congestion we need to establish a relationship between the traffic congestion patterns and the influencing factors. The work has been carried out on the basis of the previous studies and the discernment of the proficient involved in management of traffic. In this work, a methodology named Decision Making Trial and Evaluation Laboratory (DEMATEL) has been employed, for comprehending the contextual affiliation structure amongst the various key enablers.","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":"116831064","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/Confluence47617.2020.9058071
Bhat Jasra, Ayaz Hassan Moon
Transmission and distribution of multimedia data over public networks including internet and other insecure channels makes it prone to different kinds of active and passive attacks. The attacks could be mitigated by ensuring proper security measures in place. Multimedia data tends to be larger in size, more redundant and Multi-dimensional. Therefore Security requirements of multimedia data including image encryption techniques are different from that of conventional textural encryption schemes. In this paper we review and analyze different image encryption techniques in the context of security parameters used to prove efficiency of security algorithms.
{"title":"Image Encryption techniques:A Review","authors":"Bhat Jasra, Ayaz Hassan Moon","doi":"10.1109/Confluence47617.2020.9058071","DOIUrl":"https://doi.org/10.1109/Confluence47617.2020.9058071","url":null,"abstract":"Transmission and distribution of multimedia data over public networks including internet and other insecure channels makes it prone to different kinds of active and passive attacks. The attacks could be mitigated by ensuring proper security measures in place. Multimedia data tends to be larger in size, more redundant and Multi-dimensional. Therefore Security requirements of multimedia data including image encryption techniques are different from that of conventional textural encryption schemes. In this paper we review and analyze different image encryption techniques in the context of security parameters used to prove efficiency of security algorithms.","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":"115175716","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/Confluence47617.2020.9058164
Vaibhav Pandit, Rishabh Gulati, Chaitanya Singla, Sandeep Kr. Singh
Captioning of colored images has been around for quite some time now, it uses object detection and the spatial relation between the objects to generate captions. There have been numerous approaches to caption colorized images in the past, but there have been a very few. In this paper we present an approach to caption Black and white images without any attempt of colorization. We have used transfer learning to implement Inception V3, a CNN model developed by Google and a runner up in the ImageNet image classification challenge, to generate captions from Black and white images achieving an accuracy of 45.77% on the validation set.
{"title":"DeepCap: A Deep Learning Model to Caption Black and White Images","authors":"Vaibhav Pandit, Rishabh Gulati, Chaitanya Singla, Sandeep Kr. Singh","doi":"10.1109/Confluence47617.2020.9058164","DOIUrl":"https://doi.org/10.1109/Confluence47617.2020.9058164","url":null,"abstract":"Captioning of colored images has been around for quite some time now, it uses object detection and the spatial relation between the objects to generate captions. There have been numerous approaches to caption colorized images in the past, but there have been a very few. In this paper we present an approach to caption Black and white images without any attempt of colorization. We have used transfer learning to implement Inception V3, a CNN model developed by Google and a runner up in the ImageNet image classification challenge, to generate captions from Black and white images achieving an accuracy of 45.77% on the validation set.","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":"116240566","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/confluence47617.2020.9058292
U. Sharma, Naman Manchanda
The Indian Government has been promoting entrepreneurship on a nation-wide scale for many years, yet a majority of the Indian youth doesn’t prefer to start their venture. Our objective is to predict the cause behind the lack of Entrepreneurial Competency in university students and suggest potential measures to improve the same. We performed an analysis to identify a correlation between the different personality traits associated with Entrepreneurship and also cluster students into different groups and extract information from this analysis using data collected from 198 university students from across India. We have used several Machine Learning algorithms like k-NN, Logistic Regression, Naïve Bayes, Support Vector Machine, Decision Trees, Random Forests, and K-Means Clustering.
{"title":"Predicting and Improving Entrepreneurial Competency in University Students using Machine Learning Algorithms","authors":"U. Sharma, Naman Manchanda","doi":"10.1109/confluence47617.2020.9058292","DOIUrl":"https://doi.org/10.1109/confluence47617.2020.9058292","url":null,"abstract":"The Indian Government has been promoting entrepreneurship on a nation-wide scale for many years, yet a majority of the Indian youth doesn’t prefer to start their venture. Our objective is to predict the cause behind the lack of Entrepreneurial Competency in university students and suggest potential measures to improve the same. We performed an analysis to identify a correlation between the different personality traits associated with Entrepreneurship and also cluster students into different groups and extract information from this analysis using data collected from 198 university students from across India. We have used several Machine Learning algorithms like k-NN, Logistic Regression, Naïve Bayes, Support Vector Machine, Decision Trees, Random Forests, and K-Means Clustering.","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":"127544342","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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