Pub Date : 2020-01-01DOI: 10.1109/Confluence47617.2020.9058226
Magendra Singh, Pratush Kumar Shrivastava, Ammar Al Hashmi, Anurag Gupta, Amir Ansari
Now a days there are a plethora of mobile applications available in the market. A huge number of applications are being added to this list each day. As the demand for user-engagement increases, more and more rich graphical contents are provided by applications. In order to have a global footprint, applications provide support for multiple languages. All this considerably increases the size of the applications. Users install several applications on their mobile devices which results in shortage of memory space in the devices. We have proposed a technique to increase the usable memory space in the devices by leveraging the fact that each user does not require multiple languages simultaneously and many applications have duplicate/common resources. This paper proposes a common resource pool for all installed applications to optimize their memory requirements in user’s device.
{"title":"Reduce device memory using centralized common resource pool","authors":"Magendra Singh, Pratush Kumar Shrivastava, Ammar Al Hashmi, Anurag Gupta, Amir Ansari","doi":"10.1109/Confluence47617.2020.9058226","DOIUrl":"https://doi.org/10.1109/Confluence47617.2020.9058226","url":null,"abstract":"Now a days there are a plethora of mobile applications available in the market. A huge number of applications are being added to this list each day. As the demand for user-engagement increases, more and more rich graphical contents are provided by applications. In order to have a global footprint, applications provide support for multiple languages. All this considerably increases the size of the applications. Users install several applications on their mobile devices which results in shortage of memory space in the devices. We have proposed a technique to increase the usable memory space in the devices by leveraging the fact that each user does not require multiple languages simultaneously and many applications have duplicate/common resources. This paper proposes a common resource pool for all installed applications to optimize their memory requirements in user’s device.","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":"131038352","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}
Random Forest is a learning method that can be used for classification and regression problems; it operates by constructing decision trees at training time and output the predicted results. In this study, the algorithm is used to predict the Pan Evaporation for Karnal district, India. Random forest is also adopted to select the important features which highly influence the evaporation conditions. The weather of four lag weeks from the week of forecast is used to form indices that are considered for the model development. The algorithm is trained using thirty-one-year data (1973-2003) and subsequent year (2004-05) which is not utilized for model development is used as a testing set. The developed random forest model is further compared with the artificial neural network with backpropagation algorithm. The performance of the models is measured using mean square error, which shows that the predicted values are in close approximation with the observed one but the random forest model has better predictions than the artificial neural network.
{"title":"Predictive modeling of Pan Evaporation using Random Forest Algorithm along with Features Selection","authors":"Rakhee, Archana Singh, Mamta Mittal, Amrender Kumar","doi":"10.1109/Confluence47617.2020.9057856","DOIUrl":"https://doi.org/10.1109/Confluence47617.2020.9057856","url":null,"abstract":"Random Forest is a learning method that can be used for classification and regression problems; it operates by constructing decision trees at training time and output the predicted results. In this study, the algorithm is used to predict the Pan Evaporation for Karnal district, India. Random forest is also adopted to select the important features which highly influence the evaporation conditions. The weather of four lag weeks from the week of forecast is used to form indices that are considered for the model development. The algorithm is trained using thirty-one-year data (1973-2003) and subsequent year (2004-05) which is not utilized for model development is used as a testing set. The developed random forest model is further compared with the artificial neural network with backpropagation algorithm. The performance of the models is measured using mean square error, which shows that the predicted values are in close approximation with the observed one but the random forest model has better predictions than the artificial neural 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":"132867817","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/Confluence47617.2020.9058272
Nidhi Bhatia, Manish Sharma
In this communication, a superwideband square monopole antenna is presented. Proposed antenna has also the capability of rejecting three bands namely, WiMAX/C band, WLAN and Downlink Satellite System (DSS) band. Proposed antenna is designed on Arlon AD260A with radiating patch and ground on same plane of substrate and CPW-feed is used. Notched bands are obtained by embedding inverted E-shaped stub, using C-shaped parasitic element above radiating patch and etching rotated L-shaped slot in the ground plane. Optimization of antenna is carried out by training the neural network and is verified by simulator results.
{"title":"A CPW-Fed Square Monopole Triple Notch Band Superwideband Antenna for Wireless Communication Applications And Optimization by using Artificial Intelligence (Back Propagation Model)","authors":"Nidhi Bhatia, Manish Sharma","doi":"10.1109/Confluence47617.2020.9058272","DOIUrl":"https://doi.org/10.1109/Confluence47617.2020.9058272","url":null,"abstract":"In this communication, a superwideband square monopole antenna is presented. Proposed antenna has also the capability of rejecting three bands namely, WiMAX/C band, WLAN and Downlink Satellite System (DSS) band. Proposed antenna is designed on Arlon AD260A with radiating patch and ground on same plane of substrate and CPW-feed is used. Notched bands are obtained by embedding inverted E-shaped stub, using C-shaped parasitic element above radiating patch and etching rotated L-shaped slot in the ground plane. Optimization of antenna is carried out by training the neural network and is verified by simulator results.","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":"129452768","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/Confluence47617.2020.9057809
Harsh Sharma, Jai Jain, Priti Bansal, S. Gupta
Pneumonia is an infection that causes inflammation of lungs and can be deadly if not detected on time. The commonly used method to detect Pneumonia is using chest X-ray which requires careful examination of chest X-ray images by an expert. The method of detecting pneumonia using chest X-ray images by an expert is time-consuming and less accurate. In this paper, we propose different deep convolution neural network (CNN) architectures to extract features from images of chest X-ray and classify the images to detect if a person has pneumonia. To evaluate the effect of dataset size on the performance of CNN, we train the proposed CNN’s using both the original as well as augmented dataset and the results are reported.
{"title":"Feature Extraction and Classification of Chest X-Ray Images Using CNN to Detect Pneumonia","authors":"Harsh Sharma, Jai Jain, Priti Bansal, S. Gupta","doi":"10.1109/Confluence47617.2020.9057809","DOIUrl":"https://doi.org/10.1109/Confluence47617.2020.9057809","url":null,"abstract":"Pneumonia is an infection that causes inflammation of lungs and can be deadly if not detected on time. The commonly used method to detect Pneumonia is using chest X-ray which requires careful examination of chest X-ray images by an expert. The method of detecting pneumonia using chest X-ray images by an expert is time-consuming and less accurate. In this paper, we propose different deep convolution neural network (CNN) architectures to extract features from images of chest X-ray and classify the images to detect if a person has pneumonia. To evaluate the effect of dataset size on the performance of CNN, we train the proposed CNN’s using both the original as well as augmented dataset and the results are reported.","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":"133112107","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/Confluence47617.2020.9057873
Vrinda, R. Madaan, K. Bhatia, Surbhi Bhatia
the idea of Emotional Intelligence (EI) is of unmatched enthusiasm for both the literature and inside scholarly world. As a center develops in conventional psychology, emotional intelligence has risen. There are number of definitions of EI. These conceptualizations can be partitioned into two streams: ‘ability models’ where EI is characterized as cognitive abilities in emotional working versus ‘trait models’ that fuse a wide scope of personality traits and different characteristics. Emotional intelligence matters the same amount of as intellectual ability. Also in the world of the internet, emotional intelligence plays a significant role. Across the board enthusiasm for EI prompted the advancement of a wide range of scales indicating to measure the construct. This paper discusses “Emotional Intelligence” and focuses on the evolution of EI by examining the different models, the methods used to survey them, and the connection between these models and other comparable constructs. Both experimental and commercial measures are comprised here. This paper also listed some applications of emotional intelligence we used in our daily life and the social media’s effect on it.
{"title":"Understanding the Role of Emotional Intelligence in Usage of Social Media","authors":"Vrinda, R. Madaan, K. Bhatia, Surbhi Bhatia","doi":"10.1109/Confluence47617.2020.9057873","DOIUrl":"https://doi.org/10.1109/Confluence47617.2020.9057873","url":null,"abstract":"the idea of Emotional Intelligence (EI) is of unmatched enthusiasm for both the literature and inside scholarly world. As a center develops in conventional psychology, emotional intelligence has risen. There are number of definitions of EI. These conceptualizations can be partitioned into two streams: ‘ability models’ where EI is characterized as cognitive abilities in emotional working versus ‘trait models’ that fuse a wide scope of personality traits and different characteristics. Emotional intelligence matters the same amount of as intellectual ability. Also in the world of the internet, emotional intelligence plays a significant role. Across the board enthusiasm for EI prompted the advancement of a wide range of scales indicating to measure the construct. This paper discusses “Emotional Intelligence” and focuses on the evolution of EI by examining the different models, the methods used to survey them, and the connection between these models and other comparable constructs. Both experimental and commercial measures are comprised here. This paper also listed some applications of emotional intelligence we used in our daily life and the social media’s effect on it.","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":"131885525","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/Confluence47617.2020.9058265
Shivani Tanwar, D. Mehrotra, Renuka Nagpal
Network security is one of the prime concerns these days. It has become necessary for every organization to secure their data and provide the best services to their users. With the increase in network vulnerability issues, there has become a need to find out the best technology amongst the various technologies which fulfils the requirement of security for an organization. Today, we have a lot of technologies that provide security to the network, available with variations in cost, bandwidth etc. So, there occurs a need to select the best amongst the rest technologies. In this paper the aim is to utilize MCDM (Multi-Criteria-Decision-Making) approach by employing the EDAS (Evaluation Based on Distance from Average Solution) to find out the most reliable technology in networking to provide security.
{"title":"Identifying the Best Network Security Using EDAS","authors":"Shivani Tanwar, D. Mehrotra, Renuka Nagpal","doi":"10.1109/Confluence47617.2020.9058265","DOIUrl":"https://doi.org/10.1109/Confluence47617.2020.9058265","url":null,"abstract":"Network security is one of the prime concerns these days. It has become necessary for every organization to secure their data and provide the best services to their users. With the increase in network vulnerability issues, there has become a need to find out the best technology amongst the various technologies which fulfils the requirement of security for an organization. Today, we have a lot of technologies that provide security to the network, available with variations in cost, bandwidth etc. So, there occurs a need to select the best amongst the rest technologies. In this paper the aim is to utilize MCDM (Multi-Criteria-Decision-Making) approach by employing the EDAS (Evaluation Based on Distance from Average Solution) to find out the most reliable technology in networking to provide security.","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":"116226895","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/Confluence47617.2020.9058314
Chitranshu Raj, Lavanya Khular, G. Raj
Incident Handling for Cloud Infrastructures focuses on how the clustering based and non-clustering based algorithms can be implemented. Our research focuses in identifying anomalies and suspicious activities that might happen inside a Cloud Infrastructure over available datasets. A brief study has been conducted, where a network statistics dataset the NSL-KDD, has been chosen as the model to be worked upon, such that it can mirror the Cloud Infrastructure and its components. An important aspect of cloud security is to implement anomaly detection mechanisms, in order to monitor the incidents that inhibit the development and the efficiency of the cloud. Several methods have been discovered which help in achieving our present goal, some of these are highlighted as the following; by applying algorithm such as the Local Outlier Factor to cancel the noise created by irrelevant data points, by applying the DBSCAN algorithm which can detect less denser areas in order to identify their cause of clustering, the K-Means algorithm to generate positive and negative clusters to identify the anomalous clusters and by applying the Isolation Forest algorithm in order to implement decision based approach to detect anomalies. The best algorithm would help in finding and fixing the anomalies efficiently and would help us in developing an Incident Handling model for the Cloud.
{"title":"Clustering Based Incident Handling For Anomaly Detection in Cloud Infrastructures","authors":"Chitranshu Raj, Lavanya Khular, G. Raj","doi":"10.1109/Confluence47617.2020.9058314","DOIUrl":"https://doi.org/10.1109/Confluence47617.2020.9058314","url":null,"abstract":"Incident Handling for Cloud Infrastructures focuses on how the clustering based and non-clustering based algorithms can be implemented. Our research focuses in identifying anomalies and suspicious activities that might happen inside a Cloud Infrastructure over available datasets. A brief study has been conducted, where a network statistics dataset the NSL-KDD, has been chosen as the model to be worked upon, such that it can mirror the Cloud Infrastructure and its components. An important aspect of cloud security is to implement anomaly detection mechanisms, in order to monitor the incidents that inhibit the development and the efficiency of the cloud. Several methods have been discovered which help in achieving our present goal, some of these are highlighted as the following; by applying algorithm such as the Local Outlier Factor to cancel the noise created by irrelevant data points, by applying the DBSCAN algorithm which can detect less denser areas in order to identify their cause of clustering, the K-Means algorithm to generate positive and negative clusters to identify the anomalous clusters and by applying the Isolation Forest algorithm in order to implement decision based approach to detect anomalies. The best algorithm would help in finding and fixing the anomalies efficiently and would help us in developing an Incident Handling model for the Cloud.","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":"127459293","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/confluence47617.2020.9058216
Vasudev Awatramani, Pooja Gupta
Understanding textual and language information has always been one of the primary research concerns of artificial intelligence, as the crucial function it plays in communication. The biomedical domain has experienced a surge in the availability of data in the form of text. This collection of information has opened avenues to a plethora of automated applications. In this work, the nascent technique of Natural Language Transfer Learning is employed for Physiological Computing. This methodology measures the semantic similarity between medical text utilising pre-trained language models such as BERT and RoBERTa. Using the proposed methodology 90% accuracy over the BioSSES dataset has been obtained. Henceforth, transfer learning proves to be an effectual strategy for NLP tasks that belong to varied fields.
{"title":"Natural Language Transfer Learning for Physiological Textual Similarity","authors":"Vasudev Awatramani, Pooja Gupta","doi":"10.1109/confluence47617.2020.9058216","DOIUrl":"https://doi.org/10.1109/confluence47617.2020.9058216","url":null,"abstract":"Understanding textual and language information has always been one of the primary research concerns of artificial intelligence, as the crucial function it plays in communication. The biomedical domain has experienced a surge in the availability of data in the form of text. This collection of information has opened avenues to a plethora of automated applications. In this work, the nascent technique of Natural Language Transfer Learning is employed for Physiological Computing. This methodology measures the semantic similarity between medical text utilising pre-trained language models such as BERT and RoBERTa. Using the proposed methodology 90% accuracy over the BioSSES dataset has been obtained. Henceforth, transfer learning proves to be an effectual strategy for NLP tasks that belong to varied fields.","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":"129894039","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/Confluence47617.2020.9058298
Vanshika Vikas Nigam, Shreya Paul, A. Agrawal, Rishabh Bansal
During the past decade or so, knowledge graphs have stealthily made way into our daily lives, whether through voice assistants (the likes of Alexa, Google Assistant or Siri), spontaneous search results or customized personal shopping experiences through online stores recommenders. Today, we are surrounded and constantly interacting with knowledge graphs on a regular basis. The scope and impact of knowledge graphs and underlying graph databases are still a mystery to people. Considering its smooth entry into our lives, most of us are unaware of how dependent we actually are on the technology. In this paper, we have shown the working of the Google knowledge graph and how the knowledge graph works as the most effective recommendation system. We have discussed how it finds its way through daily life in various fields like Banking, Social media (Facebook, LinkedIn, Netflix), Food application (Uber Eats) and the Healthcare sector.
{"title":"A Review Paper On The Application Of Knowledge Graph On Various Service Providing Platforms.","authors":"Vanshika Vikas Nigam, Shreya Paul, A. Agrawal, Rishabh Bansal","doi":"10.1109/Confluence47617.2020.9058298","DOIUrl":"https://doi.org/10.1109/Confluence47617.2020.9058298","url":null,"abstract":"During the past decade or so, knowledge graphs have stealthily made way into our daily lives, whether through voice assistants (the likes of Alexa, Google Assistant or Siri), spontaneous search results or customized personal shopping experiences through online stores recommenders. Today, we are surrounded and constantly interacting with knowledge graphs on a regular basis. The scope and impact of knowledge graphs and underlying graph databases are still a mystery to people. Considering its smooth entry into our lives, most of us are unaware of how dependent we actually are on the technology. In this paper, we have shown the working of the Google knowledge graph and how the knowledge graph works as the most effective recommendation system. We have discussed how it finds its way through daily life in various fields like Banking, Social media (Facebook, LinkedIn, Netflix), Food application (Uber Eats) and the Healthcare sector.","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":"129014391","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/Confluence47617.2020.9057848
Anish Singh, H. Joshi, Amal Srivastava, Raja Kumar, Nitasha Hasteer
Air pollution is a serious concern to society and individuals because of its direct impact on human health and environment and over the past few years the development and expansion has led to a subsequent increase in air pollution and thus leads to extensive respiratory illness and millions of deaths. Various studies extracted from relevant research repositories discussed in paper have provided distinct solutions so as to make individuals aware and bring forth the strategies to minimize the impact of the air pollution. Monitoring, estimating and analyzing polluted air consumption through mobile application and wearable devices in one strategy. In this work, a n architecture of a system to monitor and analyze the consumption of polluted air by an individual has been proposed. The proposed system generates alerts and notification in view of the health parameters.
{"title":"An Analysis of Polluted Air Consumption and Hazards on Human Health: A Study Towards System Design","authors":"Anish Singh, H. Joshi, Amal Srivastava, Raja Kumar, Nitasha Hasteer","doi":"10.1109/Confluence47617.2020.9057848","DOIUrl":"https://doi.org/10.1109/Confluence47617.2020.9057848","url":null,"abstract":"Air pollution is a serious concern to society and individuals because of its direct impact on human health and environment and over the past few years the development and expansion has led to a subsequent increase in air pollution and thus leads to extensive respiratory illness and millions of deaths. Various studies extracted from relevant research repositories discussed in paper have provided distinct solutions so as to make individuals aware and bring forth the strategies to minimize the impact of the air pollution. Monitoring, estimating and analyzing polluted air consumption through mobile application and wearable devices in one strategy. In this work, a n architecture of a system to monitor and analyze the consumption of polluted air by an individual has been proposed. The proposed system generates alerts and notification in view of the health parameters.","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":"124523541","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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