Pub Date : 2020-11-06DOI: 10.1109/PDGC50313.2020.9315794
Shivani Inder, Gaurav Goyal
Movie critics and audience play a crucial role in the success or failure of a movie. Both audiences and critics provide their opinions for a movie using different platforms like IMDB, Rotten tomatoes and Metacritics. Our goal is to correlate the ratings of Netflix movies with the fluctuation in stock price of Netflix as an organisation. Here, multiple factors like Sequel, Genre, Actor, time of release of the movie plays a crucial role which has a direct impact on the ratings and finally on the stock value.
{"title":"Determining Relation Amongst Movie Ratings and Market Returns using Regression Analysis","authors":"Shivani Inder, Gaurav Goyal","doi":"10.1109/PDGC50313.2020.9315794","DOIUrl":"https://doi.org/10.1109/PDGC50313.2020.9315794","url":null,"abstract":"Movie critics and audience play a crucial role in the success or failure of a movie. Both audiences and critics provide their opinions for a movie using different platforms like IMDB, Rotten tomatoes and Metacritics. Our goal is to correlate the ratings of Netflix movies with the fluctuation in stock price of Netflix as an organisation. Here, multiple factors like Sequel, Genre, Actor, time of release of the movie plays a crucial role which has a direct impact on the ratings and finally on the stock value.","PeriodicalId":347216,"journal":{"name":"2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123334594","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 : 2020-11-06DOI: 10.1109/PDGC50313.2020.9315782
M. Rashid, A. Hamid, Nazir Ahmad, M. Rehman, Mir Mohammad Yousuf
A lot of data is generated from multiple sources. This data contains many hidden patterns and information. Many researchers are trying to get meaningful insights out of these patterns. Data from these sources mostly contains opinions. Opinions can be mined to lead various extractions from organizational point of view. One approach is to use Sentiment Analysis. In this paper, the authors are storing the Twitter Streaming Data into HDFS of Hadoop by using Flume and then extracting with Apache Hive. Later, Machine Learning classification algorithms are applied to decode the sentiment in this data using Apache Mahout. A novel approach based on hybrid Naïve Bayes and Decision Tree Algorithms are used to enhance the performance of sentiment analysis of streaming twitter data. The implemented research approach achieved an accuracy of 86.44% in comparison to 81.11% for Naïve Bayes Classifier.
{"title":"Novel Machine Learning Approach for Sentiment Analysis of Real Time Twitter Data with Apache Flume","authors":"M. Rashid, A. Hamid, Nazir Ahmad, M. Rehman, Mir Mohammad Yousuf","doi":"10.1109/PDGC50313.2020.9315782","DOIUrl":"https://doi.org/10.1109/PDGC50313.2020.9315782","url":null,"abstract":"A lot of data is generated from multiple sources. This data contains many hidden patterns and information. Many researchers are trying to get meaningful insights out of these patterns. Data from these sources mostly contains opinions. Opinions can be mined to lead various extractions from organizational point of view. One approach is to use Sentiment Analysis. In this paper, the authors are storing the Twitter Streaming Data into HDFS of Hadoop by using Flume and then extracting with Apache Hive. Later, Machine Learning classification algorithms are applied to decode the sentiment in this data using Apache Mahout. A novel approach based on hybrid Naïve Bayes and Decision Tree Algorithms are used to enhance the performance of sentiment analysis of streaming twitter data. The implemented research approach achieved an accuracy of 86.44% in comparison to 81.11% for Naïve Bayes Classifier.","PeriodicalId":347216,"journal":{"name":"2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123768185","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 : 2020-11-06DOI: 10.1109/PDGC50313.2020.9315809
Ranga SwamySirisati, M. S. Rao, Srinivasulu Thonukunuri
Medical Image Processing plays an essential role in human health. Many methods have played an essential role in reducing physician decision-making in diagnosis. Much caution is required and recommended, especially in cases involving the brain. Separation of tumors from normal brain cells belongs to the category of brain tumors. The dissection process can help provide the information needed for diagnosis. This process is risky due to the unusual shapes and manipulations at the border. Determining these tumors at an early stage can help provide the best treatment for patients. Typically, physicians adopt a manual method of dividing patients into patients, which leads to more time. This paper presents a well-functioning Hybrid Fusion-Neural Filter Approach (HFNF)classification system that considers various factors such as accuracy, recovery and accuracy. MRI is one of the most traditional methods for the primary diagnostic tool for brain tumors. If the tumor is malignant for successful treatment, the necessary diagnostic and treatment planning measures must be taken quickly. Physicians can make accurate decisions by applying the following procedures. The necessary treatment can be done effectively. A computer-assisted diagnostic system, MRI, can help reduce the workload of physicians.
{"title":"Analysis of Hybrid Fusion-Neural Filter Approach to detect Brain Tumor","authors":"Ranga SwamySirisati, M. S. Rao, Srinivasulu Thonukunuri","doi":"10.1109/PDGC50313.2020.9315809","DOIUrl":"https://doi.org/10.1109/PDGC50313.2020.9315809","url":null,"abstract":"Medical Image Processing plays an essential role in human health. Many methods have played an essential role in reducing physician decision-making in diagnosis. Much caution is required and recommended, especially in cases involving the brain. Separation of tumors from normal brain cells belongs to the category of brain tumors. The dissection process can help provide the information needed for diagnosis. This process is risky due to the unusual shapes and manipulations at the border. Determining these tumors at an early stage can help provide the best treatment for patients. Typically, physicians adopt a manual method of dividing patients into patients, which leads to more time. This paper presents a well-functioning Hybrid Fusion-Neural Filter Approach (HFNF)classification system that considers various factors such as accuracy, recovery and accuracy. MRI is one of the most traditional methods for the primary diagnostic tool for brain tumors. If the tumor is malignant for successful treatment, the necessary diagnostic and treatment planning measures must be taken quickly. Physicians can make accurate decisions by applying the following procedures. The necessary treatment can be done effectively. A computer-assisted diagnostic system, MRI, can help reduce the workload of physicians.","PeriodicalId":347216,"journal":{"name":"2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126875692","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 : 2020-11-06DOI: 10.1109/PDGC50313.2020.9315742
Subhanvali Shaik, Mohammad Jabirullah, Anish Kumar Vishwakarma, Rakesh Ranjan
The supermarket is a place where a wide assortment of products is accessible. The primary expectation of markets is to give accessibility of the considerable number of products and spare the hour of the buyer. As innovation advanced, lives have been essentially improved because of the development of laborsaving and intelligent utilities. In urban communities, we can watch a colossal blaze at the supermarket on weekends. This turns out to be much more when there is an assorted variety of offers and discounts. In the current scenario, people purchase an assortment of products and put them into the cart. After taking the desired products, one should move toward the counter for billing. Manual billing takes ample time which results in hauling the shopping handcart all through the shopping time and holding up in huge queues at the billing counter. To conquer these difficulties, we have proposed ARM-7 microcontroller-based smart shopping handcart for supermarkets to make the shopping experience very convenient for customers. This work can potentially reduce the human efforts and manpower requirement at the billing desk. Radio frequency identification (RFID) reader helps to scan through the tag and display the product information on LCD screen. ZigBee serves as the transceiver. All the components are interfaced with microcontroller which has database of the particular product in its memory. So whenever a tag is swiped the microcontroller checks the database and displays the details of the product. At the final stage, the list of details of the products is maintained and convenient payment solution is provided when shopping is finished. The goal of this task is to improve the speed of shopping. Hence, this system provides time-efficient, cost-effective, convenient, reliable, and user-friendly solution for shopping.
{"title":"Advancement of Shopping Handcart for Supermarket","authors":"Subhanvali Shaik, Mohammad Jabirullah, Anish Kumar Vishwakarma, Rakesh Ranjan","doi":"10.1109/PDGC50313.2020.9315742","DOIUrl":"https://doi.org/10.1109/PDGC50313.2020.9315742","url":null,"abstract":"The supermarket is a place where a wide assortment of products is accessible. The primary expectation of markets is to give accessibility of the considerable number of products and spare the hour of the buyer. As innovation advanced, lives have been essentially improved because of the development of laborsaving and intelligent utilities. In urban communities, we can watch a colossal blaze at the supermarket on weekends. This turns out to be much more when there is an assorted variety of offers and discounts. In the current scenario, people purchase an assortment of products and put them into the cart. After taking the desired products, one should move toward the counter for billing. Manual billing takes ample time which results in hauling the shopping handcart all through the shopping time and holding up in huge queues at the billing counter. To conquer these difficulties, we have proposed ARM-7 microcontroller-based smart shopping handcart for supermarkets to make the shopping experience very convenient for customers. This work can potentially reduce the human efforts and manpower requirement at the billing desk. Radio frequency identification (RFID) reader helps to scan through the tag and display the product information on LCD screen. ZigBee serves as the transceiver. All the components are interfaced with microcontroller which has database of the particular product in its memory. So whenever a tag is swiped the microcontroller checks the database and displays the details of the product. At the final stage, the list of details of the products is maintained and convenient payment solution is provided when shopping is finished. The goal of this task is to improve the speed of shopping. Hence, this system provides time-efficient, cost-effective, convenient, reliable, and user-friendly solution for shopping.","PeriodicalId":347216,"journal":{"name":"2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC)","volume":"132 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132470461","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 : 2020-11-06DOI: 10.1109/PDGC50313.2020.9315806
A. Maurya
Cloud computing consists of distributed resources and used to provide services to the applications which require huge computation power such as scientific, mathematical, weather forecasting, and biomedical applications. These applications are considered as workflow applications containing many numbers of dependent tasks. The scheduling of these dependent tasks on distributed resources is a critical problem in cloud computing. In this paper, we present a scheduling algorithm that considers clustering of resources and tasks for workflow applications in the cloud computing environment. The given algorithm is an enhancement toHySARC algorithm. Like HySARC, the proposed algorithm first forms clusters of resources and tasks and then applies list scheduling techniques on each of the clusters to schedule tasks. We have estimated and compared the performance of the proposed algorithm with HySARC algorithm on the parameters like clustering time, and makespan, and found that the proposed algorithm performed better than the compared algorithm.
{"title":"Resource and Task Clustering based Scheduling Algorithm for Workflow Applications in Cloud Computing Environment","authors":"A. Maurya","doi":"10.1109/PDGC50313.2020.9315806","DOIUrl":"https://doi.org/10.1109/PDGC50313.2020.9315806","url":null,"abstract":"Cloud computing consists of distributed resources and used to provide services to the applications which require huge computation power such as scientific, mathematical, weather forecasting, and biomedical applications. These applications are considered as workflow applications containing many numbers of dependent tasks. The scheduling of these dependent tasks on distributed resources is a critical problem in cloud computing. In this paper, we present a scheduling algorithm that considers clustering of resources and tasks for workflow applications in the cloud computing environment. The given algorithm is an enhancement toHySARC algorithm. Like HySARC, the proposed algorithm first forms clusters of resources and tasks and then applies list scheduling techniques on each of the clusters to schedule tasks. We have estimated and compared the performance of the proposed algorithm with HySARC algorithm on the parameters like clustering time, and makespan, and found that the proposed algorithm performed better than the compared algorithm.","PeriodicalId":347216,"journal":{"name":"2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC)","volume":"62 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132192494","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 : 2020-11-06DOI: 10.1109/PDGC50313.2020.9315792
Jagdish R. Yadav, J. Thakur
Botnet is a prevalent threat among the Internet that always keep on proliferating. They can mow down an entire network within a blink of an eye. Different detection techniques have been proposed to detect botnets but botmasters always keep on revamping these botnets making it onerous for detection techniques that are based on command and control (C&C) protocols and structures. Botnets also utilize encrypted communication during their propagation. As a result, a technique irrespective of the protocols and propagation mechanisms used needs to be developed. Also, the technique should be able to detect encrypted botnets. In this paper, BotEye is proposed that is a botnet detection technique based on the traffic flow behavior of the network. The fringe benefit of using a flow-based approach is that only a fraction of the total network traffic flow needs to be analyzed. The technique suggested is heedless towards the C&C protocols and structures used. It can even detect encrypted botnets as it is independent of the payload information. BotEye makes use of four features to differentiate between malicious and benign traffic. Furthermore, BotEye is evaluated against the CTU-13 dataset, using three different machine learning classifiers that incorporates a stratified 10-fold cross-validation technique. The evaluation process shows that BotEye achieved the best results, i.e., 98.5% accuracy along with a low false-positive rate when the time window is set at 240s.
{"title":"BotEye: Botnet Detection Technique Via Traffic Flow Analysis Using Machine Learning Classifiers","authors":"Jagdish R. Yadav, J. Thakur","doi":"10.1109/PDGC50313.2020.9315792","DOIUrl":"https://doi.org/10.1109/PDGC50313.2020.9315792","url":null,"abstract":"Botnet is a prevalent threat among the Internet that always keep on proliferating. They can mow down an entire network within a blink of an eye. Different detection techniques have been proposed to detect botnets but botmasters always keep on revamping these botnets making it onerous for detection techniques that are based on command and control (C&C) protocols and structures. Botnets also utilize encrypted communication during their propagation. As a result, a technique irrespective of the protocols and propagation mechanisms used needs to be developed. Also, the technique should be able to detect encrypted botnets. In this paper, BotEye is proposed that is a botnet detection technique based on the traffic flow behavior of the network. The fringe benefit of using a flow-based approach is that only a fraction of the total network traffic flow needs to be analyzed. The technique suggested is heedless towards the C&C protocols and structures used. It can even detect encrypted botnets as it is independent of the payload information. BotEye makes use of four features to differentiate between malicious and benign traffic. Furthermore, BotEye is evaluated against the CTU-13 dataset, using three different machine learning classifiers that incorporates a stratified 10-fold cross-validation technique. The evaluation process shows that BotEye achieved the best results, i.e., 98.5% accuracy along with a low false-positive rate when the time window is set at 240s.","PeriodicalId":347216,"journal":{"name":"2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132789204","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 : 2020-11-06DOI: 10.1109/PDGC50313.2020.9315791
Mansi Mathur, V. Jindal, Gitanjali Wadhwa
Ovaries are important part of female reproductive system. The importance of these tiny glands is derived from the production of female sex hormones and female gametes. The location of these ductless almond shaped small glandular organs is on just opposite sides of uterus attached with ovarian ligament. There are many factors due to which ovarian cancer can occur but it can be detected by using various techniques and among them there is one method named as convolutional neural network. This review paper tells us about how we can use Convolutional Neural Network to classify the ovarian cancer tumour and what other ways to deal with it. In this research work we have also discussed about the comparison of various machine learning algorithms like K-Nearest Neighbor, Support Vector Machine and Artificial Neural Network used in detection of ovarian cancer. After comparing the different methods for this cancer detection, it seemed Deep Learning Technique to be the best for yielding results.
{"title":"Detecting Malignancy of Ovarian Tumour using Convolutional Neural Network: A Review","authors":"Mansi Mathur, V. Jindal, Gitanjali Wadhwa","doi":"10.1109/PDGC50313.2020.9315791","DOIUrl":"https://doi.org/10.1109/PDGC50313.2020.9315791","url":null,"abstract":"Ovaries are important part of female reproductive system. The importance of these tiny glands is derived from the production of female sex hormones and female gametes. The location of these ductless almond shaped small glandular organs is on just opposite sides of uterus attached with ovarian ligament. There are many factors due to which ovarian cancer can occur but it can be detected by using various techniques and among them there is one method named as convolutional neural network. This review paper tells us about how we can use Convolutional Neural Network to classify the ovarian cancer tumour and what other ways to deal with it. In this research work we have also discussed about the comparison of various machine learning algorithms like K-Nearest Neighbor, Support Vector Machine and Artificial Neural Network used in detection of ovarian cancer. After comparing the different methods for this cancer detection, it seemed Deep Learning Technique to be the best for yielding results.","PeriodicalId":347216,"journal":{"name":"2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123853710","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 : 2020-11-06DOI: 10.1109/PDGC50313.2020.9315822
A. Khanna, Sanmeet Kaur
In context to advancements in technologies, there exist a variety of sensors that are incorporated within the fields for obtaining vital as well as auxiliary information. Among various areas of implementation for Wireless Sensor Networks (WSN), agriculture is one such domain that has experienced revolutionary advancements over the past few years. Favorable outcome for agricultural practices completely depends on correct identification and selection of sensor. In order to administer the agricultural issues in today's date, deployment of sensors has become a necessity within the domain. The basic vision of this research article is to shed light on various agricultural sensors that are available in today's date followed by proposing a framework that suggests the precise amount of fertilizer requirement by the field after accessing various associated parameters. The study proposes Requirement Based Decision Support System (RbDSS) after evaluating various parameters, i.e., Soil moisture (Sm), Soil temperature (St), Soil humidity (Sh), Volumetric Water Content (VWC), and Electrical conductivity (EC). The results of the experimentation depicts decrease in the consumption of fertilizers by 24.68 %.
{"title":"Wireless Sensor and Actuator Network(s) and its significant impact on Agricultural domain","authors":"A. Khanna, Sanmeet Kaur","doi":"10.1109/PDGC50313.2020.9315822","DOIUrl":"https://doi.org/10.1109/PDGC50313.2020.9315822","url":null,"abstract":"In context to advancements in technologies, there exist a variety of sensors that are incorporated within the fields for obtaining vital as well as auxiliary information. Among various areas of implementation for Wireless Sensor Networks (WSN), agriculture is one such domain that has experienced revolutionary advancements over the past few years. Favorable outcome for agricultural practices completely depends on correct identification and selection of sensor. In order to administer the agricultural issues in today's date, deployment of sensors has become a necessity within the domain. The basic vision of this research article is to shed light on various agricultural sensors that are available in today's date followed by proposing a framework that suggests the precise amount of fertilizer requirement by the field after accessing various associated parameters. The study proposes Requirement Based Decision Support System (RbDSS) after evaluating various parameters, i.e., Soil moisture (Sm), Soil temperature (St), Soil humidity (Sh), Volumetric Water Content (VWC), and Electrical conductivity (EC). The results of the experimentation depicts decrease in the consumption of fertilizers by 24.68 %.","PeriodicalId":347216,"journal":{"name":"2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC)","volume":"24 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120910756","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 : 2020-11-06DOI: 10.1109/PDGC50313.2020.9315745
Nidhi Sharma, Ravindara Bhatt
In today's technological world the Internet plays a significant role. Internet of Things (IoT) consists of a large number of wireless sensors and wearable sensors known as Things. These sensors are connected devices in a network and generate a vast amount of data. These sensors have limited storage and computing facility for elderly patient applications. Recently, the Internet of Things (IoT) has drawn considerable interest among the research community. Fog computing has several benefits for elderly healthcare applications such as security, efficient load distribution, and low-latency. Fog computing layer provides several advantages as improved doctor-patient relationships, reduction in medical treatment cost, and customized treatment for elderly patients. The main contribution of this article is to present a practical solution for elderly patients by taking advantage of fog computing for IoT based health systems. The system helps attendants and doctors by providing various healthcare vital parameters for preventive and corrective measures promptly.
{"title":"FoG Computing based IoT in Healthcare Application","authors":"Nidhi Sharma, Ravindara Bhatt","doi":"10.1109/PDGC50313.2020.9315745","DOIUrl":"https://doi.org/10.1109/PDGC50313.2020.9315745","url":null,"abstract":"In today's technological world the Internet plays a significant role. Internet of Things (IoT) consists of a large number of wireless sensors and wearable sensors known as Things. These sensors are connected devices in a network and generate a vast amount of data. These sensors have limited storage and computing facility for elderly patient applications. Recently, the Internet of Things (IoT) has drawn considerable interest among the research community. Fog computing has several benefits for elderly healthcare applications such as security, efficient load distribution, and low-latency. Fog computing layer provides several advantages as improved doctor-patient relationships, reduction in medical treatment cost, and customized treatment for elderly patients. The main contribution of this article is to present a practical solution for elderly patients by taking advantage of fog computing for IoT based health systems. The system helps attendants and doctors by providing various healthcare vital parameters for preventive and corrective measures promptly.","PeriodicalId":347216,"journal":{"name":"2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121219084","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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