Pub Date : 2013-07-25DOI: 10.1109/ICRTIT.2013.6844189
P. Varalakshmi, K. Maheshwari
The main objective of the proposed system is to increase the resource utilization and the provider's profit in a cloud environment. While reserving the resources, the users' requests are posted onto the Virtual Machines (VMs), and then these VMs are deployed onto the Physical Machines (PMs). The proposed system uses a Bin-Packing procedure for VM Placement which is used to reduce the number of physical machines required by the cloud providers. The proposed work has two parts, Job placement and VM placement. Job placement is done by the cloud broker which uses the best fit heuristic approach and then, the VM placement is done by the cloud broker that follows the worst fit heuristic approach. This work has been executed and the performance results are analysed with CloudSim tool, which showcased the good power conservation compared to other strategies.
{"title":"Cost-optimized resource provisioning in cloud","authors":"P. Varalakshmi, K. Maheshwari","doi":"10.1109/ICRTIT.2013.6844189","DOIUrl":"https://doi.org/10.1109/ICRTIT.2013.6844189","url":null,"abstract":"The main objective of the proposed system is to increase the resource utilization and the provider's profit in a cloud environment. While reserving the resources, the users' requests are posted onto the Virtual Machines (VMs), and then these VMs are deployed onto the Physical Machines (PMs). The proposed system uses a Bin-Packing procedure for VM Placement which is used to reduce the number of physical machines required by the cloud providers. The proposed work has two parts, Job placement and VM placement. Job placement is done by the cloud broker which uses the best fit heuristic approach and then, the VM placement is done by the cloud broker that follows the worst fit heuristic approach. This work has been executed and the performance results are analysed with CloudSim tool, which showcased the good power conservation compared to other strategies.","PeriodicalId":113531,"journal":{"name":"2013 International Conference on Recent Trends in Information Technology (ICRTIT)","volume":"122 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124404014","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 : 2013-07-25DOI: 10.1109/ICRTIT.2013.6844284
Srinivasan S, Ram Srivatsa, Ram Kumar, Bhargavi R, Vaidehi V
Existing learning systems for health prediction require batch-wise data or sub-text along with data to begin the learning process. These techniques are slow in learning and require more time to achieve a commendable accuracy. The techniques also provide less scope for adaptation to varying data. Since health parameters change dynamically, there is a need to reduce false positives. In this paper, a Regression Based Adaptive Incremental Learning Algorithm (RBAIL) is proposed. The novel RBAIL algorithm performs regression on the vital parameters such as Heart Rate, Blood Pressure and Saturated Oxygen Level to predict the abnormality. It also validates the data before learning, thus reducing the probability of a false positive. The proposed algorithm has been validated with varied data and is observed to provide increased accuracy in prediction and adaptability to fluctuating data. Simulation over real world data sets is used to validate the effectiveness of this algorithm.
{"title":"A regression based adaptive incremental algorithm for health abnormality prediction","authors":"Srinivasan S, Ram Srivatsa, Ram Kumar, Bhargavi R, Vaidehi V","doi":"10.1109/ICRTIT.2013.6844284","DOIUrl":"https://doi.org/10.1109/ICRTIT.2013.6844284","url":null,"abstract":"Existing learning systems for health prediction require batch-wise data or sub-text along with data to begin the learning process. These techniques are slow in learning and require more time to achieve a commendable accuracy. The techniques also provide less scope for adaptation to varying data. Since health parameters change dynamically, there is a need to reduce false positives. In this paper, a Regression Based Adaptive Incremental Learning Algorithm (RBAIL) is proposed. The novel RBAIL algorithm performs regression on the vital parameters such as Heart Rate, Blood Pressure and Saturated Oxygen Level to predict the abnormality. It also validates the data before learning, thus reducing the probability of a false positive. The proposed algorithm has been validated with varied data and is observed to provide increased accuracy in prediction and adaptability to fluctuating data. Simulation over real world data sets is used to validate the effectiveness of this algorithm.","PeriodicalId":113531,"journal":{"name":"2013 International Conference on Recent Trends in Information Technology (ICRTIT)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124430278","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 : 2013-07-25DOI: 10.1109/ICRTIT.2013.6844188
N. Asokan, A. Sujitha, R. Poornima, P. Sujatha, A. Kannan
A distributed environment is one in which intrusions are prevalent and drastically affect the performance of the networks. Therefore a need for Intrusion Detection Networks arises. Collaborators could be used here to enhance the detection of attacks. But still this concept suffers from lack of trustworthiness in the distributed environment. We introduce Mobile Agents (MA) to handle this problem effectively. A DCE - TRUST architecture is proposed where MAs are used to migrate from one node to another autonomously within the range of collaborators in and out of each network to inform them about the detected intrusions accurately and in a timely manner. We also found that the proposed architecture enhances detection rate and minimizes the rate in which each node gets to know about the attacks.
{"title":"Trustworthy distributed collaborative intrusion detection using mobile agents","authors":"N. Asokan, A. Sujitha, R. Poornima, P. Sujatha, A. Kannan","doi":"10.1109/ICRTIT.2013.6844188","DOIUrl":"https://doi.org/10.1109/ICRTIT.2013.6844188","url":null,"abstract":"A distributed environment is one in which intrusions are prevalent and drastically affect the performance of the networks. Therefore a need for Intrusion Detection Networks arises. Collaborators could be used here to enhance the detection of attacks. But still this concept suffers from lack of trustworthiness in the distributed environment. We introduce Mobile Agents (MA) to handle this problem effectively. A DCE - TRUST architecture is proposed where MAs are used to migrate from one node to another autonomously within the range of collaborators in and out of each network to inform them about the detected intrusions accurately and in a timely manner. We also found that the proposed architecture enhances detection rate and minimizes the rate in which each node gets to know about the attacks.","PeriodicalId":113531,"journal":{"name":"2013 International Conference on Recent Trends in Information Technology (ICRTIT)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121142180","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 : 2013-07-25DOI: 10.1109/ICRTIT.2013.6844293
J. Surana, C. Hemalatha, V. Vaidehi, S. Palavesam, M. Khan
Human activity recognition (HAR) has gained a lot of significance in monitoring the health of people, especially to detect fall among elderly people who live independently. This project proposes a novel method for recognizing activities and detecting fall of a person using body-worn sensors. Traditional algorithms like Naïve Bayes classifier and Support Vector Machine are mainly used for activity classification. However, these systems fail to capture significant association that exists between interesting patterns. Existing accelerometer based wearable systems are not sufficient to determine the fall of a person. Hence, a Fuzzy Associative Classification based Human Activity Recognition (FAC-HAR) system is proposed to overcome the aforementioned drawbacks in detecting abnormal status of a person. The proposed (FAC-HAR) system uses three different sensors namely heartbeat, breathing rate and accelerometer and employs fuzzy clustering and associative classification for abnormality detection. The proposed system introduces a novel learning mechanism is to improve classification accuracy. A classification accuracy of 92% is achieved with the proposed fuzzy frequent pattern mining based human activity recognition.
{"title":"Adaptive learning based human activity and fall detection using fuzzy frequent pattern mining","authors":"J. Surana, C. Hemalatha, V. Vaidehi, S. Palavesam, M. Khan","doi":"10.1109/ICRTIT.2013.6844293","DOIUrl":"https://doi.org/10.1109/ICRTIT.2013.6844293","url":null,"abstract":"Human activity recognition (HAR) has gained a lot of significance in monitoring the health of people, especially to detect fall among elderly people who live independently. This project proposes a novel method for recognizing activities and detecting fall of a person using body-worn sensors. Traditional algorithms like Naïve Bayes classifier and Support Vector Machine are mainly used for activity classification. However, these systems fail to capture significant association that exists between interesting patterns. Existing accelerometer based wearable systems are not sufficient to determine the fall of a person. Hence, a Fuzzy Associative Classification based Human Activity Recognition (FAC-HAR) system is proposed to overcome the aforementioned drawbacks in detecting abnormal status of a person. The proposed (FAC-HAR) system uses three different sensors namely heartbeat, breathing rate and accelerometer and employs fuzzy clustering and associative classification for abnormality detection. The proposed system introduces a novel learning mechanism is to improve classification accuracy. A classification accuracy of 92% is achieved with the proposed fuzzy frequent pattern mining based human activity recognition.","PeriodicalId":113531,"journal":{"name":"2013 International Conference on Recent Trends in Information Technology (ICRTIT)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114911774","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 : 2013-07-25DOI: 10.1109/ICRTIT.2013.6844262
P. S. A. Kumar, V. Vaidehi, E. Chandralekha
As powerful computers and cameras have become wide spread, the number of applications using vision techniques has increased enormously. One such application that has received significant attention from the computer vision community is traffic surveillance. We propose a new event detection technique for detecting abnormal events in traffic video surveillance. The main objective of this work is to detect the abnormal events which normally occur at junction, in video surveillance. Our work consists of two phases 1) Training Phase 2) Testing Phase. Our main novelty in this work is modified lossy counting algorithm based on set approach. Initially, the frames are divided into grid regions at the junction and labels are assigned. The proposed work consist of blob detection and tracking, conversion of object location to data streams, frequent item set mining and pattern matching. In the training phase, blob detection is carried out by separating the modelled static background frame using Gaussian mixture models (GMM) and this will be carried out for every frame for tracking purpose. The blobs location is determined by assigning to the corresponding grid label and numbered moving object direction to form data streams. A modified lossy counting algorithm is performed over temporal data steams for discovering regular spatial video patterns. In testing phase, the same process is repeated except frequent item set mining, for finding the spatial pattern in each frame and it is compared with stored regular video patterns for abnormal event detection. The proposed system has shown significant improvement in performance over to the existing techniques.
{"title":"Video traffic analysis for abnormal event detection using frequent item set mining","authors":"P. S. A. Kumar, V. Vaidehi, E. Chandralekha","doi":"10.1109/ICRTIT.2013.6844262","DOIUrl":"https://doi.org/10.1109/ICRTIT.2013.6844262","url":null,"abstract":"As powerful computers and cameras have become wide spread, the number of applications using vision techniques has increased enormously. One such application that has received significant attention from the computer vision community is traffic surveillance. We propose a new event detection technique for detecting abnormal events in traffic video surveillance. The main objective of this work is to detect the abnormal events which normally occur at junction, in video surveillance. Our work consists of two phases 1) Training Phase 2) Testing Phase. Our main novelty in this work is modified lossy counting algorithm based on set approach. Initially, the frames are divided into grid regions at the junction and labels are assigned. The proposed work consist of blob detection and tracking, conversion of object location to data streams, frequent item set mining and pattern matching. In the training phase, blob detection is carried out by separating the modelled static background frame using Gaussian mixture models (GMM) and this will be carried out for every frame for tracking purpose. The blobs location is determined by assigning to the corresponding grid label and numbered moving object direction to form data streams. A modified lossy counting algorithm is performed over temporal data steams for discovering regular spatial video patterns. In testing phase, the same process is repeated except frequent item set mining, for finding the spatial pattern in each frame and it is compared with stored regular video patterns for abnormal event detection. The proposed system has shown significant improvement in performance over to the existing techniques.","PeriodicalId":113531,"journal":{"name":"2013 International Conference on Recent Trends in Information Technology (ICRTIT)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116421685","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 : 2013-07-25DOI: 10.1109/ICRTIT.2013.6844186
M. Sumalatha, E. Pugazhendi, D. Archana
Cloud computing is one of the most useful environment that provides various information services in which required information can be retrieved through many web-based tools and applications. Now the new surge of interest in cloud computing is accompanied with the exponential growth of data sizes. There is a need to find the desired content quickly and efficiently by simply consulting the index. Thus there arises a question of how to effectively process these immense data sets is becoming increasingly urgent. Our existing system is searching the content through ontology in cloud which practically suffers from maintaining a consistent logic for the input documents given and taking this advantage into consideration we have brought in new concepts called Document Retrieval Algorithm. In this paper we discuss how effectively and efficiently information can be retrieved from cloud taking into account their storage space too, where we store the metadata of file in cloud and not the entire file which holds a lot of space in distributed environment. Thus we bring in the concept of Named Entity Recognition and Universal Word List with Term frequency, which maximizes the information retrieval more effective and efficient and also to bridge the gap between the semantic web and the users which reduces the complexity met by them in information retrieval.
{"title":"Concept based document management in cloud storage","authors":"M. Sumalatha, E. Pugazhendi, D. Archana","doi":"10.1109/ICRTIT.2013.6844186","DOIUrl":"https://doi.org/10.1109/ICRTIT.2013.6844186","url":null,"abstract":"Cloud computing is one of the most useful environment that provides various information services in which required information can be retrieved through many web-based tools and applications. Now the new surge of interest in cloud computing is accompanied with the exponential growth of data sizes. There is a need to find the desired content quickly and efficiently by simply consulting the index. Thus there arises a question of how to effectively process these immense data sets is becoming increasingly urgent. Our existing system is searching the content through ontology in cloud which practically suffers from maintaining a consistent logic for the input documents given and taking this advantage into consideration we have brought in new concepts called Document Retrieval Algorithm. In this paper we discuss how effectively and efficiently information can be retrieved from cloud taking into account their storage space too, where we store the metadata of file in cloud and not the entire file which holds a lot of space in distributed environment. Thus we bring in the concept of Named Entity Recognition and Universal Word List with Term frequency, which maximizes the information retrieval more effective and efficient and also to bridge the gap between the semantic web and the users which reduces the complexity met by them in information retrieval.","PeriodicalId":113531,"journal":{"name":"2013 International Conference on Recent Trends in Information Technology (ICRTIT)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122197409","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 : 2013-07-25DOI: 10.1109/ICRTIT.2013.6844213
S. R. Janani, C. Hemalatha, V. Vaidehi
In recent days, elderly people living alone at home are steadily increasing throughout the world. This situation drives to develop a health care system for monitoring the health parameters of elderly people and help them to lead ahealthy independent life. This paper presents a system that uses wireless sensors for monitoring the health parameters without disturbing the normal activities of elderly people. The proposed system provides a wearable health care solution using the wireless Shimmer sensor device for collecting ECG data in home PC. ECG data anomaly is detected using rule based classifier. Classification rules are generated based on cluster centroids obtained using a novel scheme named Hybrid Expectation Maximization and Fuzzy C Means (HEMFCM) Clustering. The proposed method is validated using real data collected from different subjects and abnormal data readings from the MIT BIH database. Experimental results show that proposed method achieves 85% classification accuracy which is better than EM and FCM clustering methods.
{"title":"ECG anomaly detection using wireless BAN and HEMFCM clustering","authors":"S. R. Janani, C. Hemalatha, V. Vaidehi","doi":"10.1109/ICRTIT.2013.6844213","DOIUrl":"https://doi.org/10.1109/ICRTIT.2013.6844213","url":null,"abstract":"In recent days, elderly people living alone at home are steadily increasing throughout the world. This situation drives to develop a health care system for monitoring the health parameters of elderly people and help them to lead ahealthy independent life. This paper presents a system that uses wireless sensors for monitoring the health parameters without disturbing the normal activities of elderly people. The proposed system provides a wearable health care solution using the wireless Shimmer sensor device for collecting ECG data in home PC. ECG data anomaly is detected using rule based classifier. Classification rules are generated based on cluster centroids obtained using a novel scheme named Hybrid Expectation Maximization and Fuzzy C Means (HEMFCM) Clustering. The proposed method is validated using real data collected from different subjects and abnormal data readings from the MIT BIH database. Experimental results show that proposed method achieves 85% classification accuracy which is better than EM and FCM clustering methods.","PeriodicalId":113531,"journal":{"name":"2013 International Conference on Recent Trends in Information Technology (ICRTIT)","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129605440","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 : 2013-07-25DOI: 10.1109/ICRTIT.2013.6844241
V. Krishnamurty, R. Natarajan, C. Babu
Web services describe the standardized way of integrating the web based applications using XML, SOAP, WSDL and UDDI. A service oriented application is developed by composing suitable services available in the registry. Monitoring of the services is enabled using Apache Jmeter, in which the load and the invocation time for each service is specified which in turn helps to simulate the response time of the services. If the services are probably expected to violate the contract, then services with the same functionality are identified to reconfigure the application without affecting the base code. This paper proposes to use the AO4BPEL technique to change the BPEL workflow at runtime and bypass the services. Towards this objective, the proposed aspect based reconfiguration framework has been tested on a prototype SOA based application in the domain of e-Shopping.
{"title":"Monitoring and reconfiguring the services in service oriented system using AOBPEL","authors":"V. Krishnamurty, R. Natarajan, C. Babu","doi":"10.1109/ICRTIT.2013.6844241","DOIUrl":"https://doi.org/10.1109/ICRTIT.2013.6844241","url":null,"abstract":"Web services describe the standardized way of integrating the web based applications using XML, SOAP, WSDL and UDDI. A service oriented application is developed by composing suitable services available in the registry. Monitoring of the services is enabled using Apache Jmeter, in which the load and the invocation time for each service is specified which in turn helps to simulate the response time of the services. If the services are probably expected to violate the contract, then services with the same functionality are identified to reconfigure the application without affecting the base code. This paper proposes to use the AO4BPEL technique to change the BPEL workflow at runtime and bypass the services. Towards this objective, the proposed aspect based reconfiguration framework has been tested on a prototype SOA based application in the domain of e-Shopping.","PeriodicalId":113531,"journal":{"name":"2013 International Conference on Recent Trends in Information Technology (ICRTIT)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128289153","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 : 2013-07-25DOI: 10.1109/ICRTIT.2013.6844205
K. Bharathwajan, S. Janani, K. Raguram, C. Hemalatha, V. Vaidehi
Recent surveys show that the number of road accidents has increased predominantly. One of the major causes to which increased accidents are attributed to is physical ailment of drivers. Continuous monitoring of driver's health condition is essential in order to reduce car accidents that occur due to health abnormality. A non-intrusive method is demanded so as to prevent hindering of driving activity. This paper presents a mobile health monitoring system which is an application running in Android based smart phone. The mobile phone acquires vital parameters such as Heart Rate (HR) and Respiration Rate (RR) from a wearable body sensor through Bluetooth. As faster heart beat and shortness of breath are the most common symptoms of heart problem, the proposed system considers heart rate and respiration rate for deciding abnormal health status. A Bayesian Belief network (BBN) is designed to analyze the vital parameters along with the driver's health history and decide whether the health status of the driver is normal or abnormal in real time. Bayesian Network is a powerful representation for uncertain domains like human health status. Also, it improves classification accuracy as it allows seamless integration of additional information such as the driver's health records with sensed vital information for deciding abnormality and thus avoids false alarms. When abnormality is detected, immediately the driver gets a beep alert call in his mobile and call alert is generated to the caregiver in the case of emergency. The proposed system is tested in real time and it gives reasonable accuracy.
{"title":"Intelligent accident mitigation system by mining vital signs using wireless body sensor","authors":"K. Bharathwajan, S. Janani, K. Raguram, C. Hemalatha, V. Vaidehi","doi":"10.1109/ICRTIT.2013.6844205","DOIUrl":"https://doi.org/10.1109/ICRTIT.2013.6844205","url":null,"abstract":"Recent surveys show that the number of road accidents has increased predominantly. One of the major causes to which increased accidents are attributed to is physical ailment of drivers. Continuous monitoring of driver's health condition is essential in order to reduce car accidents that occur due to health abnormality. A non-intrusive method is demanded so as to prevent hindering of driving activity. This paper presents a mobile health monitoring system which is an application running in Android based smart phone. The mobile phone acquires vital parameters such as Heart Rate (HR) and Respiration Rate (RR) from a wearable body sensor through Bluetooth. As faster heart beat and shortness of breath are the most common symptoms of heart problem, the proposed system considers heart rate and respiration rate for deciding abnormal health status. A Bayesian Belief network (BBN) is designed to analyze the vital parameters along with the driver's health history and decide whether the health status of the driver is normal or abnormal in real time. Bayesian Network is a powerful representation for uncertain domains like human health status. Also, it improves classification accuracy as it allows seamless integration of additional information such as the driver's health records with sensed vital information for deciding abnormality and thus avoids false alarms. When abnormality is detected, immediately the driver gets a beep alert call in his mobile and call alert is generated to the caregiver in the case of emergency. The proposed system is tested in real time and it gives reasonable accuracy.","PeriodicalId":113531,"journal":{"name":"2013 International Conference on Recent Trends in Information Technology (ICRTIT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130383869","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 : 2013-07-25DOI: 10.1109/ICRTIT.2013.6844190
K. Vanathi, C. Valliyammai
Cloud computing allows the cloud customer to hoard their data on the remote store and endow them to utilize the On-demand high quality applications with super colossal computational power. As prodigious amount of data are stored on the cloud, it inevitably acquaint new security hazards such as data integrity, data availability problems in the system. To overcome these issues, we propose an extensible mechanism to perform data integrity auditing by delegating it to the Third Party Auditor (TPA) in a privacy preserving manner. This design is formulated based on tagging and hash tag table which supports the updates in the data and also it identifies the deleterious servers. With the use of TPA, it also reduces the burden of Cloud Service Provider (CSP).
{"title":"Providing secure storage services on cloud","authors":"K. Vanathi, C. Valliyammai","doi":"10.1109/ICRTIT.2013.6844190","DOIUrl":"https://doi.org/10.1109/ICRTIT.2013.6844190","url":null,"abstract":"Cloud computing allows the cloud customer to hoard their data on the remote store and endow them to utilize the On-demand high quality applications with super colossal computational power. As prodigious amount of data are stored on the cloud, it inevitably acquaint new security hazards such as data integrity, data availability problems in the system. To overcome these issues, we propose an extensible mechanism to perform data integrity auditing by delegating it to the Third Party Auditor (TPA) in a privacy preserving manner. This design is formulated based on tagging and hash tag table which supports the updates in the data and also it identifies the deleterious servers. With the use of TPA, it also reduces the burden of Cloud Service Provider (CSP).","PeriodicalId":113531,"journal":{"name":"2013 International Conference on Recent Trends in Information Technology (ICRTIT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130271471","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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