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RISS 인기검색어
- 검색키워드
- 저자 : Saranya G. (검색결과 4 건)
- 무료
- 기관 내 무료
- 유료
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Integrated Vision and Sensor Based Analysis for Sleep Apnea Using FeatFaceNet Deep Learning
Saranya G. 대한전기학회 2024 Journal of Electrical Engineering & Technology Vol.19 No.1
For the purpose of identifying sleep apnea, it's critical to have a trustworthy, distant, and non-intrusive technology for monitoring heart rate, breathing rate, and SpO2. Polysomnography (PSG) is the standard approach for diagnosing sleep apnea, but it is also an expensive and time-consuming process. It calls for a sleep lab with specifc electrode-based gear and staf with the necessary training. When the subject is sufering from any neurological conditions like Parkinson Disease (PD), all these elements combine to make the process difcult and uncomfortable for the patient. In this work, a real-time video-based and signal-based system for diagnosing sleep apnea in persons with Parkinson's disease is presented. The criteria that are required for the detection of sleep apnea in subjects are heart rate (HR), respiration rate (RR), and oxygen saturation (SpO2). Here, it is possible to use a smartphone camera to detect and interpret imaging Photoplethysmography (iPPG) signals from video of a user's face in order to assess HR, RR, and oxygen saturation. In order to determine the volumetric changes in the blood fow, iPPG datas are processed in accordance with the video-based monitoring method. The Viola Jones method is employed in this research to identify faces. The iPPG signals are collected using the RGB channel approach after image segmentation to determine the region of interest (ROI) as the forehead. From the collected iPPG signals, the characteristics of the HR, RR, and SpO2 parameters are extracted and mathematically calculated. The time domain and frequency domain features are extracted from the ECG signal of the matching individuals. Using the FeatFaceNet Deep Learning technique, these features are decreased. The Ensemble Boosted SVM (EB-SVM) classifcation algorithm is utilized to categories the data into apneic conditions after feature selection. In the event of an emergency, the project's fnding may also be used to notify the medical assistant. As a result, our method enables remote sleep apnea detection for making the process cost-efective and comfortable for the individuals. The proposed method suggests the following performance indices: accuracy, error, precision, recall, FPR, F1-Score, and processing time.
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V.Saranya,S.Ramya,R.G. Suresh Kumar,T.Nalini 보안공학연구지원센터 2016 International Journal of Grid and Distributed Comp Vol.9 No.3
Cloud computing is a technology in which the Cloud Service Providers (CSP) provide many virtual servers to the users to store their information in the cloud. The faults occurring on the assignment and dismission of the virtual machines, the processing cost in the allocation of resources must also be considered. The parallel processing of the information on the virtual machines must be done effectively and in an efficient manner. A variety of systems were developed to facilitate Many Task Computing (MTC). These systems aim to hide the issues of parallelism and fault tolerant and they are used in many applications. In this paper, we introduced Nephele, a data processing framework to exploit dynamic resource provisioning offered by IaaS clouds. The performance evaluation of the virtual machines has been evaluated and the allocation and de-allocation of job tasks to the specific virtual machines has also been considered. A performance comparison with the well known data processing framework hadoop has been done. Thus this paper tells about the effective and efficient manner of processing the data by parallel processing and allocating the correct resources for the desired task. It also helps to reduce the cost of resource utilization by exploiting the dynamic resource utilization.
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V.Saranya,R.G. Suresh Kumar,T. Nalini 보안공학연구지원센터 2016 International Journal of Database Theory and Appli Vol.9 No.6
Cloud computing in its various forms allows users to store their information at remote location and reduce the burden at their local systems. Even though this is an advantage for users but there are also many drawbacks because of this remote storage. The main drawback which needs to be dealt with is security. Recently, security is the major concern which most of the cloud service providers are facing. The users store their information in remote location with the hope of maintaining the privacy and integrity of data. In order, to maintain the privacy and integrity of users’ data auditing has to be done by the Cloud Service Providers (CSP). CSP uses the Third Party Auditor (TPA) for performing the auditing. The TPA performs auditing on behalf of the data owner using different auditing mechanisms. Many auditing mechanisms have been introduced in literature. Each mechanism varies from one another in one or more characteristics. In this paper we have provided a study on the different auditing mechanisms required to preserve the privacy and integrity of data in cloud. We have presented the advantages and flaws in each mechanism compared to another. Many auditing mechanisms are arising in literature with the aim to maintain the integrity of users’ data and preserve the privacy. This paper remains as the basis for different auditing mechanisms that are arising in literature. With the help of auditing mechanisms the TPA can best satisfy the needs of the users.
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