Secured Cloud Computing for Medical Database Monitoring Using Machine Learning Techniques
- Title
- Secured Cloud Computing for Medical Database Monitoring Using Machine Learning Techniques
- Creator
- Balamurugan M.; Kumaresan M.; Haripriya V.; Annamalai S.; Bhuvana J.
- Description
- A growing number of people are calling on the health-care industry to adopt new technologies that are becoming accessible on the market in order to improve the overall quality of their services. Telecommunications systems are integrated with computers, connectivity, mobility, data storage, and information analytics to make a complete information infrastructure system. It is the order of the day to use technology that is based on the Internet of Things (IoT). Given the limited availability of human resources and infrastructure, it is becoming more vital to monitor chronic patients on an ongoing basis as their diseases deteriorate and become more severe. A cloud-based architecture that is capable of dealing with all of the issues stated above may be able to provide effective solutions for the health-care industry. With the purpose of building software that would mix cloud computing and mobile technologies for health-care monitoring systems, we have assigned ourselves the task of designing software. Using a method devised by Higuchi, it is possible to extract stable fractal values from electrocardiogram (ECG) data, something that has never been attempted previously by any other researcher working on the development of a computer-aided diagnosis system for arrhythmia. As a result of the results, it is feasible to infer that the support vector machine has attained the best classification accuracy attainable for fractal features. When compared to the other two classifiers, the feed forward neural network model and the feedback neural network model, the support vector machine excels them both. Furthermore, it should be noted that the sensitivity of both the feed forward neural network and the support vector machine yields results that are equivalent in quality (92.08% and 90.36%, respectively). 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
- Source
- Lecture Notes in Networks and Systems, Vol-444, pp. 91-109.
- Date
- 2022-01-01
- Publisher
- Springer Science and Business Media Deutschland GmbH
- Subject
- Cloud database; Electrocardiogram; Fractal features; Neural network
- Coverage
- Balamurugan M., Department of Computer Science and Engineering, School of Engineering and Technology, Christ (Deemed to Be University), Bangalore, India; Kumaresan M., School of Computer Science and Engineering, Jain (Deemed to Be) University, Bangalore, India; Haripriya V., School of Computer Science and IT, Jain (Deemed to Be) University, Bangalore, India; Annamalai S., School of Computing Science and Engineering, Galgotias University, Greater Noida, India; Bhuvana J., School of Computer Science and IT, Jain (Deemed to Be) University, Bangalore, India
- Rights
- Restricted Access
- Relation
- ISSN: 23673370; ISBN: 978-981192499-6
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Balamurugan M.; Kumaresan M.; Haripriya V.; Annamalai S.; Bhuvana J., “Secured Cloud Computing for Medical Database Monitoring Using Machine Learning Techniques,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 24, 2025, https://archives.christuniversity.in/items/show/20232.