An Efficient and Robust Explainable Artificial Intelligence for Securing Smart Healthcare System
- Title
- An Efficient and Robust Explainable Artificial Intelligence for Securing Smart Healthcare System
- Creator
- Seetharaman T.; Sharma V.; Balamurugan B.; Grover V.; Agnihotri A.
- Description
- The advent of IoT technologies has a tremendous impact on the healthcare sector enabling efficient monitoring of patients and utilizing the data for better analytics. Since every activity related to a patients health is monitored, the focus on smart healthcare applications has significantly transferred from service provision to a security perspective. As most healthcare applications are automated security plays a vital role. The technique of machine learning has been widely used in securing smart healthcare systems. The major challenge is that these applications require high-quality labeled images, which are difficult to acquire from real-time security applications. Further, it highly time-consuming and cost-expensive process. To address these constraints, in this paper, we define an efficient and robust explainable artificial intelligence technique that takes a small quantity of labeled data to train and de-ploy the security countermeasure for targeted healthcare applications. The proposed approach enhances the security measure through the detection of drifting samples with explainability. It is observed that the proposed approach improved accuracy, high fidelity, and explanation measures. Also, this approach is proven to be considerably resistant against numerous security threats. 2023 IEEE.
- Source
- 2023 2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023, pp. 1066-1071.
- Date
- 2023-01-01
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- Explainable Artificial Intelligence; IoT; Security; Smart Healthcare
- Coverage
- Seetharaman T., Department of CSE, CMR University, Bangalore, India; Sharma V., Computer Science Department, CHRIST (Deemed to be University), India; Balamurugan B., Shiva Nadar University, India; Grover V., School of Management, Noida Institute of Engineering and Technology, India; Agnihotri A., School of Business, Galgotias University, India
- Rights
- Restricted Access
- Relation
- ISBN: 979-835030541-8
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Seetharaman T.; Sharma V.; Balamurugan B.; Grover V.; Agnihotri A., “An Efficient and Robust Explainable Artificial Intelligence for Securing Smart Healthcare System,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 24, 2025, https://archives.christuniversity.in/items/show/19747.