ADAPTATION OF XAI-FML METHODOLOGY FOR EFFICIENT AND SECURE E-HEALTHCARE SYSTEM
| dc.contributor.author | RABIA ABID | |
| dc.date.accessioned | 2025-01-31T04:28:13Z | |
| dc.date.available | 2025-01-31T04:28:13Z | |
| dc.date.issued | 0022-06-23 | |
| dc.description.abstract | Artificial Intelligence (AI) has been applicable in many sector (like educations, healthcare, businesses, government bodies, etc) to lessen the human effort and to create an ease. All AI based systems have decision support systems (DSS) to help human in all high-pitch and low pitch situation. As a support system many machines learning (ML) based algorithms helps to make accurate decision according to situation, increase accuracy rate in data classification and enhance the performance of systems. Explainable AI (XAI) has advance feature to en hanced the decision-making feature and improve the rule base technique by using more ad vance ML and deep learning (DL) based algorithms. In this research we chose e-healthcare systems for efficient decision making and data classification, where quite massive data like patients’ health record (PHR), hospitals confidential information, administrative data, research data, physicians’ details, and many other. In this research work, we identify the existing gaps in traditional AI and ML based algorithms for efficient e-healthcare systems and trying to over come it by using XAI and advance ML based algorithm Federated Machine Learning (FML). FML is a new and advance technology which helps to maintain privacy for PHR and handle large amount of medical data effectively. In this context, XAI along with FML increase the efficiency and improve the security also of the e-healthcare systems. The performed experi ment shows the efficient system performance by implementing federated averaging algorithm on open source FL platform. The evaluating graphs shows the accuracy rate by taking epochs size 5, batch size 16 and no. of clients 5, which shows higher accuracy rate with (19, 10−4). To conclude our research, we discuss the future work with still existing some gaps in e-healthcare system like security, price, efficiency, performance evaluation and many other. | |
| dc.identifier.uri | https://repository.kinnaird.edu.pk/handle/123456789/144 | |
| dc.language.iso | en | |
| dc.publisher | KINNAIRE COLLEGE COMPUTER SCIENCE DEPARTMENT | |
| dc.title | ADAPTATION OF XAI-FML METHODOLOGY FOR EFFICIENT AND SECURE E-HEALTHCARE SYSTEM | |
| dc.type | Thesis |