This study delves into the realm of Explainable Artificial Intelligence (XAI) frameworks, aiming to empower researchers and practitioners with a deeper understanding of these tools. We establish a comprehensive knowledge base by classifying and analyzing prominent XAI solutions based on key attributes like explanation type, model dependence, and use cases. This resource equips users to navigate the diverse XAI landscape and select the most suitable framework for their specific needs. Furthermore, the study proposes a novel framework called XAIE (eXplainable AI Evaluator) for informed decision-making in XAI adoption. This framework empowers users to assess different XAI options based on their application context objectively. This will lead to more responsible AI development by fostering transparency and trust. Finally, the research identifies the limitations and challenges associated with the existing XAI frameworks, paving the way for future advancements. By highlighting these areas, the study guides researchers and developers in enhancing the capabilities of Explainable AI.

By implementing a successful and effective Electronic-Know Your Customer (e-KYC) infrastructure, conventional defects and expenses may be greatly reduced. Strong authentication procedures are used by existing e-KYC solutions to guarantee safety and confidentiality. However, these technologies have shortcomings like as key administration expenses and dependency on encryption. We suggest a revolutionary strategy that integrates blockchain technology, Web 3.0, and quantum computing to get around these restrictions. In this research, we show how current e-KYC processes could possibly be improved, making them faster and more reliable, by integrating Web 3.0 with quantum computing. Our solution makes it simple to quickly verify several consumers, increasing productivity. We provide improved safety for online requests, transcending the weaknesses inherent in traditional encryption techniques by utilizing quantum computing technology and applying the concept of quantum encryption key methodology. We contribute to the development of safe and private solutions by addressing the shortcomings of the present e-KYC systems. The results of our research show how Web 3.0 and quantum computer technology have the power to completely alter the e-KYC environment.

Annually, deceptive claims cause significant financial losses for the antiquated system now in use for processing health insurance claims. Furthermore, it runs serious security and privacy concerns due to its dependence on traditional techniques. Health insurance claims involving prescription drugs are especially susceptible to fraud. Furthermore, a lack of linkage between pharmacy and medical provider prescription data exacerbates misunderstanding and makes it easier to utilize fake prescriptions. This study suggests a way to handle claims related to health insurance that guarantees decentralized management, privacy, security, and reliability by using blockchain technology. The proposed solution makes use of a private Ethereum blockchain and includes two smart contracts that improve traceability and reliability: those for registration and approval. Authorized parties have improved access to centralized patient information through integration with decentralized apps (DApps) and off-chain storage (IPFS). A gateway filters the data that participants may access in order to protect participant privacy. To clarify the guiding principles of the suggested blockchain-based health insurance claim processing procedure for prescription pharmaceuticals, we offer a summary of the system architecture, sequence diagrams, entity-relationship diagram, and algorithms. Security evaluation and contrast with current solutions are used to assess the performance of the suggested solution.