Rakesh Kumar Bajaj

Wireless contactless sensing technologies have become vital for individualized healthcare monitoring since they let doctors check on patient’s health without having to touch them. However, it is still hard to choose the best sensing technology because of uncertainty, imprecision, and different expert perspectives. For the sake of solving such problems, a complete decision-making framework based on the Neutrosophic Soft Fuzzy TOPSIS method has been proposed which works well with vague and missing information in evaluations with more than one criterion. The model systematically evaluates different sensing technologies based on important criteria and gives a clear and strong score by calculating how near they are to the perfect solution. A comprehensive discussion and sensitivity analysis on the results obtained have also been presented along with pros and cons. A table comparing the proposed model to other decision-making methodologies has been additionally provided keeping the benchmarking into account to ensure the model is both accurate and consistent in its performance. Also, comparative trials show that the proposed method is better at coping with uncertainty and competing expert opinions. Overall, in this framework, results show that the proposed decision-support tool is dependable and easy-to-understand for healthcare professionals and policymakers. It helps them choose the best contactless human sensing technologies for real-world personalized healthcare applications.

In today’s financial systems, digital transactions are crucial for offering users significant convenience and rapid processing; however, as these networks develop, they have also become more sensitive to fraud, raising serious concerns for both consumers and financial institutions. To address these issues, particularly the presence of uncertainty and ambiguity in detecting fraud, advanced forensic analysis setups are needed for clear fraud detection and prevention. Employing the MCDM methods, our study aims to enhance fraud identification in digital transactions made through consumer devices. First, we propose some novel similarity measures for T-spherical fuzzy hypersoft set (TSFHSS) structure with their weighted version and similarity properties. The work combines TOPSIS techniques with new T-spherical fuzzy hypersoft similarity measures constructed using sine and tangent functions to enhance fraud detection accuracy and reliability. Furthermore, the study employs sensitivity analysis to determine the adaptability and predictability of the presented model across numerous conditions and decision-maker preferences, ensuring its applicability in real-world scenarios.