Powered by new advances in sensor development and artificial intelligence, the decreasing cost of computation, and the pervasiveness of handheld computation devices, biometric user authentication (and identification) is rapidly becoming ubiquitous. Modern approaches to biometric authentication, based on sophisticated machine learning techniques, cannot avoid storing either trained-classifier details or explicit user biometric data, thus exposing users’ credentials to falsification. In this paper, we introduce a secure way to handle user-specific information involved with the use of artificial neural networks for biometric authentication. Our proposed architecture, called a Neural Fuzzy Extractor (NFE), allows the coupling of pre-existing classifiers with fuzzy extractors, through an artificial-neuralnetwork-based buffer called an expander, with minimal or no performance degradation. The NFE thus offers all the performance advantages of modern deep-learningbased classifiers and all the security of standard fuzzy extractors. We demonstrate the NFE retrofit of a few classic artificial neural networks, for simple biometric authentication scenarios.

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This main objective of the study was to analyze the effectiveness of Payment Service Providers (PSPs) and examine the challenges to PSPs in Nepal with entrance of PSOs with Open Innovation such as Unified Payment Interface (UPI) and its future prospects among the users in Kathmandu valley (includes 3 districts: Kathmandu, Lalitpur, and Bhaktapur), Nepal. The research design was descriptive and analytical type; the research is based on mixed approach having both qualitative and quantitative data. Survey was conducted with total of 435 respondents, 405 respondents were selected using non random sampling for questionnaire. The collection of primary data were through unstructured interviews and structured questionnaire. The data was analyzed using the descriptive statistics and the regression   technique.  The   results   of   statistics showed that there is rising trends and adoption of PSPs among the users in Kathmandu valley.  The  result  of the ordinal regression  analysis  indicated  that  security concern (SC) showed  a  positive  and  insignificant  influence  on  PSPs’ performance  since estimate or coefficient  value  of  this    is 0.13 with  insignificant  value  of  (p=0.308),  whereas  customer service (CS), low transaction fee & Merchant Discount Rate (LTFM) and cross-border peer to peer and peer to merchant transaction (CPTM) showed a positive and significant influence on PSPs’ effectiveness having coefficient value of 0.434, 0.342 and 3.201 respectively with significant value of p=0.008, 0.004 & 0.000 respectively. However, interoperability and regulatory constraints showed a negative and insignificant influence on PSPs’ effectiveness having coefficient value of -0.206 and -0.145 respectively with insignificant value of p=0.165 and 0.241 respectively. The result of second route indicated that cross-border peer to peer and peer to merchant transaction (CPTM) which is the key feature of UPI Nepal has the highest level of influence on PSPs’ effectiveness with exponentiation of the B coefficient Exp(B) =24.548 shows UPI will set an ample challenge to the existing PSPs in Nepal. The research recommended that, PSPs need to enhance the customer service with offering low transaction and MDR. Also, diversifying their business across the border.