Your browser doesn't support javascript.
Show: 20 | 50 | 100
Results 1 - 2 de 2
Add filters

Document Type
Year range
Journal of Risk and Financial Management ; 16(5), 2023.
Article in English | Scopus | ID: covidwho-20243013


This research investigates how the uncertainty caused by the COVID-19 pandemic has affected digital banking usage in India. The study is made by utilizing a panel of data consisting of 108 firm-month observations during covid period from 2020 to 2022, with data mainly collected to analyze the impact of COVID-19 uncertainty. Most of the determinants were collected from the RBI data website. The main emphasis of this study is on the utilization of digital banking services in the context of the pandemic, and the research assesses the factors that have influenced this trend, including the number of physical bank branches, the utilization of debit and credit cards at automated teller machines (ATMs) and points of sale (PoS), as well as the level of economic policy uncertainty (EPU). The analysis was conducted using panel regression analysis, a suitable method for handling the error components in the model that are either fixed or random. The findings indicate that the uncertainty caused by the pandemic has had a negative impact on the use of digital banking services. Additionally, the study highlights that the usage of debit and credit cards at PoS has significantly contributed to promoting the progress of digital banking services during the pandemic. Overall, this study provides valuable insights into how digital banking services have evolved during a period of significant uncertainty and disruption. © 2023 by the authors.

4th International Conference on Computer and Communication Technologies, IC3T 2022 ; 606:521-530, 2023.
Article in English | Scopus | ID: covidwho-2302380


Detecting faces is a prevalent and substantial technology in current ages. It became interesting with the use of diverse masks and facial variations. The proposed method concentrates on detecting the facial regions in the digital images from real world which contains noisy, occluded faces and finally classification of images. Multi-task cascaded convolutional neural network (MTCNN)—a hybrid model with deep learning and machine learning to facial region detection is proposed. MTCNN has been applied on face detection dataset with mask and without mask images to perform real-time face detection and to build a face mask detector with OpenCV, convolutional neural networks, TensorFlow and Keras. The proposed system can be used as an application in the recent COVID-19 pandemic situations for detecting a person wears mask or not in controlling the spread of COVID-19. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.