The study showed the effect of cyber threats or technical problems on customer attitude towards e-banking services. A self-administered questionnaire was used to collect the data, and 400 respondents from eight divisions (Dhaka, Barisal, Rangpur, Chittagong, Khulna, Mymensingh, Rajshahi, and Sylhet) participated in the study. The researcher used a linear regression model to assess the relationship between dependent and independent variables. STATA and SPSS v. 25 program environment were used to conduct the statistical analysis. To determine the reliability of the data, Cronbach's Alpha was conducted and found acceptable internal consistency. Protection method (PM), Purposes of using e-banking (PUE), and Technical problems and challenges (TPC) were the independent variables. The dependent variables were ease of use (EU) and Security reason (SR). The study used two regression models for two dependent variables. The regression model (1) showed a significant effect of SR on PM, and there was no significant effect on SR on PUE and TPC. Another regression model (2) showed a significant effect of EU on PM and PUE, and there was no significant effect of EU on TPC. However, the total variance explained was still low in model 1 (22.6%) and model 2 (44.9). The people of Bangladesh should be aware of e-banking services and technical problems. Future research should be conducted to discover more data and other variables affecting customer attitudes towards e-banking.
Lossless data reduction is essential for data transmission over the Internet and the storage of data in a digital device when data loss is not permitted. The application of image compression is essential for image storing, image classification, and image recognition, and image compression techniques compress an image by reducing redundancy in the image. Many image compression standards have already been developed. This article compares the most popular state-of-the-art lossless image compression techniques, and the methods are evaluated based on the bits per pixel or compression ratio. Finally, we recommend which of the algorithms is better for a few different datasets.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.