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( Deepanjal Shrestha ),( Tan Wenan ),( Neesha Rajkarnikar ),( Seung Ryul Jeong ) 한국인터넷정보학회 2020 인터넷정보학회논문지 Vol.21 No.5
The application of Internet technology has created enormous impact on banking sector with the implementation of many techno-oriented services like Internet banking, EFT, branchless banking, Automated Clearing House (ACH) transactions etc. Study of customer’s attitude in terms of trust, perceived risk and ease of use of a particular technology is as an important parameter for acceptance or rejection of a technology. To explore the customers’attitude for Internet banking this research is undertaken. The research is carried out in Pokhara valley which is the second largest city and tourism capital of Nepal. The study employs descriptive research design with stratified sampling procedure for eight top commercial banks. A set of 25 customers is taken from each selected 8 banks making a sample size of 200 respondents. A fixed set of question related to demographic factors is provided personally or by visiting the location of the customers of Internet banking service and collected accordingly. Reliability test is performed using Cronbach's alpha and data is analyzed using inferential statistics to present the results of the study. This study provides knowledge on the current scenario of Internet banking and helps banks in cost saving, mass customization, product innovation, improved marketing and communication. This study is very important for financial institutions like banks, government agencies and business houses to understand the perception of customers towards Internet banking and technology as a whole. The study also supplements the gap in literature on technology and banking in Nepal and serves as an important knowledge base.
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Study and Evaluation of Tourism Websites based on User Perspective
( Deepanjal Shrestha ),( Tan Wenan ),( Neesha Rajkarnikar ),( Deepmala Shrestha ),( Seung Ryul Jeong ) 한국인터넷정보학회 2021 인터넷정보학회논문지 Vol.22 No.4
A well-designed website is mandatory for good marketing and proper tourism business. This research considers Nepal as a domain of study and specifically explores welcomenepal.com, the official tourism portal as a reference for the study. The work is based on the study of the existing literature, user-survey, and technical testing of the website using open-source testing tools to identify user perspective, design issues, website architecture and design quality of the tourism website. A population size of 400 respondents, which consist of both domestic and international tourist, are considered for the survey. Data is received from 360 respondents, which is analyzed using statistical tests like Cronbach’s alpha, Pearson’s correlation, cross-tabulations, bars charts and graphs to draw inferences and consclusion. The software-based test results serve as another important parameter for the evaluation of the current official website. This study brings out core needs of the tourist in terms of expectations from a tourism website and access technical quality of the current portal to provide necessary feedback and suggestions. The government officials, business houses, and web designers can utilize this work as a knowledge base to build tourism websites, which are user-centric. Further, the work is specifically important for Nepal government and tourism officials to identify shortcomings in their current website and make improvements for better design and user adaptability in future.
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Multi-dimensional Analysis and Prediction Model for Tourist Satisfaction
Deepanjal Shrestha,Tan Wenan,Bijay Gaudel,Neesha Rajkarnikar,Seung Ryul Jeong 한국인터넷정보학회 2022 KSII Transactions on Internet and Information Syst Vol.16 No.2
This work assesses the degree of satisfaction tourists receive as final recipients in a tourism destination based on the fact that satisfied tourists can make a significant contribution to the growth and continuous improvement of a tourism business. The work considers Pokhara, the tourism capital of Nepal as a prefecture of study. A stratified sampling methodology with open-ended survey questions is used as a primary source of data for a sample size of 1019 for both international and domestic tourists. The data collected through a survey is processed using a data mining tool to perform multi-dimensional analysis to discover information patterns and visualize clusters. Further, supervised machine learning algorithms, kNN, Decision tree, Support vector machine, Random forest, Neural network, Naïve Bayes, and Gradient boost are used to develop models for training and prediction purposes for the survey data. To find the best model for prediction purposes, different performance matrices are used to evaluate a model for performance, accuracy, and robustness. The best model is used in constructing a learning-enabled model for predicting tourists as satisfied, neutral, and unsatisfied visitors. This work is very important for tourism business personnel, government agencies, and tourism stakeholders to find information on tourist satisfaction and factors that influence it. Though this work was carried out for Pokhara city of Nepal, the study is equally relevant to any other tourism destination of similar nature.
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Link Prediction in Bipartite Network Using Composite Similarities
( Bijay Gaudel ),( Deepanjal Shrestha ),( Niosh Basnet ),( Neesha Rajkarnikar ),( Seung Ryul Jeong ),( Donghai Guan ) 한국인터넷정보학회 2023 KSII Transactions on Internet and Information Syst Vol.17 No.8
Analysis of a bipartite (two-mode) network is a significant research area to understand the formation of social communities, economic systems, drug side effect topology, etc. in complex information systems. Most of the previous works talk about a projection-based model or latent feature model, which predicts the link based on singular similarity. The projection-based models suffer from the loss of structural information in the projected network and the latent feature is hardly present. This work proposes a novel method for link prediction in the bipartite network based on an ensemble of composite similarities, overcoming the issues of model-based and latent feature models. The proposed method analyzes the structure, neighborhood nodes as well as latent attributes between the nodes to predict the link in the network. To illustrate the proposed method, experiments are performed with five real-world data sets and compared with various state-of-art link prediction methods and it is inferred that this method outperforms with ∼3% to ∼9% higher using area under the precision-recall curve (AUC-PR) measure. This work holds great significance in the study of biological networks, e-commerce networks, complex web-based systems, networks of drug binding, enzyme protein, and other related networks in understanding the formation of such complex networks. Further, this study helps in link prediction and its usability for different purposes ranging from building intelligent systems to providing services in big data and web-based systems.
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