The paper is to build recommendation systems leveraging Deep Learning and Big Data platform, Spark to predict item ratings of the Amazon e-commerce site. Recommendation system in e-commerce has become extremely popular in recent years and it is very important for both customers and sellers in daily life. It means providing the users with products and services they are interested in. Therecommendation systems need users’ previous shopping activities and digital footprints to make best recommendation purpose for next item shopping. We developed the recommendation models in Amazon AWS Cloud services to predict the users’ ratings for the items with the massive data set of Amazon customer reviews. We also present Big Data architecture to afford the large scale data set for storing and computation. And, we adopted deep learning for machine learning community as it is known that it has higher accuracy for the massive data set. In the end, a comparative conclusion in terms of the accuracy as well as the performance is illustrated with the Deep Learning architecture with Spark ML and the traditional Big Data architecture, Spark ML alone.

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.

This paper presents direction for efficient software education. Due to the impact of the Fourth Industrial Revolution, the whole world is interested in software education. However, simply teaching how to code is not software education. The thinking abilities used in coding for software implementation are even more important. Therefore, computational thinking is getting great attention. Several institutions suggest factors for computational thinking and encourage to teach in a relevant way based on the suggestion. In this study, the verification of the factors they suggested was conducted through a questionnaire. The total of 419 freshman students of the College of Social Sciences who were taking “Computational Thinking and Software Coding” class participated in the survey at the beginning and the end of the semester. We first analyzed Wing''s proposal that summarized the concept of computational thinking, and reviewed the proposal of ISTE (International Society for Technology in Education) for defining computational thinking factors for coding education, also checked on the suggestion of Google for factors necessary for software coding. As a result of research analysis, this paper suggests a direction for efficient software education.

The 4th Industrial Revolution is a revolutionary change through intelligence, big fusion and personalization, and the importance of sensor technology that is the basis of core technology is emerging. This study empirically analyzes the derivation of national strategy for R&D of sensor technology, and draws out the effect of technology internalization effort through strategic R&D activities on technical performance and further on national economy. The research and development results are calculated for each type of technology internalization, and the results of the research and development are verified to establish a structure that contributes to the national economic performance. As a national technology internalization strategy, considering its own R&D investment and joint research and development, we examine the impact of each factor on patents and GDP, focusing on causality and ripple effects. For causality analysis, Grandeur causality analysis shows that R&D investment and joint research and development in all countries have mutual causal relationship with GDP. The implications are as follows. First, it is necessary to establish the policy of national economic development through the internalization of technology and knowledge. Second, it is necessary to establish policies according to the type of knowledge internalization. Third, it will be necessary to create an ecosystem environment based on a virtuous relationship between knowledge internalization and national technology and economic development.