• International Journal of Computer Science & Network Security
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• 제22권2호
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• pp.250-260
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• 2022

Mobile Banking Applications (MBAPs) is one of the recent fads in mobile trading applications (Apps). MBAPs permit users to execute exchanges of money and many more whenever it might suit them; however, the primary issue for mobile banking Apps is usability. Hardly any investigation analyzes usability issues dependent on user's age, gender, exchanging accomplices, or experience. The purpose of this study is to determine the degree of usability issues, and experience of mobile banking users. The survey employs a quantitative method and performs user experiment on 240 participants with six different tasks on the application's interface. The post experiment survey is done with concerning participants. On the other hand, banking experts and Information Technology (IT) expert's group is also involved after the experiment. Expert's opinions about existing mobile banking Apps and suggestions for improving usability of MBAPs are collected through physical means (like questionnaire and interview) and online means like Google form. After that comparison of the opinions of users and experts about MBAPs is performed. The experimentation measures the tasks usability of various mobile banking apps with respect to its effectiveness, efficiency, trustfulness, learnability, memorability and satisfaction. The usability testing was led at different Universities and the outcomes acquired show that there are privacy and trust issues with their mobile banking apps. There is also a gap between users and experts which should be minimized by applying customized usability models, modes concept like other application software and also by adding complete features of banking in MBAPs. It will benefit mobile banking apps users, developers and usability engineers by providing user-friendly which are up to the mark of user's requirements.

• 한국철도학회논문집
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• 제8권5호
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• pp.411-418
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• 2005

This paper expounds the development of a user-friendly expert system for the optimal stacking sequence design of composite laminates subjected to the various rules constraints. The expert system was realized in the graphic-based design environment. Therefore, users can access and use the system easily. The optimal stacking sequence is obtained by means of integration of a genetic algorithm, finite element analysis. These systems were integrated with the rules of design heuristics under an expert system shell. The optimal stacking sequence combination for the application of interest is drawn from the discrete ply angles and design rules stored in the knowledge base of the expert system. For the integration and management of softwares, a graphic-based design environment that provides multi-tasking and graphic user interface capability is built.

• 한국정보시스템학회지:정보시스템연구
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• 제5권
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• pp.205-248
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• 1996

Historically, the en-users' acceptance of the expert systems(ES) have generally been used as a proxy for the ES' implementation success by both practitioners and academicians. However, with regard to bank loan decisions, most loan officers approach the acquisition of an ES with apprehension. In order to overcome this skepticism, more research should focus on the behavioral aspects relate to systems acquisition and usage. This research applied Vroom's(1964) expectancy theory in an effort to predict end-users' motivation to use an ES in a bank loan decision context. Because human behaviors and judgements are nonlinear rather than linear functions, accurately predicting human behavior is very difficult. To increase the prediction power for end-users' motivation to use an ES in a bank loan decision context, this research used an artificial neural network (ANN) model. In this research, an attempt was made to evaluate adequacy of the surrogates by analyzing differences between real bank loan officers and student surrogates in applying expectancy theory to estimate bank loan officers' motivation to use ES in a bank loan decision context.

• 한국기계연구소 소보
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• 통권17호
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• pp.69-74
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• 1987

This paper introduces a case-building algorithm which can determine automatically a desired design case by categorizing known-variables and unknown-variables among design variables. Common CAD programs use a case-selection technique, where a programmer sets initially a few of design cases and then users can only choose one of the given cases. The case-building technique is a powerful tool for the expert computer-aided design technology.

• 한국지능시스템학회논문지
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• 제23권1호
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• pp.7-11
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• 2013

전문가의 지식을 기반으로 한 추천시스템에 대한 다양한 연구가 최근 활발히 진행되고 있다. 지금까지의 전문가 기반 추천 시스템이 공통된 전문가 그룹의 지식을 바탕으로 모두에게 아이템을 추천하였다면, 본 논문에서는 개인의 필요와 전문가에 대한 관점을 반영한 개인화된 전문가 그룹의 지식을 기반으로 한 추천 시스템을 제안한다. 개인화된 전문가 그룹을 찾는 과정이 제안하는 추천 시스템에서 가장 중요한 부분이다. 이를 위해 개인화된 전문가를 효율적으로 찾아내는 지지 벡터 머신(SVM) 기반 기법을 제안한다. 추천 시스템에서 널리 사용되는 k 근접이웃 알고리즘과의 비교를 통하여서 개인화된 전문가를 기반으로 한 협업 필터링 추천 시스템의 효용성을 입증한다.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제7권12호
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• pp.497-504
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• 2018

본 논문은 웹 기반의 한방 전문가 시스템(Oriental Medical Expert System)에 대한 룰 기반 진단 프로세스를 제공하는 확장성, 가용성, 변경 가능성을 가진 시스템으로, 오픈 소스 Drools 기반으로 Spring MVC 프레임워크와 Ajax를 이용하여 구현하였다. 본 한방 전문가 시스템의 처리 프로세스는 일반 사용자가 웹으로 접근하여 설문을 작성한 후 기본적인 진단 및 평가를 제공하여 주는 서비스를 제공한다. 한방 전문가 시스템의 효율적인 운영을 위하여 클라이언트와 서버 간에 비동기적 통신으로 JSON 데이터를 주고받아 서버의 부담을 감소하였으며, 데이터베이스 접근은 기존의 복잡한 JDBC 단점을 보완하고자 Mybatis 프레임워크를 적용하여 RDBMS의 성능을 향상시켰다. 또한, 웹 기반의 장점을 최대한 활용하여 사용자 수가 늘어남에 따라 서버 트래픽 문제를 해결하기 위하여 Nginx를 이용한 로드밸런싱 구조를 구축하여 서비스의 가용성을 확대하였다. 연구 결과로 확장성 테스트를 통하여 서비스의 안정화에 대한 검증을 나타내었다.

• 한국정보처리학회논문지
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• 제7권5호
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• pp.1434-1445
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• 2000

An expert system executes the inference, based on the knowledge of specific domain. the reliability on the results of inference depends upon both the consistency and accuracy of knowledge. This is the reason why expert system requires the facilities which can practice an access to the various kinds of knowledge and maintain the consistency and accuracy of knowledge an maintain the consistency and accuracy of knowledge. This paper is to implement an expert system permitting an access of declarative and procedural knowledge in the knowledge base and in the data base. This paper is also to implement a knowledge acquisition system which adds the knowledge a only if its accuracy and consistency are maintained, after verifying the potential errors such as contradiction, redundancy, circulation, non-reachable rule and non-lined rule. In consequence, the expert system realizes a good access to the various sorts of knowledge and increases the reliability on the results of inference. The knowledge acquisition system contributes tro strengthening man-machine interface that enables users to add the knowledge easily to the knowledge base.

• Asia pacific journal of information systems
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• 제24권2호
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• pp.191-210
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• 2014

As mobile devices that can be connected to the Internet have spread and networking has become possible whenever/wherever, the Internet has become central in the dissemination and consumption of news. Accordingly, the ways news is gathered, disseminated, and consumed have changed greatly. In the traditional news media such as magazines and newspapers, expert editors determined what events were worthy of deploying their staffs or freelancers to cover and what stories from newswires or other sources would be printed. Furthermore, they determined how these stories would be displayed in their publications in terms of page placement, space allocation, type sizes, photographs, and other graphic elements. In turn, readers-news consumers-judged the importance of news not only by its subject and content, but also through subsidiary information such as its location and how it was displayed. Their judgments reflected their acceptance of an assumption that these expert editors had the knowledge and ability not only to serve as gatekeepers in determining what news was valuable and important but also how to rank its value and importance. As such, news assembled, dispensed, and consumed in this manner can be said to be expert-based recommended news. However, in the era of Internet news, the role of expert editors as gatekeepers has been greatly diminished. Many Internet news sites offer a huge volume of news on diverse topics from many media companies, thereby eliminating in many cases the gatekeeper role of expert editors. One result has been to turn news users from passive receptacles into activists who search for news that reflects their interests or tastes. To solve the problem of an overload of information and enhance the efficiency of news users' searches, Internet news sites have introduced numerous recommendation techniques. Recommendations based on popularity constitute one of the most frequently used of these techniques. This popularity-based approach shows a list of those news items that have been read and shared by many people, based on users' behavior such as clicks, evaluations, and sharing. "most-viewed list," "most-replied list," and "real-time issue" found on news sites belong to this system. Given that collective intelligence serves as the premise of these popularity-based recommendations, popularity-based news recommendations would be considered highly important because stories that have been read and shared by many people are presumably more likely to be better than those preferred by only a few people. However, these recommendations may reflect a popularity bias because stories judged likely to be more popular have been placed where they will be most noticeable. As a result, such stories are more likely to be continuously exposed and included in popularity-based recommended news lists. Popular news stories cannot be said to be necessarily those that are most important to readers. Given that many people use popularity-based recommended news and that the popularity-based recommendation approach greatly affects patterns of news use, a review of whether popularity-based news recommendations actually reflect important news can be said to be an indispensable procedure. Therefore, in this study, popularity-based news recommendations of an Internet news portal was compared with top placements of news in printed newspapers, and news users' judgments of which stories were personally and socially important were analyzed. The study was conducted in two stages. In the first stage, content analyses were used to compare the content of the popularity-based news recommendations of an Internet news site with those of the expert-based news recommendations of printed newspapers. Five days of news stories were collected. "most-viewed list" of the Naver portal site were used as the popularity-based recommendations; the expert-based recommendations were represented by the top pieces of news from five major daily newspapers-the Chosun Ilbo, the JoongAng Ilbo, the Dong-A Daily News, the Hankyoreh Shinmun, and the Kyunghyang Shinmun. In the second stage, along with the news stories collected in the first stage, some Internet news stories and some news stories from printed newspapers that the Internet and the newspapers did not have in common were randomly extracted and used in online questionnaire surveys that asked the importance of these selected news stories. According to our analysis, only 10.81% of the popularity-based news recommendations were similar in content with the expert-based news judgments. Therefore, the content of popularity-based news recommendations appears to be quite different from the content of expert-based recommendations. The differences in importance between these two groups of news stories were analyzed, and the results indicated that whereas the two groups did not differ significantly in their recommendations of stories of personal importance, the expert-based recommendations ranked higher in social importance. This study has importance for theory in its examination of popularity-based news recommendations from the two theoretical viewpoints of collective intelligence and popularity bias and by its use of both qualitative (content analysis) and quantitative methods (questionnaires). It also sheds light on the differences in the role of media channels that fulfill an agenda-setting function and Internet news sites that treat news from the viewpoint of markets.

• 한국경영과학회지
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• 제30권1호
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• pp.129-147
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• 2005

In Knowledge Management Systems (KMS), tacit knowledge which is usually possessed as forms like know-how, experiences, and etc. is hard to be systemized while managing explicit knowledge is comparatively easy using information technology such as databases, Recent researches in knowledge management have shown that it is more applicable in many ways to provide expert search mechanisms in KMS to pinpoint experts in the organizations with searched expertise so that users can contact them for help, In this paper, we propose an intelligent expert search framework to provide search capabilities for experts in similar or related fields according to the user's needs. In enabling intelligent expert searches, Fuzzy Abstraction Hierarchy (FAH) framework has been adopted, through' which finding experts with similar or related expertise is possible according to the subject field hierarchy defined in the system. To test applicability and practicality of the proposed framework, the prototype system, Knowledge Portal for Researchers in Science and Technology, was developed.

• Asia pacific journal of information systems
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• 제6권2호
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• pp.77-105
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• 1996

The accuracy of knowledge is a major concern for expert system developers and users. Machine learning approaches have recently been found to be useful in knowledge acquisition for expert systems. However, the accuracy of concept acquired from machine learning could not be analyzed in most cases. In this paper we develop a comprehensive knowledge error analysis methodology for practical use of expert systems. Decision tree induction is an important type of machine learning method for business expert systems. Here we start to analyze with knowledge acquired from decision tree induction method, and extend the results to develop error analysis methodology for general machine learning methods. We give several examples and illustrations for these results. We also discuss the applicability of these results to multistrategy learning approaches.