A Study on Users' Perception Towards Cash Card Usage in UPM

One of the most important significance in the emergence of e-commerce is the development of electronic payment system. This system replaces physical banknote and coins and substitutes them with electronic cash or digital cash. Smart cards used in electronic cash transactions are referred to stored-value cards or electronic purse i.e. the card will be considered as the repository for money. Units of value are stored on the card as the electronic equivalent of cash and later used for purchases. It can also be used to store value as credits for goods and services - for example, ticketing or canteen facilities. The introduction of new electronic payment scheme for substituting cash especially in Malaysia may be considered in its early age. The Malaysian Electronic Payment System (MEPS) has introduced MEPS Cash smart card as a new method of payment in the middle of year 1999. The method is expected to handle the rapid changes of technological advancement in creating the cashless society. However, consumer acceptances towards the new method of payment has not so far being tested. The consumer preference of using cash for personal consumption expenditures is still widening. On the other hand, money suffers from a few drawbacks that make it no longer practical as it took a lot of space, it cannot be transferred by any means of telecommunication network and finally, high transaction cost for handling money. This research was conducted in order to find a business model for a new method of payment i.e. by using smart card and to solve some of the existing problems that arise when using money in conventional way. The second objective of this research was to develop a smart card application prototype for the mentioned business model. This research focused on measuring user acceptance for the developed prototype and user perception towards the smart card as stored value card. The smart card business model, was then translated into a system, which showed the flow of the processes in smart card transaction. The processes clearly showed what happened between the three parties involved in the system: smart card user, merchant and bank. Selected students from the Faculty of Computer Science and Information Technology, UPM then evaluated the system by completing a questionnaire. The questionnaire intended to obtain the general background of the respondents, their attitudes towards smart card usage, acceptance towards cash card illustrated transaction processes. It was also aimed to obtain respondents' opinion if the smart card as stored value card were introduced in the near future. The result showed that the respondents are actually agreed and satisfied with the system in terms of its clarity and user interface. The findings also ascertained that it could increase the respondents' perception regarding reliability and level of convenience towards the usage of smart card. The fact that more than ninety percent of the respondents were willing to use the smart card for shopping indicated that consumer have no problem in accepting such system in the future

A Comprehensive Survey on Comparisons across Contextual Pre-filtering, Contextual Post-filtering and Contextual Modelling Approaches

Recently, there has been growing interest in recommender systems (RS) and particularly in context-aware RS. Methods for generating context-aware recommendations are classified into pre-filtering, post-filtering and contextual modelling approaches. In this paper, we present the several novel approaches of the different variant of each of these three contextualization paradigms and present a complete survey on the state-of-the-art comparisons across them. We then identify the significant challenges that require being addressed by the current RS researchers, which will help academicians and practitioners in comparing these three approaches to select the best alternative according to their strategies

Institutional repositories: Review and knowledge management perspective

This paper provides a review on current literatures on the concept Institutional Repository (IR) and to propose the use of KM theory to provide understanding on the implementation of IR. We will discuss the issues relevant to IR from the KM view by providing examples which indicated that research can be build on the knowledge gained by KM researchers to augment the understanding of IR. This paper will contribute in bringing together recent research in IR and KM and how these two concepts can collaborate in resulting better understanding of IR implementation

Domain of application in context-aware recommender systems: a review

The purpose of this research is to provide an exhaustive overview of the existing literature on the domain of applications in recommender systems with their incorporated contextual information in order to provide insight and future directions to practitioners and researchers.We reviewed published journals and conference proceedings papers from 2010 to 2016.The review finds that multimedia and e-commerce are the most focused domains of applications and that contextual information can be grouped into static, spatial and temporal contexts

A TAXONOMY OF KNOWLEDGE MANAGEMENT OUTCOMES FOR SMES

The purpose of this paper is to systematically analyze the knowledge management research within small and medium-sized companies. The study includes a systematic review of 30 peer reviewed papers on knowledge management advantages for SMEs. Balanced scorecard perspectives cover all aspects of the organization, and, consequently, the balanced scorecard approach has been applied to classify the KM benefits. The reviewed scientific studies highlight the benefits of knowledge management in the areas of economic and social perspective (increased profits, flexibility, product reputation, financial performance), commercial and customers perspective (market share, sales growth, customer satisfaction, good external relationship), internal business processes perspective (operational performance, increased productivity, product/service quality, process improvement) and organizational learning and growth perspective (employee development, innovation, organizational creativity, learning).For future studies, determining stakeholder views is recommended in order to gain sustainable competitive advantage

A Modified Fuzzy k-Partition Based on Indiscernibility Relation for Categorical Data Clustering

Categorical data clustering has been adopted by many scientific communities to classify objects from large databases. In order to classify the objects, Fuzzy k-Partition approach has been proposed for categorical data clustering. However, existing Fuzzy k-Partition approaches suffer from high computa-tional time and low clustering accuracy. Moreover, the parameter maximize of the classification like-lihood function in Fuzzy k-Partition approach will always have the same categories, hence producing the same results. To overcome these issues, we propose a modified Fuzzy k-Partition based on indiscern-ibility relation. The indiscernibility relation induces an approximation space which is constructed by equivalence classes of indiscernible objects, thus it can be applied to classify categorical data. The novelty of the proposed approach is that unlike previous approach that use the likelihood function of multi-variate multinomial distributions, the proposed approach is based on indescernibility relation. We per-formed an extensive theoretical analysis of the proposed approach to show its effectiveness in achieving lower computational complexity. Further, we compared the proposed approach with Fuzzy Centroid and Fuzzy k-Partition approaches in terms of response time and clustering accuracy on several UCI bench-mark and real world datasets. The results show that the proposed approach achieves lower response time and higher clustering accuracy as compared to other Fuzzy k-based approaches

A review on soft set-based parameter reduction and decision making

Many real world decision making problems often involve uncertainty data, which mainly originating from incomplete data and imprecise decision. The soft set theory as a mathematical tool that deals with uncertainty, imprecise, and vagueness is often employed in solving decision making problem. It has been widely used to identify irrelevant parameters and make reduction set of parameters for decision making in order to bring out the optimal choices. In this paper, we present a review on different parameter reduction and decision making techniques for soft set and hybrid soft sets under unpleasant set of hypothesis environment as well as performance analysis of the their derived algorithms. The review has summarized this paper in those areas of research, pointed out the limitations of previous works and areas that require further research works. Researchers can use our review to quickly identify areas that received diminutive or no attention from researchers so as to propose novel methods and applications

Info UPCT: diciembre 2016

Boletín informativo de la Universidad Politécnica de Cartagena con reportajes, noticias y entrevistas

Improved sine cosine algorithm with simulated annealing and singer chaotic map for Hadith classification

Feature selection (FS) represents an important task in classification. Hadith represents an example in which we can apply FS on it. Hadiths are the second major source of Islam after the Quran. Thousands of Hadiths are available in Islam, and these Hadiths are grouped into a number of classes. In the literature, there are many studies conducted for Hadiths classification. Sine Cosine Algorithm (SCA) is a new metaheuristic optimization algorithm. SCA algorithm is mainly based on exploring the search space using sine and cosine mathematical formulas to find the optimal solution. However, SCA, like other Optimization Algorithm (OA), suffers from the problem of local optima and solution diversity. In this paper, to overcome SCA problems and use it for the FS problem, two major improvements were introduced to the standard SCA algorithm. The first improvement includes the use of singer chaotic map within SCA to improve solutions diversity. The second improvement includes the use of the Simulated Annealing (SA) algorithm as a local search operator within SCA to improve its exploitation. In addition, the Gini Index (GI) is used to filter the resulted selected features to reduce the number of features to be explored by SCA. Furthermore, three new Hadith datasets were created. To evaluate the proposed Improved SCA (ISCA), the new three Hadiths datasets were used in our experiments. Furthermore, to confirm the generality of ISCA, we also applied it on 14 benchmark datasets from the UCI repository. The ISCA results were compared with the original SCA and the state-of-the-art algorithms such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Grasshopper Optimization Algorithm (GOA), and the most recent optimization algorithm, Harris Hawks Optimizer (HHO). The obtained results confirm the clear outperformance of ISCA in comparison with other optimization algorithms and Hadith classification baseline works. From the obtained results, it is inferred that ISCA can simultaneously improve the classification accuracy while it selects the most informative features

Automated Bone Age Assessment: Motivation, Taxonomies, and Challenges

Bone age assessment (BAA) of unknown people is one of the most important topics in clinical procedure for evaluation of biological maturity of children. BAA is performed usually by comparing an X-ray of left hand wrist with an atlas of known sample bones. Recently, BAA has gained remarkable ground from academia and medicine. Manual methods of BAA are time-consuming and prone to observer variability. This is a motivation for developing automated methods of BAA. However, there is considerable research on the automated assessment, much of which are still in the experimental stage. This survey provides taxonomy of automated BAA approaches and discusses the challenges. Finally, we present suggestions for future research