Excellence in Research and Innovation for Humanity

International Science Index

Commenced in January 1999 Frequency: Monthly Edition: International Paper Count: 23

Social, Behavioral, Educational, Economic, Business and Industrial Engineering

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  • 23
    Prioritizing Service Quality Dimensions:A Neural Network Approach
    One of the determinants of a firm-s prosperity is the customers- perceived service quality and satisfaction. While service quality is wide in scope, and consists of various dimensions, there may be differences in the relative importance of these dimensions in affecting customers- overall satisfaction of service quality. Identifying the relative rank of different dimensions of service quality is very important in that it can help managers to find out which service dimensions have a greater effect on customers- overall satisfaction. Such an insight will consequently lead to more effective resource allocation which will finally end in higher levels of customer satisfaction. This issue –despite its criticality- has not received enough attention so far. Therefore, using a sample of 240 bank customers in Iran, an artificial neural network is developed to address this gap in the literature. As customers- evaluation of service quality is a subjective process, artificial neural networks –as a brain metaphor- may appear to have a potentiality to model such a complicated process. Proposing a neural network which is able to predict the customers- overall satisfaction of service quality with a promising level of accuracy is the first contribution of this study. In addition, prioritizing the service quality dimensions in affecting customers- overall satisfaction –by using sensitivity analysis of neural network- is the second important finding of this paper.
    A Web Service Platform for Support Multiple Programming Language to Access Biomedical Image Databases
    Images are important in disease research, education, and clinical medicine. This paper presents a Web Service Platform (WSP) for support multiple programming languages to access image from biomedical databases. The main function WSP is to allow web users access image from biomedical databases. The WSP will receive web user-s queries. After that, it will send to Querying Server (QS) and the QS will search and retrieve data from biomedical databases. Finally, the information will display to the web users. Simple application is developed and tested for experiment purpose. Result from experiment indicated WSP can be used in biomedical environment.
    A New Dimension in Software Risk Managment
    A dynamic risk management framework for software projects is presented. Currently available software risk management frameworks and risk assessment models are static in nature and lacks feedback capability. Such risk management frameworks are not capable of providing the risk assessment of futuristic changes in risk events. A dynamic risk management framework for software project is needed that provides futuristic assessment of risk events.
    Utilizing Ontologies Using Ontology Editor for Creating Initial Unified Modeling Language (UML)Object Model
    One of object oriented software developing problem is the difficulty of searching the appropriate and suitable objects for starting the system. In this work, ontologies appear in the part of supporting the object discovering in the initial of object oriented software developing. There are many researches try to demonstrate that there is a great potential between object model and ontologies. Constructing ontology from object model is called ontology engineering can be done; On the other hand, this research is aiming to support the idea of building object model from ontology is also promising and practical. Ontology classes are available online in any specific areas, which can be searched by semantic search engine. There are also many helping tools to do so; one of them which are used in this research is Protégé ontology editor and Visual Paradigm. To put them together give a great outcome. This research will be shown how it works efficiently with the real case study by using ontology classes in travel/tourism domain area. It needs to combine classes, properties, and relationships from more than two ontologies in order to generate the object model. In this paper presents a simple methodology framework which explains the process of discovering objects. The results show that this framework has great value while there is possible for expansion. Reusing of existing ontologies offers a much cheaper alternative than building new ones from scratch. More ontologies are becoming available on the web, and online ontologies libraries for storing and indexing ontologies are increasing in number and demand. Semantic and Ontologies search engines have also started to appear, to facilitate search and retrieval of online ontologies.
    The Use of Local Knowledge and its Transferfor Community Self-Protection Development in Flood Prone Residential Area
    This paper aims to study at the use of local knowledge to develop community self-protection in flood prone residential area, Ayutthaya Island has been chosen as a case study. This study tries to examine the strength of local knowledge which is able to develop community self-protection and cope with flood disaster. In-depth, this paper focuses on the influence of social network on knowledge transfer. After conducted the research, authors reviewed the strength of local knowledge and also mentioned the obstacles of community to use and also transfer local knowledge. Moreover, the result of the study revealed that local knowledge is not always transferred by the strongest-tie social network (family or kinship) as we used to believe. Surprisingly, local knowledge could be also transferred by the weaker-tie social network (teacher/ monk) with the better effectiveness in some knowledge.
    Determining the Online Purchasing Loyalty for Thai Herbal Products
    The objective of this study is to identify the factors that influence the online purchasing loyalty for Thai herbal products. Survey research is used to gather data from Thai herb online merchants to assess factors that have impacts on enhancing loyalty. Data were collected from 300 online customers who had experience in online purchasing of Thai Herbal products. Prior experience consists of data from previous usage of online herbs, herb purchase and internet usage. E-Quality data consists of information quality, system quality, service quality and the product quality of Thai herbal products sold online. The results suggest that prior experience, Equality, attitude toward purchase and trust in online merchant have major impacts on loyalty. The good attitude and E-Quality of purchasing Thai herbal product online are the most significant determinants affecting loyalty.
    Web Personalization to Build Trust in E-Commerce: A Design Science Approach
    With the development of the Internet, E-commerce is growing at an exponential rate, and lots of online stores are built up to sell their goods online. A major factor influencing the successful adoption of E-commerce is consumer-s trust. For new or unknown Internet business, consumers- lack of trust has been cited as a major barrier to its proliferation. As web sites provide key interface for consumer use of E-Commerce, we investigate the design of web site to build trust in E-Commerce from a design science approach. A conceptual model is proposed in this paper to describe the ontology of online transaction and human-computer interaction. Based on this conceptual model, we provide a personalized webpage design approach using Bayesian networks learning method. Experimental evaluation are designed to show the effectiveness of web personalization in improving consumer-s trust in new or unknown online store.
    Effect of Network Communication Overhead on the Performance of Adaptive Speculative Locking Protocol

    The speculative locking (SL) protocol extends the twophase locking (2PL) protocol to allow for parallelism among conflicting transactions. The adaptive speculative locking (ASL) protocol provided further enhancements and outperformed SL protocols under most conditions. Neither of these protocols consider the impact of network latency on the performance of the distributed database systems. We have studied the performance of ASL protocol taking into account the communication overhead. The results indicate that though system load can counter network latency, it can still become a bottleneck in many situations. The impact of latency on performance depends on many factors including the system resources. A flexible discrete event simulator was used as the testbed for this study.

    Frames about Nanotechnology Agenda in Turkish Media, 2005-2009
    As the new industrial revolution advances in the nanotechnology have been followed with interest throughout the world and also in Turkey. Media has an important role in conveying these advances to public, rising public awareness and creating attitudes related to nanotechnology. As well as representing how a subject is treated, media frames determine how public think about this subject. In literature definite frames related to nanoscience and nanotechnology such as process, regulation, conflict and risks were mentioned in studies focusing different countries. So how nanotechnology news is treated by which frames and in which news categories in Turkey as a one of developing countries? In this study examining different variables about nanotechnology that affect public attitudes such as category, frame, story tone, source in Turkish media via framing analysis developed in agenda setting studies was aimed. In the analysis data between 2005 and 2009 obtained from the first five national newspapers with wide circulation in Turkey will be used. In this study the direction of the media about nanotechnology, in which frames nanotechnologic advances brought to agenda were reported as news, and sectoral, legal, economic and social scenes reflected by these frames to public related to nanotechnology in Turkey were planned.
    A Study of Panel Logit Model and Adaptive Neuro-Fuzzy Inference System in the Prediction of Financial Distress Periods
    The purpose of this paper is to present two different approaches of financial distress pre-warning models appropriate for risk supervisors, investors and policy makers. We examine a sample of the financial institutions and electronic companies of Taiwan Security Exchange (TSE) market from 2002 through 2008. We present a binary logistic regression with paned data analysis. With the pooled binary logistic regression we build a model including more variables in the regression than with random effects, while the in-sample and out-sample forecasting performance is higher in random effects estimation than in pooled regression. On the other hand we estimate an Adaptive Neuro-Fuzzy Inference System (ANFIS) with Gaussian and Generalized Bell (Gbell) functions and we find that ANFIS outperforms significant Logit regressions in both in-sample and out-of-sample periods, indicating that ANFIS is a more appropriate tool for financial risk managers and for the economic policy makers in central banks and national statistical services.
    Application of Feed-Forward Neural Networks Autoregressive Models in Gross Domestic Product Prediction
    In this paper we present an autoregressive model with neural networks modeling and standard error backpropagation algorithm training optimization in order to predict the gross domestic product (GDP) growth rate of four countries. Specifically we propose a kind of weighted regression, which can be used for econometric purposes, where the initial inputs are multiplied by the neural networks final optimum weights from input-hidden layer after the training process. The forecasts are compared with those of the ordinary autoregressive model and we conclude that the proposed regression-s forecasting results outperform significant those of autoregressive model in the out-of-sample period. The idea behind this approach is to propose a parametric regression with weighted variables in order to test for the statistical significance and the magnitude of the estimated autoregressive coefficients and simultaneously to estimate the forecasts.
    A Formative Assessment Model within the Competency-Based-Approach for an Individualized E-learning Path

    E-learning is not restricted to the use of new technologies for the online content, but also induces the adoption of new approaches to improve the quality of education. This quality depends on the ability of these approaches (technical and pedagogical) to provide an adaptive learning environment. Thus, the environment should include features that convey intentions and meeting the educational needs of learners by providing a customized learning path to acquiring a competency concerned In our proposal, we believe that an individualized learning path requires knowledge of the learner. Therefore, it must pass through a personalization of diagnosis to identify precisely the competency gaps to fill, and reduce the cognitive load To personalize the diagnosis and pertinently measure the competency gap, we suggest implementing the formative assessment in the e-learning environment and we propose the introduction of a pre-regulation process in the area of formative assessment, involving its individualization and implementation in e-learning.

    Context-aware Recommender Systems using Data Mining Techniques
    This study proposes a novel recommender system to provide the advertisements of context-aware services. Our proposed model is designed to apply a modified collaborative filtering (CF) algorithm with regard to the several dimensions for the personalization of mobile devices – location, time and the user-s needs type. In particular, we employ a classification rule to understand user-s needs type using a decision tree algorithm. In addition, we collect primary data from the mobile phone users and apply them to the proposed model to validate its effectiveness. Experimental results show that the proposed system makes more accurate and satisfactory advertisements than comparative systems.
    Generic Workload Management System Using Condor-Based Pilot Factory in PanDA Framework
    In the current Grid environment, efficient workload management presents a significant challenge, for which there are exorbitant de facto standards encompassing resource discovery, brokerage, and data transfer, among others. In addition, the real-time resource status, essential for an optimal resource allocation strategy, is often not readily accessible. To address these issues and provide a cleaner abstraction of the Grid with the potential of generalizing into arbitrary resource-sharing environment, this paper proposes a new Condor-based pilot mechanism applied in the PanDA architecture, PanDA-PF WMS, with the goal of providing a more generic yet efficient resource allocating strategy. In this architecture, the PanDA server primarily acts as a repository of user jobs, responding to pilot requests from distributed, remote resources. Scheduling decisions are subsequently made according to the real-time resource information reported by pilots. Pilot Factory is a Condor-inspired solution for a scalable pilot dissemination and effectively functions as a resource provisioning mechanism through which the user-job server, PanDA, reaches out to the candidate resources only on demand.
    Meta-requirements that Model Change
    One of the common problems encountered in software engineering is addressing and responding to the changing nature of requirements. While several approaches have been devised to address this issue, ranging from instilling resistance to changing requirements in order to mitigate impact to project schedules, to developing an agile mindset towards requirements, the approach discussed in this paper is one of conceptualizing the delta in requirement and modeling it, in order to plan a response to it. To provide some context here, change is first formally identified and categorized as either formal change or informal change. While agile methodology facilitates informal change, the approach discussed in this paper seeks to develop the idea of facilitating formal change. To collect, document meta-requirements that represent the phenomena of change would be a pro-active measure towards building a realistic cognition of the requirements entity that can further be harnessed in the software engineering process.
    Proposal of Additional Fuzzy Membership Functions in Smoothing Transition Autoregressive Models
    In this paper we present, propose and examine additional membership functions for the Smoothing Transition Autoregressive (STAR) models. More specifically, we present the tangent hyperbolic, Gaussian and Generalized bell functions. Because Smoothing Transition Autoregressive (STAR) models follow fuzzy logic approach, more fuzzy membership functions should be tested. Furthermore, fuzzy rules can be incorporated or other training or computational methods can be applied as the error backpropagation or genetic algorithm instead to nonlinear squares. We examine two macroeconomic variables of US economy, the inflation rate and the 6-monthly treasury bills interest rates.
    Internet Bandwidth Network Quality Management: The Case Study of Telecom Organization of Thailand

    This paper addresses a current problem that occurs among Thai internet service providers with regard to bandwidth network quality management. The IPSTAR department of Telecom Organization of Thailand public company (TOT); the largest internet service provider in Thailand, is the case study to analyze the problem that exists. The Internet bandwidth network quality management (iBWQM) framework is mainly applied to the problem that has been found. Bandwidth management policy (BMP) and quality of service (QoS) are two antecedents of iBWQM. This paper investigates internet user behavior, marketing demand and network operation views in order to determine bandwidth management policy (e.g. quota management, scheduling and malicious management). The congestion of bandwidth is also analyzed to enhance quality of service (QoS). Moreover, the iBWQM framework is able to improve the quality of service and increase bandwidth utilization, minimize complaint rate concerns to slow speed, and provide network planning guidelines through Thai Internet services providers.

    Application of Feed-Forward Neural Networks Autoregressive Models with Genetic Algorithm in Gross Domestic Product Prediction
    In this paper we present a Feed-Foward Neural Networks Autoregressive (FFNN-AR) model with genetic algorithms training optimization in order to predict the gross domestic product growth of six countries. Specifically we propose a kind of weighted regression, which can be used for econometric purposes, where the initial inputs are multiplied by the neural networks final optimum weights from input-hidden layer of the training process. The forecasts are compared with those of the ordinary autoregressive model and we conclude that the proposed regression-s forecasting results outperform significant those of autoregressive model. Moreover this technique can be used in Autoregressive-Moving Average models, with and without exogenous inputs, as also the training process with genetics algorithms optimization can be replaced by the error back-propagation algorithm.
    Success Factors of Large Scale ERP Implementation in Thailand
    The objectives of the study are to examine the determinants of ERP implementation success factors of ERP implementation. The result indicates that large scale ERP implementation success consist of eight factors: project management competence, knowledge sharing, ERP system quality , understanding, user involvement, business process re-engineering, top management support, organization readiness.
    Application of Adaptive Network-Based Fuzzy Inference System in Macroeconomic Variables Forecasting
    In this paper we apply an Adaptive Network-Based Fuzzy Inference System (ANFIS) with one input, the dependent variable with one lag, for the forecasting of four macroeconomic variables of US economy, the Gross Domestic Product, the inflation rate, six monthly treasury bills interest rates and unemployment rate. We compare the forecasting performance of ANFIS with those of the widely used linear autoregressive and nonlinear smoothing transition autoregressive (STAR) models. The results are greatly in favour of ANFIS indicating that is an effective tool for macroeconomic forecasting used in academic research and in research and application by the governmental and other institutions
    Application of Adaptive Neuro-Fuzzy Inference System in Smoothing Transition Autoregressive Models
    In this paper we propose and examine an Adaptive Neuro-Fuzzy Inference System (ANFIS) in Smoothing Transition Autoregressive (STAR) modeling. Because STAR models follow fuzzy logic approach, in the non-linear part fuzzy rules can be incorporated or other training or computational methods can be applied as the error backpropagation algorithm instead to nonlinear squares. Furthermore, additional fuzzy membership functions can be examined, beside the logistic and exponential, like the triangle, Gaussian and Generalized Bell functions among others. We examine two macroeconomic variables of US economy, the inflation rate and the 6-monthly treasury bills interest rates.
    A Study of Neuro-Fuzzy Inference System for Gross Domestic Product Growth Forecasting
    In this paper we present a Adaptive Neuro-Fuzzy System (ANFIS) with inputs the lagged dependent variable for the prediction of Gross domestic Product growth rate in six countries. We compare the results with those of Autoregressive (AR) model. We conclude that the forecasting performance of neuro-fuzzy-system in the out-of-sample period is much more superior and can be a very useful alternative tool used by the national statistical services and the banking and finance industry.
    Ontology of Collaborative Supply Chain for Quality Management
    In the highly competitive and rapidly changing global marketplace, independent organizations and enterprises often come together and form a temporary alignment of virtual enterprise in a supply chain to better provide products or service. As firms adopt the systems approach implicit in supply chain management, they must manage the quality from both internal process control and external control of supplier quality and customer requirements. How to incorporate quality management of upstream and downstream supply chain partners into their own quality management system has recently received a great deal of attention from both academic and practice. This paper investigate the collaborative feature and the entities- relationship in a supply chain, and presents an ontology of collaborative supply chain from an approach of aligning service-oriented framework with service-dominant logic. This perspective facilitates the segregation of material flow management from manufacturing capability management, which provides a foundation for the coordination and integration of the business process to measure, analyze, and continually improve the quality of products, services, and process. Further, this approach characterizes the different interests of supply chain partners, providing an innovative approach to analyze the collaborative features of supply chain. Furthermore, this ontology is the foundation to develop quality management system which internalizes the quality management in upstream and downstream supply chain partners and manages the quality in supply chain systematically.