International Journal of Services Operations and Informatics (6 papers in press)
Research on Quality of Banking Services Based on QFD and SERVQUAL Model
by Gao Chen, Zhou Yuqing
Abstract: The banking business continues to expand, and as a result, the quality of banking services put forward higher requirements. How to retain customers and how to improve marketing competitiveness became the biggest challenges for banks. Based on this analysis need, the Quality Function Deployment (QFD) and SERVQUAL model were introduced in order to enhance banks customers satisfaction. Firstly, the SERVQUAL model identifies customers needs from the customer satisfaction perspective, and the weights of customer needs then identified through the Analytic Hierarchy Process (AHP). The QFD method analyzed the relationship between customer demand and quality of service features to determine the bank's current key service features. Through effective usage of the data gathered, the analysis determines suggestions and recommendations for quality banking services improvements.
Keywords: Quality of banking services; SERVQUAL model; QFD; AHP.
An Efficient Two Stage Encryption for Securing Personal Health Records in Cloud Computing
by KrishnaKeerthi Chennam, Lakshmi Muddana
Abstract: A personal health record or PHR is a wellbeing record where wellbeing information and data identified with the care of a patient is kept up by the patient. When these records are stored in the cloud the probability of unauthorized access gets increased due to poor encryption. Further dangers of security presentation, adaptability in key administration, adaptable access and proficient client authority cancelling remained the most vital difficulties toward accomplishing fine-grained, cryptographically upheld information accessing power in the existing methods. To overcome these drawbacks and to achieve the authorized access of patients data we move on to the proposed method. Here at first, the framework is separated into two security domains such as public and personal domains (PUD and PSD) as indicated by the distinctive clients' information accessing necessities. In both domain sorts of security areas, we use vigenere encryption and two fish based encryption to acknowledge cryptographically implemented, owner driven PHR access. In PUD, the number of users is grouped with the help of hierarchical agglomerative clustering algorithm. After clustering, each group gets the secret key and then encrypts the health record and stored in the cloud. The performance of the proposed method is evaluated in terms of clustering accuracy, encryption and decryption time and memory. The experimental result shows that our proposed method has 76.05% of clustering accuracy. Our proposed method also has less encryption and decryption time when compared to the existing technique. The proposed method is implemented in JAVA with Cloud Sim.
Keywords: Patient Health Record (PHR); Personal Domain (PSD); Public Domain (PUD); Vigenere Encryption; Two Fish Encryption and Hierarchical Agglomerative Clustering Algorithm.
A systematical forecasting method for container throughput of correlated ports: a case study of Shenzhen port and Hong Kong port
by Lulu Zou
Abstract: Current studies on container throughput forecasting are mainly focused on independent forecasts of individual ports, with very limited regard for the interaction between ports. This paradigm neglects the deep underlying correlation between the ports and thus may lead to large errors of the prediction. To overcome the weaknesses, this paper proposes a new container throughput forecasting method to systematically forecast the correlated ports. A systematical forecasting model is established based on the correlation between the ports identified by the Granger causal test and estimated using the method newly proposed in this paper. For verification purposes, multiple forecasting models, including the newly proposed systematical forecasting model and the independently forecasting models, are constructed and compared in terms of the forecasting performance based on the monthly container throughput data of Shenzhen Port and Hong Kong Port, the empirical results show that the new model is superior to its rivals in terms of absolute prediction accuracy and direction accuracy.
Keywords: container throughput forecast; correlated ports; Granger causal test; ANN.
A large-scale data management and application analysis based on advanced classifier computing for the ERP system selection and adoption
by You-Shyang Chen, Chien-Ku Lin
Abstract: The promising trend of emerged large-scale data management has urgent needs to enterprises that are faced with competitions under external environment and globalization trend. Enterprise Resource Planning (ERP) plays a crucial role during the information technology integration process and enterprise management for the rational allocation of enterprise resources and management efficiency. That makes enterprises to gain advantage in the intense market competition. The application of data mining techniques and data searching technologies in business intelligence make the ERP vendor a more proactive performance. Thus, it is an important and interesting issue to help ERP system vendor selecting a suitable customer who will survey an appropriate decision to select and adopt ERP system. Furthermore, the selection and adoption of ERP system is rarely used in the field of data mining techniques. This motivates the study. Thus, we compare the empirical results of the decisional feature database constructed by two classification models, Models 1 and 2, and find out the critical factors of industrial evaluation for ERP system summarized through the analytical results and hypothesis. The empirical results include (1) Model 1: the accuracy of percentage split without feature-selection can reach 89.7810% at maximum and (2) Model 2: the accuracy of percentage split with expert feature-selection can also reach 89.7810% at maximum. It is found from this result that although Models 1 and 2 have the same classification accuracy, Model 2 uses fewer features than Model 1, and Model 2 thus has better performance than Model 1. This study provides the evidence that the expert feature-selection method has a satisfied result. This study yields the two management implications: (1) ERP vendors can find out hidden potential customers by the proposal models; and (2) expert feature-selection of given data is an effective technique used to increase the purpose of classification quality.
Keywords: large-scale data management; enterprise resource planning system; expert feature-selection; classification model.
Hybrid K-Means with Neural Network based Binary Cuckoo Search technique: A classifier for Fault Prediction in Acceptance Testing
by Yogomaya Mohapatra, Mitrabinda Ray
Abstract: One of the key challenges of tester is to determine test suite quality that is the ability to find faults. The objective of the paper is to improve the test suite quality in acceptance testing. As the use of neural network is very wide in fault prediction, we propose a meta heuristic method using Binary Cuckoo Search to classify the generated test cases that helps to improve the test suite quality. Binary Cuckoo Search algorithm is used in our approach for optimizing the weight factor of neural network. In our proposed method, test cases for acceptance testing of our case study Hospital Management System are generated automatically through the existing tool, CodePro, and then clustered by using K-Means clustering algorithm. Then, the clustered test cases are classified according to their fault detection capability. We propose a novel classifier, Hybrid K-Means with Neural Network based Binary Cuckoo Search technique, for classification of generated test cases into two classes, faulty and faultless. The classified result is experimentally evaluated against the existing software metrics, Average Percentage of Faults Detected (APFD), Problem Tracking Reports (PTR), time and memory usage. From the experimental results, we observe that the average percentage of fault detected in our approach is higher than the existing method.
Keywords: K-Means Clustering; Neural Network; Binary Cuckoo Search; Fault Prediction.
The Sovereign Debt Crisis and National Centripetal Vs Regional Centrifugal Forces in Political Economy: Empirical Lessons from Spain.
by Joaquin Gonzalez-Garcia, Joaquin Sotelo-Gonzalez, Pablo Zegarra-Saldaña
Abstract: Existing analytical frameworks are unable to establish what should be the right balance between national centrifugal and regional centripetal economic and political forces that allow for the creation of a stable and prosperous nation so this paper contributes to the body of scholarly works in political economy by concentrating on reviewing empirical evidences that the Spanish highly decentralized political system is failing to strike the right balance, especially during difficult economic times, since the regional centrifugal economic and political forces are overpowering the national centripetal ones and may signal a dangerous disassembling of the nation with the emergence of regional autarkical independence. The political balance of these forces will inevitably have a reflection on the economic variables, thus a causal relationship can be inferred by analysing the evolution of the variables sovereign debt at national and regional levels and the behaviour of the variables risk premium and the public securities credit rating.
Keywords: Spanish sovereign debt crisis; sovereign risk premium; CRAs ratings; national centripetal economic and political forces; regional centrifugal economic and political forces; regional public debt; fiscal decentralization; country default risk; public accounts transparency; regional fiscal discipline.