Earticle

Tourism in Malaysia has huge potential as the country is rich with different kinds of attractions such as historical places, different kinds of festival to enjoy and beautiful scenery to embrace. With the advances of the Internet, many investors have begun to produce different websites to promote their product; however, the quality is still valued expeditiously. The purpose of this paper is to analyze and evaluate the quality of Malaysian tourism websites in terms of usability. With this aim, five different Malaysian tourism websites were selected and evaluated based on their effectiveness, efficiency and user satisfaction. This was done using a questionnaire based evaluation.

Mining data in educational filed is an important and useful task for anybody related to educational institute. The useful information mined from the data can help with performance, guidance, teaching, planning and etc. for staff, students and instructors. Different educational data mining researches has been carried out on student data which includes the student’s basic achievements and educational background, academic scores and the amount of credit hours, but the relations between these are limited. Therefore in this paper we have described a system using data mining technologies such as decision tree. We have analyzed ID3 and J48 (C4.5) algorithms for predicting the scholarship winning chances on student data by translating the decision tree into “IF-THEN” rules and implementing these rules for prediction in our system called scholarship calculator. We have mined student data to calculate the chances of winning scholarship depending on their semester grades, position/rank of student in class, achievements, maximum and minimum amount of taken and allowed credit hours and extra curriculum activities. We found that ID3 works better even though J48 is faster in classifying data and creates a smaller tree than ID3. Because of ID3’s bigger tree it has more rule, more rules means more crosschecking and deeper decision, that’s why the predicted result was more accurate than J48. We have also described how our scholarship calculator works to predict and calculate the chances of winning scholarship. Performance evaluation is done and the results are also compared with already existing datasets. The developed system could be very useful in predicting student’s chances of winning scholarship from the first semester. It can help students to pinpoint the weak areas, which can be perfected with proper guidance from instructors and staff for better chances of winning scholarship.

This study developed a decision support system in a water treatment system capable of supporting the operator to make informed decisions about the best course of action for using multiple water resources. This system consists of a process operation and diagnosis to calculate historical and real-time data to optimize water blending ratios and diagnose each water treatment unit. According to the decision support system, the operator can easily organize calculated and analyzed data and as such can expect long-term operational and analytical benefits in terms of economic, social, and environmental effects in the future.

This study proposed an encoding scheme of sensing data for efficient data stream mining the environment which takes into account the frequency analysis and association rule mining of data stream. In this research, sensing data was encoded in the ways of presenting the features of the events detected by sensors and of including the changes of the sensing data stream. Therefore, it was found that the proposed scheme reached the point of reasoning a situation which will be detected and recognized through sensing of multiple sensors.

Big Data in the form of streaming, the data stream mining, has received a great deal of attention for some time. With disparate multiple sensors, sensors are able to gentrify the information they want to acquire. In this paper, the ways to encode a wide range of sensor data that is continuously reported is proposed. These encoding methods enable higher level analysis than identify the frequent pattern or association rules. It is essential that sensors are distributed and extract various and detailed information about the context that was sensed. This study suggests that it is essential that the sensor data is encoded in a reasonable and valid way of context inference and extracting a variety of quality information even in the data stream environment for on-off analysis of the large amount of sensor data that continuously flow in through the sensor data encoding method.

XML Reverse Engineering is a research that focuses on getting a conceptual model using an XML schema. In integration issue, previous XML reverse engineering researchers apply the reverse method of XML schema or document in order to generate a class diagram. How-ever, to generate a complete class diagram, XML constructs are not used entirely. Therefore, this paper describes XML transformation that focuses on each XML construct transforming to a class diagram. In order to generate a complete class diagram, formal method is used. There are several steps involved in constructing and transforming each XML into a class diagram. In order to ensure the formalization is complete, the ebXml case study is used and from the result obtained, this method can be used as an alternative solution to show a complete reverse XML schema.

In this paper, a new method based on Particle Swarm Optimization (PSO) for independent component analysis(ICA) is presented which can be applied for feature extraction. Due to the drawbacks of the Gradient method, it is replaced by PSO in Discriminant Independent Component Analysis (dICA ) algorithm in the proposed approach. The Gradient method may lead to local optimal and it cannot solve the problem of slow convergence since it includes a learning step which needs to be determined in advance. Moreover, Gradient-based techniques cannot achieve high level of accuracy because of the considerable complexity involved in ICA. The additional complexity of the Gradient-based algorithms leads to pseudo-optimal scenarios. The Discriminant Independent Component Analysis based on PSO is used to overcome these serious shortcomings. Most of the datasets used for simulation in this study are obtained from UCI repository. The results obtained using linear discriminant analysis (LDA), principal component analysis (PCA) and gradient-based dICA are compared with those obtained by PSO-dICA . The results show improvement in classification with PSO-dICA method compared to other methods. In other words, PSO-dICA method bought about classifier error reduction.

There is a huge debate regarding different teaching methods in terms of the choosing of appropriate methods for ‘effective’ teaching. The purpose of this study is to determine the effectiveness of various teaching methods used for university students. This research was conducted with three hundred and fourteen undergraduate students studying in 5 departments of Sun Moon University, Korea. Five methods are ‘Three-strike law’, ‘Revisiting previous lectures’, ‘Providing self-evaluation tools’, ‘Inspecting class-note of student’ and ‘A noble management of assignments’. This study used descriptive and inferential statistical techniques to examine the effectiveness of five teaching methods. In addition, student preferences for teaching methods were explored. The findings will provide some valuable direction to faculties in teaching for higher education.

Reference model is widely used in building up information system since it provides a variety of advantages such as verified framework and blueprint. However, the existing reference model that is provided particularly for each industrial group has a limitation on the application for the public domain that has a purpose of sharing the data of various business patterns. This study has its purpose of developing reference data model that is not dependent on a particular industry or business pattern. What is proposed here is to develop modelling method that interprets the real world with four elements of Party, Service, Activity and Thing and to suggest the possibility of universal application of it in public domain without being dependent on particular domains.

This paper highlights network survivable schemes in order to guarantee both protection and restoration in high speed networks. In this paper, a hybrid protection and restoration mechanism has been proposed whose objective is allocating the bandwidth in a way that minimizes the total amount of bandwidth used for working and protection paths. In the proposed mechanism, path-based protection has been considered because it yields lower overhead and is also suitable for global optimization where, in case of a single link failure, all the flows utilizing the failed link are re-routed to a pre-computed set of paths. The simulation results demonstrate that proposed mechanism is much more efficient as it provides better quality of services (QoS) in terms of network resource utilization, blocking probability etc. as compared to conventional protection and restoration schemes. The proposed mechanism seems to quantify not only the capacity savings but also offers an attractive combination of features, with both ring like speed and mesh-like efficiency.

This paper addresses a robust method for optimal Fractional order PID (FOPID) control of automatic Voltage Regulation (AVR) system to damp terminal voltage oscillation following disturbances in power systems. The optimization is carried out by the Imperialist Competitive algorithm Optimization (ICA) to improve their situation. The optimal tuning problem of the FOPID gains to control of AVR system against parametric uncertainties is formulated as an optimization problem according to time domain based objective function. It is carried out under multiple operation conditions to achieve the desired level of terminal voltage regulation. The results analysis reveals that the ICA based FOPID type AVR control system is effective and provides good terminal voltage oscillations damping ability.

In order to reduce the workload of generating test cases Data-driven testing was introduced. The method was proposed to solve the platform dependency of Record-and-playback. With Data-Driven testing, Testers could focus on generating test data itself so that it contributes to the improvement of productivity. Because a lot of testing data is required for better quality coverage, the workload of testers is explosively increased. In this paper, for efficient generation of test data, we propose the Abstract test script. The abstract test script provides various convenient operators for generating test data. With the Abstract test script, testers can design, implement and manage test data for Data-driven testing efficiently.

It was reported that golf equipment market is expected to increase continuously and quickly. However, Korean golf organizations could not produce high-end technology products through scientific research and development; despite they have a high technical competence and good infrastructure. In order to solve the problem, direction and goal of researches and developments should be clear. The domestic golf industry organizations have focused on the short-term profits and did not pursue in-depth scientific researches. Therefore, it is needed to make long-term plans which are comprehensive and systematic to strengthen the competitiveness. In this study, authors incorporate nanomaterials to golf shafts as high-end technology and suggest strategies to promote competitiveness through the nano-technology, effective planning and management.

This thesis presents an improved algorithm of v-SVDD that detects outliers in documents. The existing algorithms produce a virtual modulation vector with all elements of a power set of a feature vector from a document, and the virtual modulation vector may include vectors that do not reflect true characteristics of the document. To address the issue, this study proposes an enhanced version of the v-SVDD algorithm that produces a more precise virtual modulation vector. Test results run by this new algorithm in this paper show excellent precision in detecting leakage of tampered documents.

Predicting the existing defects in software products is one of the considerable issues in software engineering that contributes a lot toward saving time in software production and maintenance process. In fact, finding the desirable models for predicting software defects has nowadays turned into one of the main goals of software engineers. Since intricacies and restrictions of software development are increasing and unwilling consequences such as failure and errors decrease software quality and customer satisfaction, producing error-free software is very difficult and challenging. One of the efficient models in this field is multilayer neural network with proper learning algorithm. Many of the learning algorithms suffer from extra overfitting in the learning datasets. In this article, setting multilayer neural network method was used in order to improve and increase generalization capability of learning algorithm in predicting software defects. In order to solve the existing problems, a new method is proposed by developing new learning methods based on support vector machine principles and using evolutionary algorithms. The proposed method prevents from overfitting issue and maximizes classification margin. Efficiency of the proposed algorithm has been validated against 11 machine learning models and statistical methods within 3 NASA datasets. Results reveal that the proposed algorithm provides higher accuracy and precision compared to the other models.

The purpose of this research is for the establishment of VR (Virtual Reality/AR (Augmented Reality) learning based ubiquitous environments. We focus on three aspects, ubiquitous environment, collaborative learning in virtual environment, and VR/AR learning. In this paper, learner-focused course module, learner interface module, tutor interface module, learner module, solution problem module, curriculum module, management module, and diagnose module are designed. VR/AR learning system allows learner to be supported with a real time and non-real time. The devices used include smart phone, PDAs, mobile devices, portable computers, digital TV, web camera, AR device, game machine, DMB, and IPTV. This system is to become a more capable learner learning environment so that learner can get learner’s learning done more efficiently. The development of a VR/AR learning combines the advantages of a collaborative learning with the benefits of VR/AR technology and the flexibility of ubiquitous devices.

Church as one of the community service organization has a wide range of activities and transactions to accommodate the needs of the congregation. Because the church also has a fairly complex business processes and systems should have the information including data storage to facilitate an integrated and centralized church in storage, management, and presentation of data. This study discusses the analysis, design, and construction of information system of the church as the solution of the existing problems in the XYZ church; such data are not integrated and dispersed in the respective church staff.

For efficient utilization of CPU and speed up the process done by CPU inter-process synchronization became most important feature in operating system. But no operating system will directly provide support for Inter-Process Synchronization. Inter-Process Synchronization means two or more processes accessing a shared resource and performing some operation at the same time, it also provides concurrent computing among processes. In such instance, data inconsistency may occur because of updating the same shared data item by two processes at the same time. This is the standard problem which occur in any operating system. To avoid such problems some of the synchronization primitives need to be followed. Many solutions have been provided to solve the standard problem such as Blocking Mechanisms, Non-Blocking Mechanisms etc. But those mechanisms are not able to provide an optimal solution. Our idea is to develop an agent which will run in background and automatically detects Inter Process Synchronization problem and solve it without any human intervention. In this paper we are going to provide an agent based solution and develop algorithms for that which ensures smoothly execution of multiple processes without any synchronization problems and increase the performance of the CPU.

Build Verification Test (BVT) is a set of tests that is normally run on new build or daily build to primarily check the build whether all modules are integrated properly. In other words it verifies that the build is testable or not. It is done before any build is released to test team to perform further testing. It is not a full test so it should be completed within a short period of time. If there are some failures, it should be identified quickly and easily so the test team can decide whether they should continue testing with the new build or not. But sometimes BVT has too many failed test cases and test engineers need to spend a lot of time to investigate the results. In this paper we propose a way to create BVT test cases that can indicate quality of the build quicker without investigation on each of failed test cases.