Mazidah Puteh

A comparative evaluation of state-of-the-art web service composition testing approaches

More and more Web based systems are being developed by composing other single or even composite services. This is due to the fact that not all available services are able to satisfy the needs of a user. The process of composing Web services involves discovering the appropriate services, selecting the best services, combining those services together, and finally executing them. Although much research efforts have been dedicated to the discovery, selection, and composition of services, the process of testing the Web service composition has not been given the same attention. This paper discusses the importance of Web services composition testing, provides a classification of the most prominent approaches in that area, presents several criteria for comparison of those approaches, and conducts a comparative evaluation of the approaches. The results of the paper give an essential perspective to do research work on Web services composition testing.

Testing Web Services Composition: A Mapping Study

E-business systems are known for their frequent changes in business requirements, and traditional software development engineering approaches have difficulties in keeping up with this dynamicity. The use of service oriented architecture in software development has become popular as it provides a solution to frequent changes to business environments in a heterogeneous network. In service oriented architecture, new systems are quickly developed by combining services developed and owned by different organizations, and one way of realising this architecture is via Web services. Although much research effort has been put into the discovery, invocation and composition of services testing Web services has only begun to attract interest from both researchers and industry players. This paper aims to provide a mapping study of current Web services composition testing researches conducted by other researchers. Research papers on testing of Web services composition were gathered from various scholarly databases using provided search engines within a given period of time. The research papers were then classified according to issues addressed by them. The aim is to get a broad overview of the current state of research in Web services composition testing. By looking at the areas focused by existing researchers, gaps and untouched areas of Web services composition testing can be discovered.

Bess or xbest: Mining the Malaysian online reviews

Advancement in information and technology facilities especially the Internet has changed the way we communicate and express opinions or sentiments on services or products that we consume. Opinion mining aims to automate the process of mining opinions into the positive or the negative views. It will benefit both the customers and the sellers in identifying the best product or service. Although there are researchers that explore new techniques of identifying the sentiment polarization, few works have been done on opinion mining created by the Malaysian reviewers. The same scenario happens to micro-text. Therefore in this study, we conduct an exploratory research on opinion mining of online movie reviews collected from several forums and blogs written by the Malaysian. The experiment data are tested using machine learning classifiers i.e. Support VectorMachine, Naïve Baiyes and k-Nearest Neighbor. The result illustrates that the performance of these machine learning techniques without any preprocessing of the micro-texts or feature selection is quite low. Therefore additional steps are required in order to mine the opinions from these data.

Mining Opinion in Online Messages

The number of messages that can be mined from online entries increases as the number of online application users increases. In Malaysia, online messages are written in mixed languages known as ‘Bahasa Rojak’. Therefore, mining opinion using natural language processing activities is difficult. This study introduces a Malay Mixed Text Normalization Approach (MyTNA) and a feature selection technique based on Immune Network System (FS-INS) in the opinion mining process using machine learning approach. The purpose of MyTNA is to normalize noisy texts in online messages. In addition, FS-INS will automatically select relevant features for the opinion mining process. Several experiments involving 1000 positive movies feedback and 1000 negative movies feedback have been conducted. The results show that accuracy values of opinion mining using Naive Bayes (NB), k-Nearest Neighbor (kNN) and Sequential Minimal Optimization (SMO) increase after the introduction of MyTNA and FS-INS.

Flexible Immune Network Recognition System for Mining Heterogeneous Data

Artificial Immune System (AIS) is an emerging technique for the classification task and proved to be a reliable technique. In previous studies, many classifiers including AIS classifiers require the data to be in numerical or categorical data types prior to processing. The transformation of data into any other specific types from their original form can degrade the originality of the data and consume more space and pre processing time. This paper introduces AIS model using immune network for classifying heterogeneous data in its original types. The model is able to process the data with the types as represented in the database and it solves some bias problems highlighted in the AIS review papers. To ensure the consistent conditions and fair comparison, the selected existing algorithms use the same set of data as used in the proposed model. Experimental results show that this network-based model produces a better accuracy rate than the existing population-based immune algorithm and than the standard classifiers on most of the data from University of California, Irvive (UCI) Machine Learning Repository (MLR) and University of California, Riverside (UCR) Time Series Data (TSR).

Is artificial immune system suitable for opinion mining

Opinion mining is used to automate the process of identifying opinion whether it is a positive or negative view. Majority of previous works on this field uses natural language programming techniques to identify the sentiment. This paper reports the use of artificial immune system (AIS) technique in identifying Malaysian online movie reviews. This opinion mining process uses three string similarity functions namely Cosine Similarity, Jaccard Coefficient and Sorensen Coefficient. In addition, AIS performance was compared with other traditional machine learning techniques, which are Support Vector Machine, Naïve Baiyes and k-Nearest Network. The result of the findings are analyzed and discussed in this paper.

Classifying heterogeneous data with Artificial Immune System

Artificial immune system (AIS) is an emerging technique for the classification task and proved to be a reliable technique. In the previous researches, many classifiers including AIS classifiers require the data to be in numerical or categorical data types prior to processing. The transformation of data into any other specific types from their original form can degrade the originality of the data and consume more space and pre processing time. This paper introduces AIS model using clonal selection technique for classifying heterogeneous data in its original types. The model is able to process the data with the types as represented in the database and it solves some problems highlighted in the AIS reviews. To ensure the consistent conditions and fair comparison, the selected algorithms uses the same set of data as used in the proposed model. Experimental results show that this model produces a better accuracy rate than other immune algorithm and comparable to the standard classifiers on most of the benchmark data from UCI machine learning repository.

Optimization of Nano-Process Deposition Parameters Based on Gravitational Search Algorithm

This research is focusing on the radio frequency (RF) magnetron sputtering process, a physical vapor deposition technique which is widely used in thin film production. This process requires the optimized combination of deposition parameters in order to obtain the desirable thin film. The conventional method in the optimization of the deposition parameters had been reported to be costly and time consuming due to its trial and error nature. Thus, gravitational search algorithm (GSA) technique had been proposed to solve this nano-process parameters optimization problem. In this research, the optimized parameter combination was expected to produce the desirable electrical and optical properties of the thin film. The performance of GSA in this research was compared with that of Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Artificial Immune System (AIS) and Ant Colony Optimization (ACO). Based on the overall results, the GSA optimized parameter combination had generated the best electrical and an acceptable optical properties of thin film compared to the others. This computational experiment is expected to overcome the problem of having to conduct repetitive laboratory experiments in obtaining the most optimized parameter combination. Based on this initial experiment, the adaptation of GSA into this problem could offer a more efficient and productive way of depositing quality thin film in the fabrication process.

Prediction of Nanostructured ZnO Thin Film Properties Based on Neural Network

An approach in the prediction of zinc oxide (ZnO) thin films properties based on neural network is presented in this paper. The research had been focused on the electrical properties of ZnO. The sputtering power, substrate temperature, deposition time and oxygen ratio were selected as the input variables while the resistivity and conductivity were selected as the output. The numerical results obtained through the neural network model were compared with the experimental results. The result obtained from the system model of the proposed procedure was reasonably good and promising. Therefore, the prediction based on neural network model is a reliable approach compared to the traditional method of trial-and-error process.

Factors Affecting the Performance of Small Business Start-Ups Under Tunas Mekar Programme

This study scrutinises the factors that influence the performance of small business undertaken by graduates in the early stages of operation. Based on multiple regression analysis, the study shows that entrepreneurial characteristics, management practices and training and guidance have significant influence on the performance of small business start-ups. The experience of the business and the amount of initial capital are not significant to the performance of small enterprises at that stage. The findings have implications not only on the theory of the firm and human capital theory but also on small entrepreneurs, policymakers and implementers and researchers.