Mohd Sapiyan Baba

Information security - Professional perceptions of knowledge-sharing intention under self-efficacy, trust, reciprocity, and shared-language

Knowledge sharing is an important component of knowledge management systems. Security knowledge sharing substantially reduces risk and investment in information security. Despite the importance of information security, little research based on knowledge sharing has focused on the security profession. Therefore, this study analyses key factors, containing attitude, self-efficacy, trust, norm of reciprocity, and shared language, in respect of the information security workers intention to share knowledge. Information security professionals in virtual communities, including the Information Security Professional Association (ISPA), Information Systems Security Association (ISSA), Society of Information Risk Analysts (SIRA), and LinkedIn security groups, were surveyed to test the proposed research model. Confirmatory factor analysis (CFA) and the structural equation modelling (SEM) technique were used to analyse the data and evaluate the research model. The results showed that the research model fit the data well and the structural model suggests a strong relationship between attitude, trust, and norms of reciprocity to knowledge sharing intention. Hypotheses regarding the influence of self-efficacy and reciprocity, to knowledge sharing attitude were upheld. Shared language did not influence either the attitude or intention to share knowledge. We model six factors which influence security workers knowledge sharing intention.Effect of attitude, trust, reciprocity to knowledge sharing intention were upheld.Self-efficacy and reciprocity had influence on knowledge sharing attitude.Shared language affected neither intention nor attitude to share knowledge.

Evaluation model for knowledge sharing in information security professional virtual community

Abstract Knowledge sharing has been proven to have affirmative effects on both the education and business sectors. Nevertheless, many professional virtual communities (PVC) have failed due to reasons, such as the low willingness of members to share knowledge with other members. In addition, it is not explicitly evident whether knowledge sharing in information security is able to reduce risk. To date, there have been relatively few empirical studies concerning the effects of knowledge sharing and its capability to reduce risk in information security communities. This paper proposes a model that is composed of two main parts. The first part is the Triandis theory, which is adapted to understand and foster the determinants of knowledge sharing behavior in PVCs. The second part explores the quantitative relationship between knowledge sharing and security risk reduction expectation. One hundred and forty-two members from the LinkedIn information security groups participated in this study. PLS analysis shows that perceived consequences, affect, and facilitating conditions have significant effects on knowledge sharing behavior. In contrast, social factors have shown insignificant effects on knowledge sharing behavior in information security communities. The results of the study demonstrate that there is a positive and strong relationship between knowledge sharing behavior and information security risk reduction expectation.

Applying neural network technology in qualitative research for extracting learning style to improve e-learning environment

Research in social and human behavior is a very difficult and complex task in terms of collecting, analyzing and converting data into numbers. This type of researches known as qualitative research, which is a process relies on reasons behind various aspects of behavior. It investigates the why and how of decision making, as compared to what, where, and when of quantitative research. This paper describes a qualitative research to extract learning style for students. The result will be used in developing a neural network application for adaptive e-learning system to determine the most preferred learning approach for a student. As a result the paper presents how qualitative research type has direct influence and affects the accuracy result of artificial neural network application.

Information security professional perceptions of knowledge-sharing intention in virtual communities under social cognitive theory

Knowledge sharing is an important component of knowledge management systems. Security knowledge sharing substantially reduces risk and investment in information security. Despite the importance of information security, little research based on knowledge sharing has focused on the security profession. Therefore, this study analyses key factors, containing attitude, self-efficacy, personal outcome expectation, and facilitating condition, in respect of the information security workers intention to share knowledge. Information security professionals in virtual communities, including the Information Security Professional Association (ISPA), Information Systems Security Association (ISSA), Society of Information Risk Analysts (SIRA), and LinkedIn security groups, were surveyed to test the proposed research model. Confirmatory factor analysis (CFA) and the structural equation modelling (SEM) technique were used to analyse the data and evaluate the research model. The results showed that the research model fit the data well and the structural model suggests a strong relationship between attitude and knowledge sharing intention. Hypotheses regarding the influence of self-efficacy and personal outcome expectation, to knowledge sharing attitude were upheld. Facilitating condition showed significant influences on moderating between attitude and intention.

The compact Genetic Algorithm for likelihood estimator of first order moving average model

Recently Genetic Algorithms (GAs) have frequently been used for optimizing the solution of estimation problems. One of the main advantages of using these techniques is that they require no knowledge or gradient information about the response surface. The poor behavior of genetic algorithms in some problems, sometimes attributed to design operators, has led to the development of other types of algorithms. One such class of these algorithms is compact Genetic Algorithm (cGA), it dramatically reduces the number of bits reqyuired to store the poulation and has a faster convergence speed. In this paper compact Genetic Algorithm is used to optimize the maximum likelihood estimator of the first order moving avergae model MA(1). Simulation results based on MSE were compared with those obtained from the moments method and showed that the Canonical GA and compact GA can give good estimator of θ for the MA(1) model. Another comparison has been conducted to show that the cGA method has less number of function evaluations, minimum searched space percentage, faster convergence speed and has a higher optimal precision than that of the Canonical GA.

Prediction of Population Dynamics of Bacillariophyta in the Tropical Putrajaya Lake and Wetlands (Malaysia) by a Recurrent Artificial Neural Networks

Phytoplankton becomes a concern to the society when it forms a dense growth at water surface known as algae bloom. This paper discusses feasibility of applying recurrent artificial neural network to predict occurrence of selected phytoplankton population the Bacillariophyta population in Putrajaya Lake and Wetlands for one month ahead prediction. The data used are monthly data collected from August 2001 until May 2006. Network performance is measured based on the root mean square error value (RMSE). Input selection is carried out by means of correlation analysis, sensitivity analysis and unsupervised neural network SOM. Better results are achieved for simpler network where variables are selected using method stated above. Thus the capability of neural network model as a predictive tool for tropical lake cannot be disregarded at all.

Challenges of Digital Note Taking

There are world efforts to make technology act with education field for better learning achievements. Technology tries to replace the traditional learning environments, media, and activities into digital age. However, slow progress has been achieved to transfer the note taking activities into digital era. In this study, we explored current note taking tools which developed to bridge the gap between paper-based and technology-based notes. We tried to identify key specific problems and challenges that prevent note taking from existing in the digital age. This study is providing extensive investigation with systematic analysis about the impacts of current note taking tools in learning to identify constrains and limitations of typical note taking systems. Unfortunately, we agreed with similar previous studies that current tools are still inadequate and inefficient to be used for replacing the traditional note taking due to several issues. We found that developing a successful note taking applications is challenges because of four main issues, complexity, technology learning dilemma, integrity, and inefficiency issues. This study discusses the main implications to shape the future of digital notes.

Applied Genetic Algorithm for Solving Rich VRP

In this article, we present a study of the effectiveness of a genetic algorithm (GA) to solve a combinatorial problem, that is, a vehicle routing problem (VRP). We propose a new selection method, called “rank and select,” based on selection rate, and we compare it with roulette wheel selection. In this article, we use two types of crossover method and two types of mutation method. These are applied for comparing the best fitness at the end of a generation. The problem solved in this study is how to generate feasible route combinations for a rich VRP and meet all the requirements with an optimum solution. Initial test results show that the route produced by the GA was effectively used for solving rich VRP and especially for a large number of customers, depots, and vehicles. Fuel consumption by proposed routes was lower by about 20.38% compared to that of an existing route.

Applying artificial neural network theory to exploring diatom abundance at tropical Putrajaya Lake, Malaysia

This article explores the relationship between diatom abundance and water quality variables in tropical Putrajaya Lake based on limnological data collected from 2001 to 2006, using supervised and unsupervised artificial neural networks (ANNs). Recurrent artificial neural network (RANN) was used for the supervised ANNs and Kohonen Self Organizing Feature Maps (SOMs) for the unsupervised ANNs. The RANN was developed for the prediction of diatom abundance using variables selected by sensitivity analysis (water temperature, pH, dissolved oxygen, and turbidity). The RANN model performance was measured using root mean squared error (19.0 cell/mL) and the r-value (0.7). SOM was used in this study for classification and clustering of diatom abundance in relation to selected water quality variables and was validated using a sensitivity curve of diatom abundance over the selected variable range generated from RANN. SOM has been employed in this study for pattern discovery of diatom abundance at Putrajaya Lake. The extracted patterns of diatom abundance in terms of propositional IF…else rules were tested and yielded an accuracy rate of 87%.

A Comparison between Neural Network Based and Fuzzy Logic Models for Chlorophll-a Estimation

This paper describes the application of two novel computational methods such as fuzzy logic and supervised artificial neural network (ANN) to model algal biomass in tropical Putrajaya Lake and Wetlands (Malaysia). Limnological time series data collected from 2001 until 2004 was utilized using input parameters such as water temperature, pH, secchi depth, dissolved oxygen, ammoniacal nitrogen and nitrate nitrogen. Performance measure for the models developed was in terms of root mean square error (RMSE). Both models developed gave similar result with models developed using fuzzy logic approach performed slightly better compared to feed-forward artificial neural network model.