Nor Liyana Mohd Shuib

Evaluation of traditional and consolidated rice farms in Guilan Province, Iran, using life cycle assessment and fuzzy modeling.

In this study the environmental impact of consolidated rice farms (CF) - farms which have been integrated to increase the mechanization index - and traditional farms (TF) - small farms with lower mechanization index - in Guilan Province, Iran, were evaluated and compared using Life cycle assessment (LCA) methodology and adaptive neuro-fuzzy inference system (ANFIS). Foreground data were collected from farmers using face-to-face questionnaires and background information about production process and inventory data was taken from the EcoInvent®2.0 database. The system boundary was confined to within the farm gate (cradle to farm gate) and two functional units (land and mass based) were chosen. The study also included a comparison of the input-output energy flows of the farms. The results revealed that the average amount of energy consumed by the CFs was 57 GJ compared to 74.2 GJ for the TFs. The energy ratios for CFs and TFs were 1.6 and 0.9, respectively. The LCA results indicated that CFs produced fewer environmental burdens per ton of produced rice. When compared according to the land-based FU the same results were obtained. This indicates that the differences between the two types of farms were not caused by a difference in their production level, but rather by improved management on the CFs. The analysis also showed that electricity accounted for the greatest share of the impact for both types of farms, followed by P-based and N-based chemical fertilizers. These findings suggest that the CFs had superior overall environmental performance compared to the TFs in the study area. The performance metrics of the model based on ANFIS show that it can be used to predict the environmental burdens of rice production with high accuracy and minimal error.

Factors influencing the use of social media by SMEs and its performance outcomes

Purpose – The purpose of this paper is to investigate the factors that influence Facebook usage among small and medium enterprises (SMEs). In addition, it examines the impact of Facebook usage on financial and non-financial performance of the SMEs. Design/methodology/approach – Using integrated model, this study examined the influence of compatibility, cost effectiveness, interactivity and trust on Facebook usage and its subsequent impact on organizations performance. Statistical analyses were based on the data collected, through survey questionnaire from 259 SMEs in Malaysia. Partial Least Square (PLS) method was used to test the hypotheses. Findings – The study revealed that Facebook usage has a strong positive impact on financial performance of SMEs; similarly it was also found that Facebook usage positively impacts the non-financial performance of SMEs in terms of cost reduction on marketing and customer service, improved customer relations and improved information accessibility. Additionally, factors...

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.

A Review of the Applications of Bio-inspired Flower Pollination Algorithm☆

Abstract The Flower Pollination Algorithm (FPA) is a novel bio-inspired optimization algorithm that mimics the real life processes of the flower pollination. In this paper, we review the applications of the Single Flower Pollination Algorithm (SFPA), Multi-objective Flower Pollination Algorithm an extension of the SFPA and the Hybrid of FPA with other bio-inspired algorithms. The review has shown that there is still a room for the extension of the FPA to Binary FPA. The review presented in this paper can inspire researchers in the bio-inspired algorithms research community to further improve the effectiveness of the PFA as well as to apply the algorithm in other domains for solving real life, complex and nonlinear optimization problems in engineering and industry. Further research and open questions were highlighted in the paper.

Building a new taxonomy for data discretization techniques

Data preprocessing is an important step in data mining. It is used to resolve various types of problem in a large dataset in order to produce quality data. It consists of four steps, namely, data cleaning, integration, reduction and transformation. The literature shows that each preprocessing step consists of various techniques. In order to develop quality data, a data miner must decide the most appropriate techniques in every step of data preprocessing. In this study, we focus on data reduction particularly data discretization as one important data preprocessing step. Data reduction involves reducing the data distribution by reducing the range of continuous data into a range of values or categories. Data discretization plays a major role in reducing the attribute intervals of data values. Finding an appropriate number of discrete values will improve the performance of the data mining modelling, particularly in terms of classification accuracy. This paper proposes four levels of data discretization taxonomy as follows: hierarchical and non-hierarchical; splitting, merging and combination; supervised and unsupervised combinations; and binning, statistic, entropy and other related techniques. The taxonomy is developed based on a detailed review of previous discretization techniques. More than fifty techniques are investigated, and the structure of the discretization approach is outlined. Guidelines on how to use the proposed taxonomy are also discussed.

Agent based preprocessing

The current data mining tools is used to build knowledge based on a huge historical data. At present, businesses are facing with fast growing data that are very valuable in contributing knowledge. Knowledge should be updated regularly in order to ensure its quality and precision thus improve the decision making process. Data mining has shown great potential in extracting valuable knowledge from large databases. However, current data mining algorithms and tools are costly and several are too complex in their operations when dealing with large databases. In recent years, agents have become a popular paradigm in computing, because its autonomous, flexible and provides intelligence. Embedding agents in the current data mining processes and tools are believed to be able to solve the obstacle. One of the most important process in data mining is data preprocessing. It is reported that 60% of the data mining project is on preprocessing. Data preprocessing involves integration, selection, cleaning and transformation of data set that will be used for mining. This paper focuses on an agent-based preprocessing framework. The aims is to provides an auto preprocessing a set of new data, which suite to data mining novice user. The proposed agent based preprocessing framework consists of seven agents: user interface agents, coordinator agent, identify agent, CleanMiss agent, CleanNoisy agent, transformation agent and discretization agent. User interface agent is designed in such a way to provide interface suite to novice users. Coordinator agent is responsible for coordinating and cooperating with all other agents to achieve the goals. Identify agent responsible to provide an adaptive user data cleaning profiling. CleanMiss agent, CleanNoisy agent, transformation agent and discretization agent provide various types of techniques autonomously, which ended with proposing the best cleaning techniques from various types of techniques to keep in the preprocessing profile. This paper is start by introducing the data mining process problem includes data preprocessing which agent can solve data mining problems. By applying agent in data preprocessing, a tool that intelligence yet flexible can be produced.

The use of information retrieval tools: A study of computer science postgraduate students

Students may seek information for various reasons such as to understand a specific subject matter or to conduct a research. In this digital age, they may not have a problem to find information. However, they are having problem to find scholarly information that suits their learning needs. This is because they are having difficulties to find the scholarly information that suitable for their learning styles. The objectives of this research are (a) to investigate information retrieval tools that students use to find scholarly information; (b) to compare the tools in terms of access, search techniques, and search facilities and (c) to propose architecture of information retrieval tool for learning needs. Therefore, this research investigates several information retrieval tools such as Online Public Access Catalogues (OPAC), Internet search engine, online databases and digital libraries. Survey questionnaire was conducted on 129 postgraduates students of Faculty of Science Computer and Information Technology, University of Malaya. They were chosen because of their level of education and their proficiency in using IT infrastructure. The quantitative data were analyzed using statistical program SPSS. The result shows that postgraduate students having difficulties in finding information that suitable to their learning style using available information retrieval tools.

Predicting the adoption of cloud-based technology using fuzzy analytic hierarchy process and structural equation modelling approaches.

Abstract With the emergence of cloud-based technology, personalized learning mechanism has increasingly become a fundamental requirement for most learning systems. This study aimed to identify the key factors that influence user adoption of cloud-based collaborative learning technology in the educational context. Grounded on the Unified Theory of Acceptance and Use of Technology (UTAUT), personalization construct was linked to the behavioral intention, performance expectancy and effort expectancy. This research applied a new methodological approach combining both Fuzzy Analytic Hierarchy Process (FAHP) and Structural Equation Modelling (SEM) to determine the relative weight and importance of the factors as well as to test the proposed hypotheses in the research model. Using a survey questionnaire, data was collected from 150 students of four Malaysian public universities. The findings of FAHP demonstrated that performance expectancy, social influence, and personalization were the most important factors predicting behavioral intention to adopt cloud-based collaborative learning technology from experts’ point of view. The results of the SEM showed that users’ behavioral intention was significantly influenced by performance expectancy, effort expectancy, social influence and personalization. Although, personalization performed a direct influence on behavioral intention, its indirect influence through performance expectancy and effort expectancy was also considerable. This study and its findings can serve as a baseline by which cloud service providers, ministry of education, and educational institutions can make strategic and strong decisions about adoption of cloud-based technology in educational environments.

Data Mining Approach: Relevance Vector Machine for the Classification of Learning Style Based on Learning Objects

Recent researches indicate that a lot of effort has been done to provide learners with personalized learning objects. Previous studies classified learning object based on the description of the learning style preference itself without considering student preference. In this study, we propose a data mining approach to the classification of learning objects based on learning style while considering student preference use of the learning objects. Relevance Vector Machine (RVM) is used to build a classifier for the classification of learners. For the purpose of comparison, Support Vector Machine (SVM) and Neural Network (NN) were applied. Comparative simulation results indicated that the propose RVM classifier accuracy and computational time complexity is superior to the NN, and SVM classifiers. The classifier proposes in this research can be of help to educators in proposing appropriate learning objects with high level of accuracy within a short period of time. This in turn can significantly improve learners performance in understanding the subject matter.

Advances in Teaching and Learning on Facebook in Higher Institutions

Facebook has now become the most popular and extensively used social networking site among students of institutions of higher education. This makes it a widespread tool for communication and exchange of ideas. Notable to that is an active research in determining the utility of Facebook as a complementary tool in teaching and learning. The uses of the social networking sites especially, Facebook has been reported in a wide variety of results with respect to factors, such as students’ learning performance, involvement, and acceptance, have been reported in the literature. This paper presents a comprehensive review of recent studies that employ Facebook as a tool for teaching and learning in institutions of higher education. We analyze the use of Facebook as a teaching and learning tool for various courses. Thereafter, its impacts on enhancing student learning outcomes as well as its negative impact on students’ performance are evaluated. We also highlight the main limitations of the existing and previous studies. Future research directions for incorporating Facebook into the teaching and learning at institutions of higher education are suggested. This review is helpful to educators who plan to integrate Facebook into their teaching as well as to the researchers for further exploration of Facebook as a tool in teaching and learning.