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Bulletin of Electrical Engineering and Informatics

EEI
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Published by Institute of Advanced Engineering and Science

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Core Subject : Science, Engineering,

Computer Science & IT Electrical & Electronics Engineering Engineering

Bulletin of Electrical Engineering and Informatics ISSN: 2302-9285 is open to submission from scholars and experts in the wide areas of electrical, electronics, instrumentation, control, telecommunication, computer engineering, computer science, information technology and informatics from the global world. The journal publishes original papers in the field of electrical (power), electronics, instrumentation & control, telecommunication and computer engineering; computer science; information technology and informatics. Authors must strictly follow the guide for authors. Please read these instructions carefully and follow them strictly. In this way you will help ensure that the review and publication of your paper is as efficient and quick as possible. The editors reserve the right to reject manuscripts that are not in accordance with these instructions.

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Articles 17 Documents

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Search results for , issue "Vol 7, No 1: March 2018" : 17 Documents clear

Enhancing Big Data Analysis by using Map-reduce Technique Alaa Hussein Al-Hamami; Ali Adel Flayyih
Bulletin of Electrical Engineering and Informatics Vol 7, No 1: March 2018
Publisher : Institute of Advanced Engineering and Science

Database is defined as a set of data that is organized and distributed in a manner that permits the user to access the data being stored in an easy and more convenient manner. However, in the era of big-data the traditional methods of data analytics may not be able to manage and process the large amount of data. In order to develop an efficient way of handling big-data, this work enhances the use of Map-Reduce technique to handle big-data distributed on the cloud. This approach was evaluated using Hadoop server and applied on Electroencephalogram (EEG) Big-data as a case study. The proposed approach showed clear enhancement on managing and processing the EEG Big-data with average of 50% reduction on response time. The obtained results provide EEG researchers and specialist with an easy and fast method of handling the EEG big data.

A Modified Overlapping Partitioning Clustering Algorithm for Categorical Data Clustering Mohammad Alaqtash; Moayad A.Fadhil; Ali F. Al-Azzawi
Bulletin of Electrical Engineering and Informatics Vol 7, No 1: March 2018
Publisher : Institute of Advanced Engineering and Science

Clustering is one of the important approaches for Clustering enables the grouping of unlabeled data by partitioning data into clusters with similar patterns. Over the past decades, many clustering algorithms have been developed for various clustering problems. An overlapping partitioning clustering (OPC) algorithm can only handle numerical data. Hence, novel clustering algorithms have been studied extensively to overcome this issue. By increasing the number of objects belonging to one cluster and distance between cluster centers, the study aimed to cluster the textual data type without losing the main functions. The proposed study herein included over twenty newsgroup dataset, which consisted of approximately 20000 textual documents. By introducing some modifications to the traditional algorithm, an acceptable level of homogeneity and completeness of clusters were generated. Modifications were performed on the pre-processing phase and data representation, along with the number methods which influence the primary function of the algorithm. Subsequently, the results were evaluated and compared with the k-means algorithm of the training and test datasets. The results indicated that the modified algorithm could successfully handle the categorical data and produce satisfactory clusters.

Test First Model for Application in the Academic Setting Normi Sham Awang Abu Bakar; Norzariyah Yahya
Bulletin of Electrical Engineering and Informatics Vol 7, No 1: March 2018
Publisher : Institute of Advanced Engineering and Science

This research elaborates the selection of the Test First and Test Last model for a pilot experiment that was executed as a feasibility study to validate the suitability of the existing Test First model for its implementation in the series of actual experiment. The series of actual experiment is designed to investigate the quality of the project developed by the students in higher educational institution with the Test First over Test Last model. The findings gathered from the pilot experiment demonstrate that there were misunderstandings on the user stories among the participants that have led to the development of an enhanced Test First model.

Conceptualizing Information Technology Governance Model for Higher Education: An Absorptive Capacity Approach Binyamin Adeniyi Ajayi; Husnayati Hussin
Bulletin of Electrical Engineering and Informatics Vol 7, No 1: March 2018
Publisher : Institute of Advanced Engineering and Science

Information Technology (IT) governance has been emerging as a central issue in many organizations. This is because IT governance is key to realizing IT business value. Past studies have focused on the three aspects of IT governance, namely, structural capability, process capability and relational capability. At the same time, some studies have suggested that IT governance process should be viewed as a learning process rather than a problem solving process. Based on this scenario, the role of knowledge and knowledge based processes should be the central focus of IT governance. As a learning process, IT governance effectiveness can be determined by how much impact IT governance practices has influenced on decision-makers’ thinking and actions. In this case, knowledge capacity absorbed from IT governance experience reflects a certain level of organizational learning (OL) achieved which later influences the level of IT governance performance. Since studies that adopt this perspective is lacking, this paper proposes a conceptual framework based on absorptive capacity approach for an IT governance performance model in the higher education. The paper contributes theoretically by extending the knowledge of IT governance by exploring a new perspective on OL.

Antecedents of Knowledge Management Practices: Case of Malaysian Practitioners Mohamed Jalaldeen Mohamed Razi; Mohd Izzuddin Mohd Tamrin; Abdul Rahman Ahmad Dahlan; Noor Azian Mohamad Ali
Bulletin of Electrical Engineering and Informatics Vol 7, No 1: March 2018
Publisher : Institute of Advanced Engineering and Science

In this paper, we investigated the knowledge management (KM) behavior of executives in Malaysia who work in different sectors and involved in Information Technology (IT) related fields. We proposed a conceptual framework based on the Theory of Reasoned Action (TRA), the Theory of Planned Behavior (TPB) and Unified Theory of Acceptance and Use of Technology (UTAUT) to study their intention and involvement in KM initiatives. The knowledge creation theory (SECI process) was employed to operationalize KM intention and KM behavior. We proposed six independent variables that represent the social-cultural nature of KM as the antecedence of KM intention. These variables are trust, management support, decentralization, IT support, performance expectancy (PE), and effort expectancy (EE). Seventy-four executives from both private and government-linked organizations responded to our online questionnaire. SmartPLS3 was used to run the analysis. The reliability was ensured with the factor loadings, Cronbach’s alpha, Composite Reliability (CR) that met the fit requirement of above 0.6, 0.7 and 0.7 respectively. The convergent validity was confirmed through average variance extracted (AVE) that met the fit requirement of above 0.5. The discriminant validity was assessed by using Fornell and Larcker’s criterion. Finally, the structural model confirmed that only PE of KM, and EE of KM are the significant predictors of KM intention and the KM intention significantly predicts KM behavior. The implications of the findings are discussed in detail at the end of the paper.

The Design and Evaluation of DACADE Visual Tool: Theoretical Implications Madihah Sheikh Abdul Aziz; Gitte Lindgaard; Mohd Syarqawy Hamzah; T. W. Allan Whitfield
Bulletin of Electrical Engineering and Informatics Vol 7, No 1: March 2018
Publisher : Institute of Advanced Engineering and Science

A goal of every designer is to create successful products for consumers. In creating a successful product, it is crucial for a designer to understand consumers’ perceptions of a product early in the design process. Nevertheless, design students lack the necessary data collection and user testing skills to support effective design decision-making. Consequently, their products might not be acceptable to the intended consumers and are thus likely to fail in the marketplace. For design students to acquire those skills, design curricula should incorporate statistical courses teaching the concepts of data and user testing. We addressed this challenge by developing an automated visual tool named DACADE, assisting design students to systematically collect and analyze data. This paper reports the theoretical implications discovered during the process from designing through to implementing and evaluating DACADE concerning the transfer of learning, the appropriateness of graphics used in a software tool, and user motivation in a learning environment.

On Randomness of Compressed Data Using Non-parametric Randomness Tests Kamal A. Al-Khayyat; Imad F. Al-Shaikhli; V. Vijayakuumar
Bulletin of Electrical Engineering and Informatics Vol 7, No 1: March 2018
Publisher : Institute of Advanced Engineering and Science

Four randomness tests were used to test the outputs (compressed files) of four lossless compressions algorithms: JPEG-LS and JPEG-2000 algorithms are image-dedicated algorithms, while 7z and Bzip2 algorithms are general-purpose algorithms. The relationship between the result of randomness tests and the compression ratio was investigated. This paper reports the important relationship between the statistical information behind these tests and the compression ratio. It shows that, this statistical information almost the same at least, for the four lossless algorithms under test. This information shows that 50 % of the compressed data are grouping of runs, 50% of it has positive signs when comparing adjacent values, 66% of the files containing turning points, and using Cox-Stuart test, 25% of the file give positive signs, which reflects the similarity aspects of compressed data. When it comes to the relationship between the compression ratio and these statistical information, the paper shows also, that, the greater values of these statistical numbers, the greater compression ratio we get.

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