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All Journal International Journal of Electrical and Computer Engineering TELKOMNIKA (Telecommunication Computing Electronics and Control) Indonesian Journal of Electrical Engineering and Computer Science
Roslina Ibrahim
Universiti Teknologi Malaysia
Computer Science & IT Electrical & Electronics Engineering

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Hybrid deep learning model using recurrent neural network and gated recurrent unit for heart disease prediction Surenthiran Krishnan; Pritheega Magalingam; Roslina Ibrahim
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 6: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i6.pp5467-5476

Abstract

This paper proposes a new hybrid deep learning model for heart disease prediction using recurrent neural network (RNN) with the combination of multiple gated recurrent units (GRU), long short-term memory (LSTM) and Adam optimizer. This proposed model resulted in an outstanding accuracy of 98.6876% which is the highest in the existing model of RNN. The model was developed in Python 3.7 by integrating RNN in multiple GRU that operates in Keras and Tensorflow as the backend for deep learning process, supported by various Python libraries. The recent existing models using RNN have reached an accuracy of 98.23% and deep neural network (DNN) has reached 98.5%. The common drawbacks of the existing models are low accuracy due to the complex build-up of the neural network, high number of neurons with redundancy in the neural network model and imbalance datasets of Cleveland. Experiments were conducted with various customized model, where results showed that the proposed model using RNN and multiple GRU with synthetic minority oversampling technique (SMOTe) has reached the best performance level. This is the highest accuracy result for RNN using Cleveland datasets and much promising for making an early heart disease prediction for the patients.
Cloud computing acceptance among public sector employees Mohd Talmizie Amron; Roslina Ibrahim; Nur Azaliah Abu Bakar
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 19, No 1: February 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v19i1.17883

Abstract

Cloud computing is one of the platforms that drive organisations and users to be better prepared for a simpler computing platform and offers significant benefits to the quality of work. The transition from conventional computing to the virtual world helps organisations to maximise their potential. However, not all users can accept cloud computing adoption. Failure to understand the factors of user's acceptance will negatively impact the organisation's mission of empowering the technology. Therefore, this study proposes to assess to what extent the users are accepting cloud computing. This study adopts the unified theory of acceptance and use of technology (UTAUT) and six technological and human factors assessed for the Malaysian public sectors. Survey data from several ministries were analysed using partial least squares-structural equation modelling (PLS-SEM). The study found out that performance expectancy, compatibility, security, mobility, information technology (IT) knowledge, and social influence had a significant impact on the user's intention to accept cloud computing. The results of this study contribute to a clear understanding of the factors affecting the Malaysian public sectors about cloud computing.
Metrics and Benchmarks for Empirical and Comprehension Focused Visualization Research in the Sales Domain Loo Yew Jie; Doris Hooi-Ten Wong; Zarina Mat Zain; Nilam Nur Amir Sjarif; Roslina Ibrahim; Nurazean Maarop
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 3: December 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v12.i3.pp1340-1348

Abstract

Data visualization is an effort which aims to communicate data effectively and clearly to the audience through graphicalrepresentation. Data visualization efforts must be coordinated with an understanding into the Cognitive Learning Theory (CLT). In the sales domain, sales data visualization are made possible with the available Business Intelligence (BI) tools such as Microsoft Power BI, Tableau, Plotly, and others. These tools allow seamless interaction for the top management as well as the sales force with regard to the data. Sales data visualization comes with an array of advantages such as self-service analysis by business users, rapidly adapt to changing business conditions, and enable continuous on-demand reporting among others. The advantages of sales data visualization also comes with the challenges such as difficulty in identifying visual noise, high rate of image change, and high performance requirements. In an effort to reduce cognitive activity that does not enhance learning, sales visualization dashboard must be designed in a way that is neithertoo simplistic nor too complex to ensure that the Intrinsic Cognitive Load (ICL), Extrinsic Cognitive Load (ECL), and Germane Cognitive Load (GCL) are in sync with the audience. With the combination of sales data visualization and CLT, understanding complex sales details quickly is made possible by not only the top management of the organization, but also the sales force of the organization.
Co-Authors Doris Hooi-Ten Wong Loo Yew Jie Mohd Talmizie Amron Nilam Nur Amir Sjarif Nur Azaliah Abu Bakar Nurazean Maarop Pritheega Magalingam Surenthiran Krishnan Zarina Mat Zain