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All Journal Indonesian Journal of Electrical Engineering and Computer Science
Md Gapar Md Johar
Management and Science University

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Secure cloud adoption model: novel hybrid reference model Aiman Athambawa; Md Gapar Md Johar; Ali Khathibi
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 2: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i2.pp936-943

Abstract

This article discusses research conducted to conceptualise a secure cloud adoption model. The study surveyed SMEs in the Sri Lankan information technology industry using a questionnaire to determine cloud computing adoption factors. The study used Rogers' diffusion of innovation (DOI), Tornatzky and Fleischer's technology-organization-environment (TOE) framework, Venkatesh and Bala's technology acceptance model 3 (TAM3), and Venkatesh, Thong, and Xu's Unified theory of acceptance and use of technology 2 (UTAUT2) as the theoretical foundation for evaluating the reference model. Two hundred and fifty-six key officials from information technology (IT) organisations in Sri Lanka participated in the survey. The study used quantitative data coding and analysis methods with the SPSS and AMOS softwares. The findings from previous research and existing technology adoption frameworks and models were summarised to support the secure cloud adoption model (SCAM).
Poultry disease early detection methods using deep learning technology Liu Yajie; Md Gapar Md Johar; Asif Iqbal Hajamydeen
Indonesian Journal of Electrical Engineering and Computer Science Vol 32, No 3: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v32.i3.pp1712-1723

Abstract

Poultry production is a pivotal contributor to global economic growth, playing a central role in promoting human ecosystem sustainability. It offers affordable and readily accessible protein sources, encompassing meat, eggs, and other by-products. Beyond its direct nutritional benefits, poultry production enhances household income, bolsters food security, and aids in poverty reduction, making it integral to worldwide economic advancement. However, as the global population surges, so does the demand for poultry meat and eggs. Concurrently, poultry disease management emerges as a paramount challenge, leading to significant threats to food security and economic stability. Leveraging cutting-edge technology offers promising avenues to devise strategies that not only bolster farm profitability but also mitigate environmental impacts and foster the well-being of both animals and humans. This study systematically reviews the latest literature concerning poultry disease diagnosis based on deep learning techniques, elucidating the clinical manifestations associated with various ailments. The analysis indicates that emerging technological solutions, especially image processing and deep learning (DL), substantially outperform conventional manual inspection methods in early disease detection and warning in the poultry sector. Such innovations underscore their potential for revolutionizing poultry health management and disease mitigation.
Deep neural networks optimization for resource-constrained environments: techniques and models Raafi Careem; Md Gapar Md Johar; Ali Khatibi
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 3: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i3.pp1843-1854

Abstract

This paper aims to present a comprehensive review of advanced techniques and models with a specific focus on deep neural network (DNN) for resource-constrained environments (RCE). The paper contributes by highlighting the RCE devices, analyzing challenges, reviewing a broad range of optimization techniques and DNN models, and offering a comparative assessment. The findings provide potential optimization techniques and recommend a baseline model for future development. It encompasses a broad range of DNN optimization techniques, including network pruning, weight quantization, knowledge distillation, depthwise separable convolution, residual connections, factorization, dense connections, and compound scaling. Moreover, the review analyzes the established optimization models which utilizes the above optimization techniques. A comprehensive analysis is conducted for each technique and model, considering its specific attributes, usability, strengths, and limitations in the context of effective deployment in RCEs. The review also presents a comparative assessment of advanced DNN models’ deployment for image classification, employing key evaluation metrics such as accuracy and efficiency factors like memory and inference time. The article concludes with the finding that combining depthwise separable convolution, weight quantization, and pruning represents potential optimization techniques, while also recommending EfficientNetB1 as a baseline model for the future development of optimization models in RCE image classification. 
Co-Authors Aiman Athambawa Ali Khathibi Ali Khatibi Asif Iqbal Hajamydeen Liu Yajie Raafi Careem