D
Dzuraidah Abdul Wahab
Researcher at National University of Malaysia
Publications - 35
Citations - 334
Dzuraidah Abdul Wahab is an academic researcher from National University of Malaysia. The author has contributed to research in topics: Plastic bottle & Lean manufacturing. The author has an hindex of 9, co-authored 35 publications receiving 281 citations.
Papers
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A framework for organisational change management in lean manufacturing implementation
TL;DR: In this article, the authors present a thorough literature review for lean manufacturing approach in the context of organisational change management and propose an organizational change framework for Lean manufacturing implementation that would serve as the basis for further empirical research and validation.
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Application of automated image analysis to the identification and extraction of recyclable plastic bottles
TL;DR: An experimental machine vision apparatus and homemade software were used to identify and extract recyclable plastic bottles out of a conveyor belt, integrating various recognition techniques such as minimum distance in the feature space, self-organized maps, and neural networks.
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Lean manufacturing implementation in Malaysian automotive industry: An exploratory study
TL;DR: In this paper, the authors present an exploratory study of lean manufacturing implementation in Malaysian automotive industry, and examine the drivers and barriers that influence the implementation of the lean manufacturing process.
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Integration of comfort into a driver's car seat design using image analysis
Darliana Mohamad,Baba Md Deros,Dzuraidah Abdul Wahab,Dian Darina Indah Daruis,Ahmad Rasdan Ismail +4 more
TL;DR: In this article, the authors proposed a range of angles for driving posture comfort from measurement of participants and investigated the relationships between drivers' anthropometric chara cteristics, comfortable postural angles and seat adjustment.
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Histogram of Intensity Feature Extraction for Automatic Plastic Bottle Recycling System Using Machine Vision
TL;DR: The core components of machine vision for this intelligent sorting system is the image recognition and classification and the feature extraction algorithm used is discussed in detail since it is the core component of the overall system that determines the success rate.