Uzma Batool
Universiti Teknologi Malaysia
8 Papers
1 Citations
Uzma Batool is an academic researcher from Universiti Teknologi Malaysia. The author has contributed to research in topics: Deep learning & Convolutional neural network. The author has an hindex of 2, co-authored 6 publications. Previous affiliations of Uzma Batool include University of Wah.
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Papers
A Systematic Review of Deep Learning for Silicon Wafer Defect Recognition
Uzma Batool,Mohd Ibrahim Shapiai,Muhammad Tahir,Zool Hilmi Ismail,N. J. Zakaria,Ahmed Elfakharany +5 more
TL;DR: In this article, the authors present a review of the deep learning methods employed for wafer map defect recognition, which are grouped as supervised learning, unsupervised learning, and hybrid learning.
Bypassing MRI Pre-processing in Alzheimer's Disease Diagnosis using Deep Learning Detection Network
Jia Xian Fong,Mohd Ibrahim Shapiai,Yuan You Tiew,Uzma Batool,Hilman Fauzi +4 more
- 01 Feb 2020
TL;DR: This group is the first group to propose an Alzheimer's Disease diagnosis solution without requiring any MRI pre-processing technique, and introduces recent deep learning object detection architectures such as Faster R-CNN, SSD and YOLOv3 into the area of Alzheimer's disease diagnosis.
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Psychological ownership and knowledge behaviors during a pandemic: role of approach motivation
TL;DR: In this paper , the authors examined the relationship between psychological ownership, knowledge sharing, knowledge hiding and employee motivation in knowledge intensive organizations and found that stronger feelings of psychological ownership lead to both positive work behavior and negative work behavior.
Design of an Helical Spring using Single-solution Simulated Kalman Filter Optimizer
M. Abdullah Azzam,Uzma Batool,Hilman Fauzi +2 more
- 15 Jul 2019
TL;DR: In this paper, a single-solution simulated Kalman filter (ssSKF) algorithm is proposed to solve the structural engineering design problem in helical spring design, which is a single agent based optimization algorithm based on the Kalman filtering.
Convolutional Neural Network for Imbalanced Data Classification of Silicon Wafer Defects
Uzma Batool,Mohd Ibrahim Shapiai,Hilman Fauzi,Jia Xian Fong +3 more
- 01 Feb 2020
TL;DR: This research proposes a convolutional neural network while addressing class imbalance through data undersampling and shows that the imbalance mitigation with the proposed strategy offers a better solution for the wafer defects classification.