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Muhammad Hameed Siddiqi
Researcher at Kyung Hee University
Publications - 68
Citations - 1311
Muhammad Hameed Siddiqi is an academic researcher from Kyung Hee University. The author has contributed to research in topics: Feature extraction & Computer science. The author has an hindex of 17, co-authored 56 publications receiving 798 citations. Previous affiliations of Muhammad Hameed Siddiqi include Al Jouf University & Petronas.
Papers
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Human Facial Expression Recognition Using Stepwise Linear Discriminant Analysis and Hidden Conditional Random Fields
TL;DR: An accurate and robust facial expression recognition (FER) system that employs stepwise linear discriminant analysis (SWLDA), which is a significant improvement in contrast to the existing FER methods.
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Exploratory Data Analysis of Acceleration Signals to Select Light-Weight and Accurate Features for Real-Time Activity Recognition on Smartphones
TL;DR: A real-time/online SP-AR system that solves the problem of short battery life or delayed system response, and its features outperformed three commonly used features by 40% in terms of accuracy for the given SR and DW.
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Rough set-based approaches for discretization: a compact review
TL;DR: A systematic study of the rough set-based discretization techniques found in the literature and categorizes them into a taxonomy that provides a useful roadmap for new researchers in the area of RSBD.
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Traffic accident detection and condition analysis based on social networking data.
TL;DR: In this paper, a real-time monitoring framework is proposed for traffic accident detection and condition analysis using ontology and latent Dirichlet allocation (OLDA) and bidirectional long short-term memory (Bi-LSTM).
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Boosting Breast Cancer Detection Using Convolutional Neural Network.
Saad Alanazi,M. M. Kamruzzaman,Nazirul Islam Sarker,Madallah Alruwaili,Yousef Alhwaiti,Nasser Alshammari,Muhammad Hameed Siddiqi +6 more
TL;DR: In this paper, a convolutional neural network (CNN) method is proposed to boost the automatic identification of breast cancer by analyzing hostile ductal carcinoma tissue zones in whole-slide images (WSIs).