R
Rubita Sudirman
Researcher at Universiti Teknologi Malaysia
Publications - 177
Citations - 1234
Rubita Sudirman is an academic researcher from Universiti Teknologi Malaysia. The author has contributed to research in topics: Signal & Working memory. The author has an hindex of 14, co-authored 164 publications receiving 940 citations. Previous affiliations of Rubita Sudirman include University of Tulsa.
Papers
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Book Chapter
Dynamic time warping
TL;DR: DTW is considered as one effective method in speech pattern recognition, however the bad side of this method is that it requires a long processing time plus large storage capacity, especially for real time recognitions.
Proceedings ArticleDOI
Butterworth Bandpass and Stationary Wavelet Transform Filter Comparison for Electroencephalography Signal
TL;DR: The result shows that the stationary wavelet transform is more effective in removing the noise without losing the original information.
Journal ArticleDOI
Features extraction of electromyography signals in time domain on biceps brachii muscle
TL;DR: The results shows that the extracted features of the EMG signals in time domain can be implement in signal classification and could be integrated to design a signal classification based on the features extraction.
Journal ArticleDOI
Transfer Deep Learning Along With Binary Support Vector Machine for Abnormal Behavior Detection
TL;DR: The results demonstrated that VGGNet-19 has better performance than histogram of oriented gradients, background subtraction, and optical flow and shows higher detection accuracy than other pre-trained networks: GoogleNet, ResNet50, AlexNet, and V GGNet-16.
Proceedings ArticleDOI
Machine Learning for Smart Energy Monitoring of Home Appliances Using IoT
TL;DR: This project proposes a smart energy monitoring system for home appliances incorporating CIoT which consists of three parts, a Raspberry Pi-based smart plug serving as the gateway, a Tensorflow-based Long Short-term Memory model that will forecast electricity bill and notify users if abnormal energy consumption of individual home appliances is detected.