1,728,965 research outputs found
Layout and Principal Buildings of Hyderabad at the Time of Muhammad-Quli Qutb Shah
No full textfull view, Drawing depicting the layout and principal buildings of Hyderabad during the time of Muhammad-Quli Qutb Shah (reg. 1591-1600s). Map by Haroon Khan Serwani.
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Interview of Haroon Ahmed - 2009
No full textInterview of the scientist and sometime Master of Corpus Christi College, Cambridge, Haroon Ahmed. A discussion of his life and work
ACTivism interview | Haroon Gunn-Salie
No full textInterview with artist and activist Haroon Gunn-Salie focussing on the role, responsibilities of and repercussions for the artist/activist in society. The interview speaks particularly to the artist's 2013 work "Zonnebloem Renamed" as a site-specific form of occupation activism, and forms part of Dr Carla Lever's research into repertoires of shock in South African performance and politics.
Informal Zoom meeting on 2021-12-17.
Please be advised this interview contains the use of strong language.</p
Cabaret Futura
No full textCabaret Futura, Jonty Lees, Tom Humphreys, Haroon Mirza, Richard ‘Kid’ Strange. Cell Project Space 200
ECG Arrhythmia classification using Deep Convolution Neural Networks in Transfer Learning
Full text linkElectrocardiogram (ECG) is a health monitoring test which assists clinicians to detect abnormal cardiac activity based on heart’s electrical activity. Early classification of ECG signals is important towards the possible treatment measures for the patients. In principle ECG is a time series signal as a result of heart’s electrical activity. Various methods have been devised to apply Machine learning algorithms for the classification of these time series signals. These methods require feature extraction which in turn pose problems such as inconsistency in the extracted features as well as variability found in the ECG features. Deep learning methods such as algorithms based on Convolution Neural Networks (CNN) can be used to avoid manual crafting of features from ECG signals. Due to the large amount of ECG data and the complexity of CNNs, GPU processing is often required in order to train and test the models quickly. Google Colab provides a free tier with limited memory space for implementing complex and deep neural networks. Also, already trained network on some other data can be used to learn common features for new data and modified to produce desired results. Among many, Res-Net-50 and VGG-16 are well-known models being used for transfer learning.
In this project study ECG data is acquired from MIT-BIH Arrhythmia database. Using wave form database (wfdb) library in Python ECG signals were studied and observations were made for different characteristics and data variations. QRS detection was performed by locating the R peaks in ECG strips based on the annotation files for individual records. Segmented one dimensional data was converted to images for deep neural network training via CNN. Multi-layer CNN, ResNet-50 and VGG-16 were used for deep convolution and transfer learning and the computing was performed in Google Colab environment. Accuracies of 83 % via ResNet-50 and 99 % via VGG-16 were achieved using two dimensional ECG data. Higher accuracy with VGG-16 proves that Transfer learning can be applied for ECG arrhythmia classification
a vision for effective utilization of aid in Egypt
Full text linkThesis(Master) --KDI School:Master of Public Policy,2012masterpublishedNefret Zakaria El Nasser Ledin Allah Amin Haroon
Applications of machine learning in pervasive systems
No full textShakshuki, EM (corresponding author), Acadia Univ, Jodrey Sch Comp Sci, Wolfville, NS, Canada.
[email protected]; [email protected]; [email protected]
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