Browsing by Subject "Deep learning"
Now showing items 1-6 of 6
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Deep convolution neural network model to predict relapse in breast cancer
(IEEE, 2018-12-17)A mishap in anti-cancer drug distribution is critical in breast cancer patients due to poor prediction model to identify the treatment regime in ER+ve and ER-ve (Estrogen Receptor (ER)) patients. The traditional method for ... -
A deep learning approach to genomics data for population scale clustering and ethnicity prediction
(CEUR-WS.org, 2017-05-28)The understanding of variations in genome sequences assists us in identifying people who are predisposed to common diseases, solving rare diseases, and finding corresponding population group of the individuals from a ... -
Deep learning for consumer devices and services: Pushing the limits for machine learning, artificial intelligence, and computer vision
(Institute of Electrical and Electronics Engineers (IEEE), 2017-04)In the last few years, we have witnessed an exponential growth in research activity into the advanced training of convolutional neural networks (CNNs), a field that has become known as deep learning. This has been triggered ... -
Investigating the genetics of deep learning derived neuro imaging phenotypes of brain disorders
(NUI Galway, 2024-04-17)Brain disorders are collections of debilitating phenotypes that can affect cognition and general life quality via a myriad of symptoms, including mood swings, memory loss, altered thought processes, and psychosis. Despite ... -
Signal and image processing technology for smart agriculture applications
(NUI Galway, 2019-04-25)This thesis is concerned with development of signal and image processing technology for smart agriculture applications, with a particular focus on applications in automatic weeding systems. Developing an automatic weeding ... -
Toward distributed, global, deep learning using IoT devices
(Institute of Electrical and Electronics Engineers (IEEE), 2021-07-20)Deep learning (DL) using large scale, high-quality IoT datasets can be computationally expensive. Utilizing such datasets to produce a problem-solving model within a reasonable time frame requires a scalable distributed ...