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Image Classification Using Transfer Learning and Convolution Neural Networks
(North Dakota State University, 2020)
In the recent years, deep learning has shown to have a formidable impact on image classification and has bolstered the advances in machine learning research. The scope of image recognition is going to bring big changes in ...
Comparison of RNN, LSTM and GRU on Speech Recognition Data
(North Dakota State University, 2018)
Deep Learning [DL] provides an efficient way to train Deep Neural Networks [DNN]. DDNs when used for end-to-end Automatic Speech Recognition [ASR] tasks, could produce more accurate results compared to traditional ASR. ...
Brain Cancer Detection Using MRI Scans
(North Dakota State University, 2020)
An estimate of about 700,000 Americans today live with a brain tumor. Of these, 70% are benign and 30% are malicious. The average survival rate of all the malicious brain tumor patients is 35%. Diagnosing these tumors early ...
Stock Price Prediction Using Recurrent Neural Networks
(North Dakota State University, 2018)
The stock market is generally very unpredictable in nature. There are many factors that might be responsible to determine the price of a particular stock such as the market trend, supply and demand ratio, global economy, ...
Object Classification Using Stacked Autoencoder and Convolutional Neural Network
(North Dakota State University, 2016)
In the recent years, deep learning has shown to have a formidable impact on object classification and has bolstered the advances in machine learning research. Many image datasets such as MNIST, CIFAR-10, SVHN, Imagenet, ...