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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. ...
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, ...
Performance Comparison of Apache Spark MLlib
(North Dakota State University, 2018)
This study makes an attempt to understand the performance of Apache Spark and the MLlib platform. To this end, the cluster computing system of Apache Spark is set up and five supervised machine learning algorithms (Naïve-Bayes, ...
Naïve Bayes Classifier: A MapReduce Approach
(North Dakota State University, 2014)
Machine learning algorithms have the advantage of making use of the powerful Hadoop distributed computing platform and the MapReduce programming model to process data in parallel. Many machine learning algorithms have been ...
Health Risk Prediction Using Big Medical Data - a Collaborative Filtering-Enhanced Deep Learning Approach
(North Dakota State University, 2018)
Deep learning has yielded immense success on many different scenarios. With the success in other real world application, it has been applied into big medical data. However, discovering knowledge from these data can be very ...
Breast Cancer Diagnosis Using Different Machine Learning Techniques
(North Dakota State University, 2019)
Cancer is one of the dangerous diseases which causes many deaths each year and breast cancer being one of them which is quite common among women. In today’s time 12 percent of the women can develop breast cancer over her ...
Design and Development of Naive Bayes Classifier
(North Dakota State University, 2013)
The naïve Bayes classifier is a simple form of Bayesian classifiers which assumes all the features are independent of each other. Despite this assumption, the naïve Bayes classifier’s accuracy is comparable to other ...
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, ...