Browsing by Subject "Machine learning."

Now showing items 1-16 of 16

    • An Application of Natural Language Processing to Classify What Terrorists Say They Want 

      Bridgelall, Raj (2022)
      Knowing what perpetrators want can inform strategies to achieve safe, secure, and sustainable societies. To help advance the body of knowledge in counterterrorism, this research applied natural language processing and ...

    • Brain Cancer Detection Using MRI Scans 

      Thotapally, Shanthanreddy (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 ...

    • Breast Cancer Diagnosis Using Different Machine Learning Techniques 

      Roy, Souradip (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 ...

    • Comparison of RNN, LSTM and GRU on Speech Recognition Data 

      Shewalkar, Apeksha Nagesh (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. ...

    • Design and Development of Naive Bayes Classifier 

      Garg, Bandana (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 ...

    • Feature Engineering on the Cybersecurity Dataset for Deployment on Software Defined Network 

      Rifat, Nafiz Imtiaz (North Dakota State University, 2020)
      These days, due to dependency on the fast-moving world's modern technology, the increasing use of smart devices and the internet affect network traffic. Many intrusion detection studies concentrate on feature selection or ...

    • Health Risk Prediction Using Big Medical Data - a Collaborative Filtering-Enhanced Deep Learning Approach 

      Li, Xin (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 ...

    • Image Classification Using Transfer Learning and Convolution Neural Networks 

      Burugupalli, Mohan (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 ...

    • Naïve Bayes Classifier: A MapReduce Approach 

      Zheng, Songtao (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 ...

    • Object Classification Using Stacked Autoencoder and Convolutional Neural Network 

      Gottimukkula, Vijaya Chander Rao (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, ...

    • Performance Comparison of Apache Spark MLlib 

      Sharma, Pallavi (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, ...

    • Prediction Accuracy of Financial Data - Applying Several Resampling Techniques 

      Ali, Mohammad Reza (North Dakota State University, 2020)
      With the help of Data Mining and Machine Learning, prediction has been a very popular and demanding instrument to plan and accomplish a future goal. The financial sector is one of the crucial sectors of present human ...

    • Prevalence of Listeria Monocytogenes in the Pre-Harvest Environment: A Landscape Epidemiology Approach 

      Shiwakoti, Suvash (North Dakota State University, 2016)
      Listeria monocytogenes is the causative agent of the foodborne disease listeriosis. Although the incidence of listeriosis is lower than that of other foodborne diseases, it’s much higher mortality rate makes it a cause for ...

    • Railroad Accident Analysis Using Extreme Gradient Boosting 

      Bridgelall, Raj; Tolliver, Denver D. (2021)
      Railroads are critical to the economic health of a nation. Unfortunately, railroads lose hundreds of millions of dollars from accidents each year. Trends reveal that derailments consistently account for more than 70% of ...

    • Robust Capital Asset Pricing Model Estimation through Cross-Validation 

      Sakouvogui, Kekoura (North Dakota State University, 2018)
      Limitations of Capital Asset Pricing Model (CAPM) continue to present inconsistent empirical results despite its rm mathematical foundations provided in recent studies. In this thesis, we examine how estimation errors of ...

    • Stock Price Prediction Using Recurrent Neural Networks 

      Jahan, Israt (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, ...