Physical Sciences and Mathematics Commons^™

Representational Learning Approach For Predicting Developer Expertise Using Eye Movements, Sumeet Maan

Department of Computer Science and Engineering: Dissertations, Theses, and Student Research

The thesis analyzes an existing eye-tracking dataset collected while software developers were solving bug fixing tasks in an open-source system. The analysis is performed using a representational learning approach namely, Multi-layer Perceptron (MLP). The novel aspect of the analysis is the introduction of a new feature engineering method based on the eye-tracking data. This is then used to predict developer expertise on the data. The dataset used in this thesis is inherently more complex because it is collected in a very dynamic environment i.e., the Eclipse IDE using an eye-tracking plugin, iTrace. Previous work in this area only worked on …

Forecasting Vegetation Health In The Mena Region By Predicting Vegetation Indicators With Machine Learning Models, Sachi Perera, Wenzhao Li, Erik Linstead, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

Machine learning (ML) techniques can be applied to predict and monitor drought conditions due to climate change. Predicting future vegetation health indicators (such as EVI, NDVI, and LAI) is one approach to forecast drought events for hotspots (e.g. Middle East and North Africa (MENA) regions). Recently, ML models were implemented to predict EVI values using parameters such as land types, time series, historical vegetation indices, land surface temperature, soil moisture, evapotranspiration etc. In this work, we collected the MODIS atmospherically corrected surface spectral reflectance imagery with multiple vegetation related indices for modeling and evaluation of drought conditions in the MENA …

Routing Optimization In Heterogeneous Wireless Networks For Space And Mission-Driven Internet Of Things (Iot) Environments, Sara El Alaoui

Department of Electrical and Computer Engineering: Dissertations, Theses, and Student Research

As technological advances have made it possible to build cheap devices with more processing power and storage, and that are capable of continuously generating large amounts of data, the network has to undergo significant changes as well. The rising number of vendors and variety in platforms and wireless communication technologies have introduced heterogeneity to networks compromising the efficiency of existing routing algorithms. Furthermore, most of the existing solutions assume and require connection to the backbone network and involve changes to the infrastructures, which are not always possible -- a 2018 report by the Federal Communications Commission shows that over 31% …

An Investigation Into Multi-View Error Correcting Output Code Classifiers Applied To Organ Tissue Classification, Daniel Alvarez

UNLV Theses, Dissertations, Professional Papers, and Capstones

Large amounts of data is being generated constantly each day, so much data that it is difficult to find patterns in order to predict outcomes and make decisions for both humans and machines alike. It would be useful if this data could be simplified using machine learning techniques. For example, biological cell identity is dependent on many factors tied to genetic processes. Such factors include proteins, gene transcription, and gene methylation. Each of these factors are highly complex mechanism with immense amounts of data. Simplifying these can then be helpful in finding patterns in them. Error-Correcting Output Codes (ECOC) does …

Optimized Machine Learning Models Towards Intelligent Systems, Mohammadnoor Ahmad Mohammad Injadat

Electronic Thesis and Dissertation Repository

The rapid growth of the Internet and related technologies has led to the collection of large amounts of data by individuals, organizations, and society in general [1]. However, this often leads to information overload which occurs when the amount of input (e.g. data) a human is trying to process exceeds their cognitive capacities [2]. Machine learning (ML) has been proposed as one potential methodology capable of extracting useful information from large sets of data [1]. This thesis focuses on two applications. The first is education, namely e-Learning environments. Within this field, this thesis proposes different optimized ML ensemble models to …

Machine Learning For The Internet Of Things: Applications, Implementation, And Security, Vishalini Laguduva Ramnath

USF Tampa Graduate Theses and Dissertations

Artificial intelligence and ubiquitous sensor systems have seen tremendous advances in recent times, resulting in groundbreaking impact across domains such as healthcare, entertainment, and transportation through a collective ecosystem called the Internet of Things. The advent of 5G and improved wireless networks will further accelerate the research and development of tools in deep learning, sensor systems, and computing platforms by providing improved network latency and bandwidth. While tremendous progress has been made in the Internet of Things, current work has largely focused on building robust applications that leverage the data collected through ubiquitous sensor nodes to provide actionable rules and …

Advanced Techniques To Detect Complex Android Malware, Zhiqiang Li

Department of Computer Science and Engineering: Dissertations, Theses, and Student Research

Android is currently the most popular operating system for mobile devices in the world. However, its openness is the main reason for the majority of malware to be targeting Android devices. Various approaches have been developed to detect malware.

Unfortunately, new breeds of malware utilize sophisticated techniques to defeat malware detectors. For example, to defeat signature-based detectors, malware authors change the malware’s signatures to avoid detection. As such, a more effective approach to detect malware is by leveraging malware’s behavioral characteristics. However, if a behavior-based detector is based on static analysis, its reported results may contain a large number of …

An Examination Of The Smote And Other Smote-Based Techniques That Use Synthetic Data To Oversample The Minority Class In The Context Of Credit-Card Fraud Classification, Eduardo Parkinson De Castro

Dissertations

This research project seeks to investigate some of the different sampling techniques that generate and use synthetic data to oversample the minority class as a means of handling the imbalanced distribution between non-fraudulent (majority class) and fraudulent (minority class) classes in a credit-card fraud dataset. The purpose of the research project is to assess the effectiveness of these techniques in the context of fraud detection which is a highly imbalanced and cost-sensitive dataset. Machine learning tasks that require learning from datasets that are highly unbalanced have difficulty learning since many of the traditional learning algorithms are not designed to cope …

Machine Learning Assisted Gait Analysis For The Determination Of Handedness In Able-Bodied People, Hugh Gallagher

Dissertations

This study has investigated the potential application of machine learning for video analysis, with a view to creating a system which can determine a person’s hand laterality (handedness) from the way that they walk (their gait). To this end, the convolutional neural network model VGG16 underwent transfer learning in order to classify videos under two ‘activities’: “walking left-handed” and “walking right-handed”. This saw varying degrees of success across five transfer learning trained models: Everything – the entire dataset; FiftyFifty – the dataset with enough right-handed samples removed to produce a set with parity between activities; Female – only the female …