Physical Sciences and Mathematics Commons^™

Contemporary Art Authentication With Large-Scale Classification, Todd Dobbs, Abdullah-Al-Raihan Nayeem, Isaac Cho, Zbigniew Ras

Computer Science Faculty and Staff Publications

Art authentication is the process of identifying the artist who created a piece of artwork and is manifested through events of provenance, such as art gallery exhibitions and financial transactions. Art authentication has visual influence via the uniqueness of the artist’s style in contrast to the style of another artist. The significance of this contrast is proportional to the number of artists involved and the degree of uniqueness of an artist’s collection. This visual uniqueness of style can be captured in a mathematical model produced by a machine learning (ML) algorithm on painting images. Art authentication is not always possible …

Ambient Electromagnetic Radiation As A Predictor Of Honey Bee (Apis Mellifera) Traffic In Linear And Non-Linear Regression: Numerical Stability, Physical Time And Energy Efficiency, Vladimir Kulyukin, Daniel Coster, Anastasiia Tkachenko, Daniel Hornberger, Aleksey V. Kulyukin

Computer Science Faculty and Staff Publications

Since bee traffic is a contributing factor to hive health and electromagnetic radiation has a growing presence in the urban milieu, we investigate ambient electromagnetic radiation as a predictor of bee traffic in the hive’s vicinity in an urban environment. To that end, we built two multi-sensor stations and deployed them for four and a half months at a private apiary in Logan, Utah, U.S.A. to record ambient weather and electromagnetic radiation. We placed two non-invasive video loggers on two hives at the apiary to extract omnidirectional bee motion counts from videos. The time-aligned datasets were used to evaluate 200 …

Contributions To Random Forest Variable Importance With Applications In R, Kelvyn K. Bladen

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

A major focus in statistics is building and improving computational algorithms that can use data to predict a response. Two fundamental camps of research arise from such a goal. The first camp is researching ways to get more accurate predictions. Many sophisticated methods, collectively known as machine learning methods, have been developed for this very purpose. One such method that is widely used across industry and many other areas of investigation is called Random Forests.

The second camp of research is that of improving the interpretability of machine learning methods. This is worthy of attention when analysts desire to optimize …

Development Of A Machine Learning-Based Financial Risk Control System, Zhigang Hu

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

With the gradual end of the COVID-19 outbreak and the gradual recovery of the economy, more and more individuals and businesses are in need of loans. This demand brings business opportunities to various financial institutions, but also brings new risks. The traditional loan application review is mostly manual and relies on the business experience of the auditor, which has the disadvantages of not being able to process large quantities and being inefficient. Since the traditional audit processing method is no longer suitable some other method of reducing the rate of non-performing loans and detecting fraud in applications is urgently needed …

On Predicting Omnidirectional Honey Bee Traffic Using Weather And Electromagnetic Radiation, Daniel G. Hornberger

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

Honey bees are responsible for pollinating many important crops in the United States. However, honey bee populations have declined significantly since 1961. While some causes of this decline are known, others are not. By utilizing electronic bee hive monitoring (EBM) systems, bee keepers and researchers have an added resource in determining the causes of these declines so that the issues can be remedied. For nearly five months (May through October) during the 2020 honey bee foraging season in Logan, Utah, USA, we collected on-site weather and electromagnetic radiation (EMR) readings and videos of the hive entrances of six bee hives …

Comparative Study Of Machine Learning Models On Solar Flare Prediction Problem, Nikhil Sai Kurivella

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

Solar flare events are explosions of energy and radiation from the Sun’s surface. These events occur due to the tangling and twisting of magnetic fields associated with sunspots. When Coronal Mass ejections accompany solar flares, solar storms could travel towards earth at very high speeds, disrupting all earthly technologies and posing radiation hazards to astronauts. For this reason, the prediction of solar flares has become a crucial aspect of forecasting space weather. Our thesis utilized the time-series data consisting of active solar region magnetic field parameters acquired from SDO that span more than eight years. The classification models take AR …

Data-Driven Recommendation Of Academic Options Based On Personality Traits, Aashish Ghimire

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

The choice of academic major and, subsequently, an academic institution has a massive effect on a person’s career. It not only determines their career path but their earning potential, professional happiness, etc. [1] About 40% of people who are admitted to a college do not graduate within six years. Yet, very limited resources are available for students to help make those decisions, and each guidance counselor is responsible for roughly 400 to 900 students across the United States. A tool to help these decisions would benefit students, parents, and guidance counselors.

Various research studies have shown that personality traits affect …

Deep Q Learning Applied To Stock Trading, Agnibh Dasgupta

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

Developing a strategy for stock trading is a vital task for investors. However, it is challenging to obtain an optimal strategy, given the complex and dynamic nature of the stock market. This thesis aims to explore the applications of Reinforcement Learning with the goal of maximizing returns from market investment, keeping in mind the human aspect of trading by utilizing stock prices represented as candlestick graphs. Furthermore, the algorithm studies public interest patterns in form of graphs extracted from Google Trends to make predictions. Deep Q learning has been used to train an agent based on fused images of stock …

Acquisition, Processing, And Analysis Of Video, Audio And Meteorological Data In Multi-Sensor Electronic Beehive Monitoring, Sarbajit Mukherjee

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

In recent years, a widespread decline has been seen in honey bee population and this is widely attributed to colony collapse disorder. Hence, it is of utmost importance that a system is designed to gather relevant information. This will allow for a deeper understanding of the possible reasons behind the above phenomenon to aid in the design of suitable countermeasures.

Electronic Beehive Monitoring is one such way of gathering critical information regarding a colony’s health and behavior without invasive beehive inspections. In this dissertation, we have presented an electronic beehive monitoring system called BeePi that can be placed on top …

Development And Identification Of Metrics To Predict The Impact Of Dimension Reduction Techniques On Classical Machine Learning Algorithms For Still Highway Images, Wasim Akram Khan

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

We are witnessing an influx of data - images, texts, video, etc. Their high dimensionality and large volume make it challenging to apply machine learning to obtain actionable insight. This thesis explores several aspects pertaining to dimensional reduction: dimension reduction methods, metrics to measure distortion, image preprocessing, etc. Faster training and inference time on reduced data and smaller models which can be deployed on commodity hardware are a critical advantage of dimension reduction. For this study, classical machine learning methods were explored owing to their solid mathematical foundation and interpretability.

The dataset used is a time series of images from …

Applications Of Machine Learning In High-Frequency Trade Direction Classification, Jared E. Hansen

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

The correct assignment of trades as buyer-initiated or seller-initiated is paramount in many quantitative finance studies. Simple decision rule methods have been used for signing trades since many data sets available to researchers do not include the sign of each trade executed. By utilizing these decision rule methods, as well as engineering new variables from available data, we have demonstrated that machine learning models outperform prior methods for accurately signing trades as buys and sells, achieving state-of-the-art results. The best model developed was 4.5 percentage points more accurate than older methods when predicting onto unseen data. Since finance and economics …

Machine Learning Predicts Reach-Scale Channel Types From Coarse-Scale Geospatial Data In A Large River Basin, Hervé Guillon, Colin F. Byrne, Belize A. Lane, Samuel Sandoval Solis, Gregory B. Pasternack

Publications

Hydrologic and geomorphic classifications have gained traction in response to the increasing need for basin-wide water resources management. Regardless of the selected classification scheme, an open scientific challenge is how to extend information from limited field sites to classify tens of thousands to millions of channel reaches across a basin. To address this spatial scaling challenge, this study leverages machine learning to predict reach-scale geomorphic channel types using publicly available geospatial data. A bottom-up machine learning approach selects the most accurate and stable model among∼20,000 combinations of 287 coarse geospatial predictors, preprocessing methods, and algorithms in a three-tiered framework to …

Decomposing The Hamiltonian Of Quantum Circuits Using Machine Learning, Jordan Burns, Yih Sung, Colby Wight

Physics Capstone Projects

Quantum computing is one of the most promising techniques for simulating physical systems that cannot be simulated on classical computers[1]. A significant drawback of this approach is the inherent difficulty in designing circuits that can represent these systems on quantum computers. Every quantum circuit is built out of small components called quantum gates. Each of these gates manipulate the quantum system in a specific way. When used in combination, a finite subset of these gates, the set of universal gates, can be used to construct any possible quantum circuit[2].

On Video Analysis Of Omnidirectional Bee Traffic: Counting Bee Motions With Motion Detection And Image Classification, Vladmir Kulyukin, Sarbajit Mukherjee

Computer Science Faculty and Staff Publications

Omnidirectional bee traffic is the number of bees moving in arbitrary directions in close proximity to the landing pad of a given hive over a given period of time. Video bee traffic analysis has the potential to automate the assessment of omnidirectional bee traffic levels, which, in turn, may lead to a complete or partial automation of honeybee colony health assessment. In this investigation, we proposed, implemented, and partially evaluated a two-tier method for counting bee motions to estimate levels of omnidirectional bee traffic in bee traffic videos. Our method couples motion detection with image classification so that motion detection …

A Performance Comparison Of Machine Learning Algorithms For Arced Labyrinth Spillways, Fernando Salazar, Brian M. Crookston

Publications

Labyrinth weirs provide an economic option for flow control structures in a variety of applications, including as spillways at dams. The cycles of labyrinth weirs are typically placed in a linear configuration. However, numerous projects place labyrinth cycles along an arc to take advantage of reservoir conditions and dam alignment, and to reduce construction costs such as narrowing the spillway chute. Practitioners must optimize more than 10 geometric variables when developing a head–discharge relationship. This is typically done using the following tools: empirical relationships, numerical modeling, and physical modeling. This study applied a new tool, machine learning, to the analysis …

Toward Audio Beehive Monitoring: Deep Learning Vs. Standard Machine Learning In Classifying Beehive Audio Samples, Vladmir Kulyukin, Sarbajit Mukherjee, Prakhar Amlathe

Computer Science Faculty and Staff Publications

Electronic beehive monitoring extracts critical information on colony behavior and phenology without invasive beehive inspections and transportation costs. As an integral component of electronic beehive monitoring, audio beehive monitoring has the potential to automate the identification of various stressors for honeybee colonies from beehive audio samples. In this investigation, we designed several convolutional neural networks and compared their performance with four standard machine learning methods (logistic regression, k-nearest neighbors, support vector machines, and random forests) in classifying audio samples from microphones deployed above landing pads of Langstroth beehives. On a dataset of 10,260 audio samples where the training and testing …

Detecting Malicious Campaigns In Crowdsourcing Platforms, Hongkyu Choi

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

Crowdsourcing sites such as Mechanical Turk and Crowdflower provide a marketplace where requesters create tasks and recruit workers, who may perform certain tasks in order to get financial compensation. Anyone in the world can be a requester and/or a worker as long as he/she has the Internet connection. Crowdsourcing creates a new way to solve various tasks by using “human computation power”. However, crowdsourcing has been misused by malicious requesters and unethical workers for account generation, search engine optimization, content and link generation, ad posting and spam mailing, and social network linking. It creates new threats to the Web system. …

The Glass Is Half-Full: Overestimating The Quality Of A Novel Environment Is Advantageous, Oded Berger-Tal, Tal Avgar

Wildland Resources Faculty Publications

According to optimal foraging theory, foraging decisions are based on the forager's current estimate of the quality of its environment. However, in a novel environment, a forager does not possess information regarding the quality of the environment, and may make a decision based on a biased estimate. We show, using a simple simulation model, that when facing uncertainty in heterogeneous environments it is better to overestimate the quality of the environment (to be an “optimist”) than underestimate it, as optimistic animals learn the true value of the environment faster due to higher exploration rate. Moreover, we show that when the …

Comparison Of Machine Learning Algorithms For Modeling Species Distributions: Application To Stream Invertebrates From Western Usa Reference Sites, Margi Dubal

All Graduate Plan B and other Reports, Spring 1920 to Spring 2023

Machine learning algorithms are increasingly being used by ecologists to model and predict the distributions of individual species and entire assemblages of sites. Accurate prediction of distribution of species is an important factor in any modeling. We compared prediction accuracy of four machine learning algorithms-random forests, classification trees, support vector machines, and gradient boosting machines to a traditional method, linear discriminant models (LDM), on a large set of stream invertebrate data collected at 728 reference sites in the western United States. Classifications were constructed for individual species and for assemblages of sites clustered a priori by similarity on biological characteristics. …