Physical Sciences and Mathematics Commons^™

Leveraging Generative Ai For Sustainable Farm Management Techniques Correspond To Optimization And Agricultural Efficiency Prediction, Samira Samrose

All Graduate Reports and Creative Projects, Fall 2023 to Present

Sustainable farm management practice is a multifaceted challenge. Uncovering the optimal state for production while reduction of environmental negative impacts and guaranteed inter-generational assets supervision needs balanced management. Also, considering lots of different factors (cost, profit, employment etc), the agricultural based management technique requires rigorous concentration. In this project machine learning models are applied to develop, achieve and improve the farm management techniques. This experiment ensures the resultant impacts being environment friendly and necessary resource availability and efficiency. Predicting the type of crop and rotational recommendations will disclose potentiality of productive agricultural based farming. Additionally, this project is designed to …

Analysis Of Student Behavior And Score Prediction In Assistments Online Learning, Aswani Yaramala

All Graduate Theses and Dissertations, Fall 2023 to Present

Understanding and analyzing student behavior is paramount in enhancing online learning, and this thesis delves into the subject by presenting an in-depth analysis of student behavior and score prediction in the ASSISTments online learning platform. We used data from the EDM Cup 2023 Kaggle Competition to answer four key questions. First, we explored how students seeking hints and explanations affect their performance in assignments, shedding light on the role of guidance in learning. Second, we looked at the connection between students mastering specific skills and their performance in related assignments, giving insights into the effectiveness of curriculum alignment. Third, we …

An Interval-Valued Random Forests, Paul Gaona Partida

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

There is a growing demand for the development of new statistical models and the refinement of established methods to accommodate different data structures. This need arises from the recognition that traditional statistics often assume the value of each observation to be precise, which may not hold true in many real-world scenarios. Factors such as the collection process and technological advancements can introduce imprecision and uncertainty into the data.

For example, consider data collected over a long period of time, where newer measurement tools may offer greater accuracy and provide more information than previous methods. In such cases, it becomes crucial …

Stressor: An R Package For Benchmarking Machine Learning Models, Samuel A. Haycock

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

Many discipline specific researchers need a way to quickly compare the accuracy of their predictive models to other alternatives. However, many of these researchers are not experienced with multiple programming languages. Python has recently been the leader in machine learning functionality, which includes the PyCaret library that allows users to develop high-performing machine learning models with only a few lines of code. The goal of the stressor package is to help users of the R programming language access the advantages of PyCaret without having to learn Python. This allows the user to leverage R’s powerful data analysis workflows, while simultaneously …

Examining Model Complexity's Effects When Predicting Continuous Measures From Ordinal Labels, Mckade S. Thomas

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

Many real world problems require the prediction of ordinal variables where the values are a set of categories with an ordering to them. However, in many of these cases the categorical nature of the ordinal data is not a desirable outcome. As such, regression models treat ordinal variables as continuous and do not bind their predictions to discrete categories. Prior research has found that these models are capable of learning useful information between the discrete levels of the ordinal labels they are trained on, but complex models may learn ordinal labels too closely, missing the information between levels. In this …

Design Of Environment Aware Planning Heuristics For Complex Navigation Objectives, Carter D. Bailey

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

A heuristic is the simplified approximations that helps guide a planner in deducing the best way to move forward. Heuristics are valued in many modern AI algorithms and decision-making architectures due to their ability to drastically reduce computation time. Particularly in robotics, path planning heuristics are widely leveraged to aid in navigation and exploration. As the robotic platform explores and navigates, information about the world can and should be used to augment and update the heuristic to guide solutions. Complex heuristics that can account for environmental factors, robot capabilities, and desired actions provide optimal results with little wasted exploration, but …

Predicting Order Status Using Xgboost, Kegan J. Penovich

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

Invista, a Koch subsidiary, is a multinational producer of fibers, resins, and intermediaries, particularly nylon. To keep the company operating required them to take over 1.5 million orders over the course of - years, less than a third of which arrived on-time. Orders arriving other than when expected can cause many problems for any company. While arriving late is a clear problem, it also troublesome for them to arrive early. In the face of this, it becomes important to be able to tell a-priori if an order will arrive on-time or not.

To address this problem, we made use of …

Analyzing Suicidal Text Using Natural Language Processing, Cassandra Barton

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

Using Natural Language Processing (NLP), we are able to analyze text from suicidal individuals. This can be done using a variety of methods. I analyzed a dataset of a girl named Victoria that died by suicide. I used a machine learning method to train a different dataset and tested it on her diary entries to classify her text into two categories: suicidal vs non-suicidal. I used topic modeling to find out unique topics in each subset. I also found a pattern in her diary entries. NLP allows us to help individuals that are suicidal and their family members and close …

An Empirical And Theoretical Investigation Of Random Reinforced Forests And Shallow Convolutional Neural Networks, Nikhil Ganta

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

For many years, the global population of honey bees has been decreasing due to inconclusive reasons resulting in the syndrome Colony Collapse Disorder (CCD). This syndrome has been plaguing bees and affecting commercial agriculture pollination since 1998. Many researchers have suggested that pesticides, in-hive chemicals, pathogens, etc., might be the causes of CCD. Researchers also believe that any changes in a beehive can disturb the bees, which may negatively affect their health. Honey bees are the most vital among all the animal pollinators contributing to approximately 30% of the world’s commercial pollination services. As they are of keystone importance to …

Machine Learning Techniques As Applied To Discrete And Combinatorial Structures, Samuel David Schwartz

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

Machine Learning Techniques have been used on a wide array of input types: images, sound waves, text, and so forth. In articulating these input types to the almighty machine, there have been all sorts of amazing problems that have been solved for many practical purposes.

Nevertheless, there are some input types which don’t lend themselves nicely to the standard set of machine learning tools we have. Moreover, there are some provably difficult problems which are abysmally hard to solve within a reasonable time frame.

This thesis addresses several of these difficult problems. It frames these problems such that we can …

Feature Selection And Analysis For Standard Machine Learning Classification Of Audio Beehive Samples, Chelsi Gupta

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

The beekeepers need to inspect their hives regularly in order to protect them from various stressors. Manual inspection of hives require a lot of time and effort. Hence, many researchers have started using electronic beehive monitoring (EBM) systems to collect critical information from beehives, so as to alert the beekeepers of possible threats to the hive. EBM collects information by applying multiple sensors into the hive. The sensors collect information in the form of video, audio or temperature data from the hives.

This thesis involves the automatic classification of audio samples from a beehive into bee buzzing, cricket chirping and …

Rfviz: An Interactive Visualization Package For Random Forests In R, Christopher Beckett

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

Random forests are very popular tools for predictive analysis and data science. They work for both classification (where there is a categorical response variable) and regression (where the response is continuous). Random forests provide proximities, and both local and global measures of variable importance. However, these quantities require special tools to be effectively used to interpret the forest. Rfviz is a sophisticated interactive visualization package and toolkit in R, specially designed for interpreting the results of a random forest in a user-friendly way. Rfviz uses a recently developed R package (loon) from the Comprehensive R Archive Network (CRAN) to create …

Standard Machine Learning Techniques In Audio Beehive Monitoring: Classification Of Audio Samples With Logistic Regression, K-Nearest Neighbor, Random Forest And Support Vector Machine, Prakhar Amlathe

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

Honeybees are one of the most important pollinating species in agriculture. Every three out of four crops have honeybee as their sole pollinator. Since 2006 there has been a drastic decrease in the bee population which is attributed to Colony Collapse Disorder (CCD). The bee colonies fail/ die without giving any traditional health symptoms which otherwise could help in alerting the Beekeepers in advance about their situation.

Electronic Beehive Monitoring System has various sensors embedded in it to extract video, audio and temperature data that could provide critical information on colony behavior and health without invasive beehive inspections. Previously, significant …

Enhancement Of Random Forests Using Trees With Oblique Splits, Andrejus Parfionovas

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

Statistical classification is widely used in many areas where there is a need to make a data-driven decision, or to classify complicated cases or objects. For instance: disease diagnostics (is a patient sick or healthy, based on the blood test results?); weather forecasting (will there be a storm tomorrow, based on today's atmospheric pressure, air temperature, and wind velocity?); speech recognition (what was said over the phone, based on the caller's voice level and articulation); spam detection (can the unsolicited commercial e-mails be identified by their content?); and so on.

Classification trees …

Knowledge Extraction In Video Through The Interaction Analysis Of Activities, Omar Ulises Florez

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

A video is a growing stream of unstructured data that significantly increases the amount of information transmitted and stored on the Internet. For example, every minute YouTube users upload 72 GB of information. Some of the best applications for video analysis include the monitoring of activities in defense and security scenarios such as the autonomous planes that collect video and images at reduced risk and the surveillance cameras in public places like traffic lights, airports, and schools.

Some of the challenges in the analysis of video correspond to implement complex operations such as searching of activities, understanding of scenes, and …