Digital Commons Network^™

Immuno-Genomic Profiling Of Biopsy Specimens Predicts Neoadjuvant Chemotherapy Response In Esophageal Squamous Cell Carcinoma, Shota Sasagawa, Hiroaki Kato, Koji Nagaoka, Changbo Sun, Motohiro Imano, Takao Sato, Todd A Johnson, Masashi Fujita, Kazuhiro Maejima, Yuki Okawa, Kazuhiro Kakimi, Takushi Yasuda, Hidewaki Nakagawa

Faculty, Staff and Student Publications

Esophageal squamous cell carcinoma (ESCC) is one of the most aggressive cancers and is primarily treated with platinum-based neoadjuvant chemotherapy (NAC). Some ESCCs respond well to NAC. However, biomarkers to predict NAC sensitivity and their response mechanism in ESCC remain unclear. We perform whole-genome sequencing and RNA sequencing analysis of 141 ESCC biopsy specimens before NAC treatment to generate a machine-learning-based diagnostic model to predict NAC reactivity in ESCC and analyzed the association between immunogenomic features and NAC response. Neutrophil infiltration may play an important role in ESCC response to NAC. We also demonstrate that specific copy-number alterations and copy-number …

Asian Hate Speech Detection On Twitter During Covid-19, Amir Toliyat, Sarah Ita Levitan, Zeng Peng, Ronak Etemadpour

Publications and Research

Coronavirus disease 2019 (COVID-19) started in Wuhan, China, in late 2019, and after being utterly contagious in Asian countries, it rapidly spread to other countries. This disease caused governments worldwide to declare a public health crisis with severe measures taken to reduce the speed of the spread of the disease. This pandemic affected the lives of millions of people. Many citizens that lost their loved ones and jobs experienced a wide range of emotions, such as disbelief, shock, concerns about health, fear about food supplies, anxiety, and panic. All of the aforementioned phenomena led to the spread of racism and …

Human-Centered Machine Learning: Algorithm Design And Human Behavior, Wei Tang

McKelvey School of Engineering Graduate Student Theses & Dissertations

Machine learning is increasingly engaged in a large number of important daily decisions and has great potential to reshape various sectors of our modern society. To fully realize this potential, it is important to understand the role that humans play in the design of machine learning algorithms and investigate the impacts of the algorithm on humans.

Towards the understanding of such interactions between humans and algorithms, this dissertation takes a human-centric perspective and focuses on investigating the interplay between human behavior and algorithm design. Accounting for the roles of humans in algorithm design creates unique challenges. For example, humans might …

Design And Analysis Of Strategic Behavior In Networks, Sixie Yu

McKelvey School of Engineering Graduate Student Theses & Dissertations

Networks permeate every aspect of our social and professional life.A networked system with strategic individuals can represent a variety of real-world scenarios with socioeconomic origins. In such a system, the individuals' utilities are interdependent---one individual's decision influences the decisions of others and vice versa. In order to gain insights into the system, the highly complicated interactions necessitate some level of abstraction. To capture the otherwise complex interactions, I use a game theoretic model called Networked Public Goods (NPG) game. I develop a computational framework based on NPGs to understand strategic individuals' behavior in networked systems. The framework consists of three …

Enhancing The Cognition And Efficacy Of Machine Learning Through Similarity, Vishnu Pendyala, Rakesh Amireddy

Faculty Research, Scholarly, and Creative Activity

Similarity is a key element of machine learning and can make human learning much more effective as well. One of the goals of this paper is to expound on this aspect. We identify real-world concepts similar to hard-to-understand theories to enhance the learning experience and comprehension of a machine learning student. The second goal is to enhance the work in the current literature that uses similarity for transcoding. We uniquely try transcoding from Python to R and vice versa, something that was not attempted before, by identifying similarities in a latent embedding space. We list several real-world analogies to show …

Insight In Thermomechanical Constitutive Modeling Of Shape Memory Polymers, Amir Shojaei, Wei Xu, Cheng Yan, Qianxi Yang, Guoqiang Li

Faculty Publications

Shape memory polymers (SMPs) are a new member of the smart materials family. SMPs have found wide applications or potential applications in almost all manmade structures and devices. In order to better design smart structures and devices using SMPs, thermomechanical constitutive modeling is essential. In this insight paper, we will focus on presenting several multi-length-scale and multi-physics modeling frameworks, including the thermodynamics consistent model, elasto-viscoplastic model, statistical mechanics model, and phase evaluation law model. The SMPs modeled will include amorphous one-way shape memory polymers, semicrystalline one-way shape memory polymers, semicrystalline two-way shape memory polymers, and functional and mechanical damage effects …

Classification Models For 2,4-D Formulations In Damaged Enlist Crops Through The Application Of Ftir Spectroscopy And Machine Learning Algorithms, Benjamin Blackburn

Theses and Dissertations

With new 2,4-Dichlorophenoxyacetic acid (2,4-D) tolerant crops, increases in off-target movement events are expected. New formulations may mitigate these events, but standard lab techniques are ineffective in identifying these 2,4-D formulations. Using Fourier-transform infrared spectroscopy and machine learning algorithms, research was conducted to classify 2,4-D formulations in treated herbicide-tolerant soybeans and cotton and observe the influence of leaf treatment status and collection timing on classification accuracy. Pooled Classification models using k-nearest neighbor classified 2,4-D formulations with over 65% accuracy in cotton and soybean. Tissue collected 14 DAT and 21 DAT for cotton and soybean respectively produced higher accuracies than the …

Determining The Effects Of Elevated Carbon Dioxide On Soil Acidification, Cation Depletion, And Soil Inorganic Carbon And Mapping Soil Carbons Using Artificial Intelligence, Jannatul Ferdush

Theses and Dissertations

Soil carbon is the largest sink and source of the global carbon cycle and is disturbed by several natural, anthropogenic, and environmental factors. The global increase of atmospheric CO₂ affects soil carbon cycling through varied biogeochemical processes. The first chapter is a compilation of current information on potential factors triggering soil acidification and weathering mechanisms under elevated CO₂ and their consequences on soil inorganic carbon (SIC) pool and quality. Soil water content and precipitation were critical factors influencing elevated CO₂ effects on the SIC pool. The second chapter examines a detailed column experiment in which six soils …

Artificial Intelligence In The Radiomic Analysis Of Glioblastomas: A Review, Taxonomy, And Perspective, Ming Zhu, Sijia Li, Yu Kuang, Virginia B. Hill, Amy B. Heimberger, Lijie Zhai, Shenjie Zhai

Electrical & Computer Engineering Faculty Research

Radiological imaging techniques, including magnetic resonance imaging (MRI) and positron emission tomography (PET), are the standard-of-care non-invasive diagnostic approaches widely applied in neuro-oncology. Unfortunately, accurate interpretation of radiological imaging data is constantly challenged by the indistinguishable radiological image features shared by different pathological changes associated with tumor progression and/or various therapeutic interventions. In recent years, machine learning (ML)-based artificial intelligence (AI) technology has been widely applied in medical image processing and bioinformatics due to its advantages in implicit image feature extraction and integrative data analysis. Despite its recent rapid development, ML technology still faces many hurdles for its broader applications …

Data Collection And Machine Learning Methods For Automated Pedestrian Facility Detection And Mensuration, Joseph Bailey Luttrell Iv

Dissertations

Large-scale collection of pedestrian facility (crosswalks, sidewalks, etc.) presence data is vital to the success of efforts to improve pedestrian facility management, safety analysis, and road network planning. However, this kind of data is typically not available on a large scale due to the high labor and time costs that are the result of relying on manual data collection methods. Therefore, methods for automating this process using techniques such as machine learning are currently being explored by researchers. In our work, we mainly focus on machine learning methods for the detection of crosswalks and sidewalks from both aerial and street-view …

Understanding Learners' Motivation Through Machine Learning Analysis On Reflection Writing, Elizabeth Pluskwik, Yuezhou Wang, Lauren Singelmann

Integrated Engineering Department Publications

Educational data mining (EDM) is an emerging interdisciplinary field that utilizes a machine learning (ML) algorithm to collect and analyze educational data, aiming to better predict students' performance and retention. In this WIP paper, we report our methodology and preliminary results from utilizing a ML program to assess students’ motivation through their upper-division years in the XYZ project-based learning (PBL) program. ML, or more specifically, the clustering algorithm, opens the door to processing large amounts of student-written artifacts, such as reflection journals, project reports, and written assignments, and then identifies keywords that signal their levels of motivation (i.e., extrinsic vs. …

Data-Driven Research On Engineering Design Thinking And Behaviors In Computer-Aided Systems Design: Analysis, Modeling, And Prediction, Molla Hafizur Rahman

Graduate Theses and Dissertations

Research on design thinking and design decision-making is vital for discovering and utilizing beneficial design patterns, strategies, and heuristics of human designers in solving engineering design problems. It is also essential for the development of new algorithms embedded with human intelligence and can facilitate human-computer interactions. However, modeling design thinking is challenging because it takes place in the designer’s mind, which is intricate, implicit, and tacit. For an in-depth understanding of design thinking, fine-grained design behavioral data are important because they are the critical link in studying the relationship between design thinking, design decisions, design actions, and design performance. Therefore, …

Ceo Characteristics, Industry 4.0 Capabilities, And Firm Performance, Vidya Laxmanrao Ganji

Management Dissertations - Archive

Industry 4.0 has received significant attention from academia and industry in the last decade. Despite its growing popularity, this area is relatively understudied. There is a lack of thorough understanding of the disparate thematic areas under the umbrella term "Industry 4.0" in the literature. My first essay aims to elucidate the intellectual structure of Industry 4.0 publications using: (a) bibliometric techniques; and (b) topic modeling. The synthesized analyses unraveled diverse themes of Industry 4.0 and deepened our understanding of academic research on Industry 4.0. Such an understanding is important to identify opportunities to advance the boundaries of scholarship on Industry …

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 …

Simulating The Changes Of Invasive Phragmites Australis In A Pristine Wetland Complex With A Grey System Coupled System Dynamic Model: A Remote Sensing Practice, Danlin Yu, Nicholas A. Procopio, Chuanglin Fang

Department of Earth and Environmental Studies Faculty Scholarship and Creative Works

Biological invasion has been one of the reasons that coastal wetlands gradually lose their ecological services. The current study investigates the spread of a commonly found invasive species in coastal wetlands in Northeastern US, the Phragmites australis. Within a relatively pristine wetland complex in coastal New Jersey, we collected high-resolution multispectral remote sensing images for eight years (2011–2018), in both winter and summer seasons. The land cover/land use status in this wetland complex is relatively simple, contains only five identifiable vegetation covers and water. Applying high accuracy machine learning algorithms, we are able to classify the land use/land cover in …

Using Machine Learning To Classify Volleyball Jumps, Miki Jauhiainen

Theses and Dissertations

In this study, inertial measurement units (IMUs) were used to train a random forest classifier to correctly classify different jump types in volleyball. Athlete motion data were collected in a controlled setting using three IMUs, one on the waist and one on each ankle. There were 11 participants who at the time played volleyball at the collegiate level in the United States, seven male and four female. Each performed the same number of jumps across the eight jump types--five BASIC jumps and three each of the other seven--resulting in 26 jumps per subject for a total of 286. The data …

Modern Pyromes: Biogeographical Patterns Of Fire Characteristics Across The Contiguous United States, Megan E. Cattau, Adam Mahood, Jennifer K. Balch, Carol Wessman

Human-Environment Systems Research Center Faculty Publications and Presentations

In recent decades, wildfires in many areas of the United States (U.S.) have become larger and more frequent with increasing anthropogenic pressure, including interactions between climate, land-use change, and human ignitions. We aimed to characterize the spatiotemporal patterns of contemporary fire characteristics across the contiguous United States (CONUS). We derived fire variables based on frequency, fire radiative power (FRP), event size, burned area, and season length from satellite-derived fire products and a government records database on a 50 km grid (1984–2020). We used k-means clustering to create a hierarchical classification scheme of areas with relatively homogeneous fire characteristics, or modern …

Deep Active Genetic Learning With Evidential Uncertainty For Agriculture Crops And Lake Water Quality Assessment, Oguz M. Aranay

Legacy Theses & Dissertations (2009 - 2024)

Despite significant advancements in the field of machine learning, there are two issues that still require further exploration. First, how to learn from a small dataset; and second, how to select appropriate features from the data. Although there exist many techniques to address these issues, choosing a combination of the techniques from these two groups is challenging, and worth investigating. To address these concerns, this thesis presents a learning framework that is based on a deep learning model utilizing active learning (with evidential uncertainty as a basis for acquisition function) for the first issue and a genetic algorithm for the …

Stability And Differential Privacy Of Stochastic Gradient Methods, Zhenhuan Yang

Legacy Theses & Dissertations (2009 - 2024)

Recently there are a considerable amount of work devoted to the study of the algorithmic stability as well as differential privacy (DP) for stochastic gradient methods (SGM). However, most of the existing work focus on the empirical risk minimization (ERM) and the population risk minimization problems. In this paper, we study two types of optimization problems that enjoy wide applications in modern machine learning, namely the minimax problem and the pairwise learning problem.

Towards Making Transformer-Based Language Models Learn How Children Learn, Yousra Mahdy

Boise State University Theses and Dissertations

Transformer-based Language Models (LMs), learn contextual meanings for words using a huge amount of unlabeled text data. These models show outstanding performance on various Natural Language Processing (NLP) tasks. However, what the LMs learn is far from what the meaning is for humans, partly due to the fact that humans can differentiate between concrete and abstract words, but language models make no distinction. Concrete words are words that have a physical representation in the world such as “chair”, while abstract words are ideas such as “democracy”. The process of learning word meanings starts from early childhood when children acquire their …

Directed Acyclic Graph-Based Neural Networks For Tunable Low-Power Computer Vision, Abhinav Goel, Caleb Tung, Nick Eliopoulos, Xiao Hu, George K. Thiruvathukal, James C. Davis, Yung-Hisang Lu

Computer Science: Faculty Publications and Other Works

Processing visual data on mobile devices has many applications, e.g., emergency response and tracking. State-of-the-art computer vision techniques rely on large Deep Neural Networks (DNNs) that are usually too power-hungry to be deployed on resource-constrained edge devices. Many techniques improve DNN efficiency of DNNs by compromising accuracy. However, the accuracy and efficiency of these techniques cannot be adapted for diverse edge applications with different hardware constraints and accuracy requirements. This paper demonstrates that a recent, efficient tree-based DNN architecture, called the hierarchical DNN, can be converted into a Directed Acyclic Graph-based (DAG) architecture to provide tunable accuracy-efficiency tradeoff options. We …

Evaluation Of Decision-Making Prediction Models For Sewer Pipes Asset Management, Salar Shirkhanloo

Civil Engineering Dissertations - Archive

Wastewater collection systems deteriorate over time, requiring continuous adjustments and the development of asset management frameworks on the part of utility owners to maintain the performance of their assets. Any asset management framework should emphasize the importance of asset inspection and condition evaluation for efficient system operation and maintenance. Closed-circuit television (CCTV) is the most widely used tool in the United States for inspecting the interior of sewer pipes, which is a somewhat expensive and time-consuming process given the extensive inventory of pipes in a city. Due to their vast inventory of these pipes, no municipality can inspect every individual …

Exploring The Use Of Machine Learning To Parameterize Vertical Mixing In The Ocean Surface Boundary Layer, Jun Hong Liang, Jianguo Yuan, Xiaoliang Wan, Jinliang Liu, Bingqing Liu, Hakun Jang, Mayank Tyagi

Faculty Publications

In ocean and climate models, the simulation of upper-ocean temperature and salinity depends on mixing parameterizations for ocean surface boundary layer turbulence. Existing mixing parameterizations are based on physical principles with empirical parameters. However, they are still imperfect, leading to biases in the simulation of physical states in the upper ocean. In this study, we explore the use of the data-based machine learning technique, specifically, a deep neural network model, for the effects of vertical mixing in the ocean surface boundary layer. The model is trained using process-oriented simulations of the upper-ocean turbulence driven by realistic forcing conditions at the …

Process-Property Linkages Construction For Inkjet Printing With Machine Learning, Fataneh Jenabi

Boise State University Theses and Dissertations

Printed electronics are emerging technologies that can potentially revolutionize the manufacturing of electronic devices. One promising technology for printed electronics is inkjet printing. Inkjet printing offers both low-cost processing and high resolution. Being a subset of additive manufacturing, inkjet printing minimizes waste and is compatible with a wide range of inks. However, inkjet printing of electronic devices is still in its infancy. One major challenge for inkjet printing is the complexity of the process optimization and uncertain high throughput production. To achieve a high-quality print, there is a complex parameter space of materials and processing parameters that needs to be …

Optimization Of Lattice Structure Using Machine Learning Approach, Tanzila Bint Minhaj

Open Access Theses & Dissertations

The goal line of designing any structure is to get maximum performance at minimum cost. Therefore, optimization is the only method to achieve that objective. Engineers have been practicing different formats of optimization. Topological optimization is one of the well-known long-practiced methods. But it is always desired to find the most helpful design method that considers every relevant parameter associated with the structure. In the continuation of this search to enhance the efficacy of design through optimization, a new approach was explored in the following work. The motivation was to enable a model to be capable of finding out the …

Machine Learning-Enabled Model-Based Condition Assessment Of Water Pipelines By Leveraging Hydraulic Monitoring Data, Ahmad Momeni

All Dissertations

Overpopulation and climate change have direly challenged the freshwater resources, specifically potable water supplied by water distribution networks (WDNs). One aggravating issue associated with the WDNs is associated with the pipeline leakage, which accounts for almost 20% of freshwater loss in WDNs throughout the US. Leakage detection and severity measurement are of top
asset management priorities in water utilities to minimize and mitigate complicated risks attributed to background and burst leakage. Accordingly, decline in other pipe condition parameters such as effective hydraulic diameters and roughness coefficients, which are complex and uncertain in nature, abets leakage by worsening the WDN status …

Tempering The Adversary: An Exploration Into The Applications Of Game Theoretic Feature Selection And Regression, Stephen Mcgee

All Dissertations

Most modern machine learning algorithms tend to focus on an "average-case" approach, where every data point contributes the same amount of influence towards calculating the fit of a model. This "per-data point" error (or loss) is averaged together into an overall loss and typically minimized with an objective function. However, this can be insensitive to valuable outliers. Inspired by game theory, the goal of this work is to explore the utility of incorporating an optimally-playing adversary into feature selection and regression frameworks. The adversary assigns weights to the data elements so as to degrade the modeler's performance in an optimal …

Solving The Challenges Of Concept Drift In Data Stream Classification., Hanqing Hu

Electronic Theses and Dissertations

The rise of network connected devices and applications leads to a significant increase in the volume of data that are continuously generated overtime time, called data streams. In real world applications, storing the entirety of a data stream for analyzing later is often not practical, due to the data stream’s potentially infinite volume. Data stream mining techniques and frameworks are therefore created to analyze streaming data as they arrive. However, compared to traditional data mining techniques, challenges unique to data stream mining also emerge, due to the high arrival rate of data streams and their dynamic nature. In this dissertation, …

Physiognomic Artificial Intelligence, Luke Stark, Jevan Hutson

Articles

The reanimation of the pseudosciences of physiognomy and phrenology at scale through computer vision and machine learning is a matter of urgent concern. This Article—which contributes to critical data studies, consumer protection law, biometric privacy law, and antidiscrimination law—endeavors to conceptualize and problematize physiognomic artificial intelligence (“AI”) and offer policy recommendations for state and federal lawmakers to forestall its proliferation.

Physiognomic AI, as this Article contends, is the practice of using computer software and related systems to infer or create hierarchies of an individual’s body composition, protected class status, perceived character, capabilities, and future social outcomes based on their physical …

Investigating Toxicity Changes Of Cross-Community Redditors From 2 Billion Posts And Comments, Hind Almerekhi, Haewoon Kwak, Bernard J. Jansen

Research Collection School Of Computing and Information Systems

This research investigates changes in online behavior of users who publish in multiple communities on Reddit by measuring their toxicity at two levels. With the aid of crowdsourcing, we built a labeled dataset of 10,083 Reddit comments, then used the dataset to train and fine-tune a Bidirectional Encoder Representations from Transformers (BERT) neural network model. The model predicted the toxicity levels of 87,376,912 posts from 577,835 users and 2,205,581,786 comments from 890,913 users on Reddit over 16 years, from 2005 to 2020. This study utilized the toxicity levels of user content to identify toxicity changes by the user within the …