Engineering Commons^™

Uncertainty-Aware And Explainable Artificial Intelligence For Identification Of Human Errors In Nuclear Power Plants, Bhavya Reddy Kotla

Master's Projects

Nuclear Power Plants (NPPs) can face challenges in maintaining standard operations due to a range of issues, including human mistakes, mechanical breakdowns, electrical problems, measurement errors, and external influences. Swift and precise detection of these issues is crucial for stabilizing the NPPs. Identifying such operational anomalies is complex due to the numerous potential scenarios. Additionally, operators need to promptly discern the nature of an incident by tracking various indicators, a process that can be mentally taxing and increase the likelihood of human errors. Inaccurate identification of problems leads to inappropriate corrective actions, adversely affecting the safety and efficiency of NPPs. …

A Natural Language Processing Approach To Malware Classification, Ritik Mehta

Master's Projects

Many different machine learning and deep learning techniques have been successfully employed for malware detection and classification. Examples of popular learning techniques in the malware domain include Hidden Markov Models (HMM), Random Forests (RF), Convolutional Neural Networks (CNN), Support Vector Machines (SVM), and Recurrent Neural Networks (RNN) such as Long Short-Term Memory (LSTM) networks. In this research, we consider a hybrid architecture, where HMMs are trained on opcode sequences, and the resulting hidden states of these trained HMMs are used as feature vectors in various classifiers. In this context, extracting the HMM hidden state sequences can be viewed as a …

Image-Based Classification Of Malware Using T-Sne Images, Vincent Stowbunenko

Master's Projects

This Master’s project proposes a novel technique for classifying malware using image-based methods. The approach involves generating t-SNE images from the EMBER dataset, which contains one million samples of both malware and benign files, each represented by over 2,000 features. The t-SNE technique is well-suited for capturing intricate patterns in complex datasets because it effectively maintains the local structure. These t-SNE images are then used as inputs to train two lightweight image classification models, SqueezeNet and MobileNet. Additionally, to provide a benchmark for comparison, a non-image classification model using LightGBM is also explored.

As part of the investigation, the project …

Metagenomic Survey Of Marine 16s Bacterial Communities Off Palmer Station In Antarctica, Daniel Salter

Master's Projects

This project surveys the metagenomic bacterial community composition in marine surface waters off Palmer Station, Western Antarctic Peninsula and correlates findings with temperature and salinity data. Marine bacterial communities play a vital role in nutrient cycling, but data on surface waters in this region are limited. Analyzing fifteen samples of 16S sequencing data from three austral summers, consistent dominance was observed by the classes Alphaproteobacteria, Gammaproteobacteria, and Flavobacteria. Correlation analysis confirmed significant relationships between taxa and environmental conditions. The observed trends suggest varying abilities of phyla to resist and adapt to changing environmental conditions. Notably, Alphaproteobacteria demonstrated adaptability to favorable …

Malware Classification Using Opcode N-Grams And Word Embeddings, Siddhita Joshi

Master's Projects

Malware is a serious risk to any software application whether it is standalone or over the network. In order to protect computer systems, it is essential to detect and classify malware effectively. Modern malware classification research focuses on Machine Learning and Deep Learning techniques to identify advanced malicious software. This project explores malware classification by combining two robust methods: n-grams and word embedding. By extracting opcode n-grams, we make use of sequential nature of malware execution to identify any local patterns within the malware executable.

We use word embedding methods such as Word2Vec, Doc2Vec, and FastText to produce dense vector …

Evaluation Of The Effect Of Walnut Extract On Sp1-Related Pathways, Jihan Yehia

Master's Projects

Walnut extract (WE) has shown promising anti-cancer effects, such as inducing apoptosis and moderating cell cycle progression. A previous study by Dr. Brandon White’s Lab at San Jose State University hypothesizes that WE can downregulate the expression of the pro-tumoral specificity protein 1 (Sp1) in triple negative breast cancer (TNBC). This project builds an RNA-seq pipeline that runs differential gene expression (DGE) analysis to study the effect of WE on TNBC, thereby offering a wider perspective on genes that may be affected by this treatment. The data used in this project originated from Illumina and Nanopore sequencing methods, and DGE …

Efficient Video Qoe Prediction In Intelligent O-Rans, Aditya Kulkarni

Master's Projects

Open Radio Access Network (O-RAN) is a platform developed by a collaboration between wireless operators, infrastructure vendors, and service providers for deploying mobile fronthaul and midhaul networks, built entirely on cloud-native principles. The vision of O-RAN lies in the virtualization of traditional wireless infrastructure components, like Central Units (CU), Radio Units (RU), and Distributed Units (DU). O-RAN decouples the above-mentioned wireless infrastructure components into opensource elements, operating consistently with other elements of different vendors in the network. Quality of Experience (QoE) deals with a user’s subjective measure of satisfaction. RAN Intelligent Controller (RIC) in O-RAN provides flexibility to intelligently program …

Unlearning Hidden Bias Between Refugees : An Initial Empirical Investigation, Akshay Sunil Gurnaney

Master's Projects

The challenges that refugees in various regions encounter are common knowledge. One such challenge is a bias among refugees on ethnocentric grounds. In particular, there are various articles that have pointed out the struggles faced by Syrian refugees in countries like Europe as a result of implicit bias. In fact, the media coverage of Syrian crises and the government responses to the same shed negative light on the refugees themselves. On the contrary, the media coverage of Ukrainian crises is very different with lesser restrictions from the governments.

This research attempts to identify the extent of implicit bias between Ukrainian …

Poriferal Vision: Using Mobilenet To Classify Sponge Spicules Through Transfer Learning, Brian Tran

Master's Projects

Global warming is an ongoing issue where the Earth is rapidly warming up. It negatively affects the growth of coral through ocean warming and ocean acidification. Many coral communities, home to a large variety of marine life, are expected to be severely impacted by these effects. Past evidence suggests that sponges will take over as the primary reef builders since many species of sponges have skeletons made of silica or glass which is not affected by ocean acidification. More research is needed to determine which kinds of sponge will most likely be able to thrive in today’s climate.

This can …

Evalsql - Automated Assessment Of Database Queries, Damanpreet Kaur

Master's Projects

In computer science programs, database is a fundamental subject taught through several undergraduate courses. These courses develop theoretical and practical concepts of databases. Building queries is a key aspect of this learning process, and students are assessed through assignments and quizzes. However, grading these assignments can be time-consuming for professors, and students usually receive feedback only after the deadlines have passed. As a result, students may miss the opportunity to improve their work and achieve better grades. To address this issue, it would be beneficial to provide students with immediate feedback on their submissions. EvalSQL is an automated system that …

Solving The Capacitated Vehicle Routing Problem Using A New Genetic Algorithm, Cajetan Rodrigues

Master's Projects

The Capacitated Vehicle Routing Problem (CVRP) [1, 2, 3] is an extension to the Vehicle Routing Problem (VRP), a well-known NP-hard optimization problem. In our CVRP, we are given a depot, the number of vehicles and their capacity, as well as a set of customers and their demands, both the depot and the set of customers lie in the Euclidean space. The goal is to find for each vehicle an optimal route (tour) starting and finishing at the depot, such that all customers are served exactly once.

In this study, we investigate the effectiveness of using a Genetic Algorithm (GA) …

Advances In Robustness Of Image-Based Malware Detection, Rishika Pamanji

Master's Projects

In recent years, deep learning has emerged as a powerful tool for image classification tasks. However, the performance of individual deep learning models can be limited by their architecture and training data. In this project, various Convolutional Neural Network (CNN) architectures are proposed to train the malware data for feature extraction for various color coordinates such as L, CMYK, RGB, RGBA, and YCbCr. Different optimization techniques like Stochastic Gradient Descent, Root Mean Square Propagation, Ada Delta, Adam, and Adaptive Gradient are used to minimize errors in the trained data, leading to enhanced accuracy. The proposed ensemble deep learning model for …

Visual Scene Classification Using Ensemble Of Machine Learning Classifiers, Rahul Ranganath

Master's Projects

Visual scenes represent the comprehensive visual information observed in a particular environment. Whether natural landscapes, urban settings, or designed interiors, visual scenes encompass the arrangement of elements that individuals perceive through their visual senses. Visual search is perhaps one of the most typical jobs that we carry out several times a day. This is one of the main paradigms for researching visual attention. Many visual task models have been put forward in an effort to better understand visual attention. Fixations and the rapid movement of the eye - saccades, define visual exploration and visual search. When we subject viewers to …

Identification Of Copy Number Variations (Cnvs) Of Epigenetic Factors Related To The Progression Of Pancreatic Ductal Adenocarcinoma (Pdac), Pavithra Raju

Master's Projects

Pancreatic ductal adenocarcinoma (PDAC) is a formidable challenge in oncology due to its aggressive form and late-stage detection. PDAC is known to be influenced by various epigenetic factors like DNA methylation and histone modifications. This study focuses on copy number variations (CNVs) within epigenetic factors which for their role in early diagnosis. Thus, paving the way for identification of potential biomarkers. The epigenetic pipeline was extended based on CNVs and the CNV modified sequences extracted were compared with the wild type sequences of epigenetic PDAC genes. The epigenetic gene KCNJ11 with copy number gain of CNV id 46771406 was used …

Analyzing The Benthic Cover Of Crustose Coralline Algae Using Mask-R Cnn, Rachana Ravindra

Master's Projects

Coral reefs, supporting 25% of marine biodiversity, confront challenges from local and global impacts like overfishing, runoff, acidification, and warming. Crustose Coralline Algae (CCA), pivotal for reef structure and coral settlement, are underrepresented in research. Current methods like Coral Point Count with Excel Extensions (CPCe) have limitations, relying on image quality and being time-consuming. This paper proposes computer vision and Mask R-CNN, a supervised machine learning model, for CCA analysis in reef images, considering color, texture, and shape. Results indicate promise in clustering and classifying organisms. The innovative technology reduces manual labor, enhancing image analysis, simplifying the understanding of CCA’s …

Ensemble Transformer Architecture For Efficient And Real Time Sign Language Translation, Sumeet Ghegade

Master's Projects

Sign language is a form of visual language that uses face expression and hand gestures to communicate thoughts and concepts. The term refers to multiple visual languages that share some common visual cues but differ in their grammar and syntax. Sign language translation (SLT) is a crucial step in closing the communication gap between hearing and hearingimpaired people. The study of SLT using machine learning has gotten a lot of interest during the last three years but despite progress, SLT research is still in its early phases. Most of the previous approaches first convert the signs to glosses and then …

Gesture Recognition Of Sign Language Alphabet Using Machine Learning Techniques, Gursimran Singh

Master's Projects

With the rising incidence of hearing loss, effective sign language recognition has become crucial for enhancing communication for individuals with hearing impairments. Traditional sensor-based recognition systems have been challenged by the complexities of realworld settings, prompting a shift toward more adaptable vision-based recognition systems. Distinct from previous studies, this work pioneers the use of ensemble methods with advanced filtering techniques on the Sign Language MNIST dataset, offering a novel perspective on sign language recognition. This research delves into the intersection of machine learning and image processing to develop a robust framework for sign language recognition. A range of filters, including …

An Effective Transfer Learning Based Landmark Detection Framework For Uav-Based Aerial Imagery Of Urban Landscapes, Bishwas Praveen, Vineetha Menon, Tathagata Mukherjee, Bryan Mesmer, Sampson Gholston, Steven Corns

Engineering Management and Systems Engineering Faculty Research & Creative Works

Aerial imagery captured through airborne sensors mounted on Unmanned Aerial Vehicles (UAVs), aircrafts, satellites, etc. in the form of RGB, LiDAR, multispectral or hyperspectral images provide a unique perspective for a variety of applications. These sensors capture high-resolution images that can be used for applications related to mapping, surveying, and monitoring of crops, infrastructure, and natural resources. Deep learning based algorithms are often the forerunners in facilitating practical solutions for such data-centric applications. Deep learning-based landmark detection is one such application which involves the use of deep learning algorithms to accurately identify and locate landmarks of interest in images captured …

High-Temperature Interactions Between Titanium Alloys And Strontium Zirconate Refractories, R. Sharon Uwanyuze, Baris Yavas, Jiyao Zhang, Janos E. Kanyo, Lesley D. Frame, Rainer J. Hebert, Stefan Schafföner, S. Pamir Alpay

Materials Science and Engineering Faculty Research & Creative Works

We investigated interactions between Ti6Al4V alloys and strontium zirconate (SrZrO₃) ceramic to assess its potential as a refractory mold material in investment casting. We developed a robust yet simple procedure to examine both the liquid–solid and solid–solid interactions using pellets in drop casting and diffusion couple methods. Reaction layers were characterized using optical microscopy, scanning electron microscopy (SEM), transmission electron microscopy (TEM), and x-ray diffraction (XRD). The results were compared to alumina (Al₂O₃) which is still a common refractory ceramic for molds in investment casting. Our findings indicate that Ti6Al4V surfaces in contact with …

Strength Retention Of Single-Phase High-Entropy Diboride Ceramics Up To 2000°C, Lun Feng, William Fahrenholtz, Gregory E. Hilmas, Yue Zhou, Jincheng Bai

Materials Science and Engineering Faculty Research & Creative Works

The mechanical properties of single-phase (Hf_0.2,Nb_0.2,Ta_0.2,Ti_0.2,Zr_0.2)B₂ ceramics with high purity were investigated. The resulting ceramics had relative density greater than 99%, and an average grain size of 4.3 ± 1.6 μm. At room temperature (RT), the Vickers hardness was 25.2 ± 0.6 GPa at a load of 0.49 N, Young's modulus was 551 ± 7 GPa, fracture toughness was 4.5 ± 0.4 MPa m1/2, and flexural strength was 507 ± 10 MPa. Flexural strength increased by more than 50% from 507 ± 10 MPa at RT to 776 ± …

Effect Of Annealing Time On Texture Evolution Of Fe–3.4 Wt% Si Nonoriented Electrical Steel, Yizhou Du, Ronald J. O'Malley, Mario F. Buchely, Paul Kelly

Materials Science and Engineering Faculty Research & Creative Works

Herein, the Effect of Annealing Time on the Texture Evolution in Fe–3.4 Wt.% Si Non-Oriented Electrical Steel is Investigated. Strip Samples Are Cast using a Vacuum Sampling Method, Which Simulate the Solidification Conditions of an Industrial Twin Roll Thin Strip Casting (TRSC) Process. As-Cast Samples with Different Carbon and Sulfur (C&S) Levels Are Hot Rolled (HR) with Varying Levels of Hot Deformation, Cold Rolled (CR) to 0.35 Mm Thickness, and Then Annealed at 1050 °C for Different Holding Times (1, 6, 24 H). to Fe–3.4 Wt.% Si Non oriented Electrical Steel, the Observed Texture Evolution Can Be Divided into Different …

Phenomenological Analysis Of Surface Degradation Of Metallic Materials In Extreme Environment, Simon N. Lekakh, Oleg Neroslavsky

Materials Science and Engineering Faculty Research & Creative Works

The resistance to surface degradation in metallic alloys plays an important role for the lifetime of the components working in harsh environments. The mechanisms involved in degradation of metallic surface in a high-temperature aggressive gaseous atmosphere include the following: forming adherent to the surface multiphase oxide layer, partial spallation, and possible vaporization of formed compounds. The governing equation, which describes a parabolic growth of adherent layer, time-dependent vaporization, and cross-linked to instantaneous thickness of adherent layer spallation rate, was suggested and analyzed. The several relationships between the kinetic constants were defined from analysis of the governing equation. Design of routes …

Real-Time Air Gap And Thickness Measurement Of Continuous Caster Mold Flux By Extrinsic Fabry-Perot Interferometer, Abhishek Prakash Hungund, Hanok Tekle, Bohong Zhang, Ronald J. O'Malley, Jeffrey D. Smith, Rex E. Gerald, Jie Huang

Materials Science and Engineering Faculty Research & Creative Works

Mold Flux plays a critical role in continuous casting of steel. Along with many other functions, the mold flux in the gap between the solidifying steel shell and the mold serves as a medium for controlling heat transfer and as a barrier to prevent shell sticking to the mold. This manuscript introduces a novel method of monitoring the structural features of a mold flux film in real-time in a simulated mold gap. A 3-part stainless-steel mold was designed with a 2 mm, 4 mm and, 6 mm step profile to contain mold flux films of varying thickness. An Extrinsic Fabry-Perot …

Modeling Phase Selection And Extended Solubility In Rapid Solidified Alloys, Azeez Akinbo, Yijia Gu

Materials Science and Engineering Faculty Research & Creative Works

A new phase selection model based on the time-dependent nucleation theory was developed to investigate the effect of rapid solidification on extended solubility. The model was applied to predict the solubility as a function of undercooling for several binary Al alloys. The predictions of both eutectic and peritectic systems show good agreement with experimental data. It was demonstrated that the developed model is better than the T 0-line method, which neglected the kinetic process of nucleation. Furthermore, the model can also be applied to ternary and multicomponent phases assuming the nucleation is limited by the scarcest species or the slowest …

Impact Of Alumina-Based Binder On Formation Of Dense Strontium Zirconate Ceramics, Janos E. Kanyo, R. Sharon Uwanyuze, Jiyao Zhang, Rainer Hebert, Stefan Schafföner, Lesley Frame

Materials Science and Engineering Faculty Research & Creative Works

Strontium zirconate (SrZrO3) is a technical ceramic with potential for refractory applications due to its chemical stability at high temperatures, high melting temperature, and favorable thermal expansion coefficient. Practical use of SrZrO3 is limited by poor mechanical strength relative to ceramics such as alumina (Al2O3). Sintering of SrZrO3 with a hydratable Al2O3 binder is investigated as a method for improving mechanical performance. Density, phase composition, thermomechanical properties, and chemical stability in contact with alloys up to 1350 °C are considered. Results are compared with SrZrO3 samples formed using a traditional polyvinyl alcohol (PVA) binder. SrZrO3 reacts with alumina during sintering …

Microstructure Evolution And Austenitic Grain Refinement Of Ti-Modified High Mn Steels During Solution Annealing Heat Treatment, Abhinav Karanam, Arnab Sarkar, Erik Nenzen, Viraj Ashok Athavale, Mark Watson, Laura Nicole Bartlett, Lukas Bichler

Materials Science and Engineering Faculty Research & Creative Works

Austenitic high manganese (Mn) steels are often used in harsh environments due to their high toughness and wear resistance. Heat treatments are performed to engineer the microstructure of the steels and optimize their performance for the desired service conditions. This study investigated the effect of titanium (Ti) and heat treatment on the microstructure of cast high Mn steel (HMS) alloys. The results reveal that Ti addition contributed to grain refinement in the as cast and heat-treated conditions. In-situ observation of microstructure evolution up to 1125 °C confirmed the stability of precipitated TiC particles, while revealing a new mechanism of grain …

Residual Stress Distribution, Distortion, And Crack Initiation In Conventional And Intensive Quench Practices, Kingsley Tochukwu Amatanweze, Mario F. Buchely, Viraj Ashok Athavale, Laura Bartlett, Ronald J. O'Malley, Toshi Suzuki

Materials Science and Engineering Faculty Research & Creative Works

This study evaluates the effect of two different quench practices on distortion, sensitivity to quench cracking, development and distribution of residual stress, microstructural uniformity, and hardenability of standardized test castings. Navy C-rings made of AISI 4340 were quenched in this experiment. Some rings were quenched in a conventional draft tube immersion quench bath, and others were quenched in an intensive quench spray system to compare with the results from the conventional immersion quench bath. The rings were measured with a coordinate measuring machine, for distortion and flatness, before and after quenching. Hardness profiles of the quenched rings showed through hardness …

A Genome-Wide Association Study Coupled With Machine Learning Approaches To Identify Influential Demographic And Genomic Factors Underlying Parkinson’S Disease, Md Asad Rahman, Jinling Liu

Engineering Management and Systems Engineering Faculty Research & Creative Works

Background: Despite the recent success of genome-wide association studies (GWAS) in identifying 90 independent risk loci for Parkinson's disease (PD), the genomic underpinning of PD is still largely unknown. At the same time, accurate and reliable predictive models utilizing genomic or demographic features are desired in the clinic for predicting the risk of Parkinson's disease. Methods: To identify influential demographic and genomic factors associated with PD and to further develop predictive models, we utilized demographic data, incorporating 200 variables across 33,473 participants, along with genomic data involving 447,089 SNPs across 8,840 samples, both derived from the Fox Insight online study. …

Improving Social Bot Detection Through Aid And Training, Ryan Kenny, Baruch Fischhoff, Alex Davis, Casey I. Canfield

Engineering Management and Systems Engineering Faculty Research & Creative Works

Objective: We test the effects of three aids on individuals' ability to detect social bots among Twitter personas: a bot indicator score, a training video, and a warning. Background: Detecting social bots can prevent online deception. We use a simulated social media task to evaluate three aids. Method: Lay participants judged whether each of 60 Twitter personas was a human or social bot in a simulated online environment, using agreement between three machine learning algorithms to estimate the probability of each persona being a bot. Experiment 1 compared a control group and two intervention groups, one provided a bot indicator …

Additive Manufacturing Of Sic-Sialon Refractory With Excellent Properties By Direct Ink Writing, Ruoyu Chen, Saisai Li, Xinxin Jin, Haiming Wen

Materials Science and Engineering Faculty Research & Creative Works

Additive manufacturing of SiC-Sialon refractory with complex geometries was achieved using direct ink writing processes, followed by pressure less sintering under nitrogen. The effects of particle size of SiC powders, solid content of slurries and additives on the rheology, thixotropy and viscoelasticity of ceramic slurries were investigated. The optimal slurry with a high solid content was composed of 81 wt% SiC (3.5 µm+0.65 µm), Al2O3 and SiO2 powders, 0.2 wt% dispersant, and 2.8 wt% binder. Furthermore, the accuracy of the structure of specimens was improved via adjustment of the printing parameters, including nozzle size, extrusion pressure, and layer height. The …