Computer Engineering Commons^™

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 …

Interest-Based Recommendation System Using Gmail Topic Modelling, Pranav Ghaskadbi

Master's Projects

Emails are a fundamental part of modern communication. Much of communicative discourse in modern society occurs over email, resulting in personal collections for each mail user which are rich in latent user’s interests. Conventional recommendation systems require historical data of user activity and interactions to derive user interests. The absence of activity and interaction data poses an interesting challenge for generating relevant recommendations for users. We were motivated to investigate approaches to identify user interests in the absence of historical data to generate personalized content recommendations. There is opportunity to derive user interests from email data, which can be used …

Identifying Potential Alzheimer’S Disease Biomarkers Beyond Amyloid-Beta And Tau, Frank Cai

Master's Projects

Alzheimer's Disease (AD) and other forms of Mild Cognitive Impairment (MCI) affect millions of people around the world. The buildup of Amyloid-Beta (Aβ) and Tau proteins in the brain produced by amyloid precursor protein (APP) has been identified as an important cofactor in the onset and progression of AD. However, although patients diagnosed with AD exhibit Aβ and Tau buildup, about 40% of the subjects with Aβ and Tau buildup are not diagnosed with AD. In this project, we hypothesize the involvement of other epigenetic interactions between APP and related genes in addition to the buildup of Aβ and Tau …

Prediction Of 2024 Indian Pm Election Results Using Sentiment Analysis On Twitter Data, Surabhi Gupta

Master's Projects

This sentiments analysis study presents a methodical approach to predict the 2024 Indian Prime Minister Election. Data collected spanning from 2020 to 2023 from Twitter using hashtags such as IndianPMElection2024 and on topics such as the revocation of the special status of Jammu and Kashmir, the Farm Bill, and the Digital India initiative, form the core of this research. We utilized a combination of sentiment extraction tools-namely, the NLP Town's Bidirectional Encoder Representations from Transformers (BERT)-based multilingual uncased sentiment model, Valance Aware Dictionary for Sentiment Reasoning (VADER), and TextBlob. Additionally, we used a well-established machine learning model Naive Bayes, deep …

A Flexible Photonic Reduction Network Architecture For Spatial Gemm Accelerators For Deep Learning, Bobby Bose

Theses and Dissertations--Electrical and Computer Engineering

As deep neural network (DNN) models increase significantly in complexity and size, it has become important to increase the computing capability of specialized hardware architectures typically used for DNN processing. The major linear operations of DNNs, which comprise the fully connected and convolution layers, are commonly converted into general matrix-matrix multiplication (GEMM) operations for acceleration. Specialized GEMM accelerators are typically employed to implement these GEMM operations, where a GEMM operation is decomposed into multiple vector-dot-product operations that run in parallel. A common challenge that arises in modern DNNs is the mismatch between the matrices used for GEMM operations and the …

Application Of Conventional Feedforward And Deep Neural Networks To Power Distribution System State Estimation And State Forecasting, James Paul Carmichael

Theses and Dissertations--Electrical and Computer Engineering

Classical neural networks such as feedforward multilayer perceptron models (MLPs) are well established as universal approximators and as such, show promise in applications such as static state estimation in power transmission systems. This research investigates the application of conventional neural networks (MLPs) and deep learning based models such as convolutional neural networks (CNNs) and long short-term memory networks (LSTMs) to mitigate challenges in power distribution system state estimation and forecasting based upon conventional analytic methods. The ability of MLPs to perform regression to perform power system state estimation will be investigated. MLPs are considered based upon their promise to learn …

Max Fit Event Management With Salesforce, Akshay Dagwar

Electronic Theses, Projects, and Dissertations

MAX FIT Gym is looking for an event management software program to help manage activities very efficiently, along with attendees and environmental statistics. The event management program is developed and deployed using the Salesforce platform. MAX FIT can efficiently create, edit, and remove events and send email alerts to clients. This task operated on opportunities captured under MAX FIT, including all clients, and prepared information in the Salesforce cloud. This also includes product inventory with various varieties of protein products, and business owners can also add more products to their inventory. In the event management program, the event addresses within …

Learning Outcomes And Learner Satisfaction: The Mediating Roles Of Self-Regulated Learning And Dialogues, Sean Eom, Nicholas Jeremy Ashill

Journal of International Technology and Information Management

The interdependent learning process is regarded as a crucial part of e-learning success, but it has been largely ignored in e-learning empirical research. Grounded in constructivist and social constructivist theory, we present and test an e-learning success model consisting of eight e-learning critical success factors (CSF) derived from constructivist and social constructivist models. Three hundred seventy-two on-line students from a Midwestern university in the United States participated in the survey. The data collected from the survey was used to examine the partial least squares structural equation model. The results highlight the importance of self-regulated learning and dialogical processes to explain …

Acceptance Of Interoperable Electronic Health Record (Ehrs) Systems: A Tanzanian E-Health Perspective, Emmanuel Mbwambo, Herman Mandari

Journal of International Technology and Information Management

The study assessed factors that influence the acceptance of interoperable electronic Health Records (EHRs) Systems in Tanzania Public Hospitals. The study applied a hybrid model that combined the Technology Acceptance Model (TAM) and Technology-Organization-Environment (TOE). Snowball sampling technique was applied and a total of 340 questionnaires were distributed to selected clinics, polyclinics and hospitals, of which 261 (77%) received questionnaires were considered to be valid and reliable for subsequent data analysis. IBM SPSS software version 27.0 was employed for data analysis. Findings indicated that relative advantage, compatibility, management support, organizational competency, training and education, perceived ease of use, perceived usefulness, …

Analysis Of The Impact Of Vaccinations On Pandemic Metrics In The New York Metropolitan Area, Oredola A. Soluade, Heechang Shin, Robert Richardson

Journal of International Technology and Information Management

This study evaluates the relationship between pandemic cases and vaccination usage, ICU bed utilization, hospitalizations, and deaths in the New York City metropolitan area. The study includes variables for the lockdown period and confirmed infections. The evaluation addresses three periods: (1) before vaccinations, (2) after vaccinations, and (3) the lockdown period. In addition, the number of vaccines per day for the manufacturers (Pfizer, Moderna, and Johnson & Johnson) are included in the study. Comparisons with New Jersey and Connecticut are used to validate that New York statistics are consistent with other states. The results provide a general model of the …

Enhanced Load Balancing Based On Hybrid Artificial Bee Colony With Enhanced Β-Hill Climbing In Cloud, Maha Zeedan, Gamal Attiya, Nawal El-Fishawy

Mansoura Engineering Journal

This paper proposes enhanced load balancer based artificial bee colony and β-Hill climbing for improving the performance metrics such as response time, processing cost, and utilization to avoid overloaded or under loaded situations of virtual machines. In this study, the suggested load balancer is called enhanced load balancing based on hybrid artificial bee colony with enhanced β-Hill climbing (ELBABCEβHC) to improve the response time, processing cost and the resource utilization. Our proposed approach starts by ranking the task then the greedy randomized adaptive search procedure (GRASP) is used in initializing populations. Further, the binary artificial bee colony (BABC) enhanced with …

Enhancing The Performance Of Federated Learning With Diffusion Models: Leveraging Synthetic Data To Address Non-Iid Data Challenges, Karin Huangsuwan

Chulalongkorn University Theses and Dissertations (Chula ETD)

In the context of machine learning in healthcare, federated learning (FL) is frequently seen as an effective approach to tackling issues of data privacy and distribution. Nonetheless, many real-world datasets exhibit non-identical and independently distributed (non-IID) characteristics, meaning that data features vary across different institutions. This non-IID nature presents challenges for FL model convergence, such as client drifting, where model weights lean towards local optima rather than global optimum. To address these issues, we introduce a new framework called "FedDrip (Federated Learning with Diffusion Reinforcement at Pseudo-site)," which leverages diffusion-generated synthetic data to mitigate data-related problems in non-IID settings. Our …

Predicting Newcomer’S Turnover Using Predictive Analytics : A Case Study Of Thai Financial Firm In Bangkok, Thailand, Meena Kittikunsiri

Chulalongkorn University Theses and Dissertations (Chula ETD)

Employee turnover, a critical issue impacting workplace productivity, has prompted organizations to leverage machine learning techniques for predictive analysis. This study specifically targets the prediction of turnover among new employees, utilizing data obtained from a survey conducted at a Thai financial firm in Bangkok, Thailand. Through an evaluation of various machine learning models, the results indicate that the Random Forest model surpasses others. Furthermore, this research highlights crucial factors influencing newcomer turnover, such as comfort with workplace culture, work-from-home policies, onboarding programs, and satisfaction with the recruitment process. These findings offer actionable insights for HR professionals to focus on these …

Enhancing Traffic Safety In Unpredicted Environments With Integration Of Adas Features With Sensor Fusion In Intelligent Electric Vehicle Platform With Implementation Of Environmental Mapping Technology, David S. Obando Ortegon

College of Graduate Studies: Theses & Dissertations

A major objective on society is to reduce the number of accidents and fatalities on the road for drivers, and pedestrians. Therefore, the automotive engineering field is working on this problem through the development and integration of safety technologies such as advanced driving assistance systems. For this reason, this work was intended to develop and evaluate the performance of different ADAS features and IV technologies under unexpected scenarios. This by the development of safety algorithms applied to the intelligent electric vehicle designed and built in this work, through the use of ADAS sensors based on sensor fusion. Evaluation of AEB, …

Comparative Analytics On Chilli Plant Disease Using Machine Learning Techniques, Sai Abhishta Roy Seelam

College of Graduate Studies: Theses & Dissertations

This thesis concerns the detection of diseases in chilli plants using machine learning techniques. Three algorithms, viz., Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Multi-Layer Perceptron (MLP), and their variants have been employed. Chilli-producing countries, India, Mexico, China, Indonesia, Spain, the United States, and Turkey. India has the world’s largest chilli production of about 49% (according to 2020). Andhra Pradesh (Guntur) is the largest market in India, where their varieties are more popular for pungency and color. This study classifies five kinds of diseases that affect the chilli, namely, leaf spot, whitefly, yellowish, healthy, and leaf curl. A …

Comparative Analysis Of Fullstack Development Technologies: Frontend, Backend And Database, Qozeem Odeniran

College of Graduate Studies: Theses & Dissertations

Accessing websites with various devices has brought changes in the field of application development. The choice of cross-platform, reusable frameworks is very crucial in this era. This thesis embarks in the evaluation of front-end, back-end, and database technologies to address the status quo. Study-a explores front-end development, focusing on angular.js and react.js. Using these frameworks, comparative web applications were created and evaluated locally. Important insights were obtained through benchmark tests, lighthouse metrics, and architectural evaluations. React.js proves to be a performance leader in spite of the possible influence of a virtual machine, opening the door for additional research. Study b …

Ai Usage In Development, Security, And Operations, Maurice Ayidiya

Walden Dissertations and Doctoral Studies

Artificial intelligence (AI) has become a growing field in information technology (IT). Cybersecurity managers are concerned that the lack of strategies to incorporate AI technologies in developing secure software for IT operations may inhibit the effectiveness of security risk mitigation. Grounded in the technology acceptance model, the purpose of this qualitative exploratory multiple case study was to explore strategies cybersecurity professionals use to incorporate AI technologies in developing secure software for IT operations. The participants were 10 IT professionals in the United States with at least 5 years of professional experience working in DevSecOps and managing teams of at least …

Chatgpt: Open Possibilities, Mohammad Aljanabi, Mohanad Ghazi, Ahmed Hussein Ali, Saad Abas Abed

Iraqi Journal for Computer Science and Mathematics

ChatGPT-3 is a powerful language model developed by OpenAI that has the potential to revolutionize the way we interact with technology. This model has been trained on a massive amount of data, allowing it to understand and generate human-like text with remarkable accuracy.One of the most exciting possibilities of ChatGPT-3 is its potential to improve natural language processing (NLP) and natural language understanding (NLU) in a wide range of applications. In particular, ChatGPT-3 can be used to power chatbots, virtual assistants, and other conversational interfaces. These types of systems are becoming increasingly important as more and more people use voice …

Face Image And Video Analysis In Biometrics And Health Applications, Na Zhang

Graduate Theses, Dissertations, and Problem Reports (ETD)

Computer Vision (CV) enables computers and systems to derive meaningful information from acquired visual inputs, such as images and videos, and make decisions based on the extracted information. Its goal is to acquire, process, analyze, and understand the information by developing a theoretical and algorithmic model. Biometrics are distinctive and measurable human characteristics used to label or describe individuals by combining computer vision with knowledge of human physiology (e.g., face, iris, fingerprint) and behavior (e.g., gait, gaze, voice). Face is one of the most informative biometric traits. Many studies have investigated the human face from the perspectives of various different …

Imitation Learning For Swarm Control Using Variational Inference, Hafeez Olafisayo Jimoh

Graduate Theses, Dissertations, and Problem Reports (ETD)

Swarms are groups of robots that can coordinate, cooperate, and communicate to achieve tasks that may be impossible for a single robot. These systems exhibit complex dynamical behavior, similar to those observed in physics, neuroscience, finance, biology, social and communication networks, etc. For instance, in Biology, schools of fish, swarm of bacteria, colony of termites exhibit flocking behavior to achieve simple and complex tasks. Modeling the dynamics of flocking in animals is challenging as we usually do not have full knowledge of the dynamics of the system and how individual agent interact. The environment of swarms is also very noisy …

Motion Planning In Artificial And Natural Vector Fields, Bernardo Martinez Rocamora Junior

Graduate Theses, Dissertations, and Problem Reports (ETD)

This dissertation advances the field of autonomous vehicle motion planning in various challenging environments, ranging from flows and planetary atmospheres to cluttered real-world scenarios. By addressing the challenge of navigating environmental flows, this work introduces the Flow-Aware Fast Marching Tree algorithm (FlowFMT*). This algorithm optimizes motion planning for unmanned vehicles, such as UAVs and AUVs, navigating in tridimensional static flows. By considering reachability constraints caused by vehicle and flow dynamics, flow-aware neighborhood sets are found and used to reduce the number of calls to the cost function. The method computes feasible and optimal trajectories from start to goal in challenging …

Machine Learning And Deep Learning Approaches For Gene Regulatory Network Inference In Plant Species, Sai Teja Mummadi

Dissertations, Master's Theses and Master's Reports

The construction of gene regulatory networks (GRNs) is vital for understanding the regulation of metabolic pathways, biological processes, and complex traits during plant growth and responses to environmental cues and stresses. The increasing availability of public databases has facilitated the development of numerous methods for inferring gene regulatory relationships between transcription factors and their targets. However, there is limited research on supervised learning techniques that utilize available regulatory relationships of plant species in public databases.

This study investigates the potential of machine learning (ML), deep learning (DL), and hybrid approaches for constructing GRNs in plant species, specifically Arabidopsis thaliana, …

Experimental Evaluation Of Micro-Epidermal Actuators On Flexible Substrates, Courtney D. Bradley

Graduate Research Theses & Dissertations

Does embedding actuators in a flexible substrate increase their performance in hearing aids? What are the differences in damping experienced by actuators of different diameters and at different locations? At what frequency is peak acceleration achieved and what role does the size of the actuator and embedding it in a flexible substrate play? These questions will form the basis of this thesis. This work was done to develop a small non-invasive Band-Aid-©-like hearing aid. The novelty of this device requires a detailed analysis of piezoelectric actuators. This is a continuation of past students’ work on the topic. The main parameters …

Design And Development Of A Smartphone App For Health Monitoring And Alerts For Farm Workers, Omer Oztoprak

Graduate Research Theses & Dissertations

This thesis presents the design and development of a novel smartphone applicationfor simultaneous health monitoring and alerts for agricultural workers using multiple wearable sensor devices via Bluetooth Low Energy (BLE). The application combines health data from a range of sensors such as an IMU that detects body movement, a pulse oximeter that captures oxygen saturation and heart rate, and a thermometer that measures body temperature. The software framework is built using Jetpack Compose in Android Studio, and it incorporates the MVVM (Model- View-ViewModel) model to provide optimized data flow and robust efficiency. By integrating the Dagger Hilt Dependency Injection framework, …

To The Moon: Strategic Competition In The Cislunar Region, Shawn M. Willis

Faculty Publications

China’s advancing space capabilities, particularly in the cislunar region, call for increased cislunar space domain awareness on the part of the United States. US military and civilian decisionmakers must take into account the full scope of China’s cislunar plans and capabilities as the military builds space strategies and future force designs. The United States must also increase near-term investments that support more robust cislunar space domain awareness.

Spatio-Temporal Deep Learning Approaches For Addressing Track Association Problem Using Automatic Identification System (Ais) Data, Md Asif Bin Syed

Graduate Theses, Dissertations, and Problem Reports (ETD)

In the realm of marine surveillance, track association constitutes a pivotal yet challenging task, involving the identification and tracking of unlabelled vessel trajectories. The need for accurate data association algorithms stems from the urge to spot unusual vessel movements or threat detection. These algorithms link sequential observations containing location and motion information to specific moving objects, helping to build their real-time trajectories. These threat detection algorithms will be useful when a vessel attempts to conceal its identity. The algorithm can then identify and track the specific vessel from its incoming signal. The data for this study is sourced from the …

Machine Learning For Biosensors, Gayathri Anapanani

Graduate Theses, Dissertations, and Problem Reports (ETD)

Biosensors have become increasingly popular as diagnostic tools due to their ability to detect and quantify biological analytes in a wide range of applications. With the growing demand for faster and more reliable biosensing devices, machine learning has become a valuable tool in enhancing biosensor performance. In this report, we review recent progress in the application of machine learning to biosensors. We discuss the potential benefits of using machine learning in biosensors, including improved sensitivity, selectivity, and accuracy. We also discuss the various machine learning techniques that have been applied to biosensors, including data preprocessing, feature extraction, and classification and …

Use Of Bioheat Modeling To Characterize And Optimize Implantable Medical Devices And Neuromodulation Technologies, Adantchede Louis Zannou

Dissertations and Theses

Medical device development includes prototyping, benchtop characterization, preclinical studies, and clinical trials. Understanding the limitations and potential adverse effects of medical devices prior to their administration in humans is a crucial first step. Optimizing medical devices is essential to employing technology and improving patients care. Computational modeling is widely adopted as a powerful tool to predict stimulation/recording parameter optimization, rapid electrode/device prototyping, investigating novel mechanism of action, and testing working principles of any medical devices. Many implantable neuromodulation technologies including Spinal Cord Stimulation (SCS), which provide substantial therapeutic benefit for patient population with lower back pain, produces heat via the …

Anomaly Based Intrusion Detection System Through Remote Virtual Machine Introspection, Huseyn Huseynov

Dissertations and Theses

Research on identifying malicious applications is an important direction in information security, especially when it comes to detection of evasive malware such as keyloggers, trojans, rootkits and their derivatives. Inspired by a biological immune system and based on negative selection algorithm approach to detect various types of malwares is proposed in this paper.

By deeply studying Linux kernel, understanding links behind different internal system processes, examining, and experimenting with hundreds of various keyloggers we propose a single Artificial Intelligence based solution as a comprehensive protection against wide range of malwares. Developed Intrusion Detection System (IDS) can be deployed in the …

Tutorial - Shodhguru Labs: Knowledge-Infused Artificial Intelligence For Mental Healthcare, Kaushik Roy

Publications

Artificial Intelligence (AI) systems for mental healthcare (MHCare) have been ever-growing after realizing the importance of early interventions for patients with chronic mental health (MH) conditions. Social media (SocMedia) emerged as the go-to platform for supporting patients seeking MHCare. The creation of peer-support groups without social stigma has resulted in patients transitioning from clinical settings to SocMedia supported interactions for quick help. Researchers started exploring SocMedia content in search of cues that showcase correlation or causation between different MH conditions to design better interventional strategies. User-level Classification-based AI systems were designed to leverage diverse SocMedia data from various MH conditions, …