Computer Engineering Commons^™

Recommendation System Using Machine Learning For Fertilizer Prediction, Durga Rajesh Bommireddy

Electronic Theses, Projects, and Dissertations

This project presents the development of a sophisticated machine-learning model aimed at enhancing agricultural productivity by predicting the optimal fertilizer suited to specific crop requirements. Leveraging a diverse set of features including soil color, pH levels, rainfall, temperature, and crop type, our model offers tailored recommendations to farmers. Three powerful algorithms, Support Vector Machines (SVM), Artificial Neural Networks (ANN), and XG-Boost, were implemented to facilitate the prediction process. Through comprehensive experimentation and evaluation, we assessed the performance of each algorithm in accurately predicting the best fertilizer for maximizing crop yield. The project not only contributes to the advancement of machine …

Detection Of Myofascial Trigger Points With Ultrasound Imaging And Machine Learning, Benjamin Formby

All Theses

Myofascial Pain Syndrome (MPS) is a common chronic muscle pain disorder that affects a large portion of the global population, seen in 85-93% of patients in specialty pain clinics [10]. MPS is characterized by hard, palpable nodules caused by a stiffened taut band of muscle fibers. These nodules are referred to as Myofascial Trigger Points (MTrPs) and can be classified by two states: active MTrPs (A-MTrPs) and latent MtrPs (L-MTrPs). Treatment for MPS involves massage therapy, acupuncture, and injections or painkillers. Given the subjectivity of patient pain quantification, MPS can often lead to mistreatment or drug misuse. A deterministic way …

Accelerating Machine Learning Inference For Satellite Component Feature Extraction Using Fpgas., Andrew Ekblad

Theses and Dissertations

Running computer vision algorithms requires complex devices with lots of computing power, these types of devices are not well suited for space deployment. The harsh radiation environment and limited power budgets have hindered the ability of running advanced computer vision algorithms in space. This problem makes running an on-orbit servicing detection algorithm very difficult. This work proposes using a low powered FPGA to accelerate the computer vision algorithms that enable satellite component feature extraction. This work uses AMD/Xilinx’s Zynq SoC and DPU IP to run model inference. Experiments in this work centered around improving model post processing by creating implementations …

Autonomous Shipwreck Detection & Mapping, William Ard

LSU Master's Theses

This thesis presents the development and testing of Bruce, a low-cost hybrid Remote Operated Vehicle (ROV) / Autonomous Underwater Vehicle (AUV) system for the optical survey of marine archaeological sites, as well as a novel sonar image augmentation strategy for semantic segmentation of shipwrecks. This approach takes side-scan sonar and bathymetry data collected using an EdgeTech 2205 AUV sensor integrated with an Harris Iver3, and generates augmented image data to be used for the semantic segmentation of shipwrecks. It is shown that, due to the feature enhancement capabilities of the proposed shipwreck detection strategy, correctly identified areas have a 15% …

User Profiling Through Zero-Permission Sensors And Machine Learning, Ahmed Elhussiny

Theses and Dissertations

With the rise of mobile and pervasive computing, users are often ingesting content on the go. Services are constantly competing for attention in a very crowded field. It is only logical that users would allot their attention to the services that are most likely to adapt to their needs and interests. This matter becomes trivial when users create accounts and explicitly inform the services of their demographics and interests. Unfortunately, due to privacy and security concerns, and due to the fast nature of computing today, users see the registration process as an unnecessary hurdle to bypass, effectively refusing to provide …

Detection Of Crypto-Ransomware Attack Using Deep Learning, Muna Jemal

Master of Science in Computer Science Theses

The number one threat to the digital world is the exponential increase in ransomware attacks. Ransomware is malware that prevents victims from accessing their resources by locking or encrypting the data until a ransom is paid. With individuals and businesses growing dependencies on technology and the Internet, researchers in the cyber security field are looking for different measures to prevent malicious attackers from having a successful campaign. A new ransomware variant is being introduced daily, thus behavior-based analysis of detecting ransomware attacks is more effective than the traditional static analysis. This paper proposes a multi-variant classification to detect ransomware I/O …

Acat 2.0: An Ai Transformer-Based Approach To Predictive Speech Generation, Kairan Quazi

Computer Science and Engineering Senior Theses

While constituting a rare family of diseases that afflicts 268,000 people worldwide, motor neuron diseases carry a high fatality rate with one-third of people dying within a year of diagnosis and 50% of people dying within two years (MND Association, 2022). MNDs rapidly and progressively impair muscle movement, making everyday activities like walking, chewing, and speaking almost impossible. In collaboration with famed physicist Dr. Stephen Hawking, Intel Labs developed an assistive communications platform known as ACAT to simulate speech and facilitate electronic tasks. However, the original ACAT can be slow to use, leading to awkward pauses in conversations. This paper …

Wearable Sensor Gait Analysis For Fall Detection Using Deep Learning Methods, Haben Girmay Yhdego

Electrical & Computer Engineering Theses & Dissertations

World Health Organization (WHO) data show that around 684,000 people die from falls yearly, making it the second-highest mortality rate after traffic accidents [1]. Early detection of falls, followed by pneumatic protection, is one of the most effective means of ensuring the safety of the elderly. In light of the recent widespread adoption of wearable sensors, it has become increasingly critical that fall detection models are developed that can effectively process large and sequential sensor signal data. Several researchers have recently developed fall detection algorithms based on wearable sensor data. However, real-time fall detection remains challenging because of the wide …

Analyzing The Impact Of Automation On Employment In Different Us Regions: A Data-Driven Approach, Thejaas Balasubramanian

Electronic Theses, Projects, and Dissertations

Automation is transforming the US workforce with the increasing prevalence of technologies like robotics, artificial intelligence, and machine learning. As a result, it is essential to understand how this shift will impact the labor market and prepare for its effects. This culminating experience project aimed to examine the influence of computerization on jobs in the United States and answer the following research questions: Q1. What factors affect how likely different jobs will be automated? Q2. What are the possible effects of automation on the US workforce across states and industries? Q3. What are the meaningful predictors of the likelihood of …

Information-Theoretic Model Diagnostics (Infomod), Armin Esmaeilzadeh

UNLV Theses, Dissertations, Professional Papers, and Capstones

Model validation is a critical step in the development, deployment, and governance of machine learning models. During the validation process, the predictive power of a model is measured on unseen datasets with a variety of metrics such as Accuracy and F1-Scores for classification tasks. Although the most used metrics are easy to implement and understand, they are aggregate measures over all the segments of heterogeneous datasets, and therefore, they do not identify the performance variation of a model among different data segments. The lack of insight into how the model performs over segments of unseen datasets has raised significant challenges …

Machine-Learning Approaches For Developing An Autograder For High School-Level Cs-For-All Initiatives, Sirazum Munira Tisha

LSU Doctoral Dissertations

Most existing autograders used for grading programming assignments are based on unit testing, which is tedious to implement for programs with graphical output and does not allow testing for other code aspects, such as programming style or structure. We present a novel autograding approach based on machine learning that can successfully check the quality of coding assignments from a high school-level CS-for-all computational thinking course. For evaluating our autograder, we graded 2,675 samples from five different assignments from the past three years, including open-ended problems from different units of the course curriculum. Our autograder uses features based on lexical analysis …

Real-Time Facial Expression Recognition Using Edge Ai Accelerators, Mark Heath Smith

Theses and Dissertations

Facial expression recognition is a popular and challenging area of research in machine learning applications. Facial expressions are critical to human communication and allow us to convey complex thoughts and emotions beyond spoken language. The complexity of facial expressions creates a difficult problem for computer vision systems, especially edge computing systems. Current Deep Learning (DL) methods rely on large-scale Convolutional Neural Networks (CNN) which require millions of floating point operations (FLOPS) to accomplish similar image classification tasks. However, on edge and IoT devices, large-scale convolutional models can cause problems due to memory and power limitations. The intent of this work …

Metabolomic Differentiation Of Tumor Core And Edge In Glioma., Mary E. Baxter

Electronic Theses and Dissertations

Glioma is one of the most aggressive forms of brain cancer. It has been shown that the microenvironments differ significantly between the core and edge regions of glioma tumors. This study obtained metabolomic profiles of glioma core and edge regions using paired glioma core and edge tissue samples from 27 human patients. Data was acquired by performing liquid-liquid metabolite extraction and 2DLC-MS/MS on the tissue samples. In addition, a boosted generalized linear machine learning model was employed to predict the metabolomic profiles associated with O-6-methylguanine-DNA methyltransferase (MGMT) promoter methylation.

A panel of 66 metabolites was found to be statistically significant …

Design, Determination, And Evaluation Of Gender-Based Bias Mitigation Techniques For Music Recommender Systems, Sunny Shrestha

Electronic Theses and Dissertations

The majority of smartphone users engage with a recommender system on a daily basis. Many rely on these recommendations to make their next purchase, download the next game, listen to the new music or find the next healthcare provider. Although there are plenty of evidence backed research that demonstrates presence of gender bias in Machine Learning (ML) models like recommender systems, the issue is viewed as a frivolous cause that doesn’t merit much action. However, gender bias poses to effect more than half of the population as by default ML systems are designed to cater to a cisgender man. This …

Machine-Learning-Powered Cyber-Physical Systems, Enrico Casella

Theses and Dissertations--Computer Science

In the last few years, we witnessed the revolution of the Internet of Things (IoT) paradigm and the consequent growth of Cyber-Physical Systems (CPSs). IoT devices, which include a plethora of smart interconnected sensors, actuators, and microcontrollers, have the ability to sense physical phenomena occurring in an environment and provide copious amounts of heterogeneous data about the functioning of a system. As a consequence, the large amounts of generated data represent an opportunity to adopt artificial intelligence and machine learning techniques that can be used to make informed decisions aimed at the optimization of such systems, thus enabling a variety …

Adversarial Training Of Deep Neural Networks, Anabetsy Termini

CCE Theses and Dissertations

Deep neural networks used for image classification are highly susceptible to adversarial attacks. The de facto method to increase adversarial robustness is to train neural networks with a mixture of adversarial images and unperturbed images. However, this method leads to robust overfitting, where the network primarily learns to recognize one specific type of attack used to generate the images while remaining vulnerable to others after training. In this dissertation, we performed a rigorous study to understand whether combinations of state of the art data augmentation methods with Stochastic Weight Averaging improve adversarial robustness and diminish adversarial overfitting across a wide …

Reinforcement Learning For Stock Option Trading, James Garza

ICT

Reinforcement learning has recently seen an increase in popularity due to its ability to learn from past experience and its capability of adapting quickly and effectively to new market conditions. This research will focus on reinforcement learning and its importance in trading stock options. Option traders can trade options with one of two option expirations: American or European style. This research will base the analysis on the American expiration style, considered more challenging in trading than the European expiration style. This could lead to the possibility of improving the current trading techniques. In addition, this research aims to understand the …

Evaluation Of Different Machine Learning, Deep Learning And Text Processing Techniques For Hate Speech Detection, Nabil Shawkat

MSU Graduate Theses

Social media has become a domain that involves a lot of hate speech. Some users feel entitled to engage in abusive conversations by sending abusive messages, tweets, or photos to other users. It is critical to detect hate speech and prevent innocent users from becoming victims. In this study, I explore the effectiveness and performance of various machine learning methods employing text processing techniques to create a robust system for hate speech identification. I assess the performance of Naïve Bayes, Support Vector Machines, Decision Trees, Random Forests, Logistic Regression, and K Nearest Neighbors using three distinct datasets sourced from social …

Estimating Air Pollution Levels Using Machine Learning, Srujay Rao Devaraneni

Master's Projects

Air pollution has emerged as a substantial concern, especially in developing countries worldwide. An important aspect of this issue is the presence of PM2.5. Air pollutants with a diameter of 2.5 or less micrometers are known as PM2.5. Due to their size, these particles are a serious health risk and can quickly infiltrate the lungs, leading to a variety of health problems. Due to growing concerns about air pollution, technology like automatic air quality measurement can offer beneficial assistance for both personal and business decisions. This research suggests an ensemble machine learning model that can efficiently replace the standard air …

Cyber Resilience Analytics For Cyber-Physical Systems, Md Ariful Haque

Electrical & Computer Engineering Theses & Dissertations

Cyber-physical systems (CPSs) are complex systems that evolve from the integrations of components dealing with physical processes and real-time computations, along with networking. CPSs often incorporate approaches merging from different scientific fields such as embedded systems, control systems, operational technology, information technology systems (ITS), and cybernetics. Today critical infrastructures (CIs) (e.g., energy systems, electric grids, etc.) and other CPSs (e.g., manufacturing industries, autonomous transportation systems, etc.) are experiencing challenges in dealing with cyberattacks. Major cybersecurity concerns are rising around CPSs because of their ever-growing use of information technology based automation. Often the security concerns are limited to probability-based possible attack …

Iot In Smart Communities, Technologies And Applications., Muhammad Zaigham Abbas Shah Syed

Electronic Theses and Dissertations

Internet of Things is a system that integrates different devices and technologies, removing the necessity of human intervention. This enables the capacity of having smart (or smarter) cities around the world. By hosting different technologies and allowing interactions between them, the internet of things has spearheaded the development of smart city systems for sustainable living, increased comfort and productivity for citizens. The Internet of Things (IoT) for Smart Cities has many different domains and draws upon various underlying systems for its operation, in this work, we provide a holistic coverage of the Internet of Things in Smart Cities by discussing …

A Framework For Stable Robot-Environment Interaction Based On The Generalized Scattering Transformation, Kanstantsin Pachkouski

Electronic Thesis and Dissertation Repository

This thesis deals with development and experimental evaluation of control algorithms for stabilization of robot-environment interaction based on the conic systems formalism and scattering transformation techniques. A framework for stable robot-environment interaction is presented and evaluated on a real physical system. The proposed algorithm fundamentally generalizes the conventional passivity-based approaches to the coupled stability problem. In particular, it allows for stabilization of not necessarily passive robot-environment interaction. The framework is based on the recently developed non-planar conic systems formalism and generalized scattering-based stabilization methods. A comprehensive theoretical background on the scattering transformation techniques, planar and non-planar conic systems is presented. …

Machine Learning And Artificial Intelligence Methods For Cybersecurity Data Within The Aviation Ecosystem, Anna Baron Garcia

Doctoral Dissertations and Master's Theses

Aviation cybersecurity research has proven to be a complex topic due to the intricate nature of the aviation ecosystem. Over the last two decades, research has been centered on isolated modules of the entire aviation systems, and it has lacked the state-of-the-art tools (e.g. ML/AI methods) that other cybersecurity disciplines have leveraged in their fields. Security research in aviation in the last two decades has mainly focused on: (i) reverse engineering avionics and software certification; (ii) communications due to the rising new technologies of Software Defined Radios (SDRs); (iii) networking cybersecurity concerns such as the inter and intra connections of …

Deception Detection Across Domains, Languages And Modalities, Subhadarshi Panda

Dissertations, Theses, and Capstone Projects

With the increase of deception and misinformation especially in social media, it has become crucial to develop machine learning methods to automatically identify deception. In this dissertation, we identify key challenges underlying text-based deception detection in a cross-domain setting, where we do not have training data in the target domain. We analyze the differences between domains and as a result develop methods to improve cross-domain deception detection. We additionally develop approaches that take advantage of cross-lingual properties to support deception detection across languages. This involves the usage of either multilingual NLP models or translation models. Finally, to better understand multi-modal …

Analyzing Microarchitectural Residue In Various Privilege Strata To Identify Computing Tasks, Tor J. Langehaug

Theses and Dissertations

Modern multi-tasking computer systems run numerous applications simultaneously. These applications must share hardware resources including the Central Processing Unit (CPU) and memory while maximizing each application’s performance. Tasks executing in this shared environment leave residue which should not reveal information. This dissertation applies machine learning and statistical analysis to evaluate task residue as footprints which can be correlated to identify tasks. The concept of privilege strata, drawn from an analogy with physical geology, organizes the investigation into the User, Operating System, and Hardware privilege strata. In the User Stratum, an adversary perspective is taken to build an interrogator program that …

Artificial Neural Networks And Their Applications To Intelligent Fault Diagnosis Of Power Transmission Lines, Fatemeh Mohammadi Shakiba

Dissertations

Over the past thirty years, the idea of computing based on models inspired by human brains and biological neural networks emerged. Artificial neural networks play an important role in the field of machine learning and hold the key to the success of performing many intelligent tasks by machines. They are used in various applications such as pattern recognition, data classification, stock market prediction, aerospace, weather forecasting, control systems, intelligent automation, robotics, and healthcare. Their architectures generally consist of an input layer, multiple hidden layers, and one output layer. They can be implemented on software or hardware. Nowadays, various structures with …

Efficient Discovery And Utilization Of Radio Information In Ultra-Dense Heterogeneous 3d Wireless Networks, Mattaka Gamage Samantha Sriyananda

Electronic Thesis and Dissertation Repository

Emergence of new applications, industrial automation and the explosive boost of smart concepts have led to an environment with rapidly increasing device densification and service diversification. This revolutionary upward trend has led the upcoming 6th-Generation (6G) and beyond communication systems to be globally available communication, computing and intelligent systems seamlessly connecting devices, services and infrastructure facilities. In this kind of environment, scarcity of radio resources would be upshot to an unimaginably high level compelling them to be very efficiently utilized. In this case, timely action is taken to deviate from approximate site-specific 2-Dimensional (2D) network concepts in radio resource utilization …

Data Parallel Frameworks For Training Machine Learning Models, Guoyi Zhao

Doctoral Dissertations

Machine learning is the study of computer algorithms that focuses on analyzing and interpreting patterns and structures in data. It has been successfully applied to many areas in computer science and achieved state-of-the-art results to enable learning, reasoning, and decision-making without human interactions. This research aims to develop innovated data parallel frameworks to accommodate the computing resources to parallelize different machine learning and deep learning algorithms and speed up the training. To achieve that, we explore three interesting frameworks in this dissertation: (1) Sync-on-the-fly framework for gradient descent algorithms on transient resources; (2) Asynchronous Proactive Data Parallel framework for both …

Models And Machine Learning Techniques For Improving The Planning And Operation Of Electricity Systems In Developing Regions, Santiago Correa Cardona

Doctoral Dissertations

The enormous innovation in computational intelligence has disrupted the traditional ways we solve the main problems of our society and allowed us to make more data-informed decisions. Energy systems and the ways we deliver electricity are not exceptions to this trend: cheap and pervasive sensing systems and new communication technologies have enabled the collection of large amounts of data that are being used to monitor and predict in real-time the behavior of this infrastructure. Bringing intelligence to the power grid creates many opportunities to integrate new renewable energy sources more efficiently, facilitate grid planning and expansion, improve reliability, optimize electricity …

Local Learning Algorithms For Stochastic Spiking Neural Networks, Bleema Rosenfeld

Dissertations

This dissertation focuses on the development of machine learning algorithms for spiking neural networks, with an emphasis on local three-factor learning rules that are in keeping with the constraints imposed by current neuromorphic hardware. Spiking neural networks (SNNs) are an alternative to artificial neural networks (ANNs) that follow a similar graphical structure but use a processing paradigm more closely modeled after the biological brain in an effort to harness its low power processing capability. SNNs use an event based processing scheme which leads to significant power savings when implemented in dedicated neuromorphic hardware such as Intel’s Loihi chip.

This work …