Doctor of Philosophy (PhD), Wright State University, 2020, Computer Science and Engineering PhD

As the population of older individuals increases worldwide, the number of people with cardiovascular issues and diseases is also increasing. The rate at which individuals in the United States of America and worldwide that succumb to Cardiovascular Disease (CVD) is rising as well. Approximately 2,303 Americans die to some form of CVD per day according to the American Heart Association. Furthermore, the Center for Disease Control and Prevention states that 647,000 Americans die yearly due to some form of CVD, which equates to one person every 37 seconds. Finally, the World Health Organization reports that the number one cause of death globally is from CVD in the form of either myocardial infarctions or strokes. The primary ways of assisting individuals affected with CVD are from either improved treatments, monitoring research, or primary and secondary prevention measures. In the form of cardiovascular structural monitoring, there are multiple ways of viewing the human heart. That is, Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET), Computed Tomography (CT), and Ultrasonography are the four fundamental imaging techniques. Though, continuous monitoring with these imaging techniques is far from currently possible. Large financial cost and size (MRI), radiation exposure (PET and CT), or necessary physician assistance (Ultrasonography) are the current primary problems. Though, of the four methodologies, Ultrasonography allows for multiple configurations, is the least expensive, and has no detrimental side effects to the patient. Therefore, in an effort to improve continuous monitoring capabilities for cardiovascular health, we design a novel wearable ultrasound vest to create a near 3D model of the heart in real-time. Specifically, we provide a structural modeling approach specific to this system's design via a Stereo Vision 3D modeling algorithm. Similarly, we introduce multiple Stochastic Petri Net (SPN) models of the heart for future functiona (open full item for complete abstract)

Master of Science in Mechanical Engineering, Cleveland State University, 2023, Washkewicz College of Engineering

In structural design, the distribution of wind-induced pressure exerted on structures is crucial. The pressure distribution for a particular building is often determined by scale model tests in boundary layer wind tunnels (BLWTs). For all combinations of interesting building shapes and wind factors, experiments with BLWTs must be done. Resource or physical testing restrictions may limit the acquisition of needed data because this procedure might be time- and resource-intensive. Finding a trustworthy method to cyber-enhance data-collecting operations in BLWTs is therefore sought. This research analyzes how machine learning approaches may improve traditional BLWT modeling to increase the information obtained from tests while proportionally lowering the work needed to complete them. The more general question centers on how a machine learning-enhanced method ultimately leads to approaches that learn as data are collected and subsequently optimize the execution of experiments to shorten the time needed to complete user-specified objectives. 3 Different Machine Learning models, namely, Support vector regressors, Gradient Boosting regressors, and Feed Forward Neural networks were used to predict the surface Averaged Mean pressure coefficients cp on Isolated high-rise buildings. The models were trained to predict average cp for missing angles and also used to train for varying dimensions. Both global and local approaches to training the models were used and compared. The Tokyo Polytechnic University's Aerodynamic Database for Isolated High-rise buildings was used to train all the models in this study. Local and global prediction approaches were used for the DNN and GBRT models and no considerable difference has been found between them. The DNN model showed the best accuracy with (R2 > 99%, MSE < 1.5%) among the used models for both missing angles and missing dimensions, and the other two models also showed high accuracy with (R2 > 97%, MSE < 4%).

PhD, University of Cincinnati, 2020, Engineering and Applied Science: Computer Science and Engineering

Musicians who perform with electronic synthesizers often adjust synthesis parameters during live performance to achieve more expressive sounds. Enabling the performer to teach a computer to make these adjustments automatically during performance frees the performer from this responsibility, while maintaining an expressive sound in line with the performer's musical vision. We have created a machine learning system called Larasynth that can be trained by a musician to make these parameter adjustments in real-time during live performances. Larasynth is trained using examples in the form of MIDI files created by the user. Learning is achieved using Long Short-Term Memory (LSTM) recurrent neural networks. To accomplish this, we have devised a set of features which capture the state of the synthesizer controller at regular intervals and are used to make regular predictions of parameter values using an LSTM network. To achieve sufficient generalization during training, transformations are applied to the training data set before each training epoch to simulate variations that may occur during performance. We have also created a new lightweight LSTM library suitable for small networks under real-time constraints. In this thesis we present details behind Larasynth's implementation and use, and experiments that were performed to demonstrate Larasynth's ability to learn behaviors based on different musical situations.

Master of Science, Miami University, 2024, Computer Science

Causal maps are graphical models that are well-understood in small scales. When created through a participatory modeling process, they become a strong asset in decision making. Furthermore, those who participate in the modeling process may seek to understand the problem from various perspectives. However, as causal maps increase in size, the information they contain becomes clouded, which results in the map being unusable. In this thesis, we transform causal maps into various mediums to improve the usability and accessibility of large causal models; our proposed algorithms can also be applied to small-scale causal maps. In particular, we transform causal maps into meaningful paragraphs using GPT and network traversal algorithms to attain full-coverage of the map. Then, we compare automatic text summarization models with graph reduction algorithms to reduce the amount of text to a more approachable size. Finally, we combine our algorithms into a visual analytics environment to provide details-on-demand for the user by displaying the summarized text, and interacting with summaries to display the detailed text, causal map, and even generate images in an appropriate manner. We hope this research provides more tools for decision-makers and allows modelers to give back to participants the final result of their work.

Doctor of Philosophy, University of Toledo, 2023, Engineering

Intrusion detection systems (IDSs) have evolved significantly since the first time they were introduced and have become one of the most essential defenses in a network. With the advent of machine learning (ML), several improvements and enhancements have been made to the capabilities of traditional IDSs. However, every advancement brings with it a range of new challenges and threats. Although ML expanded the abilities of IDSs, there are certain problems that need to be investigated and this research attempts to highlight and address some of the existing problems. One of the problems is that a major portion of the research progress involving IDSs has been achieved using decades-old datasets. This work aims to study recently published research IDS datasets and analyze the performances of ML-based IDS models when trained with such datasets. Another problem focused on in this research is the vulnerabilities of ML models to adversarial environments. The work identifies that a majority of research progress achieved relevant to ML-powered IDSs is toward the direction of improving the performance efficiency of the IDS models under normal settings, i.e., toward optimizing the detection rates with genuine data. Relatively little progress is made towards making the IDS models robust to adversarial environments and deceptive inputs that target the IDSs rather than the premises (networks or hosts) guarded by them. This is a serious concern in cybersecurity which needs more investigation and problem-solving. In regard to this concern, various types of adversarial attacks are studied, and the behaviors of IDSs in certain white-box adversarial settings are assessed when the models are trained with modern research datasets. The study extends further by developing a defense mechanism against a white-box evasion attack which is considered to be very powerful for image-classication-based models. As the IDS models deployed in real-world environments are more susceptible to black-bo (open full item for complete abstract)

Master of Computing and Information Systems, Youngstown State University, 2023, Department of Computer Science and Information Systems

The performance of machine learning (ML) methods, including deep learning, for classification and regression tasks applied to tabular datasets is sensitive to hyperparameters values. Therefore, finding the optimal values of these hyperparameters is integral to improving the prediction accuracy of a machine learning algorithm and the model selection. However, manually searching for the best configuration is a tedious task, and many AutoML (automated machine learning) frameworks have been proposed recently to help practitioners solve this problem. Hyperparameters are the values or configurations used to control the algorithm's behavior while building the model. Hyperparameter optimization is the guided process of finding the best combination of hyperparameters that delivers the best performance on the data and task at hand in a reasonable amount of time. In this work, the performance of two frequently used AutoML hyperparameter optimization frameworks, Optuna and HyperOpt, are compared on popular OpenML tabular datasets to identify the best framework for tabular data. The results of the experiments show that the performance score of Optuna is better than that of HyperOpt, while HyperOpt is the fastest for hyperparameter optimization.

Doctor of Philosophy (PhD), Wright State University, 2019, Computer Science and Engineering PhD

With massive data collections and needs for building powerful predictive models, data owners may choose to outsource storage and expensive machine learning computations to public cloud providers (Cloud). Data owners may choose cloud outsourcing due to the lack of in-house storage and computation resources or the expertise of building models. Similarly, users, who subscribe to specialized services such as movie streaming and social networking, voluntarily upload their data to the service providers' site for storage, analytics, and better services. The service provider, in turn, may also choose to benefit from ubiquitous cloud computing. However, outsourcing to a public cloud provider may raise privacy concerns when it comes to sensitive personal or corporate data. Cloud and its associates may misuse sensitive data and models internally. Moreover, if Cloud's resources are poorly secured, the confidential data and models become vulnerable to privacy attacks by external adversaries. Such potential threats are out of the control of the data owners or general users. One way to address these privacy concerns is through confidential machine learning (CML). CML frameworks enable data owners to protect their data with encryption or other data protection mechanisms before outsourcing and facilitates Cloud training the predictive models with the protected data. Existing cryptographic and privacy-protection methods cannot be immediately lead to the CML frameworks for outsourcing. Although theoretically sound, a naive adaptation of fully homomorphic encryption (FHE) and garbled circuits (GC) that enable evaluation of any arbitrary function in a privacy-preserving manner is impractically expensive. Differential privacy (DP), on the other hand, cannot specifically address the confidentiality issues and threat model in the outsourced setting as DP generally aims to protect an individual's participation in a dataset from an adversarial model consumer. Moreover, a practical CM (open full item for complete abstract)

Master of Science (MS), Ohio University, 2019, Electrical Engineering & Computer Science (Engineering and Technology)

Network-on-chip (NoC) continues to be the preferred communication fabric in multicore and manycore architectures as the NoC seamlessly blends the resource efficiency of the bus with the parallelization of the crossbar. However, without adaptable power management the NoC suffers from excessive static power consumption at higher core counts. Static power consumption will increase proportionally as the size of the NoC increases to accommodate higher core counts in the future. NoC also suffers from excessive dynamic energy as traffic loads fluctuate throughout the execution of an application. Power-gating (PG) and Dynamic Voltage and Frequency Scaling (DVFS) are two highly effective techniques proposed in literature to reduce static power and dynamic energy in the NoC respectively. DVFS is a popular technique that allows dynamic energy to be saved but may potentially lead to a loss in throughput. Power-gating allows static power to be saved but can introduce new problems incurred by isolating network routers. Further complications include the introduction of long wake-up delays and break-even times. However, both DVFS and power-gating are critical for realizing energy proportional computing as core counts race into the hundreds for multi-cores. In this thesis, we propose two distinct but related techniques that enable energy proportional computing for NoC. We first propose LEAD - Learning-enabled Energy Aware Dynamic voltage/frequency scaling for NoC architectures. LEAD applies machine learning (ML) techniques to enable improvements in both energy and performance with reduced overhead cost. This allows LEAD to enact a proactive energy management strategy that relies on an offline trained regression model while also providing a wide variety of voltage/frequency (VF) pairs. In this work, we will refer to various VF pairs as modes. LEAD groups each router and the router's outgoing links locally into the same V/F domain allowing energy management at a finer granularity wit (open full item for complete abstract)

Master of Science (M.S.), University of Dayton, 2017, Electrical and Computer Engineering

Division of focal plane imaging polarimeters have the distinct advantage of being capable of obtaining temporally synchronized intensity measurements across a scene; however, they sacrifice spatial resolution in doing so due to their spatially modulated arrangement of the pixel-to-pixel polarizers and often result in aliased imagery. This shortcoming is often overcome through advanced demosaicing strategies that minimize the effects of false polarization while preserving as much high frequency content as possible. While these techniques can yield acceptable imagery, they tend to be computationally complex and the spatial resolution is often reduced below the native capabilities of the focal plane array. This thesis proposes a super-resolution method based upon a previously trained regularized extreme learning regression (RELR) that aims to recover missing high-frequency content beyond the spatial resolution of the sensor and correct low-frequency content, while maintaining good contrast between polarized and unpolarized artifacts presented in this thesis. For each of the four channels of the image, the modified RELR predicts the missing high-frequency and lowfrequency components that result from upsampling. These missing high-frequency components are then refined with a high pass filter and added back to the upsampled image. This provides a fast and computationally simple way of recovering missing high frequency components that are lost with current state-of-the-art demosaicing algorithms. The modified RELR provides better results than other visible band single-image super-resolution techniques and is much faster, thus making it applicable to real-time applications. The obtained results demonstrate the effectiveness of the modified RELR for a truth scenario (no aliasing resulting from undersampling) and a derived microgrid scenario (aliasing resulting from undersampling). The truth scenario shows that the modified RELR performs exceptionally better than other algori (open full item for complete abstract)

Master of Science (MS), Wright State University, 2014, Computer Science

Deep learning is an emerging area in machine learning that exploits multi-layered neural networks to extract invariant relationships from large data sets. Deep learning uses layers of non-linear transformations to represent data in abstract and discrete forms. Several different architectures have been developed over the past few years specifically to process images including the Convolutional Restricted Boltzmann Machine. The Boltzmann Machine is trained using contrastive divergence, a depth-first gradient based training algorithm. Gradient based training methods have no guarantee of reaching an optimal solution and tend to search a limited region of the solution space. In this thesis, we present an alternative method for synthesizing deep networks using evolutionary algorithms. This is a breadth-first stochastic search process that utilizes reconstruction error along with additional properties to encourage evolution of unique features. Using this technique, potentially a larger region of the solution space is explored allowing identification of different types of solutions using less training data. The process of developing this method is discussed along with its potential as a viable replacement to contrastive divergence.

PhD, University of Cincinnati, 2006, Engineering : Computer Science and Engineering

The objective of supervised learning is to estimate unknowns based on labeled training samples. For example, one may have aerial spectrographic readings for a large field planted in corn. Based on spectrographic observation, one would like to determine whether the plants in part of the field are weeds or corn. Since the unknown to be estimated is categorical or discrete, the problem is one of classification. If the unknown to be estimated is continuous, the problem is one of regression or numerical estimation. For example, one may have samples of ozone levels from certain points in the atmosphere. Based on those samples, one would like to estimate the ozone level at other points in the atmosphere. Algorithms for supervised learning are useful tools in many areas of agriculture, medicine, and engineering, including estimation of proper levels of nutrients for cows, prediction of malignant cancer, document analysis, and speech recognition. A few general references on supervised learning include [1], [2], [3], and [4]. Two recent reviews of the supervised learning literature are [5] and [6]. In general, univariate learning tree algorithms have been particularly successful in classification problems, but they can suffer from several fundamental difficulties, e.g., "a representational limitation of univariate decision trees: the orthogonal splits to the feature's axis of the sample space that univariate tree rely on" [8] and overfit [17]. In this thesis, we present a classification procedure for supervised classification that consists of a new univariate decision tree algorithm (Margin Algorithm) and two other related algorithms (Hyperplane and Box Algorithms). The full algorithm overcomes all of the usual limitations of univariate decision trees and is called the Paired Planes Classification Procedure. The Paired Planes Classification Procedure is compared to Support Vector Machines, K-Nearest Neighbors, and decision trees. The Hyperplane Algorithm allows direct user in (open full item for complete abstract)

Master of Science (MS), Ohio University, 2010, Computer Science (Engineering and Technology)

A software pipeline has been developed to aide in SNP microarray analysis in case/control genome-wide association (GWA) studies. The pipeline uses data taken from previous GWA studies from the NCBI Gene Expression Omnibus website and analyzes the SNP information from these studies to reate predictive classifiers. These classifiers attempt to accurately predict if individuals have a particular phenotype based on their genotypes. Two dierent methods were used to create these predictive models. One makes use of a popular machine learning technique, support vector machines, and the other is a simpler method that uses genotype total dierences between cases and controls. One major benefit of using the support vector machine method is the ability to integrate and consider many combinations of SNPs in a computationally inexpensive manner. The GSE13117 dataset, which consists of mentally retarded children and their parents, and the GSE9222 dataset, which consists of autistic patients and their parents, were used to test the software pipeline. A Bayesian confidence interval was used in reporting classifier performance in addition to 5-repeated 10-fold cross-validation (5r-10cv). For the GSE9222 data set, the top performing model achieved a balanced accuracy of 70.8% and a normal accuracy of 71.7% using 5r-10cv. The model that had the distribution with the highest upper bound had a 95% confidence balanced accuracy interval of 62.1% to 75.3%. For the GSE13117 data set, the top performing classifier achieved a balanced accuracy of 56.2% and a normal accuracy of 65.7% using 5r-10cv. The model that had the distribution with the highest upper bound for the GSE13117 data set had a 95% confidence balanced accuracy interval of 49.6% to 68.3%. Such classifiers will eventually lead to new insights into disease and allow for simpler and more accurate diagnoses in the future. The work in this thesis contains ideas and work that is a continuation of previously published abstracts and post (open full item for complete abstract)

Master of Science, Miami University, 2024, Electrical and Computer Engineering

In this thesis, a thorough investigation into the optimization of user connectivity in ad hoc communication networks using robust policy creation and intelligent UAV location in stochastic environments is presented. In order to handle the dynamic and decentralized character of ad hoc networks, we identified the optimal UAV positions by applying a multi-agent deep Q-learning technique. To train stochastic environment-adaptive policies, a novel simple algorithm was devised with an emphasis on the usefulness of these policies under different scenarios. Through an empirical investigation, the study offered information on the generalizability and adaptability of learnt behaviors by examining how well policies based on one distribution of settings performed when applied to different, unseen distributions. In this thesis, we also explored the resilience of UAV networks against jamming attempts and propose a method for unaffected UAVs to self-adjust their placements. This approach ensured optimal user coverage even in adversarial situations. By demonstrating the potential of machine learning techniques to maximize network performance and enhance user connectivity in the face of environmental uncertainties and security risks, these contributions will collectively advance the field of UAV-assisted communication.

Doctor of Philosophy (PhD), Ohio University, 2024, Civil Engineering (Engineering and Technology)

Colloidal aggregation is a critical phenomenon influencing various environmental processes. However, limited research has been conducted on the aggregation of particles with heterogeneous physical and chemical properties, which are more representative of practical environmental systems than homogeneous particles. The central hypothesis of this dissertation is that primary particle size polydispersity along with chemical and material heterogeneity of primary particles exert non-trivial effects on the aggregate growth rate and the fractal dimensions of aggregates. In this dissertation, the aggregation and stability of heterogeneous nano- and sub-micrometer particles in aqueous systems were investigated using in situ microscopy and image analysis. Initially, the study examined the growth kinetics and structures of aggregates formed by polystyrene microplastics in mono- and bidisperse systems. Findings indicated that while the primary particle size distribution did not affect the scaling behavior of aggregate growth, it delayed the onset of rapid aggregation. Structural analysis revealed a power law dependence of the aggregate fractal dimension in both mono- and bidisperse systems, with mean fractal dimensions consistent with aggregates from diffusion-limited cluster aggregation. The results also suggested that aggregate fractal dimension was insensitive to shape anisotropy. The dissertation further explored the structure of DLCA aggregates in heterogeneous systems composed of particles with varying sizes, surface charges, and material compositions. The fractal dimensions of DLCA aggregates in these heterogeneous particle systems were similar, ranging from 1.6 to 1.7, and consistent with theoretical predictions and experimental evidence for homogeneous DLCA aggregates. This confirmed the universality of aggregate structures in the DLCA regime, regardless of particle composition. Additionally, a scaling relationship was demonstrated between aggregat (open full item for complete abstract)

Master of Science in Computer Engineering, University of Dayton, 2024, Electrical and Computer Engineering

Single-image super-resolution (SISR) is the task of increasing an image's resolution using one lower resolution image. This task has been used in many areas of life to capture finer details in medical imagery, images with distant objects, and compressed images. Compressing images can save computational resources and bandwidth. Deep Learning (DL) techniques for image compression and SISR have become abundant as such methods have yielded promising results, such as in the Convolutional Neural Network for Enhanced Compression Techniques (CONNECT) compression framework [1] [2] and SwinIR [3], the multi-scale attention network [4], and the Real-ESRGAN [5] super-resolution models. In this thesis, these super-resolution models are to be analyzed and compared with each other using previous work and direct testing on the Set14 dataset with one being selected to be used on the backend of CONNECT as an alternative compression framework. This framework could yield higher compression ratios while maintaining or improving reconstructed image quality. This thesis attempts to improve the existing CONNECT compression framework by analyzing and selecting a DL-based super-resolution model to reconstruct the compressed images after they have been fed through CONNECT. Varying compression methods are then compared using widely used image quality metrics and the compression ratio metric.

PhD, University of Cincinnati, 2024, Engineering and Applied Science: Computer Science and Engineering

The field of medicine has witnessed remarkable progress in recent years, largely owing to the technological advancements in machine learning and deep learning frameworks. The healthcare industry has been a significant contributor to this massive influx of data, generating approximately 30% of the world's data volume. While data mining has been a crucial tool for discovering hidden patterns from data and to extract valuable insights. Effective feature learning, on the other hand, plays an important role in the performance of machine learning models in attaining increased predictive accuracies and learning efficiencies. This research aims to understand and explore the feature selection techniques on both clinical data and medical image analysis, and to attain comprehensive insights into image understanding (IU) by focusing on the segmentation methods for both object recognition and scene classification. The first part of this research studies two feature selection approaches namely, the principal component analysis (PCA) and particle swarm optimization (PSO) on the clinical data using Wisconsin Diagnostic Breast Cancer (WDBC) dataset, to study and extract the top features and evaluate the predictive performances across the five of the most widely used supervised classification algorithms. It is inferred that the study yields significant insights into the effectiveness and efficiency of various classification algorithms for predicting breast cancer type. In the context of PCA, it is imperative to have a good understanding of how features may have a positive/negative impact on the PCs. The study emphasizes the critical role of feature selection in enhancing classification accuracy. The second part of this research delves into IU, as it plays a pivotal role in various computer vision tasks, such as extraction of essential features from images, object detection, and segmentation. At a higher level of granularity, both semantic and instance segmentation are (open full item for complete abstract)

Doctor of Philosophy (Ph.D.), Bowling Green State University, 2024, Statistics

Deep learning, neural network, has been penetrating into almost every corner of data analyses. With advantages on computing power and speed, adding more layers in a neural network analysis becomes a common practice to improve the prediction accuracy. However, over depleting information in the training dataset may consequently carry data noises into the learning process of neural network and result in over-fitting errors. Neural Network has been used to predict the future time series data. It had been claimed by several authors that the Neural Network (Recurrent Neural Network) can predict the time series data, although time series models have also been used to predict the future time series data. This dissertation is thus motivated to investigate the prediction performances of neural networks versus the conventional inference method of time series analysis. After introducing basic concepts and theoretical background of neural network and time series prediction in Chapter 1, Chapter 2 analyzes fundamental structure of time series, along with estimation, hypothesis testing, and prediction methods. Chapter 3 discusses details of computing algorithms and procedures in neural network with theoretical adjustment for time series prediction. In conjunction with terminologies and methodologies in the previous chapters, Chapter 4 directly compares the prediction results of neural networks and conventional time series for the squared error function. In terms of methodology assessment, the evaluation criterion plays a critical role. The performance of the existing neural network models for time series predictions has been observed. It has been experimentally observed that the time series predictions by time series models are better compared to the neural network models both computationally and theoretically. The conditions for the better performances of the Time Series Models over the Neural Network Models have been discovered. Theorems have also been pro (open full item for complete abstract)

Doctor of Philosophy in Engineering, Cleveland State University, 2024, Washkewicz College of Engineering

Leveraging electronic health record data requires sophisticated methods that can optimally process this information to improve clinical decision-making. Artificial Intelligence (AI) promises to process healthcare data faster, for lower costs, and more accurately than conventional processes. Deep learning applied to longitudinal electronic health records (EHR) holds promise for disease prediction, but a systematic methods comparison has yet to be reported. Despite the promises of this technology, challenges remain in the current approaches to predicting a disease. There remains an unmet need for developing a gold-standard disease prediction framework for EHR data that can be reliably applied across many diseases. This research proposes a generalized deep learning approach that is amenable to predicting a vast number of diseases by integrating multiple streams of longitudinal clinical data and genomic features to maximize the predictive power over a broad spectrum of diseases. I provide empirical validation of the proposed solution using data from multiple datasets; including comprehensive simulated datasets and a real-world EHR datasets. The ultimate goal of this research is to develop a generalized deep learning approach that is amenable to predicting a vast number of diseases using longitudinal clinical data from the EHR.

Master of Science in Computer Engineering (MSCE), Wright State University, 2024, Computer Engineering

Hardware components are becoming prone to threats with increasing technological advances. Malicious modifications to such components are increasing and are known as hardware Trojans. Traditional approaches rely on functional assessments and are not sufficient to detect such malicious actions of Trojans. Machine learning (ML) assisted techniques play a vital role in the overall detection and improvement of Trojan. Our novel approach using various ML models brings an improvement in hardware Trojan identification with power signal side channel analysis. This study brings a paradigm shift in the improvement of Trojan detection in integrated circuits (ICs). In addition to this, our further analysis towards hardware authentication extends towards PUFs (Physical Unclonable Functions). Arbiter PUFs were chosen for this purpose. These are also Vulnerable towards ML attacks. Advanced ML assisted techniques predict the responses and perform attacks which leads to the integrity of PUFs. Our study helps improve ML-assisted hardware authentication for ML attacks. In addition, our study also focused on the defense part with the addition of noise and applying the same attack ML-assisted model. Detection of Trojan in hardware components is achieved by implementing machine learning techniques. For this Purpose, several Machine learning models were chosen. Among them, Random Forest classifier (RFC) and Deep neural network shows the highest accuracy. This analysis plays a vital role in the security aspect of all hardware components and sets a benchmark for the overall security aspects of hardware. Feature extraction process plays major role for the improvement of accuracy and reliability of hardware Trojan classification. Overall, this study brings significant improvement in the field of overall hardware security. Our study shows that RFC performs best in hardware classification with an average of 98. 33% precision of all chips, and deep learning techniques give 93. 16% prec (open full item for complete abstract)

Master of Computing and Information Systems, Youngstown State University, 2024, Department of Computer Science and Information Systems

The field of coding education is rapidly evolving, with emerging technologies playing a pivotal role in transforming traditional learning methodologies. This thesis introduces Dr. Lego, an innovative framework designed to revolutionize the assessment and understanding of block-based coding through the integration of sophisticated deep learning models. Dr. Lego combines cutting-edge technologies such as MobileNetV3 (Howard, 2019), for visual recognition and BERT (Devlin et al., 2018), and XLNet (Yang et al., 2019) for natural language processing to offer a comprehensive approach to evaluating coding proficiency. The research methodology involves the meticulous curation of a diverse dataset comprising projects from the LEGO SPIKE app (LEGO Education, 2022), ensuring that the models are subjected to a broad range of coding scenarios. Leveraging the dynamic educational environment provided by the LEGO SPIKE app (LEGO Education, 2022), Dr. Lego empowers users to design and implement various coding projects, fostering hands-on learning experiences. This thesis delves into methodologies aimed at enhancing coding education by exploring model integration, data generation, and fine-tuning of pre-trained models. Dr. Lego not only evaluates coding proficiency but also provides cohesive and insightful feedback, enhancing the learning experience for users. The adaptability of the framework highlights its potential to shape the future of coding education, paving the way for a new era of interactive and engaging learning experiences.