Digital Commons Network^™

Energy Planning Model Design For Forecasting The Final Energy Consumption Using Artificial Neural Networks, Haidy Eissa

Theses and Dissertations

“Energy Trilemma” has recently received an increasing concern among policy makers. The trilemma conceptual framework is based on three main dimensions: environmental sustainability, energy equity, and energy security. Energy security reflects a nation’s capability to meet current and future energy demand. Rational energy planning is thus a fundamental aspect to articulate energy policies. The energy system is huge and complex, accordingly in order to guarantee the availability of energy supply, it is necessary to implement strategies on the consumption side. Energy modeling is a tool that helps policy makers and researchers understand the fluctuations in the energy system. Over the …

A Longitudinal Analysis Of Pathways To Computing Careers: Defining Broadening Participation In Computing (Bpc) Success With A Rearview Lens, Mercy Jaiyeola

Theses and Dissertations

Efforts to increase the participation of groups historically underrepresented in computing studies, and in the computing workforce, are well documented. It is a national effort with funding from a variety of sources being allocated to research in broadening participation in computing (BPC). Many of the BPC efforts are funded by the National Science Foundation (NSF) but as existing literature shows, the growth in representation of traditionally underrepresented minorities and women is not commensurate to the efforts and resources that have been directed toward this aim.

Instead of attempting to tackle the barriers to increasing representation, this dissertation research tackles the …

Advanced Analytics In Smart Manufacturing: Anomaly Detection Using Machine Learning Algorithms And Parallel Machine Scheduling Using A Genetic Algorithm, Meiling He

Theses and Dissertations

Industry 4.0 offers great opportunities to utilize advanced data processing tools by generating Big Data from a more connected and efficient data collection system. Making good use of data processing technologies, such as machine learning and optimization algorithms, will significantly contribute to better quality control, automation, and job scheduling in Smart Manufacturing. This research aims to develop a new machine learning algorithm for solving highly imbalanced data processing problems, implement both supervised and unsupervised machine learning auto-selection frameworks for detecting anomalies in smart manufacturing, and develop a genetic algorithm for optimizing job schedules on unrelated parallel machines. This research also …

An Interactive Health Data Science Platform For Exploratory Analysis Of Health Outcomes – A Case Study With Colon Cancer, Hemanth Kumar Alapati

Theses and Dissertations

Disease prediction is an important aspect of early disease detection and preventive care with wide range of applications in healthcare domain. Previous studies used image processing techniques, statistical and machine learning models to predict diseases. Prediction accuracies vary with data type and the target. Often the data is processed through models under different data conditions to identify what works best for a scenario. This results in tweaking the code, running multiple iterations making these methods usable only for people with technical skills. An interactive platform is developed that hides the technicalities and allows the users to change options like target …

Predicting Occurrence Of The Term Sarcopenia With Semi-Supervised Machine Learning, Kevin Flasch

Theses and Dissertations

Sarcopenia is a medical condition that involves loss of muscle mass. It has been difficult todefine and only recently assigned an official medical code, leading to many medical records lacking a coded diagnosis although the clinical note text may discuss it or symptoms of it. This thesis investigates the application of machine learning and natural language processing to analyze clinical note text to see how well the term ’sarcopenia’ can be predicted in clinical note text from records concerning the condition.

A variety of machine learning models combined with different features and text processingare tested against training data that mentions …

Human Capital In The Knowledge Economy : A 3-Country Case Study In Healthcare, James Scott Mccallum

Theses and Dissertations

During the present knowledge economy there appear to be labor shortages at the same time and in the same regions in which there is an excess of labor supply. Such a pattern would run counter to previous major economic disruptions, as well as questioning traditional free market economic theory of supply and demand principles. Implications for policy where there are global labor shortages along with surplus labor availability in a market economy, are significant. It will likely indicate a drag on economic growth for business sectors, for regions and perhaps globally. It would indicate an accompanying growing disparity of income. …

A Deep Recurrent Neural Network With Iterative Optimization For Inverse Image Processing Applications, Masaki Ikuta

Theses and Dissertations

Many algorithms and methods have been proposed for inverse image processing applications, such as super-resolution, image de-noising, and image reconstruction, particularly with the recent surge of interest in machine learning and deep learning methods.

As for Computed Tomography (CT) image reconstruction, the most recently proposed methods are limited to image domain processing, where deep learning is used to learn the mapping between a true image data set and a noisy image data set in the image domain. While deep learning-based methods can produce higher quality images than conventional model-based algorithms, these methods have a limitation. Deep learning-based methods used in …

An Assessment Of Image-Cloaking Techniques Against Automated Face Recognition For Biometric Privacy, Brandon Scott Ledford

Theses and Dissertations

Over the past two decades, Americans have aggressively increased the amount of facial data uploaded to the internet primarily via social media. This data is largely unprotected due to the dire lack of existing regulations protecting users from large scale face recognition in the United States, where the value of data trade is in the tens of billions. In its current state, facial privacy in the United States depends on American corporations opting not to collect the public data, an option rarely chosen. Much research has been done in the area of suppressing recognition abilities, giving users the ability to …

Explainable Transfer-Learning And Knowledge Distillation For Fast And Accurate Head-Pose Estimation, Nima Aghli

Theses and Dissertations

Head-pose estimation from facial images is an important research topic in computer-vision. It has many applications in detecting the focus of attention, monitoring driver behavior, and human-computer interaction. As with other computer-vision topics, recent research on head-pose estimation has been focused on using deep convolutional neural networks (CNNs). Although deeper networks improve prediction accuracy, they suffer from dependency on expensive hardware such as GPUs to perform real-time inference. As a result, CNN model compression becomes an important concept. In this work, we propose a novel CNN compression method by combing weight pruning and knowledge distillation. Additionally, we improve the state-of-the-art …

Unveiling Migration Patterns Using Data And Network Science, Firas Aswad

Theses and Dissertations

Many real-world phenomena have recently been witnessed, motivating scientists to provide a comprehensive understanding of worldwide networks, such as the human migration phenomenon. Human migration research is multidisciplinary and has yielded work in social sciences, physics, and the new field of smart city designing and planning based on big data analytics. The importance of the research comes from the impacts on both countries, sources, and distention. Impacts occur at several levels such as economy, city planning, politics, and law enforcement, leading to changes in demographics. Thus, studies on human migration help policymakers prepare and handle crises when they occur in …

Beware Of Ips In Sheep's Clothing: Measurement And Disclosure Of Ip Spoofing Vulnerabilities, Alden Douglas Hilton

Theses and Dissertations

Networks not employing destination-side source address validation (DSAV) expose themselves to a class of pernicious attacks which could be prevented by filtering inbound traffic purporting to originate from within the network. In this work, we survey the pervasiveness of networks vulnerable to infiltration using spoofed addresses internal to the network. We issue recursive Domain Name System (DNS) queries to a large set of known DNS servers world-wide using various spoofed-source addresses. In late 2019, we found that 49% of the autonomous systems we tested lacked DSAV. After a large-scale notification campaign run in late 2020, we repeated our measurements in …

Correct Web Service Transactions In The Presence Of Malicious And Misbehaving Transactions, John Thomas Ravan Iii

Theses and Dissertations

Concurrent database transactions within a web service environment can cause a variety of problems without the proper concurrency control mechanisms in place. A few of these problems involve data integrity issues, deadlock, and efficiency issues. Even with today’s industry standard solutions to these problems, they have taken a reactive approach rather than proactively preventing these problems from happening. We deliver a solution, based on prediction-based scheduling to ensure consistency while keeping execution time the same or faster than current industry solutions. The first part of this solution involves prototyping and formally proving a prediction-based scheduler.

The prediction-based scheduler leverages a …

Multi-Objective Routing For Distributed Controllers, Konstantin Y. Rubin

Theses and Dissertations

A long-term goal of future naval shipboard power systems is the ability to manage energy flow with sufficient flexibility to accommodate future platform requirements such as better survivability, continuity, and support of pulsed and other demanding loads. To facilitate scalable, low-latency global distributed system control, each control module can include an integrated network interface connected through multiple channels onto a direct, multi-hop network topology. In this work, we focus on a 2D Torus, in which control nodes are arranged in a regular 2D grid, with each node connected through point-to-point connections to its four immediate neighbors. An important advantage of …

Towards More Trustworthy Deep Learning: Accurate, Resilient, And Explainable Countermeasures Against Adversarial Examples, Fei Zuo

Theses and Dissertations

Despite the great achievements made by neural networks on tasks such as image classification, they are brittle and vulnerable to adversarial example (AE) attacks, which are crafted by adding human-imperceptible perturbations to inputs in order that a neural-network-based classifier incorrectly labels them. Along with the prevalence of deep learning techniques, the threat of AEs attracts increasingly attentions since it may lead to serious consequences in some vital applications such as disease diagnosis.

To defeat attacks based on AEs, both detection and defensive techniques attract the research community’s attention. Given an input image, the detection system outputs whether it is an …

Robot Area Coverage Path Planning In Aquatic Environments, Nare Karapetyan

Theses and Dissertations

This thesis is motivated by real world problems faced in aquatic environments. It addresses the problem of area coverage path planning with robots - the problem of moving an end-effector of a robot over all available space while avoiding existing obstacles. The problem is considered first in a 2D space with a single robot for specific environmental monitoring operations, and then with multi-robot systems — a known NP-complete problem. Next we tackle the coverage problem in 3D space - a step towards underwater mapping of shipwrecks or monitoring of coral reefs.

The first part of this thesis leverages human expertise …

Sampling And Robustness In Multi-Robot Visibility-Based Pursuit-Evasion, Trevor Vincent Olsen

Theses and Dissertations

Given a two-dimensional polygonal space, the multi-robot visibility-based pursuit-evasion problem tasks several pursuer robots with the goal of establishing visibility with an arbitrarily fast evader. The best-known complete algorithm for this problem takes time doubly exponential in the number of robots. However, sampling-based techniques have shown promise in generating feasible solutions in these scenarios.

Existing sampling-based algorithms have long execution times and high failure rates for complex environments. We first address that limitation by proposing a new algorithm that takes an environment as its input and returns a joint motion strategy which ensures that the evader is captured by one …

Procesi I Web Dizajnimit, Albin Ibrahimi

Theses and Dissertations

Numri i web sajteve në internet gjithnjë e më shumë po rritet, këto sajte zhvillohen nga programerë të ndryshëm duke përdorur gjuhë të ndryshme programuese. Ashtu që produkti final të jetë i suksesshëm, këto web sajte duhet të zhvillohen në bazë të një procesi, i cili do të shpjegohet në detaje dhe është objektiv kryesor i këtij punimi.

Procesi i tillë ndihmon në efikasitet më të lartë të produktit final, pasi që kërkesat kryesore ndahen në kërkesa më të vogla deri ne maksimum ashtu që krijohet një lloj hallke e cila mundëson planifikim shumë të mirë.

Në këtë proces përfshihen …

Ftth, Blerant Gashi

Theses and Dissertations

Rrjetet fibra për në shtepi (FTTH) i përkasin familjes së sistemeve të transmetimit FTT-x brenda botës së telekomunikacionit. Këto rrjete, të cilat konsiderohen broadband, kanë aftësinë për të transportuar sasi të mëdha të të dhënave dhe informacioneve me ritme shumë të larta të bitit. Teknologjia FTTH përfshin futjen e fibrave optike në rrjetin global, si operatori i rrjetit kryesor si linja e fundit e teknologjis. Rrjetet FTTH për kominikimin e dy pajisve ndermjet tyre perdor driten dhe si medium për transmetim të sinjalve përdor fibrin optikë, ku shpejtsia e transmetimin të të dhënave është Gigabitshte. Fibri optik ka shumë avantazhe …

Characterizing Convolutional Neural Network Early-Learning And Accelerating Non-Adaptive, First-Order Methods With Localized Lagrangian Restricted Memory Level Bundling, Benjamin O. Morris

Theses and Dissertations

This dissertation studies the underlying optimization problem encountered during the early-learning stages of convolutional neural networks and introduces a training algorithm competitive with existing state-of-the-art methods. First, a Design of Experiments method is introduced to systematically measure empirical second-order Lipschitz upper bound and region size estimates for local regions of convolutional neural network loss surfaces experienced during the early-learning stages. This method demonstrates that architecture choices can significantly impact the local loss surfaces traversed during training. Next, a Design of Experiments method is used to study the effects convolutional neural network architecture hyperparameters have on different optimization routines' abilities to …

Enterprise Resource Allocation For Intruder Detection And Interception, Adam B. Haywood

Theses and Dissertations

This research considers the problem of an intruder attempting to traverse a defender's territory in which the defender locates and employs disparate sets of resources to lower the probability of a successful intrusion. The research is conducted in the form of three related research components. The first component examines the problem in which the defender subdivides their territory into spatial stages and knows the plan of intrusion. Alternative resource-probability modeling techniques as well as variable bounding techniques are examined to improve the convergence of global solvers for this nonlinear, nonconvex optimization problem. The second component studies a similar problem but …

Determining Physical Characteristics Through Information Leakage In 802.11ac Beamforming, Albert D. Taglieri

Theses and Dissertations

The risk of information leakage in 802.11ac allows an eavesdropper to monitor wireless traffic and correlate physical locations between devices, as well as environment changes such as the motion of a person. Previous pattern-analysis mitigation methods, which used nonexistent devices to fool an eavesdropper, are not effective in an 802.11ac network, because devices on the network can be correlated to their physical location, which a nonexistent device does not have. Further, additional information about motion in the target environment can be observed and analyzed, providing a new potential for pattern analysis and sensing. 802.11ac makes it possible to plug in …

Advancing Proper Dataset Partitioning And Classification Of Visual Search And The Vigilance Decrement Using Eeg Deep Learning Algorithms, Alexander J. Kamrud

Theses and Dissertations

Electroencephalography (EEG) classification of visual search and vigilance tasks has vast potential in its benefits. In future human-machine teaming systems, EEG could act as the tool for operator state assessment, enabling AI teammates to know when to assist the operator in these tasks, with the potential to lead to increased safety of operations, better training systems for our operators, and improved operational effectiveness. This research investigates deep learning methods which utilize EEG signals to classify the efficiency of an operator's search and to classify whether an operator is in a decrement during a vigilance type task, and investigates performing these …

A Machine Learning Pipeline With Switching Algorithms To Predict Lung Cancer And Identify Top Features, Anika Tasnim

Theses and Dissertations

Lung cancer is the leading cause of cancer-related death around the world. Early detection is a critical factor for its effective treatment. To facilitate early-stage prediction, a Machine Learning (ML) pipeline has been built that uses inpatient admission data to train four ML models. The data is dynamically loaded into a database, cleaned, and passed through the SelectKBest selector to identify the top features influencing the prognosis, which are then fed into the pipeline and fitted to the ML models to make the forecast. Among the models used, Decision Tree provides the highest accuracy (97.09%), followed by Random Forest (94.07%). …

Medical Image Segmentation Using Machine Learning, Masoud Khani

Theses and Dissertations

Image segmentation is the most crucial step in image processing and analysis. It can divide an image into meaningfully descriptive components or pathological structures. The result of the image division helps analyze images and classify objects. Therefore, getting the most accurate segmented image is essential, especially in medical images. Segmentation methods can be divided into three categories: manual, semiautomatic, and automatic. Manual is the most general and straightforward approach. Manual segmentation is not only time-consuming but also is imprecise. However, automatic image segmentation techniques, such as thresholding and edge detection, are not accurate in the presence of artifacts like noise …

Nb-Iot: Iot Përmes Sistemeve Celulare Brez-Ngushta, Besart Haziri

Theses and Dissertations

Me rritjen e numrit të pajisjeve IoT në ditët e sotme, kërkesa për t’i mbështetur ato vetëm sa rritet, me rreth 7.6 miliardë pajisje IoT aktive në fund të vitit 2019, si dhe me një pritshmëri të rritjes deri në 24.1 miliardë pajisje IoT në vitin 2030. Me numër kaq të madh të pajisjeve që do të lidhen në Internet atëherë na duhet siguri dhe rrjet i besueshëm që i mbështet të gjitha këto pajisje. Mundësia më e mirë për këtë çështje është NB-IoT (Narrowband–Internet of Things).

NB-IoT mundëson konektimin e qindra-mijëra pajisjeve të vogla (sensorëve) në Internet përmes sistemeve …

Essays On Fake Review Detection, Managerial Response, And Consumer Perceptions, Long Chen

Theses and Dissertations

This dissertation investigates how online reviews and managerial responses jointly affect consumer perceptions. I first examine and compare the outcomes of multiple fake review classifiers using various algorithms, including traditional machine learning methods and recently developed deep learning methods (essay I). Then, based on the findings of the first essay, I examine the interrelationship between fake review detection, managerial response, and hotel ratings and ratings’ growths (essay II).The first essay is a comparative study on the methodology of identifying fake reviews. Although online reviews have attracted much attention from academia and industry for over fifteen years, how to identify fake …

Development Of A 7 Dof Exoskeleton Robot For Rehabilitation Of Lower Extremity, Sk Hasan

Theses and Dissertations

The World Health Organization reports that worldwide about 1 billion people have some form ofdisability. Of these, 110-190 million people have significant difficulties in functioning (mainly upper and lower extremity disability) independently. The major causes of human lower extremity disability include stroke, trauma, spinal cord injuries, and muscular dystrophy. Every 40 seconds, someone in the United States has a stroke. A statistic shows that approximately 65% of post-stroke patients suffer lower extremity impairment. Rehabilitation programs are the main method to promote functional recovery in disabled individuals. The conventional therapeutic approach requires a long commitment from a therapist or a clinician. …

Analysis Of Music Genre Clustering Algorithms, Samuel Walter Stern

Theses and Dissertations

Classification and clustering of music genres has become an increasingly prevalent focusin recent years, prompting a push for research into relevant algorithms. The most successful algorithms have typically applied the Naive Bayes or k-Nearest Neighbors algorithms, or used Neural Networks to perform classification. This thesis seeks to investigate the use of unsupervised clustering algorithms such as K-Means or Hierarchical clustering, and establish their usefulness in comparison to or conjunction with established methods.

Prediction Of Concurrent Hypertensive Disorders In Pregnancy And Gestational Diabetes Mellitus Using Machine Learning Techniques, Mary Ejiwale

Theses and Dissertations

Gestational diabetes mellitus and hypertensive disorders in pregnancy are serious maternal health conditions with immediate and lifelong mother-child health consequences. These obstetric pathologies have been widely investigated, but mostly in silos, while studies focusing on their simultaneous occurrence rarely exist. This is especially the case in the machine learning domain. This retrospective study sought to investigate, construct, evaluate, compare, and isolate a supervised machine learning predictive model for the binary classification of co-occurring gestational diabetes mellitus and hypertensive disorders in pregnancy in a cohort of otherwise healthy pregnant women. To accomplish the stated aims, this study analyzed an extract (n=4624, …

Semantic Analysis Of Vaccine And Mask Sentiments In Covid-19 Twitter Data, Mohammadreza Sediqin

Theses and Dissertations

SARS CoV-2 (COVID-19) was identified as the cause of severe respiratory disease in China in 2019. It is a virus that will be transferred person-to-person by sneezing, coughing, or talking. This phenomenon not only affects public health and economics but also mental health as well. SARS-CoV-2 vaccines and wearing masks plays significant rolesin preventing the spread of the COVID-19 virus, but vaccine hesitancy and anti-mask beliefs threaten the efficacy of the government orders in prevention and immunization against Coronavirus. The impact of the COVID-19 pandemic has been investigated from different aspects, but few large-scale studies focus on the opinion of …