Engineering Commons^™

On Stability And Similarity Of Network Embeddings, Apurva Shriniwas Mulay

Theses - ALL

Machine Learning on graphs has become an active research area due to the prevailing graph-structured data in the real world. Many real-world applications can be modeled with graphs. Modern application domains include web-scale social networks [26], recommender systems, knowledge graphs, and biological or protein networks. However, there are various challenges. First, the graphs generated from such applications are often large. Moreover, in some scenarios, the complete graph is not available, e.g., for privacy reasons. Thus, it becomes impractical to perform network analysis or compute various graph measures. Hence, graph sampling becomes an important task.Sampling is often the first step to …

Real-Time Monitoring Of Fdm 3d Printer For Fault Detection Using Machine Learning: A Bibliometric Study, Vaibhav Kisan Kadam, Satish Kumar, Arunkumar Bongale

Library Philosophy and Practice (e-journal)

Additive Manufacturing has wide application range including healthcare, Fashion, Manufacturing, Prototypes, Tooling etc. AM techniques are subjected to various defects that may be printing defects or anomalies in machine. There is gap between current AM techniques and smart manufacturing since current AM lacks in build sensors necessary for process monitoring and fault detection. Both of these issues can be solved by incorporating real-time monitoring into AM. So the study is carried out to identify recent work done in AM to improve current system. For this bibliometric study Scopus database is used, study is kept limited to year 2010-2021 and English …

Implementing Artificial Intelligence And Machine Learning Into Advanced Qualification Programs, Jennifer R. Herr

Journal of Aviation/Aerospace Education & Research

Since its start, the Advanced Qualification Program (AQP) has encouraged new and innovative strategies for training airline crewmembers. The foundation of AQP is to train crew the way they fly and to find new and innovative ways to increase safety through training. By using data collected through the AQP process, training methods can be refined and improved. New technologies, such as artificial intelligence (AI) and machine learning can make data analysis and training more effective and efficient. This paper will explore these concepts and how AI and machine learning could be implemented in the AQP process to make training more …

Bibliometric Survey On Flood Prediction Using Machine Learning, Seema Patil Prof., Daksh Khurana Mr., Kartik Rao Mr, Priyanshu Meena Mr, Shivendra Singh Mr

Library Philosophy and Practice (e-journal)

Floods are one of the most devastating natural hazards, and modelling them is extremely difficult. Flood prediction model advancement study led to factors such as loss of human and animal life, property damage, and risk mitigation. The focus of this bibliometric survey is to recognise the few studies which have upheld on the factors affecting the floods. The analysis is done based on 254 documents such as articles, conference papers, article reviews and some reviews and notes. India contributes to the maximum number of documents followed by China and the United States of America. This bibliometric survey is conducted using …

Prediction Of Stocks And Stock Price Using Artificial Intelligence : A Bibliometric Study Using Scopus Database, Priyanka Tupe-Waghmare, Priyanka Tupe-Waghmare

Library Philosophy and Practice (e-journal)

Prediction of stocks and the prices of the stock is one of the most crucial points of discussion amongst the researchers and analysts in the financial domain to date. Every stakeholder and most importantly the investor desires to earn higher profit for his investment in the market and try to use several different strategies to invest their money. There are numerous methods to predict and analyse the movement of the stock prices. They are broadly divided into – statistical and artificial intelligence-based methods. Artificial intelligence is used to predict the futuristic prices of stocks and use wide range of algorithms …

Machine Learning Applied To Colloidal Properties Of Perfluorocarbon Nanoemulsions For Imaging In Ards/Ali, Marco Hosfeld

Electronic Theses and Dissertations

Acute Respiratory distress Syndrome (ARDS) and Acute Lung Injury (ALI) are inflammatory lung pathologies consisting of non-hydrostatic pulmonary edema leading to hypoxia and impaired gas exchange in the lungs. ARDS/ALI is both difficult to study and treat as it is not in itself a specific pathology but rather a syndrome consisting of many pathologies that vary case by case. It is, however, consistently characterized by an explosive acute inflammatory response in the lung parenchyma leading to hypoxia. Although time has seen to an increase in the understanding of ARDS/ALI, the mortality rate remains in the range of 30-50%. For these …

Decentralized Aggregation Design And Study Of Federated Learning, Venkata Naga Surya Sameeraja Malladi

Master of Science in Software Engineering Theses

The advent of machine learning techniques has given rise to modern devices with built-in models for decision making and providing rich content to users. This typically involves processing huge volumes of data in central servers and sending updated models to end-user devices. There are two main concerns on this server architecture, one is the privacy of data that is being transferred to a central server and the other is volumes of data sent over the network for the model update. Federated Learning helps solve these problems by training models on local data within the device and aggregating the model with …

Field Demonstration Of Gpr And Uav Technologies For Evaluation Of Two Us 75/77 Bridges, Sepehr Pashoutani, Jinying Zhu, Chungwook Sim, Ji-Yong Lee

Nebraska Department of Transportation: Research Reports

Two Nebraska bridges with asphalt overlay were selected for nondestructive testing and evaluation (NDT/NDE). Three NDT techniques were conducted on these two bridges, including Ground Penetrating Radar (GPR), Half-Cell Potential (HCP) and Unmanned Aerial Vehicle (UAV) imaging. NDT data were collected during three construction stages of the bridges: (1) before repair on existing asphalt overlay; (2) on bare concrete after asphalt removal; (3) and after repairing delaminated concrete.

A machine learning technique, autoencoder, was used to build quantitative relationships between different NDT datasets. On bare concrete, the GPR amplitude and HCP voltage show a strong linear relationship. Then a threshold …

Statistical Machine Learning For Breast Cancer Detection With Terahertz Imaging, Tanny Andrea Chavez Esparza

Graduate Theses and Dissertations

Breast conserving surgery (BCS) is a common breast cancer treatment option, in which the cancerous tissue is excised while leaving most of the healthy breast tissue intact. The lack of in-situ margin evaluation unfortunately results in a re-excision rate of 20-30% for this type of procedure. This study aims to design statistical and machine learning segmentation algorithms for the detection of breast cancer in BCS by using terahertz (THz) imaging. Given the material characterization properties of the non-ionizing radiation in the THz range, we intend to employ the responses from the THz system to identify healthy and cancerous breast tissue …

Analog Spiking Neural Network Implementing Spike Timing-Dependent Plasticity On 65 Nm Cmos, Luke Vincent

Graduate Theses and Dissertations

Machine learning is a rapidly accelerating tool and technology used for countless applications in the modern world. There are many digital algorithms to deploy a machine learning program, but the most advanced and well-known algorithm is the artificial neural network (ANN). While ANNs demonstrate impressive reinforcement learning behaviors, they require large power consumption to operate. Therefore, an analog spiking neural network (SNN) implementing spike timing-dependent plasticity is proposed, developed, and tested to demonstrate equivalent learning abilities with fractional power consumption compared to its digital adversary.

Synthesizer Parameter Approximation By Deep Learning, Daniel Faronbi, Alisa Gilmore

Theses/Capstones/Creative Projects

Synthesizers have been an essential tool for composers of any style of music including computer generated sound. They allow for an expansion in timbral variety to the orchestration of a piece of music or sound scape. Sound designers are trained to be able to recreate a timbre in their head using a synthesizer. This works well for simple sounds but becomes more difficult as the number of parameters required to produce a specific timbre increase. The goal of this research project is to formulate a method for synthesizers to approximate a timbre given an input audio sample using deep learning. …

A Study Of Deep Reinforcement Learning In Autonomous Racing Using Deepracer Car, Mukesh Ghimire

Honors Theses

Reinforcement learning is thought to be a promising branch of machine learning that has the potential to help us develop an Artificial General Intelligence (AGI) machine. Among the machine learning algorithms, primarily, supervised, semi supervised, unsupervised and reinforcement learning, reinforcement learning is different in a sense that it explores the environment without prior knowledge, and determines the optimal action. This study attempts to understand the concept behind reinforcement learning, the mathematics behind it and see it in action by deploying the trained model in Amazon's DeepRacer car. DeepRacer, a 1/18^th scaled autonomous car, is the agent which is trained …

Application Of Machine Learning In Flood Depth Prediction, Armando Esquivel

Open Access Theses & Dissertations

Machine learning technologies have helped provide answers for problems with a high degree of complexity. Machine learning has been utilized by various disciplines within the Civil Engineering profession and has proven to be efficient in solving complex problems. Although machine learning is being used in the Civil Engineering profession, a formal framework on developing and integrating machine learning has not been developed for flood depth prediction. The proposed word uses machine learning to predict the depth of flood at Houston, TX, due to a 100-year 24-hour storm. The proposed work can be used to collect, store and analyze data to …

Optimal Analytical Methods For High Accuracy Cardiac Disease Classification And Treatment Based On Ecg Data, Jianwei Zheng

Computational and Data Sciences (PhD) Dissertations

This work constitutes six projects. In the first project, a newly inaugurated research database for 12-lead electrocardiogram signals was created under the auspices of Chapman University and Shaoxing People's Hospital (Shaoxing Hospital Zhejiang University School of Medicine). This database aims to enable the scientific community in conducting new studies on arrhythmia and other cardiovascular conditions. In the second project, we created a new 12-lead ECG database under the auspices of Chapman University and Ningbo First Hospital of Zhejiang University that aims to provide high quality data enabling detection of the distinctions between idiopathic ventricular arrhythmia from right ventricular outflow tract …

Characterizing Students’ Engineering Design Strategies Using Energy3d, Jasmine Singh, Viranga Perera, Alejandra Magana, Brittany Newell

Discovery Undergraduate Interdisciplinary Research Internship

The goals of this study are to characterize design actions that students performed when solving a design challenge, and to create a machine learning model to help future students make better engineering design choices. We analyze data from an introductory engineering course where students used Energy3D, an open source computer-aided design software, to design a zero-energy home (i.e. a home that consumes no net energy over a period of a year). Student design actions within the software were recorded into text files. Using a sample of over 300 students, we first identify patterns in the data to assess how students …

Five Degree-Of-Freedom Property Interpolation Of Arbitrary Grain Boundaries Via Voronoi Fundamental Zone Octonion Framework, Sterling Gregory Baird

Theses and Dissertations

In this work we introduce the Voronoi fundamental zone octonion (VFZO) interpolation framework for grain boundary (GB) structure-property models and surrogates. The VFZO framework offers an advantage over other five degree-of-freedom (5DOF) based property interpolation methods because it is constructed as a point set in a Riemannian manifold. This means that directly computed Euclidean distances approximate the original octonion distance with significantly reduced computation runtime (∼7 CPU minutes vs. 153 CPU days for a 50000×50000 pairwise-distance matrix). This increased efficiency facilitates lower interpolation error through the use of significantly more input data. We demonstrate grain boundary energy (GBE) interpolation results …

Prediction Of Self-Diffusion Constants In Model And Real Systems Using Machine Learning, Joshua P. Allers

Chemical and Biological Engineering ETDs

Understanding diffusion of chemical compounds is important for the design and optimization of many chemical engineering and energy processes. Recent modifications to the Darken equation allow for accurate prediction of Maxwell-Stefan (MS) diffusion in mixtures. Still, there are few practical applications due to the requirement of individual self-diffusion constants. A reliable predictive model for self-diffusion constants would be highly valuable when used in conjunction with the modified Darken equation. Here, we show that Machine Learning (ML) can be used to develop generalized models for self-diffusion in Lennard Jones (LJ) systems and real systems of pure solutions. The use of Artificial …

How The Power Of Machine – Machine Learning, Data Science And Nlp Can Be Used To Prevent Spoofing And Reduce Financial Risks, Sasibhushan Rao Chanthati

Harrisburg University Other Works

This paper discusses the potential of machine learning, data science, and natural language processing (NLP) in mitigating the incidence of spoofing and financial risks hinged on cyber threats. Another one is spoofing; it is the act of impersonating legitimate entities to gain unauthorized information and it is indeed a threat to the public and companies to some extent. The research introduces two primary methodologies to combat spoofing: an email filtering system using a machine learning algorithm and an encryption and decryption system using a Caesar Cipher and Python programming language. It distinguishes between approved domains and unapproved domains by using …

Fiber Optic Sensor Embedded Smart Helmet For Real-Time Impact Sensing And Analysis Through Machine Learning, Yiyang Zhuang, Qingbo Yang, Taihao Han, Ryan O'Malley, Aditya Kumar, Rex E. Gerald Ii, Jie Huang

Electrical and Computer Engineering Faculty Research & Creative Works

Background: Mild traumatic brain injury (mTBI) strongly associates with chronic neurodegenerative impairments such as post-traumatic stress disorder (PTSD) and mild cognitive impairment. Early detection of concussive events would significantly enhance the understanding of head injuries and provide better guidance for urgent diagnoses and the best clinical practices for achieving full recovery. New method: A smart helmet was developed with a single embedded fiber Bragg grating (FBG) sensor for real-time sensing of blunt-force impact events to helmets. The transient signals provide both magnitude and directional information about the impact event, and the data can be used for training machine learning (ML) …

Deep Reinforcement Learning Applied To Spacecraft Attitude Control And Moment Of Inertia Estimation Via Recurrent Neural Networks, Nathaniel A. Enders

Theses and Dissertations

This study investigated two distinct problems related to unknown spacecraft inertia. The first problem explored the use of a recurrent neural network to estimate spacecraft moments of inertia using angular velocity measurements. Initial results showed that, for the configuration examined, the neural network can estimate the moments of inertia when there is a known external torque. The second problem trained a reinforcement learning agent, via proximal policy optimization, to control the attitude of a spacecraft. The results demonstrated that reinforcement learning may be a viable option for guidance and control solutions where the spacecraft model may be unknown. The trained …

Pain Recognition Performance On A Single Board Computer, Iyonna L. Tynes

USF Tampa Graduate Theses and Dissertations

Emotion recognition is a quickly growing field of study due to the increased interest in building systems which can classify and respond to emotions. Recent medical crises, such as the opioid overdose epidemic in the United States and the global COVID-19 pandemic has emphasized the importance of emotion recognition applications is areas like Telehealth services. Considering this, this thesis focuses specifically on pain recognition. The problem of pain recognition is approached from both a hardware and software perspective, as we propose a real-time pain recognition system, from facial images, that is deployed on an NVIDIA Jetson Nano single-board computer. We …

Reinforcement Learning-Based Access Schemes In Cognitive Radio Networks, Ehab Maged Elguindy

Theses and Dissertations

In this thesis, we propose different MAC protocols based on three Reinforcement Learning (RL) approaches, namely Q-Learning, Deep Q-Network (DQN), and Deep Deterministic Policy Gradient (DDPG). We exploit the primary user (PU) feedback, in the form of ARQ and CQI bits, to enhance the performance of the secondary user (SU) MAC protocols. Exploiting the PU feedback information can be applied on the top of any SU sensing-based MAC protocol. Our proposed model relies on two main pillars, namely, an infinite-state Partially Observable Markov Decision Process (POMDP) to model the system dynamics besides a queuing-theoretic model for the PU queue; the …

Source Localization With Machine Learning, Arjun Gupta

Electrical and Computer Engineering ETDs

Source localization with sensor arrays have found applications across domains beginning with radar and sonar, astronomy, acoustics, bio-medical devices and more recently in autonomous cars and adaptive communication systems. The knowledge of the spatial spectrum not only provide information about the source and interference but also assists in increasing signal integrity and avoid interference. This provides an added degree of freedom in the form of spatial diversity. This research investigates spatial spectrum estimation of waveforms from the signals sampled by arbitrarily distributed sensors. Conventional high resolution algorithms such as root-MuSiC fails to perform accurate source localization due to the reliance …

Time Series Data Analysis Using Machine Learning-(Ml) Approach, Mvv Prasad Kantipudi Dr., Pradeep Kumar N.S Dr., S.Sreenath Kashyap Dr., Ss Anusha Vemuri Ms

Library Philosophy and Practice (e-journal)

Healthcare benefits related to continuous monitoring of human movement and physical activity can potentially reduce the risk of accidents associated with elderly living alone at home. Based on the literature review, it is found that many studies focus on human activity recognition and are still active towards achieving practical solutions to support the elderly care system. The proposed system has introduced a joint approach of machine learning and signal processing technology for the recognition of human's physical movements using signal data generated by accelerometer sensors. The framework adopts the concept of DSP to select very descriptive feature sets and uses …

Diabetes Prediction Using Machine Learning : A Bibliometric Analysis, Vijayshri Nitin Khedkar, Sina Patel

Library Philosophy and Practice (e-journal)

Diabetes Mellitus is a chronic disease which can be deadly if undetected for longer time. Artificial intelligence is helping in healthcare industry to a great extent by helping professionals to derive useful information and patterns from data available in various formats: Survey data, electronic health records, laboratory data.. Diabetes, if predicted at an early stage can help many people to save lives and cost for healthcare. Decision-making, diagnosing and predicting diabetes have become an increasing trend in recent years. There are numerous publications in diabetes prediction and yet it’s an ongoing research topic with availability of new data and methods. …

Machine Learning Morphisms: A Framework For Designing And Analyzing Machine Learning Work Ows, Applied To Separability, Error Bounds, And 30-Day Hospital Readmissions, Eric Zenon Cawi

McKelvey School of Engineering Graduate Student Theses & Dissertations

A machine learning workflow is the sequence of tasks necessary to implement a machine learning application, including data collection, preprocessing, feature engineering, exploratory analysis, and model training/selection. In this dissertation we propose the Machine Learning Morphism (MLM) as a mathematical framework to describe the tasks in a workflow. The MLM is a tuple consisting of: Input Space, Output Space, Learning Morphism, Parameter Prior, Empirical Risk Function. This contains the information necessary to learn the parameters of the learning morphism, which represents a workflow task. In chapter 1, we give a short review of typical tasks present in a workflow, as …

A Literature Survey And Bibliometric Analysis Of Application Of Artificial Intelligence Techniques On Wireless Mesh Networks, Smita R. Mahajan Mrs., Harikrishnan R Dr., Ketan Kotecha Dr.

Library Philosophy and Practice (e-journal)

Recent years have seen a surge in the use of technology for executing transactions in both online and offline modes. Various industries like banking, e-commerce, and private organizations use networks for the exchange of confidential information and resources. Network security is thus of utmost importance, with the expectation of effective and efficient analysis of the network traffic. Wireless Mesh Networks are effective in communicating information over a vast span with minimal costs. A network is evaluated based on its security, accessibility, and extent of interoperability. Artificial Intelligence techniques like machine learning and deep learning have found widespread use to solve …

Medical Surge Capability: Performance Modeling Of Hospital Emergency Departments, Egbe-Etu Emmanuel Etu

Wayne State University Dissertations

Hospitals are faced with significant challenges during and after natural or human-caused disasters. Surge planning is a critical component of every healthcare facility’s emergency plan and response system. The process of managing and allocating scarce resources by tackling the vulnerability inherent to patients means that defining improvement priorities is one of the main challenges healthcare systems face when responding to a medical surge event (e.g., COVID-19). The consequences of these challenges include increased patient mortality, ambulance diversion, long wait times, and unavailability of beds. Previous efforts in hospital operations management have successfully applied operations research techniques in analyzing and optimizing …

Data Driven Analysis And Characterization Of Modern Android Malware, Qian Han

Dartmouth College Ph.D Dissertations

Google’s Android operating system was first announced to the public in 2007 and was installed on more than three billion mobile devices by 2019. With the prevalence of Android OS, Android malware has since proliferated. Android malware is malicious software designed to exploit Android operating systems running on smart devices. Some variants of Android malware have the capability of disabling the device, allowing a malicious actor to remotely control the device, track the user’s activity, lock the device, and so on. Moreover, the evolution and sophistication of modern Android malware obfuscation and detection bypassing methods have significantly improved in recent …

Exposure Assessment Of Emerging Contaminants: Rapid Screening And Modeling Of Plant Uptake, Majid Bagheri

Doctoral Dissertations

"With the advent of new chemicals and their increasing uses in every aspect of our life, considerable number of emerging contaminants are introduced to environment yearly. Emerging contaminants in forms of pharmaceuticals, detergents, biosolids, and reclaimed wastewater can cross plant roots and translocate to various parts of the plants. Long-term human exposure to emerging contaminants through food consumption is assumed to be a pathway of interest. Thus, uptake and translocation of emerging contaminants in plants are important for the assessment of health risks associated with human exposure to emerging contaminants. To have a better understanding over fate of emerging contaminants …