Digital Commons Network^™

Early Detection Of Fake News On Social Media, Yang Liu

Dissertations

The ever-increasing popularity and convenience of social media enable the rapid widespread of fake news, which can cause a series of negative impacts both on individuals and society. Early detection of fake news is essential to minimize its social harm. Existing machine learning approaches are incapable of detecting a fake news story soon after it starts to spread, because they require certain amounts of data to reach decent effectiveness which take time to accumulate. To solve this problem, this research first analyzes and finds that, on social media, the user characteristics of fake news spreaders distribute significantly differently from those …

Non-Invasive Hyperglycemia Detection Using Ecg And Deep Learning, Renato Silveira Cordeiro

Master's Theses

Hyperglycemia is characterized by an elevated level of glucose in the blood. It is normally asymptomatic, except for an extremely high level, and thus a person can live in that state for years before the negative - sometimes irreversible - health impacts appear. Unexpected hyperglycemia can also be an indication of diabetes, a chronic disease that, when not treated, can lead to serious consequences, including limb amputations and even death. Therefore, identifying hyperglycemic state is important. The most common and direct way to measure a person’s glucose level is by directly assessing it from a blood sample by pricking a …

Detecting Myocardial Infarctions Using Machine Learning Methods, Aniruddh Mathur

Master's Projects

Myocardial Infarction (MI), commonly known as a heart attack, occurs when one of the three major blood vessels carrying blood to the heart get blocked, causing the death of myocardial (heart) cells. If not treated immediately, MI may cause cardiac arrest, which can ultimately cause death. Risk factors for MI include diabetes, family history, unhealthy diet and lifestyle. Medical treatments include various types of drugs and surgeries which can prove very expensive for patients due to high healthcare costs. Therefore, it is imperative that MI is diagnosed at the right time. Electrocardiography (ECG) is commonly used to detect MI. ECG …

Assessing Wildfire Damage From High Resolution Satellite Imagery Using Classification Algorithms, Ai-Linh Alten

Master's Projects

Wildfire damage assessments are important information for first responders, govern- ment agencies, and insurance companies to estimate the cost of damages and to help provide relief to those affected by a wildfire. With the help of Earth Observation satellite technology, determining the burn area extent of a fire can be done with traditional remote sensing methods like Normalized Burn Ratio. Using Very High Resolution satellites can help give even more accurate damage assessments but will come with some tradeoffs; these satellites can provide higher spatial and temporal resolution at the expense of better spectral resolution. As a wildfire burn area …

Analyze Informant-Based Questionnaire For The Early Diagnosis Of Senile Dementia Using Deep Learning, Fubao Zhu, Xiaonan Li, Daniel Mcgonigle, Haipeng Tang, Zhuo He, Chaoyang Zhang, Guang-Uei Hung, Pai-Yi Chu, Weihua Zhou

Faculty Publications

Objective: This paper proposes a multiclass deep learning method for the classification of dementia using an informant-based questionnaire.

Methods: A deep neural network classification model based on Keras framework is proposed in this paper. To evaluate the advantages of our proposed method, we compared the performance of our model with industry-standard machine learning approaches. We enrolled 6,701 individuals, which were randomly divided into training data sets (6030 participants) and test data sets (671 participants). We evaluated each diagnostic model in the test set using accuracy, precision, recall, and F1-Score.

Results: Compared with the seven conventional machine learning …

Smartphone-Based Human Fatigue Detection In An Industrial Environment Using Gait Analysis, Swapnali Babasaheb Karvekar

Theses

Human fatigue due to repetitive and physically challenging jobs may result in poor performance and a Work-related Musculoskeletal Disorder (WMSD). Thus, the importance of being able to monitor fatigue to implement preventative interventions cannot be overstated. This study was designed to monitor fatigue through the development of a methodology that objectively classifies an individual’s level of fatigue in the workplace by utilizing the motion sensors embedded in smartphones. An experiment consisting of squatting tasks, primarily involving the lower extremity musculature, was conducted with 24 participants using a smartphone attached to their right shank. Using Borg’s Ratings of Perceived Exertion (RPE) …

On Action Quality Assessment, Paritosh Parmar

UNLV Theses, Dissertations, Professional Papers, and Capstones

In this dissertation, we tackle the task of quantifying the quality of actions, i.e., how well an

action was performed using computer vision. Existing methods used human body pose-based features to express the quality contained in an action sample. Human body pose estimation in actions such as sports actions, like diving and gymnastic vault, is particularly challenging, since the athletes undergo convoluted transformations while performing their routines. Moreover, pose-based features do not take into account visual cues such as water splash in diving. Visual cues are taken into account by human judges. In our first work, we show that using …

Neural Network In Hardware, Jiong Si

UNLV Theses, Dissertations, Professional Papers, and Capstones

This dissertation describes the implementation of several neural networks built on a field programmable gate array (FPGA) and used to recognize a handwritten digit dataset – the Modified National Institute of Standards and Technology (MNIST) database. A novel hardwarefriendly activation function called the dynamic ReLU (D-ReLU) function is proposed. This activation function can decrease chip area and power of neural networks when compared to traditional activation functions at no cost to prediction accuracy.

The implementations of three neural networks on FPGA are presented: 2-layer online training fully-connected neural network, 3-layer offline training fully-connected neural network, and two solutions of Super-Skinny …

An Application Of Deep Learning Models To Automate Food Waste Classification, Alejandro Zachary Espinoza

Dissertations and Theses

Food wastage is a problem that affects all demographics and regions of the world. Each year, approximately one-third of food produced for human consumption is thrown away. In an effort to track and reduce food waste in the commercial sector, some companies utilize third party devices which collect data to analyze individual contributions to the global problem. These devices track the type of food wasted (such as vegetables, fruit, boneless chicken, pasta) along with the weight. Some devices also allow the user to leave the food in a kitchen container while it is weighed, so the container weight must also …

Characteristic Index Digging Of Combat Sos Capability Based On Machine Learning, Yongli Yang, Xiaofeng Hu, Rong Ming, Xiaojing Yin, Wenxiang Wang

Journal of System Simulation

Abstract: Aiming at the two difficulties in characteristic index digging of combat system of systems (CSoS), namely operation data generation and digging method selection, this paper proposes a new digging method, that is, using the simulation testbed to generate operation data, then adopting the machine learning to dig characteristic index. Two methods of characteristic index digging based on machine learning are researched: (1) the method based on network convergence, divides the communities for fundamental indexes based on their relationship, and obtains the characteristic indexes by principal component analysis (PCA); this method is applied to dig the characteristic indexes of …

Chaotic Time Series Prediction Based On Gaussian Processes Mixture, Zhenjie Feng, Fan Yu

Journal of System Simulation

Abstract: Aiming at the problem that the existing learning algorithms of Gaussian processes mixture (GPM) model, such as Markov Chain Monte Carlo (MCMC), variation or leave one out, have high computational complexity, a hidden variables posterior hard-cut iterative training algorithm is proposed, which simplifies the training process of the model. The GPM model based on the proposed algorithm is applied to chaotic time series prediction. The effects of embedding dimension, time delay, learning sample number, and testing sample numbers on predictive ability are discussed. It is demonstrated by the experimental results that the prediction of the GPM model is more …

Machine Learning On Acoustic Signals Applied To High-Speed Bridge Deck Defect Detection, Yao Chou

Theses and Dissertations

Machine learning techniques are being applied to many data-intensive problems because they can accurately provide classification of complex data using appropriate training. Often, the performance of machine learning can exceed the performance of traditional techniques because machine learning can take advantage of higher dimensionality than traditional algorithms. In this work, acoustic data sets taken using a rapid scanning technique on concrete bridge decks provided an opportunity to both apply machine learning algorithms to improve detection performance and also to investigate the ways that training of neural networks can be aided by data augmentation approaches. Early detection and repair can enhance …

Self-Driving Toy Car Using Deep Learning, Fahim Ahmed, Suleyman Turac, Mubtasem Ali

Publications and Research

Our research focuses on building a student affordable platform for scale model self-driving cars. The goal of this project is to explore current developments of Open Source hardware and software to build a low-cost platform consisting of the car chassis/framework, sensors, and software for the autopilot. Our research will allow other students with low budget to enter into the world of Deep Learning, self-driving cars, and autonomous cars racing competitions.

Truck Activity Pattern Classification Using Anonymous Mobile Sensor Data, Taslima Akter

Graduate Theses and Dissertations

To construct, operate, and maintain a transportation system that supports the efficient movement of freight, transportation agencies must understand economic drivers of freight flow. This is a challenge since freight movement data available to transportation agencies is typically void of commodity and industry information, factors that tie freight movements to underlying economic conditions. With recent advances in the resolution and availability of big data from Global Positioning Systems (GPS), it may be possible to fill this critical freight data gap. However, there is a need for methodological approaches to enable usage of this data for freight planning and operations.

To …

Approxml: Efficient Approximate Ad-Hoc Ml Models Through Materialization And Reuse, Faezeh Ghaderi

Computer Science and Engineering Theses - Archive

Machine Learning (ML) has become an essential tool in answering complex predictive analytic queries. Model building for large scale datasets is one of the most time-consuming parts of the data science pipeline. Often data scientists are willing to sacrifice some accuracy in order to speed up this process during the exploratory phase. In this report, we aim to demonstrate ApproxML, a system that efficiently constructs approximate ML models for new queries from previously constructed ML models using the concepts of model materialization and reuse. ApproxML supports a wide variety of ML models such as generalized linear models for supervised learning …

Social Media Text Analysis Using Multi-Kernel Convolutional Neural Network, Anna Philips

Computer Science and Engineering Theses - Archive

Transportation planners and ride hailing platforms such as Uber and Lyft use their riders feedback to assess their services and monitor customer satisfaction. Social media websites such as Facebook, Instagram, LinkedIn and in particular Twitter provides a large dataset of micro-texts by users who regularly post to their social media accounts about their grievances with their ride experience. This data is often unorganized and intractable to process because of it’s extremely large size which is continuously increasing daily. In this project, we collected ride hailing service relevant text data from Twitter around New York and developed a novel Convolutional Neural …

Use Of Word Embedding To Generate Similar Words And Misspellings For Training Purpose In Chatbot Development, Sanjay Thapa

Computer Science and Engineering Theses - Archive

The advancement in the field of Natural Language Processing and Machine Learning has played a significant role in the huge improvement of conversational Artificial Intelligence (AI). The use of text-based conversation AI such as chatbots have increased significantly for the everyday purpose to communicate with real people for a variety of tasks. Chatbots are deployed in almost all popular messaging platforms and channels. The rise of chatbot development frameworks based on machine learning is helping to deploy chatbot easily and promptly. These chatbot development frameworks use machine learning and natural language understanding (NLU) to understand users' messages and intents and …

Learning Robot Manipulation Tasks Via Observation, Michail Theofanidis

Computer Science and Engineering Dissertations - Archive

The coexistence of humans and robots has been the aspiration of many scientific endeavors in the past century. Most anthropomorphic or industrial robots are highly articulated and complex machines, which are designed to carry out tasks that often involve the manipulation of physical objects. Traditionally, robots learn how to perform such tasks with the aid of a human programmer or operator. In this regard, the human acts as a teacher who provides a demonstration of a task. From the data of the demonstration, the robot must learn a state-action mapping that accomplishes the task. This state-action mapping is often addressed …

Feature Extraction In Noise-Diverse Environments For Human Activities Recognition Using Wi-Fi, Sheheryar Arshad

Computer Science and Engineering Dissertations - Archive

With the rapid development of 802.11 standard and Internet of Things (IoT) applications, Wi-Fi (IEEE 802.11) has emerged as the most widely used wireless communication technology. Wi-Fi based sensing has found widespread use cases involving activity recognition, indoor localization, design of smart spaces and in healthcare applications. This dissertation presents the study of human activities’ sensing and recognition using channel state information (CSI) of Wi-Fi. We highlight the limitations of existing methods and consequently design the frameworks for collecting stable CSI and monitoring different indoor and outdoor environments for human activities. Specifically, this dissertation provide means to define and extract …

Think2act: Using Multimodal Data To Assess Human Cognitive And Physical Performance, Maher Abujelala

Computer Science and Engineering Dissertations - Archive

As computers become more advanced, affordable, and smaller in size, we start to use them in almost every aspect of our daily life. Nowadays, the use of computers is not just limited to accomplish work-related tasks. Instead, we use computers for education, entertainment, healthcare, and in many other areas to facilitate our daily life activities. From here, the Human-Computer Interaction (HCI) field emerged. HCI is a multidisciplinary field of study that focuses on utilizing computers and technology to interact with humans, improve their quality of life, and enhance their performance. The rapid advancements in other related research fields, such as …

Deep Representation Learning For Clustering And Domain Adaptation, Mohsen Kheirandishfard

Computer Science and Engineering Dissertations - Archive

Representation learning is a fundamental task in the area of machine learning which can significantly influence the performance of the algorithms used in various applications. The main goal of this task is to capture the relationships between the input data and learn feature representations that contain the most useful information of the original data. Such representations can be further leveraged in many machine learning applications such as clustering, natural language analysis, recommender systems, etc. In this dissertation, we first present a theoretical framework for solving a broad class of non-convex optimization problems. The proposed method is applicable to various tasks …

Multivariate Time Series Pattern Recognition Using Machine Learning And Deep Learning Methods, Sai Abhishek Devar

Industrial, Manufacturing, and Systems Theses - Archive

In this research work, we have implemented machine learning & deep-learning algorithms on real-time multivariate time series datasets in the manufacturing & health care fields. The research work is organized in two case-studies. The case study-1 is about rare event classification in multivariate time series in a pulp and paper manufacturing industry, data was collected of multiple sensors at each stage of production line, the data contains a rare event of paper break that commonly occurs in the industry. For preprocessing we have implemented sliding window approach for calculating first order difference method to capture the variation in the data …

Domain Adaptation In Unmanned Aerial Vehicles Landing Using Reinforcement Learning, Pedro Lucas Franca Albuquerque

School of Computing: Dissertations, Theses, and Student Research

Landing an unmanned aerial vehicle (UAV) on a moving platform is a challenging task that often requires exact models of the UAV dynamics, platform characteristics, and environmental conditions. In this thesis, we present and investigate three different machine learning approaches with varying levels of domain knowledge: dynamics randomization, universal policy with system identification, and reinforcement learning with no parameter variation. We first train the policies in simulation, then perform experiments both in simulation, making variations of the system dynamics with wind and friction coefficient, then perform experiments in a real robot system with wind variation. We initially expected that providing …

Toward Self-Reconfigurable Parametric Systems: Reinforcement Learning Approach, Ting-Yu Mu

Dissertations

For the ongoing advancement of the fields of Information Technology (IT) and Computer Science, machine learning-based approaches are utilized in different ways in order to solve the problems that belong to the Nondeterministic Polynomial time (NP)-hard complexity class or to approximate the problems if there is no known efficient way to find a solution. Problems that determine the proper set of reconfigurable parameters of parametric systems to obtain the near optimal performance are typically classified as NP-hard problems with no efficient mathematical models to obtain the best solutions. This body of work aims to advance the knowledge of machine learning …

Development Of A Machine Learning-Based Prediction Model For Construction And Environmental Costs Of Trenchless Spray-Applied Pipe Linings, Cured-In-Place Pipe, And Sliplining Methods In Large Diameter Culverts, Ramtin Serajiantehrani

Civil Engineering Dissertations - Archive

According to U.S. Department of Transportation, Federal Highway Administration there are more than 4.1 million miles of road in total length, making it the world’s longest and biggest road network with millions of culverts hidden underneath the road in the United States. The development of underground infrastructure, concerns about environmental impacts, and economic trends are influencing society, resulting in the advancement of technology for more environment-friendly and cost-effective pipeline rehabilitation. Trenchless technologies employ innovative methods, materials, and equipment that require minimum surface excavation and access area for rehabilitation of old and deteriorated culverts. Trenchless technologies can be used when other …

Extracting Patterns In Medical Claims Data For Predicting Opioid Overdose, Ryan Sanders

Graduate Theses and Dissertations

The goal of this project is to develop an efficient methodology for extracting features from time-dependent variables in transaction data. Transaction data is collected at varying time intervals making feature extraction more difficult. Unsupervised representational learning techniques are investigated, and the results compared with those from other feature engineering techniques. A successful methodology provides features that improve the accuracy of any machine learning technique. This methodology is then applied to insurance claims data in order to find features to predict whether a patient is at risk of overdosing on opioids. This data covers prescription, inpatient, and outpatient transactions. Features created …

Comparison Of Rl Algorithms For Learning To Learn Problems, Adolfo Gonzalez Iii

Theses and Dissertations

Machine learning has been applied to many different problems successfully due to the expressiveness of neural networks and simplicity of first order optimization algorithms. The latter being a vital piece needed for training large neural networks efficiently. Many of these algorithms were produced with behavior produced by experiments and intuition. An interesting question that comes to mind is that rather than observing and then designing algorithms with beneficial behaviors, can these algorithms be learned through a reinforcement learning by modeling optimization as a game. This paper explores several reinforcement learning algorithms which are applied to learn policies suited for optimization.

Materials Prediction Using High-Throughput And Machine Learning Techniques, Chandramouli Nyshadham

Theses and Dissertations

Predicting new materials through virtually screening a large number of hypothetical materials using supercomputers has enabled materials discovery at an accelerated pace. However, the innumerable number of possible hypothetical materials necessitates the development of faster computational methods for speedier screening of materials reducing the time of discovery. In this thesis, I aim to understand and apply two computational methods for materials prediction. The first method deals with a computational high-throughput study of superalloys. Superalloys are materials which exhibit high-temperature strength. A combinatorial high-throughput search across 2224 ternary alloy systems revealed 102 potential superalloys of which 37 are brand new, all …

Machine Learning Predictions Electronic Couplings For Charge Transport Calculations Of P3ht, Evan D. Miller, Matthew L. Jones, Mike M. Henry, Bryan Stanfill, Eric Jankowski

Materials Science and Engineering Faculty Publications and Presentations

The purpose of this work is to lower the computational cost of predicting charge mobilities in organic semiconductors, which will benefit the screening of candidates for inexpensive solar power generation. We characterize efforts to minimize the number of expensive quantum chemical calculations we perform by training machines to predict electronic couplings between monomers of poly-(3-hexylthiophene). We test five machine learning techniques and identify random forests as the most accurate, information-dense, and easy-to-implement approach for this problem, achieving mean-absolute-error of 0.02 [× 1.6 × 10⁻¹⁹ J], R² = 0.986, predicting electronic couplings 390 times faster than quantum chemical calculations, …

Application Of Artificial Neural Networks In The Drilling Processes: Can Equivalent Circulation Density Be Estimated Prior To Drilling?, Husam Hasan Alkinani, Abo Taleb Al-Hameedi, Shari Dunn-Norman, David Lian

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

As the drilling environment became more challenging nowadays, managing equivalent circulating density (ECD) is a key factor to minimize non-productive time (NPT) due to many drilling obstacles such as stuck pipe, formation fracturing, and lost circulation. The goal of this work was to predict ECD prior to drilling by using artificial neural network (ANN). Once ECD is recognized, the crucial drilling variables impact ECD can be modified to control ECD within the acceptable ranges. Data from over 2000 wells collected worldwide were used in this study to create an ANN to predict ECD prior to drilling. Into training, validation, and …