Physical Sciences and Mathematics Commons^™

Neural Network Supervised And Reinforcement Learning For Neurological, Diagnostic, And Modeling Problems, Donald Wunsch Iii

Masters Theses

“As the medical world becomes increasingly intertwined with the tech sphere, machine learning on medical datasets and mathematical models becomes an attractive application. This research looks at the predictive capabilities of neural networks and other machine learning algorithms, and assesses the validity of several feature selection strategies to reduce the negative effects of high dataset dimensionality. Our results indicate that several feature selection methods can maintain high validation and test accuracy on classification tasks, with neural networks performing best, for both single class and multi-class classification applications. This research also evaluates a proof-of-concept application of a deep-Q-learning network (DQN) to …

Metric Learning Via Linear Embeddings For Human Motion Recognition, Byoungdoo Kong

Masters Theses

We consider the application of Few-Shot Learning (FSL) and dimensionality reduction to the problem of human motion recognition (HMR). The structure of human motion has unique characteristics such as its dynamic and high-dimensional nature. Recent research on human motion recognition uses deep neural networks with multiple layers. Most importantly, large datasets will need to be collected to use such networks to analyze human motion. This process is both time-consuming and expensive since a large motion capture database must be collected and labeled. Despite significant progress having been made in human motion recognition, state-of-the-art algorithms still misclassify actions because of characteristics …

Automatically Classifying Non-Functional Requirements With Feature Extraction And Supervised Machine Learning Techniques, Mahtab Ezzatikarami

Electronic Thesis and Dissertation Repository

Abstract. Context and Motivation: Non-functional requirements (NFRs) of a system need to be classified into different types such as usability, performance, etc. This would enable stakeholders to ensure the completeness of their work by extracting specific NFRs related to their expertise. Question/Problem: Because of the size and complexity of requirement specification documents, the manual classification of NFRs is time-consuming, labour-intensive, and error-prone. We thus need an automated solution that can provide a highly accurate and efficient categorization of NFRs. Principal ideas/results: In this investigation, using natural language processing and supervised machine learning (SML) techniques, we investigate with feature extraction techniques …

Development And Identification Of Metrics To Predict The Impact Of Dimension Reduction Techniques On Classical Machine Learning Algorithms For Still Highway Images, Wasim Akram Khan

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

We are witnessing an influx of data - images, texts, video, etc. Their high dimensionality and large volume make it challenging to apply machine learning to obtain actionable insight. This thesis explores several aspects pertaining to dimensional reduction: dimension reduction methods, metrics to measure distortion, image preprocessing, etc. Faster training and inference time on reduced data and smaller models which can be deployed on commodity hardware are a critical advantage of dimension reduction. For this study, classical machine learning methods were explored owing to their solid mathematical foundation and interpretability.

The dataset used is a time series of images from …

Computational Astronomy: Classification Of Celestial Spectra Using Machine Learning Techniques, Gayatri Milind Hungund

Master's Projects

Lightyears beyond the Planet Earth there exist plenty of unknown and unexplored stars and Galaxies that need to be studied in order to support the Big Bang Theory and also make important astronomical discoveries in quest of knowing the unknown. Sophisticated devices and high-power computational resources are now deployed to make a positive effort towards data gathering and analysis. These devices produce massive amount of data from the astronomical surveys and the data is usually in terabytes or petabytes. It is exhaustive to process this data and determine the findings in short period of time. Many details can be missed …

Randomized And Evolutionary Approaches To Dataset Characterization, Feature Weighting, And Sampling In K-Nearest Neighbors, Suryoday Basak

Computer Science and Engineering Theses

K-Nearest Neighbors (KNN) has remained one of the most popular methods for supervised machine learning tasks. However, its performance often depends on the characteristics of the dataset and on appropriate feature scaling. In this thesis, characteristics of a dataset that make it suitable for being used within KNN are explored. As part of this, two new measures for dataset dispersion, called mean neighborhood target variance (MNTV), and mean neighborhood target entropy (MNTE) are developed to help determine the performance we expect while using KNN regressors and classifiers, respectively. It is empirically demonstrated that these measures of dispersion can be indicative …

An Exploration Of Methods For Classifying Air-Written Letters From The Spanish Alphabet, Manuel Serna-Aguilera

Computer Science and Computer Engineering Undergraduate Honors Theses

The ability to recognize human activity, especially air-writing, is an interesting challenge as one could identify any letter from many languages. I intend to investigate this problem of air-writing, but with the added twist of including the following letters from the Spanish alphabet: Á, É, Í, Ó, Ú, Ü, and Ñ. With this new alphabet, I set out to see what kinds of classifiers work best and on what kinds of data, since letters can be represented in multiple ways.

My tracking system will consist of a regular camera and a subject who will draw with a brightly colored marker …

Towards Multi-Modal Data Classification, Henry Ng

UNLV Theses, Dissertations, Professional Papers, and Capstones

A feature fusion multi-modal neural network (MMN) is a network that combines different modalities at the feature level to perform a specific task. In this paper, we study the problem of training the fusion procedure for MMN. A recent study has found that training a multi-modal network that incorporates late fusion produces a network that has not learned the proper parameters for feature extraction. These late fusion models perform very well during training but fall short to its single modality counterpart when testing. We hypothesize that jointly trained MMN have weight space that is too large for effective training. To …

Novel Inference Methods For Generalized Linear Models Using Shrinkage Priors And Data Augmentation., Arinjita Bhattacharyya

Electronic Theses and Dissertations

Generalized linear models have broad applications in biostatistics and sociology. In a regression setup, the main target is to find a relevant set of predictors out of a large collection of covariates. Sparsity is the assumption that only a few of these covariates in a regression setup have a meaningful correlation with an outcome variate of interest. Sparsity is incorporated by regularizing the irrelevant slopes towards zero without changing the relevant predictors and keeping the resulting inferences intact. Frequentist variable selection and sparsity are addressed by popular techniques like Lasso, Elastic Net. Bayesian penalized regression can tackle the curse of …

Prediction Of Sudden Cardiac Death Using Ensemble Classifiers, Ayman Momtaz El-Geneidy

CCE Theses and Dissertations

Sudden Cardiac Death (SCD) is a medical problem that is responsible for over 300,000 deaths per year in the United States and millions worldwide. SCD is defined as death occurring from within one hour of the onset of acute symptoms, an unwitnessed death in the absence of pre-existing progressive circulatory failures or other causes of deaths, or death during attempted resuscitation. Sudden death due to cardiac reasons is a leading cause of death among Congestive Heart Failure (CHF) patients. The use of Electronic Medical Records (EMR) systems has made a wealth of medical data available for research and analysis. Supervised …

Development Of Criteria For Mobile Device Cybersecurity Threat Classification And Communication Standards (Ctc&Cs), Emmanuel Jigo

CCE Theses and Dissertations

The increasing use of mobile devices and the unfettered access to cyberspace has introduced new threats to users. Mobile device users are continually being targeted for cybersecurity threats via vectors such as public information sharing on social media, user surveillance (geolocation, camera, etc.), phishing, malware, spyware, trojans, and keyloggers. Users are often uninformed about the cybersecurity threats posed by mobile devices. Users are held responsible for the security of their device that includes taking precautions against cybersecurity threats. In recent years, financial institutions are passing the costs associated with fraud to the users because of the lack of security.

The …

A Computational Method For The Image Segmentation Of Pigmented Skin Lesions, Kaila M. Piscitelli

Senior Projects Spring 2020

Senior Project submitted to The Division of Science, Mathematics and Computing of Bard College.

Multi-Label Classification Models For Heterogeneous Data: An Ensemble-Based Approach., Jose Maria Moyano Murillo

Theses and Dissertations

In recent years, the multi-label classification gained attention of the scientific community given its ability to solve real-world problems where each instance of the dataset may be associated with several class labels simultaneously, such as multimedia categorization or medical problems.

The first objective of this dissertation is to perform a thorough review of the state-of-the-art ensembles of multi-label classifiers (EMLCs). Its aim is twofold: 1) study state-of-the-art ensembles of multi-label classifiers and categorize them proposing a novel taxonomy; and 2) perform an experimental study to give some tips and guidelines to select the method that perform the best according to …

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 …

Multimodal Emotion Recognition Using 3d Facial Landmarks, Action Units, And Physiological Data, Diego Fabiano

USF Tampa Graduate Theses and Dissertations

To fully understand the complexities of human emotion, the integration of multiple physical features from different modalities can be advantageous. Considering this, this thesis presents an approach to emotion recognition using handcrafted features that consist of 3D facial data, action units, and physiological data. Each modality independently, as well as the combination of each for recognizing human emotion were analyzed.

This analysis includes the use of principal component analysis to determine which dimensions of the feature vector are most important for emotion recognition. The proposed features are shown to be able to be used to accurately recognize emotion and that …

Adaptive Feature Engineering Modeling For Ultrasound Image Classification For Decision Support, Hatwib Mugasa

Doctoral Dissertations

Ultrasonography is considered a relatively safe option for the diagnosis of benign and malignant cancer lesions due to the low-energy sound waves used. However, the visual interpretation of the ultrasound images is time-consuming and usually has high false alerts due to speckle noise. Improved methods of collection image-based data have been proposed to reduce noise in the images; however, this has proved not to solve the problem due to the complex nature of images and the exponential growth of biomedical datasets. Secondly, the target class in real-world biomedical datasets, that is the focus of interest of a biopsy, is usually …

A Machine Learning Approach To Predicting Community Engagement On Social Media During Disasters, Adel Alshehri

USF Tampa Graduate Theses and Dissertations

The use of social media is expanding significantly and can serve a variety of purposes. Over the last few years, users of social media have played an increasing role in the dissemination of emergency and disaster information. It is becoming more common for affected populations and other stakeholders to turn to Twitter to gather information about a crisis when decisions need to be made, and action is taken. However, social media platforms, especially on Twitter, presents some drawbacks when it comes to gathering information during disasters. These drawbacks include information overload, messages are written in an informal format, the presence …

An Adaptive Weighted Average (Wav) Reprojection Algorithm For Image Denoising, Halimah Alsurayhi

Electronic Thesis and Dissertation Repository

Patch-based denoising algorithms have an effective improvement in the image denoising domain. The Non-Local Means (NLM) algorithm is the most popular patch-based spatial domain denoising algorithm. Many variants of the NLM algorithm have proposed to improve its performance. Weighted Average (WAV) reprojection algorithm is one of the most effective improvements of the NLM denoising algorithm. Contrary to the NLM algorithm, all the pixels in the patch contribute into the averaging process in the WAV reprojection algorithm, which enhances the denoising performance. The key parameters in the WAV reprojection algorithm are kept fixed regardless of the image structure. In this thesis, …

Classifying Challenging Behaviors In Autism Spectrum Disorder With Neural Document Embeddings, Abigail Atchison

Computational and Data Sciences (MS) Theses

The understanding and treatment of challenging behaviors in individuals with Autism Spectrum Disorder is paramount to enabling the success of behavioral therapy; an essential step in this process being the labeling of challenging behaviors demonstrated in therapy sessions. These manifestations differ across individuals and within individuals over time and thus, the appropriate classification of a challenging behavior when considering purely qualitative factors can be unclear. In this thesis we seek to add quantitative depth to this otherwise qualitative task of challenging behavior classification. We do so through the application of natural language processing techniques to behavioral descriptions extracted from the …

Human Activity Recognition Based On Multimodal Body Sensing, Anish Hemant Narkhede

Master's Projects

In the recent years, human activity recognition has been widely popularized by a lot of smartphone manufacturers and fitness tracking companies. It has allowed us to gain a deeper insight into our physical health on a daily basis. However, with the evolution of fitness tracking devices and smartphones, the amount of data that is being captured by these devices is growing exponentially. This paper aims at understanding the process of dimensionality reduction such as PCA so that the data can be used to make meaningful predictions along with novel techniques using autoencoders with different activation functions. The paper also looks …

Toward On-Demand Profile Hidden Markov Models For Genetic Barcode Identification, Jessica Sheu

Master's Projects

Genetic identification aims to solve the shortcomings of morphological identification. By using the cytochrome c oxidase subunit 1 (COI) gene as the Eukaryotic “barcode,” scientists hope to research species that may be morphologically ambiguous, elusive, or similarly difficult to visually identify. Current COI databases allow users to search only for existing database records. However, as the number of sequenced, potential COI genes increases, COI identification tools should ideally also be informative of novel, previously unreported sequences that may represent new species. If an unknown COI sequence does not represent a reported organism, an ideal identification tool would report taxonomic ranks …

Species Classification Using Dna Barcoding And Profile Hidden Markov Models, Sphoorti Poojary

Master's Projects

Traditional classification systems for living organisms like the Linnaean taxonomy involved classification based on morphological features of species. This traditional system is being replaced by molecular approaches which involve using gene sequences. The COI gene, also known as the ”DNA barcode” since it is unique in every species, can be used to uniquely identify organisms and thus, classify them. Classifying using gene sequences has many advantages, including correct identification of cryptic species(individuals which appear similar but belong to different species) and species which are extremely small in size. In this project, I worked on classifying COI sequences of unknown species …

Teaching Computers To Teach Themselves: Synthesizing Training Data Based On Human-Perceived Elements, James Little

Honors Projects

Isolation-Based Scene Generation (IBSG) is a process for creating synthetic datasets made to train machine learning detectors and classifiers. In this project, we formalize the IBSG process and describe the scenarios—object detection and object classification given audio or image input—in which it can be useful. We then look at the Stanford Street View House Number (SVHN) dataset and build several different IBSG training datasets based on existing SVHN data. We try to improve the compositing algorithm used to build the IBSG dataset so that models trained with synthetic data perform as well as models trained with the original SVHN training …

Classification Of Vegetation In Aerial Imagery Via Neural Network, Gevand Balayan

UNLV Theses, Dissertations, Professional Papers, and Capstones

This thesis focuses on the task of trying to find a Neural Network that is best suited for identifying vegetation from aerial imagery. The goal is to find a way to quickly classify items in an image as highly likely to be vegetation(trees, grass, bushes and shrubs) and then interpolate that data and use it to mark sections of an image as vegetation. This has practical applications as well. The main motivation of this work came from the effort that our town takes in conserving water. By creating an AI that can easily recognize plants, we can better monitor the …

Computer-Aided Classification Of Impulse Oscillometric Measures Of Respiratory Small Airways Function In Children, Nancy Selene Avila

Open Access Theses & Dissertations

Computer-aided classification of respiratory small airways dysfunction is not an easy task. There is a need to develop more robust classifiers, specifically for children as the classification studies performed to date have the following limitations: 1) they include features derived from tests that are not suitable for children and 2) they cannot distinguish between mild and severe small airway dysfunction.

This Dissertation describes the classification algorithms with high discriminative capacity to distinguish different levels of respiratory small airways function in children (Asthma, Small Airways Impairment, Possible Small Airways Impairment, and Normal lung function). This ability came from innovative feature selection, …

Deep Neural Network Learning-Based Classifier Design For Big-Data Analytics, Krishnan Raghavan

Doctoral Dissertations

"In this digital age, big-data sets are commonly found in the field of healthcare, manufacturing and others where sustainable analysis is necessary to create useful information. Big-data sets are often characterized by high-dimensionality and massive sample size. High dimensionality refers to the presence of unwanted dimensions in the data where challenges such as noise, spurious correlation and incidental endogeneity are observed. Massive sample size, on the other hand, introduces the problem of heterogeneity because complex and unstructured data types must analyzed. To mitigate the impact of these challenges while considering the application of classification, a two step analysis approach is …

Change Descriptors For Determining Nodule Malignancy In Lung Ct Screening Images, Benjamin Geiger

USF Tampa Graduate Theses and Dissertations

Computed tomography (CT) imagery is an important weapon in the fight against lung cancer; various forms of lung cancer are routinely diagnosed from CT imagery. The growth of the suspect nodule is known to be a prognostic factor in the diagnosis of pulmonary cancer, but the change in other aspects of the nodule, such as its aspect ratio, density, spiculation, or other features usable for machine learning, may also provide prognostic information.

We hypothesized that adding combined feature information from multiple CT image sets separated in time could provide a more accurate determination of nodule malignancy. To this end, we …

Topological And Feature Based Identification Of Hole Boundaries In Point Cloud Data And Differentiation Between Surface And Physical Holes, Aaqif Muhtasim

Computer Science and Engineering Theses

With the advent of autonomous agents becoming prominent in everyday lives, the importance of processing the surroundings into understandable features becomes more and more important. 3D point clouds play a major role in the perception of such agents and thus having the ability to correctly decipher features from point clouds is crucial to the planning of actions that the agent would need to undertake. This thesis analyzes holes found in point clouds. Based on two approaches that center around topological data analysis and local point set features respectively. It studies how each of the methods works and how a combination …

Classification Of Clinical Narratives Using Convolutional Neural Network, Nikit Rajiv Lonari

Computer Science and Engineering Theses

Patient safety is a key aspect for good consumer care. When an individual is hospitalized or receives medication the family wants the patient safety to be above all factors. For instance, a drug can do both either cure the disease or perhaps, give rise to an adverse event. A drug administered for an indicated condition has substantial power to reduce or cure a disease and further to prevent it from happening again in the future but at the risk of side effects. At present, there are several methods in patient safety and in particular in the area of signal detection …

Cleaver: Classification Of Everyday Activities Via Ensemble Recognizers, Samantha Hsu

Master's Theses

Physical activity can have immediate and long-term benefits on health and reduce the risk for chronic diseases. Valid measures of physical activity are needed in order to improve our understanding of the exact relationship between physical activity and health. Activity monitors have become a standard for measuring physical activity; accelerometers in particular are widely used in research and consumer products because they are objective, inexpensive, and practical. Previous studies have experimented with different monitor placements and classification methods. However, the majority of these methods were developed using data collected in controlled, laboratory-based settings, which is not reliably representative of real …