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Articles 1 - 24 of 24
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Probabilistic Forecasting Of Winter Mixed Precipitation Types In New York State Utilizing A Random Forest, Brian Chandler Filipiak
Probabilistic Forecasting Of Winter Mixed Precipitation Types In New York State Utilizing A Random Forest, Brian Chandler Filipiak
Legacy Theses & Dissertations (2009 - 2024)
Operational forecasters face a plethora of challenges when making a forecast; they must consider multiple data sources ranging from radar and satellites to surface and upper air observations, to numerical weather prediction output. Forecasts must be done in a limited window of time, which adds an additional layer of difficulty to the task. These challenges are exacerbated by winter mixed precipitation events where slight differences in thermodynamic profiles or changes in terrain create different precipitation types across small areas. In addition to being difficult to forecast, mixed precipitation events can have large-scale impacts on our society.
Deep Active Genetic Learning With Evidential Uncertainty For Agriculture Crops And Lake Water Quality Assessment, Oguz M. Aranay
Deep Active Genetic Learning With Evidential Uncertainty For Agriculture Crops And Lake Water Quality Assessment, Oguz M. Aranay
Legacy Theses & Dissertations (2009 - 2024)
Despite significant advancements in the field of machine learning, there are two issues that still require further exploration. First, how to learn from a small dataset; and second, how to select appropriate features from the data. Although there exist many techniques to address these issues, choosing a combination of the techniques from these two groups is challenging, and worth investigating. To address these concerns, this thesis presents a learning framework that is based on a deep learning model utilizing active learning (with evidential uncertainty as a basis for acquisition function) for the first issue and a genetic algorithm for the …
Stability And Differential Privacy Of Stochastic Gradient Methods, Zhenhuan Yang
Stability And Differential Privacy Of Stochastic Gradient Methods, Zhenhuan Yang
Legacy Theses & Dissertations (2009 - 2024)
Recently there are a considerable amount of work devoted to the study of the algorithmic stability as well as differential privacy (DP) for stochastic gradient methods (SGM). However, most of the existing work focus on the empirical risk minimization (ERM) and the population risk minimization problems. In this paper, we study two types of optimization problems that enjoy wide applications in modern machine learning, namely the minimax problem and the pairwise learning problem.
Exposing Gan-Generated Faces Using Deep Neural Network, Hui Guo
Exposing Gan-Generated Faces Using Deep Neural Network, Hui Guo
Legacy Theses & Dissertations (2009 - 2024)
Generative adversarial network (GAN) generated high-realistic human faces are visually challenging to discern from real ones. They have been used as profile images for fake social media accounts, which leads to high negative social impacts.In this work, we explore a universal physiological cue of the eye, namely the pupil shape consistency, to identify GAN-generated faces reliably. We show that GAN-generated faces can be exposed via irregular pupil shapes. This phenomenon is caused by the lack of physiological constraints in the GAN models. We demonstrate that such artifacts exist widely in high-quality GAN-generated faces. We design an automatic method to segment …
Dynamic Instance-Wise Decision-Making For Machine Learning, Yasitha Warahena Liyanage
Dynamic Instance-Wise Decision-Making For Machine Learning, Yasitha Warahena Liyanage
Legacy Theses & Dissertations (2009 - 2024)
In a typical supervised machine learning setting, the predictions on all test instances are based on a common subset of features discovered during model training. However, using a different subset of features that are most informative for each test instance individually may improve not only the quality of prediction but also the overall interpretability of the model. To this end, in this dissertation, we study the problem of optimizing the trade-off between instance-level sparsity and the quality of prediction using a dynamic instance-wise decision-making approach. Specifically, this approach sequentially reviews features one at a time for each data instance given …
Two Dimensional Nanoparticles/Nanozymes For Biosensing Augmented By Machine Learning, Nidhi Chintan Nandu
Two Dimensional Nanoparticles/Nanozymes For Biosensing Augmented By Machine Learning, Nidhi Chintan Nandu
Legacy Theses & Dissertations (2009 - 2024)
Biosensing is an ever-evolving field with many resources devoted towards new approaches in sensing and refining the existing methods. For a long time, such approaches involved use of state-of-the-art instruments and were often proved to be time consuming and expensive. Nanoparticles with their unique properties, inexpensive synthesis and ease of manipulation have found purpose in myriad fields including biosensing. Different nanoparticles based on their composition, morphology, dimensionality, and surface modifications have already gained foot hold in the biosensor ranks. The unique opto-electric properties due to the quantum effect in the nanoparticles have also made them one of the leading alternatives …
Using Machine Learning To Predict Super-Utilizers Of Healthcare Services, Kevin Paul Buchan Jr.
Using Machine Learning To Predict Super-Utilizers Of Healthcare Services, Kevin Paul Buchan Jr.
Legacy Theses & Dissertations (2009 - 2024)
In this dissertation, I aim to forecast high utilizers of emergency care and inpatient Medicare services (i.e., healthcare visits). Through a literature review, I demonstrate that accurate and reliable prediction of these future high utilizers will not only reduce healthcare costs but will also improve the overall quality of healthcare for patients. By identifying this population at risk before manifestation, I propose that there is still time to reverse undesirable healthcare trajectories (i.e., individuals whose clinical risk increases an excessive healthcare and treatment burden) through timely attention and proper care coordination. My dissertation culminates in the delivery of state-of-the-art predictive …
Learning Graphs For Object Tracking And Counting, Shengkun Li
Learning Graphs For Object Tracking And Counting, Shengkun Li
Legacy Theses & Dissertations (2009 - 2024)
As important problems in computer vision, object tracking and counting attract increasing amounts of attention in recent years due to its wide range of applications, such as video surveillance, human- computer interaction, smart city. Despite much progress has been made in object tracking and counting with the arriving of deep neural networks (DNN), there still remains much room for improvement to satisfy the real-world applications.
The Role Of Ammonia In Atmospheric New Particle Formation And Implications For Cloud Condensation Nuclei, Arshad Arjunan Nair
The Role Of Ammonia In Atmospheric New Particle Formation And Implications For Cloud Condensation Nuclei, Arshad Arjunan Nair
Legacy Theses & Dissertations (2009 - 2024)
Atmospheric ammonia has received recent attention due to (a) its increasing trend across various regions of the globe; (b) the associated direct and indirect (through PM2.5) effects on human health, the ecosystem, and climate; and (c) recent evidence of its role in significantly enhancing atmospheric new particle formation (NPF or nucleation) rates. The mechanisms behind nucleation in the atmosphere are not fully understood, although over the last decade there have been significant developments in our understanding. This dissertation aims at improving our understanding of atmospheric ammonia in the atmosphere, its spatiotemporal variability, its role in atmospheric new particle formation, and …
Uncertainty Learning In Subjective Logic And Pattern Discovery In Network Data, Adilijiang Alimu
Uncertainty Learning In Subjective Logic And Pattern Discovery In Network Data, Adilijiang Alimu
Legacy Theses & Dissertations (2009 - 2024)
Uncertainty caused by unreliable or insufficient data and vulnerable machine learning models
Towards Machine Learning In Chemical Sensing : Milk Differentiation And Quality Control Through Two-Dimensional Nano-Sensor Array, Yu Sheng Chen
Towards Machine Learning In Chemical Sensing : Milk Differentiation And Quality Control Through Two-Dimensional Nano-Sensor Array, Yu Sheng Chen
Legacy Theses & Dissertations (2009 - 2024)
Herein, we developed a novel artificial tongue using machine learning and 12 nanoassemblies (2D-NAs) to identify and analyzed different kinds of milk beverages for quality control. This biomimetic sensor array was trained to “taste” different milk types as an “artificial tongue” which is the first time we demonstrated that this sensor array can be implemented to complex systems. Two-dimensional nanoparticles (2D-nps) and nine fluorescently labeled single stranded oligonucleotides (ssDNA) with different length and nucleobases were assembled to create 12 2D-NAs. The artificial tongue was deployed to identify and analyze five milk types. All five milk types were discriminated with 95% …
Pose Based Human Activity Recognition, Wenbo Li
Pose Based Human Activity Recognition, Wenbo Li
Legacy Theses & Dissertations (2009 - 2024)
Pose based human activity recognition is an important step towards video understanding. The last decade has witnessed the great progress in this field which is driven by multiple technical innovations, i.e., kinect, pose estimation techniques, deep learning, etc.
Efficient Detection Of Diseases By Feature Engineering Approach From Chest Radiograph, Avishek Mukherjee
Efficient Detection Of Diseases By Feature Engineering Approach From Chest Radiograph, Avishek Mukherjee
Legacy Theses & Dissertations (2009 - 2024)
Deep Learning is the new state-of-the-art technology in Image Processing. We applied Deep Learning techniques for identification of diseases from Radiographs made publicly available by NIH. We applied some Feature Engineering approach to augment the data from Anterior-Posterior position to Posterior-Anterior position and vice-versa for all the diseases, at the same point we suppressed ‘No Finding’ radiographs which contributed to more than 50% (approximately 60,000) of the dataset to top 1000 images. We also prepared a model by adding a huge amount of noise to the augmented data, which if need be can be deployed at rural locations which lack …
Communications Using Deep Learning Techniques, Priti Gopal Pachpande
Communications Using Deep Learning Techniques, Priti Gopal Pachpande
Legacy Theses & Dissertations (2009 - 2024)
Deep learning (DL) techniques have the potential of making communication systems
Emotion Forecasting In Dyadic Conversation : Characterizing And Predicting Future Emotion With Audio-Visual Information Using Deep Learning, Sadat Shahriar
Emotion Forecasting In Dyadic Conversation : Characterizing And Predicting Future Emotion With Audio-Visual Information Using Deep Learning, Sadat Shahriar
Legacy Theses & Dissertations (2009 - 2024)
Emotion forecasting is the task of predicting the future emotion of a speaker, i.e., the emotion label of the future speaking turn–based on the speaker’s past and current audio-visual cues. Emotion forecasting systems require new problem formulations that differ from traditional emotion recognition systems. In this thesis, we first explore two types of forecasting windows(i.e., analysis windows for which the speaker’s emotion is being forecasted): utterance forecasting and time forecasting. Utterance forecasting is based on speaking turns and forecasts what the speaker’s emotion will be after one, two, or three speaking turns. Time forecasting forecasts what the speaker’s emotion will …
Optimization Methods For Learning Graph-Structured Sparse Models, Baojian Zhou
Optimization Methods For Learning Graph-Structured Sparse Models, Baojian Zhou
Legacy Theses & Dissertations (2009 - 2024)
Learning graph-structured sparse models has recently received significant attention thanks to their broad applicability to many important real-world problems. However, such models, of more effective and stronger interpretability compared with their counterparts, are difficult to learn due to optimization challenges. This thesis presents optimization algorithms for learning graph-structured sparse models under three different problem settings. Firstly, under the batch learning setting, we develop methods that can be applied to different objective functions that enjoy linear convergence guarantees up to constant errors. They can effectively optimize the statistical score functions in the task of subgraph detection; Secondly, under stochastic learning setting, …
Clinical Information Extraction From Unstructured Free-Texts, Mingzhe Tao
Clinical Information Extraction From Unstructured Free-Texts, Mingzhe Tao
Legacy Theses & Dissertations (2009 - 2024)
Information extraction (IE) is a fundamental component of natural language processing (NLP) that provides a deeper understanding of the texts. In the clinical domain, documents prepared by medical experts (e.g., discharge summaries, drug labels, medical history records) contain a significant amount of clinically-relevant information that is crucial to the overall well-being of patients. Unfortunately, in many cases, clinically-relevant information is presented in an unstructured format, predominantly consisting of free-texts, making it inaccessible to computerized methods. Automatic extraction of this information can improve accessibility. However, the presence of synonymous expressions, medical acronyms, misspellings, negated phrases, and ambiguous terminologies make automatic extraction …
Mass Spectrometric Analysis And Machine Learning Enable Microorganism Classification Based On Rna Posttranscriptional Modifications, Colin Christopher Aldrich
Mass Spectrometric Analysis And Machine Learning Enable Microorganism Classification Based On Rna Posttranscriptional Modifications, Colin Christopher Aldrich
Legacy Theses & Dissertations (2009 - 2024)
RNA post-transcriptional modifications (PTMs) are dynamic features that can be up- or down-regulated by the health and metabolic state of a cell. These covalent modifications are installed and removed on RNA nucleosides by enzymes controlled by the activation and deactivation of specific genes. The goal of this research was to demonstrate that RNA PTMs can serve as a unique feature for the classification/identification of microorganisms. We utilized a scheme based on electrospray ionization mass spectrometry (ESI-MS) to obtain global PTM profiles from total RNA extracted from various microorganisms in optimal growth conditions as well as Salmonella typhimurium (S. typhimurium) spiked …
Mathematical Foundations Of Sentiment Classification : A Probabilistic Approach, Syed Shahzad Raza
Mathematical Foundations Of Sentiment Classification : A Probabilistic Approach, Syed Shahzad Raza
Legacy Theses & Dissertations (2009 - 2024)
This thesis is an introduction to the mathematical formalization of sentiment classification. It presents two popular probabilistic machine learning models to classify tweets downloaded from Twitter during the US Election Period, 2016. The thesis analyses accuracy of the two classification algorithms used. Namely, Multinomial Naïve Bayes and Bernoulli Naïve Bayes algorithms. Supervised learning approaches implemented in this thesis use approximately 600 manually labeled tweets containing information regarding the US presidential candidates. It is shown with 80% accuracy that majority of twitter users spoke in favor of Donald Trump before and after the presidential election through their tweets. We also discuss …
Visual Saliency Estimation : A Pre-Attentive Cognitive And Context-Aware Approach, Amanda Shannon Danko
Visual Saliency Estimation : A Pre-Attentive Cognitive And Context-Aware Approach, Amanda Shannon Danko
Legacy Theses & Dissertations (2009 - 2024)
At each glance, biological vision systems organize a tremendous amount of input and
Bus Stop Usage Evaluation And Brt Station Selection Strategy By Machine Learning Methods, Tianchi Zhang
Bus Stop Usage Evaluation And Brt Station Selection Strategy By Machine Learning Methods, Tianchi Zhang
Legacy Theses & Dissertations (2009 - 2024)
According to Commuting in the United States 2009, 86.1% of Americans commuted by car, light truck, or van, and about three-quarters of these individuals were driving alone, causing traffic congestion and raising environmental and energy-saving concerns in society. Therefore, transportation experts encourage the public to take public transportation and recommend the development of Bus Rapid Transit (BRT). Currently, bus service restructuring and BRT plans are based on rider surveys, community meetings and on-street interviews. However, these methods require large investments in manpower and material resources, and produce potentially biased results. In this research, the author used the machine learning method, …
Automated Classification Of The Narrative Of Medical Reports Using Natural Language Processing, Ira J. Goldstein
Automated Classification Of The Narrative Of Medical Reports Using Natural Language Processing, Ira J. Goldstein
Legacy Theses & Dissertations (2009 - 2024)
In this dissertation we present three topics critical to the document level classification of the narrative in medical reports: the use of preferred terminology in light of the presence of synonymous terms, the less than optimal performance of classification systems when presented with a non-uniform distribution of classes, and the problems associated with scarcity of labeled data when presented with an imbalance of classes in the data sets.
Machine Learned Melody Matching Using Strictly Relative Musical Abstractions, Michael Joseph Kolta
Machine Learned Melody Matching Using Strictly Relative Musical Abstractions, Michael Joseph Kolta
Legacy Theses & Dissertations (2009 - 2024)
We implement and evaluate a machine learning approach to improve systems for searching a database of music via melodic sample. We explore symbolic and aural input queries and test our prototypes with extensive user surveys. Our main contribution is to combine the following four elements. First is to create a unique musical abstraction that accounts for both pitch and rhythm in a relative manner. Second, our system allows for approximate matching of imperfect queries via the utilization of the Smith-Waterman algorithm that was originally designed for approximate matching of molecular subsequences, such as DNA samples. Third is to design our …
Bootstrapping Events And Relations From Text, Ting Liu
Bootstrapping Events And Relations From Text, Ting Liu
Legacy Theses & Dissertations (2009 - 2024)
Information Extraction (IE) is a technique for automatically extracting structured data from text documents. One of the key analytical tasks is extraction of important and relevant information from textual sources. While information is plentiful and readily available, from the Internet, news services, media, etc., extracting the critical nuggets that matter to business or to national security is a cognitively demanding and time consuming task. Intelligence and business analysts spend many hours poring over endless streams of text documents pulling out reference to entities of interest (people, locations, organizations) as well as their relationships as reported in text. Such extracted "information …