Digital Commons Network^™

Deep Visual Recommendation System, Raksha Sunil

Master's Projects

Recommendation system is a filtering system that predicts ratings or preferences that a user might have. Recommendation system is an evolved form of our trivial information retrieval systems. In this paper, we present a technique to solve new item cold start problem. New item cold start problem occurs when a new item is added to a shopping website like Amazon.com. There is no metadata for this item, no ratings and no reviews because it’s a new item in the system. Absence of data results in no recommendation or bad recommendations. Our approach to solve new item cold start problem requires …

Modeling The Bioactivation And Subsequent Reactivity Of Drugs, Tyler Brian Hughes

Arts & Sciences Electronic Theses and Dissertations

Metabolism can convert drugs to harmful reactive metabolites that conjugate to DNA and off-target proteins. Reactive metabolites are a significant driver of both drug candidate attrition and withdrawal from the market of already approved drugs. Unfortunately, reactive metabolites are difficult to study in vivo, because they are transitory and generally do not circulate. Instead, this work computationally models both metabolism and reactivity. Using deep learning, predictive models were developed for the metabolic formation of quinones and epoxides, which together account for about half of known reactive metabolites. Additionally, an accurate model of DNA and protein reactivity was constructed, which predicts …

Uas-Based Object Tracking Via Deep Learning, Marc Dinh

UNLV Theses, Dissertations, Professional Papers, and Capstones

Tracking is the task of identifying an object of interest and detect its position over time, and has numerous applications like surveillance, security and traffic control. In present times, unmanned aerial vehicles (UAV) have been more and more common which provides us with a new and less explored domain, with an ideal vantage point for surveillance and monitoring applications.. Aerial tracking is a particularly challenging task as it introduces new environmental variables such as rapid motion in 3D space. We propose a new deep learned tracker architecture catered to aerial tracking.

First, a study of six state-of-the-art deep learned trackers …

Emotion Recognition Using Deep Convolutional Neural Network With Large Scale Physiological Data, Astha Sharma

USF Tampa Graduate Theses and Dissertations

Classification of emotions plays a very important role in affective computing and has real-world applications in fields as diverse as entertainment, medical, defense, retail, and education. These applications include video games, virtual reality, pain recognition, lie detection, classification of Autistic Spectrum Disorder (ASD), analysis of stress levels, and determining attention levels. This vast range of applications motivated us to study automatic emotion recognition which can be done by using facial expression, speech, and physiological data.

A person’s physiological signals such are heart rate, and blood pressure are deeply linked with their emotional states and can be used to identify a …

A Deep Learning Approach To Recognizing Bees In Video Analysis Of Bee Traffic, Astha Tiwari

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

Colony Collapse Disorder (CCD) has been a major threat to bee colonies around the world which affects vital human food crop pollination. The decline in bee population can have tragic consequences, for humans as well as the bees and the ecosystem. Bee health has been a cause of urgent concern for farmers and scientists around the world for at least a decade but a specific cause for the phenomenon has yet to be conclusively identified.

This work uses Artificial Intelligence and Computer Vision approaches to develop and analyze techniques to help in continuous monitoring of bee traffic which will further …

Word Recognition In Nutrition Labels With Convolutional Neural Network, Anuj Khasgiwala

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

Nowadays, everyone is very busy and running around trying to maintain a balance between their work life and family, as the working hours are increasing day by day. In such hassled life people either ignore or do not give enough attention to a healthy diet. An imperative part of a healthy eating routine is the cognizance and maintenance of nourishing data and comprehension of how extraordinary sustenance and nutritious constituents influence our bodies. Besides in the USA, in many other countries, nutritional information is fundamentally passed on to consumers through nutrition labels (NLs) which can be found in all packaged …

Extractive Text Summarization With Deep Learning, Garrett G. Chan

Computer Engineering

This project explores extractive text summarization using the capabilities of Deep Learning. The goal of this project is to create an application with a neural network to take in text as its input, and create a summary that is a shorter, condensed version of the input text. This has been implemented in Python by configuring and training a neural network that takes in a vector of features that are extracted from the text using various Natural Language Processing libraries. The implementation demonstrates that we can train simple deep neural networks to successfully summarize text.

Improving Asynchronous Advantage Actor Critic With A More Intelligent Exploration Strategy, James B. Holliday

Graduate Theses and Dissertations

We propose a simple and efficient modification to the Asynchronous Advantage Actor Critic (A3C)

algorithm that improves training. In 2016 Google’s DeepMind set a new standard for state-of-theart

reinforcement learning performance with the introduction of the A3C algorithm. The goal of

this research is to show that A3C can be improved by the use of a new novel exploration strategy we

call “Follow then Forage Exploration” (FFE). FFE forces the agents to follow the best known path

at the beginning of a training episode and then later in the episode the agent is forced to “forage”

and explores randomly. In …

A Continuous Space Generative Model, Erzen Komoni

Graduate Theses and Dissertations

Generative models are a class of machine learning models capable of producing digital images with plausibly realistic properties. They are useful in such applications as visualizing designs, rendering game scenes, and improving images at higher magnifications. Unfortunately, existing generative models generate only images with a discrete predetermined resolution. This paper presents the Continuous Space Generative Model (CSGM), a novel generative model capable of generating images as a continuous function, rather than as a discrete set of pixel values. Like generative adversarial networks, CSGM trains by alternating between generative and discriminative steps. But unlike generative adversarial networks, CSGM uses only one …

Detecting Speakers In Video Footage, Michael Williams

Master's Theses

Facial recognition is a powerful tool for identifying people visually. Yet, when the end goal is more specific than merely identifying the person in a picture problems can arise. Speaker identification is one such task which expects more predictive power out of a facial recognition system than can be provided on its own. Speaker identification is the task of identifying who is speaking in video not simply who is present in the video. This extra requirement introduces numerous false positives into the facial recognition system largely due to one main scenario. The person speaking is not on camera. This paper …

Multimodal Sensing And Data Processing For Speaker And Emotion Recognition Using Deep Learning Models With Audio, Video And Biomedical Sensors, Farnaz Abtahi

Dissertations, Theses, and Capstone Projects

The focus of the thesis is on Deep Learning methods and their applications on multimodal data, with a potential to explore the associations between modalities and replace missing and corrupt ones if necessary. We have chosen two important real-world applications that need to deal with multimodal data: 1) Speaker recognition and identification; 2) Facial expression recognition and emotion detection.

The first part of our work assesses the effectiveness of speech-related sensory data modalities and their combinations in speaker recognition using deep learning models. First, the role of electromyography (EMG) is highlighted as a unique biometric sensor in improving audio-visual speaker …

Data-Driven Modeling For Decision Support Systems And Treatment Management In Personalized Healthcare, Milad Zafar Nezhad

Wayne State University Dissertations

Massive amount of electronic medical records (EMRs) accumulating from patients and populations motivates clinicians and data scientists to collaborate for the advanced analytics to create knowledge that is essential to address the extensive personalized insights needed for patients, clinicians, providers, scientists, and health policy makers. Learning from large and complicated data is using extensively in marketing and commercial enterprises to generate personalized recommendations. Recently the medical research community focuses to take the benefits of big data analytic approaches and moves to personalized (precision) medicine. So, it is a significant period in healthcare and medicine for transferring to a new paradigm. …

Deep Learning Models For Scoring Protein-Ligand Interaction Energies, Md Mahmudulla Hassan

Open Access Theses & Dissertations

In recent years, the cheminformatics community has seen an increased success with machine learning-based scoring functions for estimating binding affinities. The prediction of protein-ligand binding affinities is crucial for drug discovery research. Many physics-based scoring functions have been developed over the years. Lately, machine learning approaches are proven to boost the performance of traditional scoring functions. In this study, two scoring functions were developed; one is based on the Convolutional Neural Networks and the other one, called DLSCORE, is based on an ensemble of fully connected neural networks. Both the models were trained on the refined PDBbind (v.2016) dataset using …

Scalable Feature Selection And Extraction With Applications In Kinase Polypharmacology, Derek Jones

Theses and Dissertations--Computer Science

In order to reduce the time associated with and the costs of drug discovery, machine learning is being used to automate much of the work in this process. However the size and complex nature of molecular data makes the application of machine learning especially challenging. Much work must go into the process of engineering features that are then used to train machine learning models, costing considerable amounts of time and requiring the knowledge of domain experts to be most effective. The purpose of this work is to demonstrate data driven approaches to perform the feature selection and extraction steps in …