Browsing Theses and Dissertations(library) by Subject "Deep learning"
Now showing items 1-20 of 46
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ADVANCING THE RADIATION ONCOLOGY CLINIC WITH MOTION MANAGEMENT AND AUTOMATIC TREATMENT PLANNING
(2022-06-09)The leading cause of premature death (death under the age of 70) is cancer. The top five cancers for both male and female are: lung, colorectum, pancreas, breast cancer, and prostate. In 2020 there was an estimated 19.3 ... -
Artificial Intelligence For Cognitive Behavior Assessment In Children
(2018-06-25)Cognitive impairments in early childhood can lead to poor academic performance and require proper remedial intervention at the appropriate time. ADHD affects about 6-7% of children and is a psychiatric neurodevelopmental ... -
Automated multistep classifier sizing and training for deep learner
(2018-08-13)Training algorithms for deep learning have recently been proposed with notable success, beating the start-of-the-art in certain areas like audio, speech and language processing. The key role is played by learning multiple ... -
CELL SEGMENTATION IN CANCER HISTOPATHOLOGY IMAGES USING CONVOLUTIONAL NEURAL NETWORKS
(2016-12-07)Cancer, the second most dreadful disease causing large scale deaths in humans is characterized by uncontrolled growth of cells in the human body and the ability of those cells to migrate from the original site and spread ... -
COMPREHENSIVE STUDY OF GENERATIVE METHODS ON DRUG DISCOVERY
(2019-12-09)Observing the recent success of the deep learning (DL) technology in multiple life-changing application areas, e.g., autonomous driving, image/video search and discovery, natural language processing, etc., many new ... -
Constructing Large Open-Source Corpora and Leveraging Language Models for Simulink Toolchain Testing and Analysis
(2023-12-06)In several safety-critical industries such as automotive, aerospace, healthcare, and industrial automation, MATLAB/Simulink has emerged as the de-facto standard tool for system modeling and analysis, model compilation into ... -
Convex and Non-convex Optimization Methods for Machine Learning
(2019-08-01)This dissertation is concerned with modeling fundamental and challenging machine learning tasks as convex/non-convex optimization problems and designing a mechanism that could solve them in a cost and time-effective manner. ... -
Deep Generative Sculpting Models for Single Image 3D Reconstruction
(2023-12-18)In the field of computer vision, learning representations of images is an important task. This dissertation introduces deep generative sculpting models (DGSM), deep learning models that learn 3D representations of objects ... -
Deep Learning Based Multi-Label Classification for Surgical Tool Presence Detection in Laparoscopic Videos
(2017-08-07)Laparoscopic surgery, Modern surgery, where the surgery is performed far away from the patient by inserting small incisions on the patient's body and the surgery is performed with a help of a video recorder and through ... -
DEEP LEARNING FOR PROTEIN PROPERTY AND STRUCTURE PREDICTION
(2022-08-15)I present my work towards solving the fundamental, challenging, and valuable problem for protein property and structure prediction. Specifically, I focus on solving the problem from three critical aspects: (1) designing ... -
Deep Learning for Recognition of Objects, Activities, Faces, and Spatio-Temporal Patterns
A popular method in machine learning is Convolutional Neural Network (CNN). CNN had was of high interest to the research community in the 1990s, but after that its popularity receded compared to the Support Vector Machine ... -
DEEP LEARNING METHODS FOR IMAGE RESTORATION AND RECONSTRUCTION
(2021-04-19)The problem of image reconstruction and restoration refers to recovering the clean images from corrupted ones. Corruption or degradation can occur due to atmospheric conditions such as rain, fog, mist, snow, dust, and air ... -
Deep Reinforcement Learning-based Portfolio Management
(2019-05-16)Machine Learning is at the forefront of every field today. The subfields of Machine Learning called Reinforcement Learning and Deep Learning, when combined have given rise to advanced algorithms which have been successful ... -
Deep Representation Learning for Clustering and Domain Adaptation
(2019-12-05)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 ... -
DEEPSIGN: A DEEP-LEARNING ARCHITECTURE FOR SIGN LANGUAGE
(2018-11-13)Sign languages are used by deaf people for communication. In sign languages, humans use hand gestures, body, facial expressions and movements to convey meaning. Humans can easily learn and understand sign languages, but ... -
Defending Neural Networks Against Adversarial Examples
(2018-12-12)Deep learning is becoming a technology central to the safety of cars, the security of networks, and the correct functioning of many other types of systems. Unfortunately, attackers can create adversarial examples, small ... -
DOMAIN ADAPTIVE TRANSFER LEARNING FOR VISUAL CLASSIFICATION
(2021-08-16)Deep Neural Networks have made a significant impact on many computer vision applications with large-scale labeled datasets. However, in many applications, it is expensive and time-consuming to gather large-scale labeled ... -
Efficient Network Design for High Dimensional Data
(2020-05-14)Due to the powerful feature representation capabilities, deep learning has became a powerful tool in the field of computer vision. Especially in the aspect of high-dimensional images, deep learning can achieve fast inference ... -
ENHANCING THE CLASSIFICATION OF AUTISM SPECTRUM DISORDER FROM RS-FMRI FUNCTIONAL CONNECTIVITY DATA USING TEMPORAL INFORMATION
(2023-12-15)Autism Spectrum Disorder (ASD) affects the patient’s cognitive development which leads to difficulties in social functioning, daily tasks, and independent living. This necessitates intervention at an early age to take ... -
Exploring Deep Learning in Finance
(2022-05-20)Financial market analysis is process of analyzing market closely and predict the next move of market whether it will go up or down using historical data. Financial market is stochastic and has rapid changes over time, ...