Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
Results
Showing 1 to 20 of 135 for “"Deep learning (Machine learning)"”.
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Applications of Deep Learning, Machine Learning, and Remote Sensing to Improving Air Quality and Solar Energy Production
… from Planet Labs. In this study, we employ a deep convolutional neural network (CNN) to process the imagery by extracting image features that characterize the day-to-day dynamic changes in the built environment and more importantly the image colors related to aerosol loading, and a random …
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Predicting SMEs’ credit risk using artificial intelligence applications: Evidence from the UK SMEs
… artificial intelligence techniques, such as<br/>deep learning, machine learning algorithms, and ensemble methods.<br/>The results show that macroeconomic factors greatly improve bankruptcy models'<br/>forecast accuracy. Furthermore, the findings show that machine learning techniques<br/>typically …
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Application of Machine Learning and Deep Learning Approaches for Traffic Operation and Safety Assessment at Signalized Intersections
… This thesis addresses such applications of machine learning and deep learning approaches using emerging traffic datasets. A novel deep learning model, MGCNN is suggested for short-term turning volume prediction using GRIDSMART data from the MLK corridor in Chattanooga, Tennessee. During …
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Synthesizing Realistic Data for Vision Based Drone-to-Drone Detection
In the thesis, we aimed at building a robust UAV(drone) detection algorithm through which, one drone could detect another drone in flight. Though this was a straight forward object detection problem, the biggest challenge we faced for drone detection is the limited amount of drone images for …
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High performance Deep Learning based Digital Pre-distorters for RF Power Amplifiers
In this work, we present different deep learning-based digital pre-distorters and compare them based on their performance towards improving the linearity of highly non-linear power amplifiers. The simulation results show that BiLSTM based DPDs work the best in terms of improving the linearity …
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Dynamic reconfigurable battery systems via graph-based deep reinforcement learning
… as dynamic graphs and investigate graph-based deep reinforcement learning approaches for adaptively optimizing cell-to-cell topology under practical operational constraints. We evaluate the proposed methods by using the open-source battery simulation platform PyBaMM, measuring performance in …
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A unified algebraic framework extending from a 6-set discrete probability algebra and its application in deep learning
… that can be used to delve into the neuron-level deep neural network structure and aims at improving the transparency of how the black box works and making advancements in detailed applications. Our approach extends a 6-Set Discrete Probability Algebra to a more systematic quantitative framework …
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Predicting hand grasping orientation for prosthetic hand control using multimodal sensor data (EEG, EMG, and IMU) with machine learning approach
… limb loss. Our research delves into advanced machine learning (ML) methodologies to accurately predict hand-grasping orientations, thereby improving the precision of prosthetic control. We present a comprehensive framework that amalgamates inputs from multiple sensors. This includes …
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Deep learning-based framework for traffic estimation for the MLK Smart Corridor in downtown Chattanooga, TN
In this Thesis we introduced a deep learning-based framework for vehicles detection, tracking, movement direction identification, and speed estimation. We chose YOLOv7 for objects detection given its ability to run up to 160 fps. We trained YOLOv7 to detect and classify vehicles into four classes …
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Addressing the challenges facing deep learning based Specific Emitter Identification via preamble based waveforms
… in-depth experiments that analyze the effects on Deep Learning (DL) based Specific Emitter Identification (SEI) and address three issues facing the field. SEI is targeted as a physical-layer security measure that can identify radios within an Internet of Things (IoT) deployment without the need of …
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An evaluation of the robustness of the natural-adversarial mutual information-based defense and malware classification against adversarial attacks for deep learning
In today’s technology driven world, the use of Machine Learning (ML) systems is becoming ubiquitous, albeit often in the background, in many areas of daily life. ML systems are being used to detect malware, control autonomous vehicles, classify images, assist with medical diagnosis, and block …
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Valuazione automatica della complessità di frasi in Italiano e Inglese tramite Deep Learning.
… complexity in English and Italian languages via Deep Learning methodologies. The developed systems have shown learning abilities capable of inferring and relating in a non-trivial way features which affect the text comprehension to assess the text complexity, and versatility for tackling the …
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Performance benchmarking, analysis, and optimization of deep learning inference
The world sees a proliferation of deep learning (DL) models and their wide adoption in different application domains. This has made the performance benchmarking, understanding, and optimization of DL inference an increasingly pressing task for both hardware designers and system providers, as they …
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End-To-End Text Detection Using Deep Learning
… to the next. Another line of work is the use of deep learning techniques. Some of the deep methods used for text detection are box detection models and fully convolutional models. Box detection models suffer from the nature of the annotations, which may be too coarse to provide detailed …
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Learning to handle occlusion for motion analysis and view synthesis
… we can improve the performance of vision-based deep learning models by harnessing the power of occlusion handling. We first visit the problem of optical flow estimation for motion analysis. We present a deep learning module that builds upon occlusion handling methods in classic Computer Vision …
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Applying Natural Language Processing and Deep Learning Techniques for Raga Recognition in Indian Classical Music
… classification problem, solved by applying a deep learning technique. A digital audio excerpt is hierarchically processed and split into subsequences and gamaka sequences to mimic a textual document structure, so our model can learn the resulting tonal and temporal sequence patterns using a …
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Synthetic Electronic Medical Record Generation using Generative Adversarial Networks
… a patient's condition quickly. In recent years, Deep Learning models have proved their value and have become state-of-the-art in computer vision, natural language processing, speech and other areas. The private nature of EHR data has prevented public access to EHR datasets. There are many …
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Deep Learning for Enhancing Precision Medicine
… have been limited. Meanwhile, advances in deep learning, one of the most promising branches of artificial intelligence, have produced unprecedented performance in various fields. Although several deep learning-based methods have been proposed to predict individual phenotypes, they have not …
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Deep Learning Neural Network-based Sinogram Interpolation for Sparse-View CT Reconstruction
… views possesses severe artifacts. Recently, Deep Learning-based methods are increasingly being used to interpret the missing data by learning the nature of the image formation process. The current methods are promising but operate mostly in the image domain presumably due to lack of …
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CloudCV: Deep Learning and Computer Vision on the Cloud
… problems: building and maintaining a cluster of machines, formulating each component of the computer vision pipeline, designing new deep learning layers, writing custom hardware wrappers, etc. This thesis introduces CloudCV, an ambitious system that contain algorithms for end-to-end processing of …
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