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 17 of 17 for “"Deep Learning (DL) models"”.
-
Comparative Analysis of Machine Learning Models for ERCOT Short Term Load Forecasting
… investigates the efficacy of various machine learning (ML) and deep learning (DL) models for short-term load forecasting (STLF) in the Electric Reliability Council of Texas (ERCOT) grid. A dual comparative approach is employed, evaluating models based on temporal features alone as well as in …
-
Machine Learning Application in Energy Storage System’s State Estimation: State of Health (SOH)
… and temperature towards developing different deep learning (DL) models to estimate the cell’s SOH cycled under a variety of extreme fast charging protocols. The results obtained from the different DL models have been compared with those obtained from the conventional feed forward neural …
-
Improving Computational Efficiency of MRI Brain Tumour Analysis Using Hybrid Machine Learning Models
… focuses on evaluating the usefulness of machine learning (ML) and deep learning (DL) models in classifying brain tumor and non-tumor cases using a dataset sourced from Kaggle. After preprocessing, the dataset was analyzed using Support Vector Machines (SVM), VGG-19, and YOLOv10 models. Metrics …
-
Towards a Reliable Deep Learning Framework for Prostate Cancer Diagnosis using Ultrasound
… for improving patient outcomes. Developing deep learning (DL) models for PCa detection is hindered by noisy labels and cancer heterogeneity. The purpose of this work is to develop a clinically applicable framework for DL-based detection of PCa from ultrasound that is robust to noise and …
-
Topic-Based Video Classification and Retrieval Using Machine Learning
Machine learning has made significant progress for many real-world problems. The Deep Learning (DL) models proposed primarily concentrate on object detection, image classification, and image captioning. However, very little work has been shown in DL-based video-content analysis and retrieval. Due …
-
Deep Learning Domain Adaptation in Brain MRI: Investigating Motion Mitigation in Adult and Neonatal Scans
… non-compliant patients like newborns. While Deep Learning (DL) models have emerged as powerful retrospective solutions for motion mitigation, their performance is expected to degrade when applied to data from different scanners, protocols, or patient populations. This thesis investigates the …
-
Using deep learning to classify community network traffic
… traffic classification make use of the Machine Learning (ML) and single Deep Learning (DL) models. ML classification models are effective to a certain degree. However, studies have shown they record low prediction and accuracy scores. In contrast, the proliferation of various deep learning …
-
Trustworthy Federated Learning Systems: From Secure Distributed Training to Reliable Fine-tuning
Training and fine-tuning Deep Learning (DL) models require vast amounts of domain-specific data. However, this data is often distributed across different organizations and devices, restricted from direct sharing by privacy, ownership, and regulatory constraints. While Federated Learning (FL) …
-
Application of foundation models for molecular representation in cancer drug discovery and precision oncology
… could be accelerated by leveraging advances in deep learning (DL) models to identify promising hit candidates and improve the prediction of drug response in cancer. Development of cancer drugs that will be effective on a predictable set of targets remains a major challenge. We are developing a …
-
NON-INVASIVE METHODS TO PREDICT AND MONITORING METABOLIC DYSFUNCTION ASSOCIATED STEATOTIC LIVER DISEASE BY USING ULTRASOUND IMAGING
… artificial intelligence (AI)-based (from machine learning to deep learning) models applied to imaging data give the potential to improve MASLD quantification, allowing for more widespread use of these technologies in both research and clinical settings and helping and supporting clinicians in …
-
Exploring Machine Learning, Feature Engineering, and Explainability to Constrain Spica’s Apsidal Constant through MESA Simulations
… <p>This research aims to incorporate machine learning techniques to effectively constrain Spica’s apsidal constant, focusing on three main objectives. First, it seeks to apply a range of machine learning (ML) and deep learning (DL) models, from basic to more complex architectures, to analyze …
-
Deep Learning Architectures for Improving Weather Research and Forecasting (WRF) Precipitation Estimates and Landslide Susceptibility Mapping: A Case Study of Puerto Rico
… address these challenges, the implementation of deep learning (DL) models utilizing high-resolution radar data offers a promising strategy for increasing the accuracy of precipitation estimations. Additionally, DL models can be employed to predict landslide susceptibility maps (LSM) that rely on …
-
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 …
-
Using Deep Learning to Understand and Design Heterogeneous Materials
… multiscale modeling techniques and machine learning (ML) models to accelerate property calculation and design of heterogeneous materials. We begin with developing artificial intelligence (AI)-based surrogate models for building multiscale "structure-to-physical field" linkage. At the …
-
AI-DRIVEN ATRIAL ARRHYTHMIA DETECTION: DEVELOPMENT, CROSS-COMPARISON AND UNCERTAINTY QUANTIFICATION OF ALGORITHMS FOR CLINICAL CONTINUOUS ECGS
… Challenges: Developing AI-based, particularly deep learning (DL), models to accurately detect atrial arrhythmias presents several significant challenges. First, extracting invariant representations across subjects of these arrhythmias is complex, necessitating high-quality annotated data and a …
-
Biologically plausible energy-efficient context-sensitive neural networks
… in artificial intelligence (AI), particularly in deep neural networks (DNNs), have significantly driven the demand for specialised hardware, such as GPUs and TPUs, to meet the growing computational requirements. However, this surge in computational power has led to substantial increases in energy …
-
Energy Efficient Deep Spiking Recurrent Neural Networks: A Reservoir Computing-Based Approach
… (PU). Therefore, it is essential to sense the idle frequency bands and assign them to the secondary user (SU). The effectiveness of SDFR in capturing the spatio-temporal correlation of MIMO-OFDM time-series and predicting the availability of frequency bands in the future time slots is studied as …