Browsing by Advisor
Now showing items 1-10 of 10
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Adding time-series data to enhance performance of naural language processing tasks
(2023)In the past few decades, with the explosion of information, a large number of computer scientists have devoted themselves to analyzing collected data and applying these findings to many disciplines. Natural language ... -
Boosting feature extraction performance on the aspect of representation learning efficiency
(2022)Machine learning is famous for its automatic data handling. While there is a slow growth in the performance of the state-of-the-art models in the most recent well-known learning frameworks, the number of parameters and ... -
A comparison and analysis of explainable clinical decision making using white box and black box models
(2024)Explainability is a crucial element of machine learning-based making in high stake scenarios such as risk assessment in criminal justice [80], climate modeling [79], disaster response [82], education [81] and critical ... -
Deep learning based image super resolution
(2021)Image super resolution is one of the most significant computer vision researches aiming to reconstruct high resolution images with realistic details from low resolution images. In the past years, a number of traditional ... -
Feature learning boosts network performance
(2020)Features are an important part of machine learning. Features are often the reduced-dimensional representation of input data, feature calculation, extraction, and fusion directly affect the final result of the network. ... -
Lightweight deep learning for monocular depth estimation
(2021)Monocular depth estimation is a challenging but significant part of computer vision with many applications in other areas of study. This estimation method aims to provide a relative depth prediction for a single input ... -
Online sequential learning with non-iterative strategy for feature extraction, classification and data augmentation
(2020)Network aims to optimize for minimizing the cost function and provide better performance. This experimental optimization procedure is widely recognized as gradient descent, which is a form of iterative learning that starts ... -
Quantifying the impact of Twitter activity in political battlegrounds
(2022)It may be challenging to determine the reach of the information, how well it corresponds with the domain design, and how to utilize it as a communication medium when utilizing social media platforms, notably Twitter, to ... -
Semi-supervised framework for clustering and semantic segmentation
(2021)During the past couple of decades, machine learning and deep learning methods have achieved remarkable results in many real-world applications. However, it is difficult to develop and train these artificial intelligence ... -
Vapnik-Chervonenkis dimension in neural networks
(2023)This thesis aims to explore the potential of statistical concepts, specifically the Vapnik-Chervonenkis Dimension (VCD)[33], in optimizing neural networks. With the increasing use of neural networks in replacing human ...
