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Accurate estimation of signal-to-noise ratio (SNR) in Scanning Electron Microscopy (SEM) is crucial because it evaluates the image quality. SEM images faced a challenge whereby Gaussian noise commonly appears in the imag...

For the structural health monitoring of composite materials, data analysis technology must be very sophisticated, capable of detecting fault patterns that are multi-level and complicated. A comprehensive deep learning pa...

Car-following models (CFMs) are fundamental to traffic flow analysis and autonomous driving. Although calibrated physics-based and trained data-driven CFMs can replicate human driving behavior, their reliance on specific...

Recent advancements in artificial intelligence have transformed three-dimensional (3D) optical imaging and metrology, enabling high-resolution and high-precision 3D surface geometry measurements from one single fringe pa...

The recent Zero-Trust Architecture (ZTA) is progressively adopted to the develop network security by assuming no implicit trust within or outside an organization’s boundary. Though, ZTA faces substantial challenge...

Recent advances in computer vision and deep learning, particularly Convolutional Neural Networks (CNNs), have significantly increased road safety. CNNs were used in this work to automatically detect and categorise traffi...

Abstract Microwave radar, utilizing the differences in Doppler frequencies from moving target echoes, offers remote sensing capabilities and continuous all‐weather monitoring for geological disasters. However, intellig...

The need for early and precise identification of abnormalities has made the detection and classification of brain tumours essential components of medical diagnosis. Because brain tumours are naturally complex and can hav...

Car classification is important in daily life because there are many distinct types of automobiles made by various manufacturers. Although there are numerous methods for classifying autos, machine learning technologies h...

Abstract High-throughput analysis of EEG data has significantly contributed to understanding neural dynamics in Alzheimer’s disease diagnosis. However, the complexity and high dimensionality of EEG signals pose challen...

This paper addresses the growing need for implementing intelligent Natural Language Processing (NLP) systems on low-power, memory-limited devices such as Raspberry Pi, mobile phones, and IoT edge hardware. As edge comput...

In recent years, the flipped learning (FL) model has emerged as an innovative and increasingly popular approach to education, transforming how students engage with content and enhancing overall learning outcomes. This mo...

"This thesis investigates the application of machine learning methods to the task of deforestation monitoring using time-series satellite data. The objective is to assess how different algorithmic approaches perform in d...