Bashir Kazimi
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Accelerated quantification of reinforcement degradation in additively manufactured Ni-WC metal matrix composites via SEM and vision transformers Open
Machine learning (ML) applications have shown potential in analyzing complex patterns in additively manufactured (AMed) structures. Metal matrix composites (MMC) offer the potential to enhance functional parts through a metal matrix and re…
Enhancing Semantic Segmentation in High-Resolution TEM Images: A Comparative Study of Batch Normalization and Instance Normalization Open
Integrating deep learning into image analysis for transmission electron microscopy (TEM) holds significant promise for advancing materials science and nanotechnology. Deep learning is able to enhance image quality, to automate feature dete…
Self-Supervised Learning with Generative Adversarial Networks for Electron Microscopy Open
In this work, we explore the potential of self-supervised learning with Generative Adversarial Networks (GANs) for electron microscopy datasets. We show how self-supervised pretraining facilitates efficient fine-tuning for a spectrum of do…
View article: A comparison of deep learning segmentation models for synchrotron radiation based tomograms of biodegradable bone implants
A comparison of deep learning segmentation models for synchrotron radiation based tomograms of biodegradable bone implants Open
Introduction: Synchrotron radiation micro-computed tomography (SRμCT) has been used as a non-invasive technique to examine the microstructure and tissue integration of biodegradable bone implants. To be able to characterize parameters rega…
Self-Supervised Learning for Semantic Segmentation of Archaeological Monuments in DTMs Open
Deep learning models need a lot of labeled data to work well. In this study, we use a Self-Supervised Learning (SSL) method for semantic segmentation of archaeological monuments in Digital Terrain Models (DTMs). This method first uses unla…
Extraction of linear structures from digital terrain models using deep learning Open
This paper explores the role deep convolutional neural networks play in automated extraction of linear structures using semantic segmentation techniques in Digital Terrain Models (DTMs). DTM is a regularly gridded raster created from laser…
DETECTION OF TERRAIN STRUCTURES IN AIRBORNE LASER SCANNING DATA USING DEEP LEARNING Open
Automated recognition of terrain structures is a major research problem in many application areas. These structures can be investigated in raster products such as Digital Elevation Models (DEMs) generated from Airborne Laser Scanning (ALS)…
SEMANTIC SEGMENTATION OF MANMADE LANDSCAPE STRUCTURES IN DIGITAL TERRAIN MODELS Open
We explore the use of semantic segmentation in Digital Terrain Models (DTMS) for detecting manmade landscape structures in archaeological sites. DTM data are stored and processed as large matrices of depth 1 as opposed to depth 3 in RGB im…
Coverage for Character Based Neural Machine Translation Open
In recent years, Neural Machine Translation (NMT) has achieved state-of-the-art performance in translating from a language; source language, to another; target language. However, many of the proposed methods use word embedding techniques t…