Recently, I am computer vision engineer in VecTech. I have one year of expertise. I have finished my PhD Degree from Southern Illinois University Carbondale (2022), where I were working on diverse deep learning problems.…
Recently, I am computer vision engineer in VecTech. I have one year of expertise. I have finished my PhD Degree from Southern Illinois University Carbondale (2022), where I were working on diverse deep learning problems. My research revolves around creating new algorithms for machine and deep learning and applying them to improve generative approaches for computer vision (images, videos) and natural language processing. I have expertise in data analysis.
I am a computer vision engineer. Create AI models for image processing. also, I worked for 5 years as TA. I have industrial and teaching experience .Recently, I am computer vision engineer in VecTech. I have one year of expertise. I have finished my PhD Degree from Southern
Illinois University Carbondale (2022), where I were working on diverse deep learning problems. My research revolves around
creating new algorithms for machine and deep learning and applying them to improve generative approaches for computer vision
(images, videos) and natural language processing. I have expertise in data analysis.
This study aims to train and validate machine/deep learning models within the context of SBIRT for identifying who with alcohol/drugs misuse based on Alcohol Use Disorders Identification Test (AUDIT) and Drug Abuse Screening Test (DAST-10) scores.
The cleaning data methods and advanced pre-processing (e.g., a missing data imputation technique and an augmented sampling data method) of Electronic Health Records (EHRs) were performed.
The primary analysis was the multi-class classification of alcohol/drugs misuse using machine/deep learning models.
Test AUDIT and DAST-10 served as the reference standards.
The Vision Transformer is a model for image classification that employs a Transformer-like architecture over patches of the image.
An image is split into fixed-size patches, each of them are then linearly embedded, position embeddings are added, and the resulting sequence of vectors is fed to a standard Transformer encoder.
Spiking neural networks (SNNs) are artificial neural networks that more closely mimic natural neural networks.
SNNs are believed to be highly computationally and energy efficient for specific neurochip hardware real-time solutions.
The purpose of this study is to detect hydraulic structures (i.e., bridges and culverts) that are important to overland flow modeling and environmental applications.
Used advanced CNN techniques, including Siamese Neural Networks (SNNs), Capsule Networks (CapsNets), and Graph Convolutional Networks (GCNs), to classify samples with similar topographic and spectral characteristics, an objective which is challenging utilizing traditional machine learning techniques, such as Support Vector Machine (SVM), Gaussian Classifier (GC), and Gaussian Mixture Model (GMM).
Used a model combining between VAE and GAN architecture.
VAE aims to produce diverse predictions, while GAN aims to produce naturalistic frames.
Combining the two produces predictions that look more realistic to human raters and better cover the range of possible futures.
Proposed a two-stage model to generate short Arabic poetry.
Built a hierarchical recurrent neural network (HRNN) which is a combination of a word-level language model and a sentence-level language model.
Used FastText as embedding method.
Developed the main model by creating a version of a poetry generation model with extended phonetic and semantic embeddings.
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