Tiba Zaki Abdulhameed

Tiba Zaki Abdulhameed
Al-Nahrain University · Department of Computer Science

Doctor of Philosophy

About

7
Publications
5,088
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10
Citations
Introduction
Tiba Zaki Abdulhameed currently a PhD student at the department of computer science , Western Michigan University. Sponsored by HCED. Tiba does research in Natural Language Processing, Words Embeddings, and Algorithms and Artificial Intelligence. mainly coding with Bash scrpits, Python, and C

Publications

Publications (7)
Article
Full-text available
Word clustering is a serious challenge in low-resource languages. Since words that share semantics are expected to be clustered together, it is common to use a feature vector representation generated from a distributional theory-based word embedding method. The goal of this work is to utilize Modern Standard Arabic (MSA) for better clustering perfo...
Article
Full-text available
Introduction: Coronavirus disease 2019 (COVID-19) is one of the serious infectious diseases that is caused by a specific virus called syndrome coronavirus 2 viruses (SARSCoV-2). The rapid spread of COVID19 raises serious concerns about the globally growing death rate. Currently, cases are doubled in one week around the world. Recorded data shows th...
Conference Paper
Full-text available
Utilizing Machine Learning Models to Predict the Car Crash Injury Severity for Elderly Drivers Abstract— Car crash can cause serious and severe injuries that impact people every day. Those injuries could be especially damaging for elderly drivers of age 60 or more. The goal of this research is to investigate the risk factors that contribute to cra...
Poster
Full-text available
Abstract Background :Designing accurate predictive models for injury severity prediction of traffic accidents of the elderly population is a critical task for transportation systems. Objective: A set of influential factors are selected to build five machine learning-based predictive models to classify the severity of injuries. Methods: Machin...
Conference Paper
Neural word embedding, such as word2vec, produces very large features' vectors. In this paper, we are investigating the length of the feature vector aiming to optimize the word representation results, and also to speed up the algorithm by addressing noise impact. Principal Component Analysis (PCA) has a proven record in dimensionality reduction as...

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Projects

Project (1)
Project
We would like to improve the conversational language modeling used in developing Automatic Speech Recognition (ASR) system .