Tiba Zaki Abdulhameed

Al-Nahrain University · Department of Computer Science

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...

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...

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...

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...

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...