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A Comparative Study of Hybridized Neural Networks in Estimating Traffic Accident Severity

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dc.contributor.author Anik, Md. Mydul Islam
dc.contributor.author Akram, Wasim
dc.contributor.author Md. Ashikuzzaman
dc.date.accessioned 2022-09-13T06:11:39Z
dc.date.available 2022-09-13T06:11:39Z
dc.date.issued 2019-12-24
dc.identifier.uri http://dspace.ewubd.edu:8080/handle/123456789/3728
dc.description This thesis submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering of East West University, Dhaka, Bangladesh. en_US
dc.description.abstract The increasing number of populations causing increase of vehicles which leads to traffic accident. As transportation system expands, it needs to be monitored to assure safety to citizen. Cities are trying to adopt technological advancement in order to minimize traffic accident. Traffic accidents have become one of the largest national health issues and many factors like weather condition, road condition, light condition, etcetera is related to it. In the current paper, several hybridize machine learning models are used on dataset of city Leeds, UK to estimate traffic accident severity. Hybridize Machine learning models are Artificial Neural Network (ANN) with Gradient decent, Principle Component Analysis (PCA) with ANN, Genetic Algorithm with ANN, Particle Swarm Optimization with ANN. These models are also compared with other machine learning models such as Support Vector Machine (SVM), Naïve Bayes, Nearest Centroid, Logistic Regression, K Nearest Neighbor Classification and Random Forest. Comparison was done considering performance evaluation of each model’s accuracy result. Genetic Algorithm with ANN showed promising result of 86.63% accuracy which is the highest score of all model results. Whereas, Nearest Centroid Method gave 55% of accuracy resulting lowest of all. The Results and findings obtained in this study are significant which can provide invaluable information on reducing traffic accident. en_US
dc.language.iso en_US en_US
dc.publisher East West University en_US
dc.relation.ispartofseries ;CSE00212
dc.subject Hybridized Neural Networks, Estimating Traffic Accident Severity en_US
dc.title A Comparative Study of Hybridized Neural Networks in Estimating Traffic Accident Severity en_US
dc.type Thesis en_US


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