Zeng, Q., Huang, H.-L., Xu, P.-P. and Ma, M. (2014) Developing an Optimized Artificial Neural Network to Predict Traffic Crash Injury Severity. 14th COTA International Conference of Transportation Professionals, Changsha, 4-7 July 2014, 2396-2407.
https://doi.org/10.1061/9780784413623.229

has been cited by the following article:

• TITLE: Prediction of Accident Severity Using Artificial Neural Network: A Comparison of Analytical Capabilities between Python and R

  AUTHORS: Imran Chowdhury Dipto, Md Ashiqur Rahman, Tanzila Islam, H M Mostafizur Rahman

  KEYWORDS: Artificial Neural Network, Accident Severity, Machine Learning, Python, R

  JOURNAL NAME: Journal of Data Analysis and Information Processing, Vol.8 No.3, August 7, 2020

  ABSTRACT: Large amount of data has been generated by Organizations. Different Analytical Tools are being used to handle such kind of data by Data Scientists. There are many tools available for Data processing, Visualisations, Predictive Analytics and so on. It is important to select a suitable Analytic Tool or Programming Language to carry out the tasks. In this research, two of the most commonly used Programming Languages have been compared and contrasted which are Python and R. To carry out the experiment two data sets have been collected from Kaggle and combined into a single Dataset. This study visualizes the data to generate some useful insights and prepare data for training on Artificial Neural Network by using Python and R language. The scope of this paper is to compare the analytical capabilities of Python and R. An Artificial Neural Network with Multilayer Perceptron has been implemented to predict the severity of accidents. Furthermore, the results have been used to compare and tried to point out which programming language is better for data visualization, data processing, Predictive Analytics, etc.