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

Journals Menu

Follow SCIRP

ISSN Print: 2327-7211
ISSN Online: 2327-7203
www.scirp.org/journal/jdaip
E-mail: jdaip@scirp.org

"Prediction of Accident Severity Using Artificial Neural Network: A Comparison of Analytical Capabilities between Python and R"
written by Imran Chowdhury Dipto, Md Ashiqur Rahman, Tanzila Islam, H M Mostafizur Rahman,
published by Journal of Data Analysis and Information Processing, Vol.8 No.3, 2020

has been cited by the following article(s):

[1]	Comparative Ergonomic Posture Analysis of CNC Milling Machine Workers through Digital Human Modeling and Artificial Neural Networks
	Indian Journal …, 2024

[2]	Short-term traffic flow prediction based on optimized MSTSAN model.
	Advances in Transportation …, 2024

[3]	Enhance Intelligent Method for Accident Detection and Best Road Choosing to Prevent Congestion in VANET Vehicle
	2023 IEEE Afro …, 2023

[4]	Study and implementation IoT smart home using packet tracer and python
	2023

[5]	Electric device classification by power consumption for optimal power management in IoT network
	2023

[6]	Data-Driven Decision-Making: real-time construction market monitoring to enhance operational efficiency
	2023

[7]	Data-Driven Decision-Making: A Graphical User Interface for Real-Time Market Analysis & Quotation Evaluation Visualising and comparing internal & external …
	2023

[8]	Análisis Comparativo de Modelos de Machine Learning Y GLM de Procesos de Tarificación de Seguros de Automóviles
	2022

[9]	A Review on Impact Analysis of Accident Using AI
	2018

Follow SCIRP

[1]	Innovations of Intelligent Informatics, Networking, and Cybersecurity Communications in Computer and Information Science, 2025 DOI:10.1007/978-3-031-81065-7_14

[2]	Enhance Intelligent Method for Accident Detection and Best Road Choosing to Prevent Congestion in VANET Vehicle 2023 IEEE Afro-Mediterranean Conference on Artificial Intelligence (AMCAI), 2023 DOI:10.1109/AMCAI59331.2023.10431524