Comparative Analysis of Machine Learning Models for the Prediction of Pedestrian Crash Severity: Focused on Balancing Pedestrian Crash Dataset

2021 ◽  
Vol 29 (2) ◽  
pp. 3-15
Author(s):  
Hojun Lee ◽  
Sugie Lee
Keyword(s):  
Machine Learning ◽  
Comparative Analysis ◽  
Crash Severity ◽  
Learning Models ◽  
Machine Learning Models

scholarly journals A Comparative Analysis of Machine Learning Algorithms in Design Process of Adaptive Traffic Signal Control System

2022 ◽  
Vol 2161 (1) ◽  
pp. 012054
Author(s):  
R M Savithramma ◽  
R Sumathi ◽  
H S Sudhira
Keyword(s):  
Machine Learning ◽  
Comparative Analysis ◽  
Regression Tree ◽  
Gradient Boosting ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Suitable Model ◽  
Traffic Classification ◽  
Learning Models ◽  
Machine Learning Models

Abstract In recent decades machine learning technology has proved its efficiency in most sectors by making human life easier. With this popularity and efficiency, it is applied to design traffic signal control systems to mitigate traffic congestion and distribute waiting delays. Hence, many researchers around the world are working to address this issue. As a part of the solution, this article presents a comparative analysis of various machine learning models to come up with a suitable model for an isolated intersection. In this context, eight machine learning models including Linear Regression, Ridge, Lasso, Support Vector Regression, k-Nearest Neighbour, Decision Tree, Random Forest, and Gradient Boosting Regression Tree are selected. Shivakumara Swamiji Circle (SSC), one of the intersections in Tumakuru, Karnataka, India is selected as a case study area. Essential data is collected from SSC through videography. The selected models are developed to predict green time based on traffic classification and volume in Passenger Car Units (PCU) for each phase on the PyCharm platform. The models are evaluated based on various performance metrics. Results revealed that all the selected models predict green splits with 91% accuracy using traffic classification as input, whereas, models showed 85% accuracy with PCU as input. And also, Gradient Boosting Regression Tree is the best suitable model for the selected intersection, whereas, Decision Tree is not referred model for this application.

scholarly journals Spam-Detection with Comparative Analysis and Spamming Words Extractions

2021 ◽  
Author(s):  
Md Khairul Islam ◽  
Md Al Amin ◽  
Md Rakibul Islam ◽  
Md Nosin Ibna Mahbub ◽  
Md Imran Hossain Showrov ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Comparative Analysis ◽  
Spam Detection ◽  
Learning Models ◽  
Electronic Medium ◽  
Machine Learning Models

Communication through email plays an essential part especially in every sector of our day-to-day life. Considering its significance, it is important to filter spam emails from emails. Spam email, also known as junk email, is unwanted messages that are sent by the electronic medium in large quantities. Most of the spam emails are commercial in nature that is not only irritating but also harmful due to malicious scams or malware-hosting sites or use viruses attached to the message. In this paper, we identify spam emails and expose how spam emails can be distinguished from legitimate/normal emails. We deployed four machine learning models and two deep learning models over the datasets including the combined dataset. Besides, we also try to find the important keywords that are found repeatedly from spam emails repository. This type of knowledge will enable us to detect spam emails for our personnel and community security purpose.<br>

A comparative analysis of machine learning models for quality pillar assessment of SaaS services by multi-class text classification of users’ reviews

2019 ◽  
Vol 101 ◽  
pp. 341-371 ◽  
Author(s):  
Muhammad Raza ◽  
Farookh Khadeer Hussain ◽  
Omar Khadeer Hussain ◽  
Ming Zhao ◽  
Zia ur Rehman
Keyword(s):  
Machine Learning ◽  
Comparative Analysis ◽  
Text Classification ◽  
Learning Models ◽  
Machine Learning Models
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