Logo Lanfrica
  • Accueil
  • Atlas
  • Analyses
  • Documentation
  • Sign in

© 2026 Lanfrica. Tous droits réservés. Tous les droits d'auteur des ressources affichées sur le site Web Lanfrica appartiennent aux détenteurs de droits d'auteur d'origine, sauf indication contraire explicite.

ANALYSIS OF SOKOTO STATE ROAD ACCIDENT AND PREDICTION OF ACCIDENT SEVERITY USING MACHINE LEARNING TECHNIQUE

Créateur:
MuhUmaMaiSul
Éditeur:
Tej
Hôte:

Visit

doi.org

Languages

Fulfulde, NigerianHausa

Similaires

Explainable Machine Learning for Road Accident Severity PredictionPredicting Road Traffic Accident Severity Using Machine Learning Algorithms in Kano State, Nigeriapochengstar0203/prediction-of-road-traffic-accident-severityayelefransi/Ethiopian-Road-Traffic-Accident-Severity-PredictionParisaGhahreman/Road-Accident-Severity-XAIAppraising Machine Learning Algorithms for Modeling of Road Accident Counts by Severity in Nigeria

Explainable Machine Learning for Road Accident Severity Prediction

Initial public release of the Road Accident Severity XAI framework. This release includes: Cross

Predicting Road Traffic Accident Severity Using Machine Learning Algorithms in Kano State, Nigeria

Road traffic accidents (RTAs) are a major global public health and safety crisis, leading to million

pochengstar0203/prediction-of-road-traffic-accident-severity

using kaggle data to predict south Africa road traffic accident severity # prediction-of-road-traff

ayelefransi/Ethiopian-Road-Traffic-Accident-Severity-Prediction

# Ethiopian RTA Severity Predictor — Full Stack Web App A production-grade full stack application t

ParisaGhahreman/Road-Accident-Severity-XAI

Cross-country road accident severity prediction and explainable AI framework using UK, France, and E

Appraising Machine Learning Algorithms for Modeling of Road Accident Counts by Severity in Nigeria

Road accidents are caused by several causative factors leading to various degrees of injury and deat