Classifying collisions in road accidents using XGBOOST, CATBOOST and SALP SWARM based optimization algorithms

Traffic accidents are the leading cause of death and injury in many developed nations. Anyone utilizing the road can meet an accident at any moment of time. The type of collision also plays a role in determining who is accountable for the accident. The biggest advantage of classifying collisions in...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	Multimedia tools and applications Jg. 83; H. 13; S. 38387 - 38410
Hauptverfasser:	Altaf, Insha, Kaul, Ajay
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.04.2024 Springer Nature B.V
Schlagworte:	Algorithms Classification Collisions Computer Communication Networks Computer Science Data Structures and Information Theory Datasets Feature selection Multimedia Information Systems Pedestrians Roads & highways Special Purpose and Application-Based Systems Traffic accidents Traffic accidents & safety Type of collision Improved salp swarm algorithm Time-varying binary salp swarm algorithm Enhanced salp swarm algorithm Nature inspired algorithm
ISSN:	1573-7721, 1380-7501, 1573-7721
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!