A novel approach to predict the traffic accident assistance based on deep learning DOI Creative Commons
José F. Vicent, Manuel Curado, José Luis Tejera Oliver

и другие.

Neural Computing and Applications, Год журнала: 2024, Номер unknown

Опубликована: Дек. 31, 2024

Abstract According to the World Health Organization, thousands of people die every year in road traffic accidents. A crucial problem is prediction medical assistance these For this purpose, we propose a new deep learning model whose goal distinguish whether accident requires assistance. The proposed perspective general, so valid for any dataset from city. present divided into three differentiated stages. In first pre-processing stage, general data treatment performed, collection and cleaning balancing. Secondly, post-processing stage employs genetic boosting algorithms obtain importance all set variables used prediction. last Model Training, based on two-dimensional convolutional neural networks applied need Finally, test effectiveness accuracy by applying it datasets six different cities. obtained experimental results show that our framework achieves higher cities compared state-of-the-art models, confirming its suitability applicability, even real time.

Язык: Английский

Towards an Approach of Traffic Information Extraction Through ChatGPT DOI
Quang Tran Minh, Trong Nhan Phan,

Bui Tien Duc

и другие.

Lecture notes on data engineering and communications technologies, Год журнала: 2024, Номер unknown, С. 45 - 54

Опубликована: Янв. 1, 2024

Язык: Английский

Процитировано

0

A novel approach to predict the traffic accident assistance based on deep learning DOI Creative Commons
José F. Vicent, Manuel Curado, José Luis Tejera Oliver

и другие.

Neural Computing and Applications, Год журнала: 2024, Номер unknown

Опубликована: Дек. 31, 2024

Abstract According to the World Health Organization, thousands of people die every year in road traffic accidents. A crucial problem is prediction medical assistance these For this purpose, we propose a new deep learning model whose goal distinguish whether accident requires assistance. The proposed perspective general, so valid for any dataset from city. present divided into three differentiated stages. In first pre-processing stage, general data treatment performed, collection and cleaning balancing. Secondly, post-processing stage employs genetic boosting algorithms obtain importance all set variables used prediction. last Model Training, based on two-dimensional convolutional neural networks applied need Finally, test effectiveness accuracy by applying it datasets six different cities. obtained experimental results show that our framework achieves higher cities compared state-of-the-art models, confirming its suitability applicability, even real time.

Язык: Английский

Процитировано

0