A Stacking Ensemble Machine Learning Model for Emergency Call Forecasting with Gaëlle Talotsing скачать в хорошем качестве

A Stacking Ensemble Machine Learning Model for Emergency Call Forecasting with Gaëlle Talotsing

KmerAI Talks#11 with Gaëlle patricia Talotsing, PhD student, Polytechnique Montréal, Canada. About the Guest: Gaelle Patricia Talotsing received the B.Eng. and M.Eng. degrees in networks and telecommunications from the Ecole Nationale Superiéure Polytechnique de Douala (ENSP, ex-FGI), Cameroon, in 2017, and the M.Sc. degree in applied mathematics from AIMS, Limbe, Cameroon, in 2020. She is currently pursuing the Ph.D. degree in computer engineering with Polytechnique Montréal, QC, Canada. She worked on several projects combining machine learning, telecommunications, and advanced networks (SDN and NFV). In 2018, she received a MasterCard scholarship as a student with African Institute for Mathematical Science (AIMS), Cameroon. In 2019, she received the IVADO Excellence Scholarship, giving her the opportunity to intern with Canada Excellence Research Chair in Real-Time Decision-Making (CERC), Montreal, Canada. Her research interests include ambulance demand prediction, ambulance allocation, and routing using deep learning and machine learning algorithms. Topic: A Stacking Ensemble Machine Learning Model for Emergency Call Forecasting Gaelle will be presenting her paper "A Stacking Ensemble Machine Learning Model for Emergency Call Forecasting." This study explores the development of a robust machine learning model that combines multiple algorithms to accurately forecast emergency call demands. By integrating spatial, temporal, and weather data, the model aims to improve ambulance dispatch planning and reduce response times, offering valuable insights for emergency management in complex urban settings. Don’t miss this opportunity to learn about innovative solutions for enhancing EMS efficiency! Don’t miss this insightful presentation on cutting-edge advancements in AI! Checkout the paper: https://ieeexplore.ieee.org/document/...