Đề tài nghiên cứu tỷ lệ huyết thanh dương tính Toxocara spp. với mục tiêu nhằm đánh giá tình hình nhiễm ký sinh trùng ở Tây Nguyên, nhấn mạnh chẩn đoán sớm và phòng ngừa.

This study advances forest fire susceptibility mapping in Gia Lai province by leveraging optimized machine learning models. We evaluated five models -Deep Neural Networks (DNN), Random Forest (RF), Gradient Boosting (GB), Logistic Regression (LR), and Support Vector Machines (SVM) - using a dataset of 2,827 fire incidents (2007÷2021), an equal number of non-fire points, and 12 influencing factors: slope, aspect, elevation, curvature, land use, NDVI (Normalized Difference Vegetation Index), NDWI (Normalized Difference Water Index), NDMI (Normalized Difference Moisture Index), temperature, wind speed, relative humidity, and rainfall.