日期:
2020 年 — 2026 年
2020
2021
2022
2023
2024
2025
2026
影响因子:

District-Level Dengue Early Warning Prediction System in Bangladesh Using Hybrid Explainable AI and Bayesian Deep Learning

孟加拉国基于混合可解释人工智能和贝叶斯深度学习的区级登革热早期预警预测系统

Shiddik, Md Abu Bokkor; Toshi, Farzana Zannat; Yesmin, Sadia; Rahman, Md Siddikur

Bayesian spatiotemporal modelling of neonatal, infant and under-5 mortality (2000-2022) in 41 Asian countries: a population-level observational study

贝叶斯时空模型分析亚洲41个国家2000-2022年新生儿、婴儿和5岁以下儿童死亡率:一项基于人群的观察性研究

Rahman, Md Siddikur; Shiddik, Md Abu Bokkor

Global Prediction of Dengue Incidence Using an Explainable Artificial Intelligence-Driven ConvLSTM Integrating Environmental, Health, and Socio-Economic Determinants

利用可解释的人工智能驱动的卷积长短期记忆网络(ConvLSTM)整合环境、健康和社会经济因素,对登革热发病率进行全球预测

Shiddik, Md Abu Bokkor

Utilizing artificial intelligence to predict and analyze socioeconomic, environmental, and healthcare factors driving tuberculosis globally

利用人工智能预测和分析全球结核病蔓延的社会经济、环境和医疗保健因素。

Rahman, Md Siddikur; Shiddik, Abu Bokkor

Unraveling global malaria incidence and mortality using machine learning and artificial intelligence-driven spatial analysis

利用机器学习和人工智能驱动的空间分析揭示全球疟疾发病率和死亡率

Rahman, Md Siddikur; Shiddik, Md Abu Bokkor

Leveraging explainable artificial intelligence and spatial analysis for communicable diseases in Asia (2000-2022) based on health, climate, and socioeconomic factors

基于健康、气候和社会经济因素,利用可解释人工智能和空间分析方法应对亚洲传染病(2000-2022年)

Rahman, Md Siddikur; Shiddik, Md Abu Bokkor

Explainable artificial intelligence for predicting dengue outbreaks in Bangladesh using eco-climatic triggers

利用生态气候触发因素预测孟加拉国登革热疫情的可解释人工智能

Rahman, Md Siddikur; Shiddik, Md Abu Bokkor

Reflections on explainable artificial intelligence for predicting dengue outbreaks in Bangladesh

关于利用可解释人工智能预测孟加拉国登革热疫情的思考

Rahman, Md Siddikur; Shiddik, Md Abu Bokkor