Ajeet Kumar Mohanty
Biographic Data
| ID | 7027557 |
|---|---|
| NAME | Ajeet Kumar Mohanty |
| GIVEN NAMES | Ajeet Kumar |
| FAMILY NAME | Mohanty |
| SIGNATURE | MOHANTY A K |
| AFFILIATIONS | National Institute of Malaria Research |
| ORCID | 0000-0001-8573-5353 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2025 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 0 |
Hybrid models combining trend and seasonality components with machine learning algorithms provide accurate forecasting of malaria incidence
Forecasting malaria incidence is vital for effective resource allocation during malaria elimination. In this study, we highlight robust models for forecasting incidence using climatic and malaria data from Goa, India. Multi-collinearity and Shapley Additive Explanations (SHAP) were used to identify most important predictors of malaria transmission among 15 climatic variables. Three machine-learning models (Support vector machines, Random Forest, …
Spatiotemporal patterns of urban heat in indoor and outdoor microclimates
As global temperatures rise due to climate change, urban heat islands have emerged as an important public health concern, significantly exacerbating heat stress in urban populations. Meteorological data is critical for assessing heat stress, and localized microclimate data provide more precise measurements of heat hazards than traditional weather station data. Our study explored microclimate patterns in space and time in tropical cities with rapi…
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Hybrid models combining trend and seasonality components with machine learning algorithms provide accurate forecasting of malaria incidence
Forecasting malaria incidence is vital for effective resource allocation during malaria elimination. In this study, we highlight robust models for forecasting incidence using climatic and malaria data from Goa, India. Multi-collinearity and Shapley Additive Explanations (SHAP) were used to identify most important predictors of malaria transmission among 15 climatic variables. Three machine-learning models (Support vector machines, Random Forest, …
Spatiotemporal patterns of urban heat in indoor and outdoor microclimates
As global temperatures rise due to climate change, urban heat islands have emerged as an important public health concern, significantly exacerbating heat stress in urban populations. Meteorological data is critical for assessing heat stress, and localized microclimate data provide more precise measurements of heat hazards than traditional weather station data. Our study explored microclimate patterns in space and time in tropical cities with rapi…
Climate change (1 work) · Confidence interval (1 work) · Ensemble learning (1 work) · Extreme Learning Machine (1 work) · Forecasting Techniques and Applications (1 work) · Heat stress (1 work) · Humidity (1 work) · Land cover (1 work) · Malaria (1 work) · Malaria Research and Control (1 work)