DEEP-LEARNING MODEL FOR INFLUENZA PREDICTION FROM MULTISOURCE HETEROGENEOUS DATA IN A MEGACITY: MODEL DEVELOPMENT AND EVALUATION

Deep-Learning Model for Influenza Prediction From Multisource Heterogeneous Data in a Megacity: Model Development and Evaluation

BackgroundIn megacities, there is an urgent need to establish more sensitive forecasting and early warning methods for acute respiratory infectious diseases.Existing prediction and early warning models for influenza and other acute respiratory infectious diseases have limitations and therefore there is room for improvement.ObjectiveThe aim of this

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Non sustainable development. CO2, Plants and Climate.

The crucial role of plant photosynthesis has been emphasized in relation to the atmospheric CO2 concentration and climate change.Because of the irreplaceable role of plants in ecosystem services, potential changes of both crop and Cheese Grater wild plants could impact sustainability of human societies.The dependence of the rate of photosynthesis o

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MRI v/s Non contrast CT brain in evaluation of stroke

Introduction: Stroke is a common and leading cause of death and disability in the world.The guidelines for use of recombinant tissue plasminogen activator (rtPA) include stroke onset within 3 to 4.5 hours, preceded by a computed tomographic (CT) scan to exclude the presence of haemorrhage.Aims/Objectives: The importance of MRI/CT perfusion of brain

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