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Handbook on Intelligent Healthcare Analytics


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Dry image Landslide image Flood image Extreme weather image Extreme temperature

      Weather changes as non-probabilistic distance variation and then likelihood also become a problem such that maximization for change of directions remains same. The next state can be predicted using Markov chain model from the sequence of random generation of updates. Let us consider the state variables as state, followed with variables

      Using the state estimation from Kaggle dataset data has been fed as an input layer; later, the hidden layer along with the weight (W) and bias (b) are initiated to classify the preprocessed data to predict the climatic change. The outputs expected from the reliable entity from a dataset such as extreme weather, dry, drought, and temperature change can be categorized using the value that creates the DAG form, which can avoid statelessness in nature. This model from the proposed work uses this stateless approach where the updates can memorize the information as analyzed from the buffer for unique classification on time series.

Schematic illustration of proposed system for predicting disaster using improved Bayesian hidden Markov frameworks.

      From the observations on each state moves, the probability with the power of the matrix is generated. Let us consider the steps as state-to-state transition.

      Figure 2.4 shows the entities that are identified from the united states of America on various years that have the impact according to the climate changes. Prediction using this dataset based on the developed feature analysis can be performed. Each and every disaster along with the disasters reported year-wise with the count is also reported.

Bar graph depicts total number of disaster analysis using improved Bayesian Markov chain model.



Year Total economic damage from natural disasters (US$)
count 561.000000 5.610000e+02
mean