Abstract:

Monitoring the Earth's vital signs is essential to assess its condition and maintain the safety of living organisms. The current system utilizes the Multinomial Naïve Bayes algorithm for the image-based identification of critical Earth phenomena, such as seismic events, cyclones, floods, and wildfires. Although effective in its simplicity, the algorithm's presumption of feature independence may constrain its capacity to discern complex relationships within picture collections. These systems may find it challenging to adjust to the dynamic characteristics of the environment. In the realm of Earth observation, the Naive Bayes model is utilized to analyze data pertaining to seismic events, cyclones, floods, and wildfires. The fundamental premise of independence among characteristics in the Naive Bayes model may restrict its ability to effectively represent complicated interactions within the varied and dynamic datasets related to Earth's vital signs. The suggested system utilizes a Convolutional Neural Network (CNN) with the VGG16 architecture and the Random Forest algorithm, constituting an Ensemble Learning model. The VGG model, founded on CNN architecture, is employed to extract features from the input picture, preprocess, and train and test the data; subsequently, the Random Forest method is utilized to forecast accuracy and labels for the input data. The benefits of Earth's vital signs include real-time monitoring, predictive analytics, and enhanced accuracy. A multifunctional instrument for environmental monitoring and facilitating the establishment of early warning systems for swift reactions to ecological hazards and natural calamities.

Description:

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