Classification and Analysis of Weather Images Using Machine Intelligent Based Approach

Purpose: Weather information plays a crucial role in the human society. It helps to lower weather related losses and enhance societal benefits such as protection of life, health, property, etc., is very much essential for proper classification images (WIs) into several categories dew, fogsmog, frost, glaze, hail, lightning, rain, rainbow, rime, sandstorm, snow, etc. so that appropriate can be provided people well organizations further analysis. Approach: In this work, machine intelligent (MI) based approach proposed WIs snow types. The focused on stacking (hybridization) Logistic Regression (LRG), Support Vector Machine (SVMN), Random Forest (RFS) Neural Network (NNT) methods carry out classification. method compared with other learning (ML) LRG, SVMN, RFS, NNT, Decision Tree (DTR), AdaBoost (ADB), Naïve Bayes (NBY), K-Nearest Neighbor (KNNH) Stochastic Gradient Descent (SGDC) performance Result: ML have been implemented using Python Orange 3.26.0. 1604 having 149, 141, 146, 150, 144, 142, 147, 143 numbers types respectively are taken from Kaggle source. all assessed parameters accuracy (CA), F1, Precision (PR) Recall (RC). From results, it found capable providing better results terms CA, PR RC DTR, ADB, NBY, KNNH SGD. Originality: MI by focusing RFS NNT type. performs SGDC methods. Paper Type: Conceptual Research.