Modified Penguin Search Algorithm to Improve Feature Selection in Wireless Sensor Network

A feature selection approach focuses on lowering the features' dimension while retaining or improving the classifier's predicted accuracy. The importance of the feature selection algorithm increases, particularly in data sets with many variables and features. It will get rid of pointless variables and boost classification performance and accuracy. This article describes the Modified Penguin search algorithm to improve wireless sensor networks' feature selection (MPSA). It handles the forest environment features like temperature, humidity, light intensity, and carbon dioxide in various climatic zones during the morning, afternoon, and night to detect the fire. The objective of the MPSA mechanism is to remove unnecessary features and enhance classification accuracy. The Modified Penguin search algorithm selects the important feature for classification. The proposed system uses the Support Vector Machine (SVM) algorithm to improve the classification performance. The experimental outcomes demonstrate the MPSA is more efficient than the baseline mechanism. The simulation results demonstrate that the MPSA mechanism improves the feature selection accuracy and minimizes the processing time.