Abstract

Microelectromechanical Systems (MEMS) sensors play a pivotal role in collecting data for various applications, yet their computational load often poses a challenge, leading to increased power consumption and reduced efficiency. This study addresses this issue by integrating Decision Tree algorithms to enhance AI-driven MEMS sensors. The primary problem is the high computational burden faced by MEMS sensors when processing large volumes of data, which can impair performance and battery life. The proposed method involves applying Decision Tree algorithms to preprocess and filter data, thereby reducing the volume of information processed directly by the MEMS sensors. Experimental results show a significant reduction in computational load, with a 35% decrease in processing time and a 28% improvement in battery efficiency. Additionally, the accuracy of data classification improved by 20% compared to traditional methods. These improvements demonstrate the effectiveness of Decision Trees in optimizing MEMS sensor performance for advanced data science applications.

Authors

B. Yuvaraj, Karanam Ramesh Rao, R. Anbarasu, G. Kadirvelu
Sphoorthy Engineering College, India

Keywords

MEMS Sensors, Decision Tree Algorithms, Computational Load, Data Preprocessing, Battery Efficiency