An Overview on KSK Approach: AI-driven IoT based Decision Making System

Abstract

The heart of the system is the KSK approach in decision-making. The proliferation of data from interconnected devices necessitates intelligent decision-making, moving beyond traditional, human-dependent IoT systems. The KSK approach utilizes AI-driven algorithms (like ANN, DT and KNN) to analyze data collected by IoT sensors, providing instantaneous knowledge and decision-making capabilities. By enabling localized data processing (edge intelligence), the KSK approach reduces latency and enhances operational efficiency in specialized applications like smart farming, patient monitoring, and robotic control. It offers a robust framework for improving accuracy (ranging from 87% to over 99% in various applications) by providing a synergistic mix of AI and IoT technologies. The Key Focus Areas: Real-time analysis, predictive analytics, intelligent decision systems, and high accuracy.  The KSK approach integrates Artificial Intelligence (AI) with the Internet of Things (IoT) to enable autonomous, real-time, and high-accuracy decision-making, often achieving up to 99.9% accuracy. This methodology transforms raw data into actionable intelligence for diverse sectors, including healthcare, smart agriculture, and robotics, by leveraging machine learning algorithms. It enhances decision-making in healthcare, agriculture, and manufacturing by analyzing data from sensors to provide insights, reduce operational costs, and improve reliability. The framework provides real-time analytics, lower operational costs, improved safety, and personalized experiences. Initiated by Dr. Kutubuddin S. Kazi, the approach is designed for scenarios where intelligent, automated, and swift decision-making is critical.