ADVANCED AI-DRIVEN SYSTEM FOR PERSONALIZED TREATMENT RECOMMENDATIONS: A TECHNICAL OVERVIEW - Balamurugan Sivakolunthu Vel, (INTERNATIONAL JOURNAL OF RESEARCH IN COMPUTER APPLICATIONS AND INFORMATION TECHNOLOGY (IJRCAIT), 2025)
The convergence of the Industrial Internet of Things (IIoT) with artificial intelligence has created unprecedented opportunities for real-time process optimization, yet manufacturers face significant challenges in processing and analyzing high-dimensional sensor data for immediate decision-making. This article presents an innovative cloud-based Industrial Process Optimization Engine that combines sensor analytics, equipment monitoring, and machine learning to provide real-time, equipment-specific operational recommendations while ensuring scalability and manufacturing accuracy. Processing data from over 450,000 industrial sensors against 23,450 equipment parameters in under 185ms, maintaining 99.2% accuracy in fault prediction. Implementation across eight manufacturing networks demonstrates substantial improvements, including a 68.5% reduction in maintenance planning time and a 31.5% increase in first-pass yield rates. The architecture's scalability has been validated across 245 production facilities, supporting up to 50,000 concurrent sensor streams while maintaining sub-second response times. Article results show that the system significantly enhances manufacturing efficiency through real-time integration of sensor data with production systems, representing a major advancement in smart manufacturing implementation.