Production-grade ensemble framework combining XGBoost, PyTorch & Sklearn - 70%+ test coverage with Optuna optimization for time-series prediction

EN : Water Pipe Leakage Risk Prediction System v0-3 | Industrial-grade GPR system achieving R²=0.9894 accuracy with 1,907x parallel processing efficiency | Complete scalability proven from N=44 to N=6000 datasets. JP : 水道管漏水リスク予測GPRシステム v0-3 | 産業レベル予測精度R²=0.9894、1,907倍並列処理効率化を実現したガウス過程回帰による高精度漏水リスク分析システム | N=44→N=6000完全スケーラビリティ実証済み

Monitoring various parameters such as temperature, pressure, and vibration, the system detects early signs of malfunction. It helps reduce downtime, optimize maintenance schedules, and enhance equipment lifespan, resulting in cost savings and improved operational efficiency.

This repository contains code for the paper "A Multimodal Deep Learning Framework for Metadata-Assisted Classification of IoT Sensor Data" , published in IEEE Internet of Things Journal