Production-grade ensemble framework combining XGBoost, PyTorch & Sklearn - 70%+ test coverage with Optuna optimization for time-series prediction
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Updated
Nov 10, 2025 - Jupyter Notebook
Production-grade ensemble framework combining XGBoost, PyTorch & Sklearn - 70%+ test coverage with Optuna optimization for time-series prediction
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
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.
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