NeuroFusionNet Adaptive Deep Learning for Intelligent Real-Time Industrial IoT Decisions

Abstract

The rapid development of Industrial IoT (IIoT) has facilitated real-time observation and decision-making in smart factories, even though current methods suffer from constraints like processing noisy, high-dimensional sensor data and modeling both spatial and temporal relationships well. Classical models like CNN, LSTM, and GRU tend to fail in handling sequential patterns and context-aware anomaly detection, which restricts predictive maintenance and operational efficiency. To address these limitations, this research introduces NeuroFusionNet, a CNN–BiGRU–Attention hybrid framework, developed using Python and TensorFlow, to pull localized spatial features using CNN, capture bidirectional temporal relationships using BiGRU, and highlight key time steps using Attention for improved anomaly detection and predictive maintenance. The framework is tested on the Environmental Sensor Telemetry dataset, with multivariate industrial signals such as gas levels, temperature, and equipment vibrations. Experimental results demonstrate that NeuroFusionNet achieves 95.2% accuracy, 94.8% precision, 94.1% recall, and 94.4% F1-score, representing an improvement of approximately 2 to 7% over baseline models (CNN, RNN, LSTM) across multiple performance metrics. The method provides faster convergence and robust real-time inference, supporting scalable deployment for smart manufacturing environments. These results highlight that NeuroFusionNet not only outperforms conventional hybrid models such as CNN–LSTM and CNN–GRU but also offers actionable insights for predictive maintenance, safety, and efficiency, establishing a foundation for adaptive AI-driven monitoring in Industry 4.0 applications.