Article
Unified Emergency Condition Detection
The escalating frequency of natural and man-made disasters demands intelligent, automated frameworks capable of detecting emergencies in real time. This paper presents the Unified Emergency Condition Detection (UECD) system, an integrated platform that fuses Internet of Things (IoT) sensor networks, convolutional neural networks (CNN), and deep neural networks (DNN) to detect, classify, and communicate diverse emergency conditions including fires, gas leaks, seismic events, health crises, and vehicular accidents. The proposed architecture employs a hybrid CNN-DNN model trained on structured multi-sensor datasets encompassing temperature, smoke, vibration, heart-rate, and gas-concentration readings, achieving a detection accuracy of 96.7% with a false alarm rate below 1.8%. A Random Forest baseline achieves 93.4% accuracy, while the hybrid deep model surpasses it by leveraging both spatial feature extraction and sequential decision boundaries. Sensor data fusion across heterogeneous modalities substantially reduces ambiguity and false positives. The system generates geo-tagged, severity-ranked alerts distributed via SMS, email, and mobile push notifications to relevant authorities within subsecond latency. An experimental Flask-based web interface demonstrates real-time prediction, dataset management, and model loading capabilities. Experimental results indicate that the UECD system achieves superior performance compared to state-of-the-art singlemodality emergency detection approaches. The system is scalable to smart city deployments, healthcare environments, and industrial facilities, representing a significant advancement toward fully autonomous emergency management infrastructure.
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