Application of Machine Learning and IoT for Enhancing Safety and Security in Industries - Prasad, K. V. S.; K. V., Pradeep; Chitra, D.; Tale, Vithoba Tukaram; S., Sathyakala; Patil, Suvarna Mahavir (International Journal of Intelligent Systems and Applications in Engineering, 2024)
The present research advances a transformational technique to enhancing occupational safety within the forging sector by applying integrating machine learning models into a sophisticated environmental protection device. The look at leverages multiple sensors, this contains temperature, pressure, fire, sound, and proximity sensors, to continually show and speak actual-time environmental conditions. The major controller, receptive to these sensor inputs, acts on relevant safety measures which incorporate alarms, water sprinklers, and maintenance indications. To improve safety beyond rapid replies, machine learning to know designs along with Artificial Neural Network (ANN), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Recurrent Neural Network (RNN), and Decision Tree (DT) are employed to are anticipating future environmental circumstances. Results show off excellent accuracies, with the ANN primary at ninety eight.77%, underlining its accuracy in anticipating actuator responses. The SVM, KNN, RNN, and DT models show off remarkable overall performance, jointly contributing to a proactive threat prediction framework. Confusion matrices further improve the models' prediction skills. This study provides a paradigm change in occupational protection, where sensible systems not only adapt to real-time situations however depend on and minimize capability hazards. The results create a strong foundation for adaptive protection systems in industrial contexts, providing a precedent for the integration of machine learning as a crucial instrument in promoting safe and resilient places of work.