Integrated Implementation of Hybrid Deep Learning Models and IoT Sensors for Analyzing Solider Health and Emergency Monitoring

Facing death by soldiers is happening all the time. They never wriggle out of their responsibility. They fight in utmost hard places in territories, on peaks and foothills, in savannahs and jungles. Their role in protecting the borders of our uncertain terrestrial is exceptional. They sacrifice their life for the nation. There are many conflicts concerning the soldier’s health. Integrated Implementation of Hybrid Deep Learning approach would be convenient for armed forces engaged in various operations in warrior activities. The nano GPS tracing (Global Positioning Systems) will be placed on soldier. Soldier health monitoring model embedded and interfaced with mobile computing, health devices and health care networking facilities. In the control scheme, the soldier’s uniform will be mountedwith smart sensors. Sensors data obtained by the detectors connected to the soldiers is processed by a modified machine learning algorithm such as Autoencoder and long short-term memory structure (AUTO-LSTM). This research implies a computationalintelligence system that can understand both individual events and the changes among two distinct accomplishments of short length and the squat amount for medical applications. In this research, an autoencoder for collecting attributes for classification obtained by detectors. The LTSM system is then used to collect further substantial extended period relationships among datasets to enhance data analysis accuracy further. The experimental outcomes designate that the suggested resolution will help escalation the classification accuracy up to 97 per cent and thefinding accuracy for transformations greater than 90 per cent that are larger than that of several current related frameworks.