Two layer Neural Networks
In many countries, drowning is one of the leading causes of death for children. Drowning kills silently and quickly. A person drowning cannot get air so he cannot shout for help. There are commercial drowning alert systems such as SenTAG pool safety system and Wahooo Swim Monitor System are available. These systems consider the behavioral changes of the victim at early drowning stage. These systems consider mainly the time lapse spent by the swimmer underwater. But they do not consider the behavioral changes of the victim at early drowning stage what is known as near drowning situation.
Neural Network – Lifeguard
Researchers, Mohamed Kharrat et.al. aimed to create a system that helps detecting victims in near drowning situation. They used supervised neural networks method for making the system classify normal swimming and near drowning patterns automatically. The researchers collected the patterns of victims’ body posture and feed them as training dataset to the two layers of feed-foreword neural network. The data set was split into 160 vectors of which 149 vectors were for normal swimming situation and 11 vectors for near drowning situation. Matlab’s Neural Network Pattern Recognition Tool Box was used to process the data. The system was successful in classifying all the input vectors into near drowning and drowning classes.
Cloud Computing Service and Deployment Models: Layers and Management
Book (IGI Global)
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