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International Journal of Engineering and Advanced Research Technology

Volume 4 Issue 6 (June 2018)

S.No. Title & Authors Page No View

Title : Real-Time IoT-Based Health Care Monitoring for Prediction and Analysis

Authors : Komal Mukesh Adkane, Prof. Anil Bavaskar

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Abstract :

Care of critically ill patient, requires spontaneous & accurate decisions so that life protecting & lifesaving therapy can be properly applied. Statistics reveal that every minute a human is losing his or her life across the globe. More close in India, everyday many lives are affected by heart attacks and more importantly because the patients did not get timely and proper help .This paper is based on monitoring of patients. We have designed and developed a reliable, energy efficient patient monitoring system. It is able to send parameters of patient in real time. It enables the doctors to monitor patient health parameters in real time. Here the parameters of patient are measured continuously and wirelessly transmitted using Zigbee. The project provides a solution for enhancing the reliability and flexibility by improving the performance and patient monitoring system. In the current proposed system the patient health is continuously monitored and the acquired data is analyzed at a centralized system. If a particular patient health parameter falls below the threshold value, a . Here, we are using Zigbee for wireless transmission. The Doctor can get are cord of a particular information by just accessing the database of the patient on his PC which is continuously updated through Zigbee receiver module.


Title : A Method for Power Load Forecasting Base on SVM and Wavelet Neural Network

Authors : Tongna Liu

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Abstract :

This paper put forward a new method of the SVM and wavelet neural network model for short-term load forecasting. The neural call function is basis of nonlinear wavelets. We overcome the shortcoming of single train set of SVM. It can be seen from the example this method can improve effectively the forecast accuracy and speed. The forecast model was tested and the result showed that it was an effective way to forecast short-term electric load.