Recognizing ECG cardiac arrhythmia automatically is an essential task for diagnosing the abnormalities of cardiac muscle. The proposal of few algorithms has been made for classifying the ECG cardiac arrhythmias, however the system of classification efficiency is determined on the basis of its prediction and diagnosis accuracy. Hence, in this study the proposal of an efficient system has been made for classifying the ECG cardiac arrhythmia as an expertise. Discrete Wavelet Transform (DWT) is being utilized for the preprocessing mechanism of ECG signal, Independent Component Analysis (ICA) is being utilized for dimensionality reduction and Feature Extraction process of ECG signal and Multi-Layer Perceptron (MLP) neural network is being utilized for performing the task of classification. As an outcome of classification, the results have been acquired on categorizing Normal Beats under the class of Non-Ectopic beat, Atrial Premature Beat under the class of Supra-Ventricular ectopic beat and Ventricular Escape beat under the class of Ventricular ectopic beat on the basis of standardization given by ANSI/AAMI EC57: 1998. For the acquisition of ECG signal, MIT-BIH physionet arrhythmia database is being utilized in this study added to that its being utilized for training process and testing process of the classifier on the basis of MLP-NN. The results obtained from the simulation has been inferred that the accuracy of classification of the proposed algorithm is 96.50% on utilizing 10 files inclusive of normal beats, Atrial Premature Beat and Ventricular Escape beat.

This effort examines and likens a collection of active methods to dimensionally reduction and select salient features since the electrocardiogram database. ECG signal classification and feature selection plays a vital part in identifies of cardiac illness. An accurate ECG classification could be a difficult drawback. This effort also examines of ECG classification into arrhythmia kinds. This effort discusses the problems concerned in Classification ECG signal and exploration of ECG databases (MIT-BIH), pre-processing, dimensionally reduction, Feature selection techniques, classification and optimization techniques. Machine learning techniques give offers developed classification accurateness with imprecation of dimensionality.

Communication during disaster plays a vital role. In this paper, a system is proposed to provide communication during disaster like earthquakes, sandstorms, tsunamis floods. etc when disaster happens it causes a black-out in communication, a serious of network failure which could cause the delays in rescue team especially in remote areas. The foremost important thing is generating the first response from the people or family who had a close encounter to that if it gets broken then communications with other people’s get collapsed which lead to major impact to society by natural calamity. Li-Fi is a booming technology and plays a role on this scenario with the speed of transmission is up to 150mbps. This idea has been implemented as a demo using Arduino Uno processor with bit transfer and receives via serial communication ports. The bits are transmitted as a pack of 8 bits and they are sent in a binary encoded format. Then the primary parameters such as BER, MSE, and PSNR are computed for the different field of distance by varying the position of Light source. Further, to enhance the modern trending technology, a mobile app was developed which act as an emergency alert system where they can alert the team at the initial stage of disaster begins, within a fraction seconds information will be shared to the team with high speed of approx 150 Mbps with high PSNR and low value of MSE. They can also report the current scenario to natural disaster management authority.