REAL TIME SYSTEM FOR EFFICIENT PROCESSING OF CARDIAC ARRHYTHMIAS SIGNALS

Cardiac arrhythmias is a very uncommon life threating arrhythmia which can even cause sudden death. Healthcare professionals are always looking to find out the ways in order to reduce the death rate. The new method of feature extraction and classification of arrhythmias has been developed by the authors of this paper in their previous works. In this paper, authors have proposed the methodology for the development of a real-time system for efficient processing of arrhythmic signals in order to differentiate between normal and abnormal patients. The purpose of this work is to develop a real-time system for processing the real-time signals or signals obtained from MIT-BIH arrhythmia database. For carrying out this work, we have taken the signals from MIT-BIH Supraventricular arrhythmia   database and MIT-BIH Fantasia database. Authors have achieved 100% accuracy by using this method. Keywords: MIT-BIH, Cardiac arrhythmias, real-time system

Applicability of Augis–Bennett relation for determination of activation energy of glass transition in some Se rich chalcogenide glasses

The present work reports the results of non-isothermal DSC measurements on some Se-based ternary glasses for evaluation of activation of glass transition. The activation energy of glass transition (Eg) is determined using Augis–Bennett's relation, which is basically derived for amorphous to crystalline phase transition. Moynihan's relation which is derived on the concept of thermal relaxation and is basically used for glass transition is also used for determination of Eg values. We have observed that Eg values obtained from Augis–Bennett's relation are in admirable agreement with the Eg values which are obtained using Moynihan's relation

Advanced detection of fungi-bacterial diseases in plants using modified deep neural network and DSURF

Food is indispensable for humans as their growth and survival depend on it. But nowadays, crop is getting spoiled due to fungi and bacteria as soil temperature are changes very rapidly according to sudden climate changes. Due to fungi-bacterial crop, the quality of food is declining day by day and this is really not good for human health. The goal of this research paper is the advanced detection of fungi-bacterial diseases in plants using modified deep neural network approach and DSURF method in order to enhance the detection process. Proposed approach of this research is to use the artificial intelligence techniques like neural network model and dynamic SURF method in order to identify and classify the plant diseases for fungus and bacteria. Additionally, support dynamic feature extraction DSURF & classifier combinations for creating image clusters with the help of Clustering. Deep learning model is employed for training and testing the classifier. The quantitative experimental results of this research work are claimed that authors have achieved the 99.5% overall accuracy by implementing DNNM and DSURF which is much higher than other previous proposed methods in this field. This proposed work is a step towards finding the best practices to detect plant diseases from any bacterial and fungal infection so that humans can get healthy food