Identification of COVID-19 Spreaders using 5-Layer Multiple Network Approach
Authors: Jafar Ali Ibrahim, T Sravani, G Chaitanya Sai, V Sandhya Rani, R Rakesh, N Sai Sri Harsha
Abstract
Since the onset of COVID-19, radiographic image analysis coupled with artificial intelligence (AI) has become popular due to insufficient RT-PCR test kits. In this paper, an automated AI- assisted COVID-19 diagnosis scheme is proposed utilizing the ensembling approach of multiple convolutional neural networks (CNNs). Two different strategies have been carried out for ensembling: A feature level fusionbasedensembling method and a decision level ensembling method. Several traditional CNN architectures are tested and finally in the ensembling operation, MobileNet, InceptionV3, DenseNet201, DenseNet121 and Xception are used. To handle the computational complexity of multiple networks, transfer learning strategy is incorporated through ImageNet pre-trained weight initialization. For feature-level ensembling scheme, global averages of the convolutional feature maps generated from multiple networks are aggregated and undergo through fully connected layers for combined optimization. Additionally, for decision level ensembling scheme, final prediction generated from multiple networks are converged into a single prediction by utilizing the maximum voting criterion. Both strategies perform better than any individual network. Outstanding performances have been achieved through extensive experimentation on a public database with 96% accuracy on 3-class (COVID- 19/normal/pneumonia) diagnosis and 89.21% on 4- class (COVID-19/normal/viral pneumonia/bacterial pneumonia)diagnosis.
Design of a Bio Signal Based Stress Detection System Using Machine Learning Techniques
Authors: U. Srinivasulu, K. Sai, Ch. Sai Likitha, T. Prem, P. Sadiq Ali
Abstract
This study represents a design of a detection system of stress through machine learning using some available bio signals in human body. Stress can be commonly defined as the disturbance in psychological equilibrium. Stress detectionis one of the major research areas in biomedical engineering as proper detection of stress can conveniently prevent many psychological and physiological problems like cardiac rhythm abnormalities or arrhythmia. There are several bio-signals available (i.e. ECG, EMG, Respiration, GSR etc.) which are helpful in detecting stress levels as these signals shows characteristic changes with stress induction. In this paper, ECG was selected as the primary candidate because of the easily available recording (i.e. several mobile clinical grade recorders are available now in the market) and ECG feature extraction techniques. Another advantage of ECG is that respiratory signal information can also be detected form ECG which is known as EDR (ECG derived Respiration) without having separate sensor system for respiration measurement. Features of ECG signals are distinctive and collection of the signals is cost- efficient. From ECG we derived RR interval, QT interval, and EDR features for the development of the model. For the implementation of a supervised machine learning (SVM) method in MATLAB, Physionet’s “drivedb” database was used as the training dataset and validation. SVM was chosen for classification, as there are two classes of labeled data; ‘stressed’ or ‘non- stressed’. Several SVM model types were verified by changing the feature number and Kernel type. Our results showed an accuracy level of 98.6% with Gaussian Kernel function and using all available features (RR, QT and EDR), which also emphasizes the importance of respiratory information in stress detection through Machine Learning.
Smart Shoes for Blind Person
Authors: Kinnari Tavde, Prof. Vishakha Khambhati
Abstract
Eyes-we all know that this is the best gift god give us. All the things happen around us, this whole world is visible to us, because of eyes. We can also say that we are very blessing for this precious gift. But there are some people who can’t able to see the things happen around them, they can’t see what’s going on their surrounding areas, because they are blind or they lost their vision permanently. Those people face many challenges in their daily life. They used walking cane, or stick to move and detection of any object. But stick is not give proper results and it is used for limited range. There are many types of sticks and other devices are available in market but they are very expensive, so poor people can’t buy it. This paper presents smart shoes for blind person. This smart shoe is easy to use and it creates user-friendly environment for blind person. It is wearable system. And it is not expensive. In this ultrasonic sensor, buzzer, battery, Arduino Uno and jumper wires are used.
Bipolar Interval Valued Intuitionistic Fuzzy Necessity Operator
Authors: M. Suganya, A. Manonmani
Abstract
n this paper we have introduced the necessity operator on a Bipolar Interval Valued Intuitionistic Fuzzy Subset of a Bipolar Interval Valued Intuitionistic Fuzzy Topological space and verified its property.