Abstract-This paper gives an overview of the current state of radio frequency identification (RFID) technology. Aside from a brief introduction to the principles of the technology, major current and envisaged fields of application, as well as advantages, and limitations of use are discussed. Radio frequency identification (RFID) is a generic term that is used to describe a system that transmits the identity (in the form of a unique serial number) of an object or person wirelessly, using radio waves. It's grouped under the broad category of automatic identification technologies. RFID is increasingly used with biometric technologies for security. In this paper Basic Principles of RFID technology along with its types are discussed.

Artificial Neural Network (ANN) has been shown to be an efficient tool for non-parametric modelling of data in a variety of different contexts where the output is a non-linear function of the inputs. These include business forecasting, credit scoring, bond rating, business failure prediction, medicine, pattern recognition and image processing. A large number of studies have been reported in literature with reference to the use of ANN in modelling stock prices in western countries. However, not much work along these lines has been reported in the Indian context. In this article we discuss the modelling of the Indian stock market (price index) data using ANN. We study the efficacy of ANN in modelling the Bombay Stock Exchange (BSE) SENSEX weekly closing values. We develop two networks with three hidden layers for the purpose of this study which are denoted as ANN1 and ANN2. ANN1 takes as its inputs the weekly closing value, 52-week moving average of the weekly closing SENSEX values, 5-week moving average of the same, and the 10-week Oscillator for the past 200 weeks. ANN2 takes as its inputs the weekly closing value, 52-week moving average of the weekly closing SENSEX values, 5-week moving average of the same and the 5-week volatility for the past 200 weeks. Both the neural networks are trained using data for 250 weeks starting January 1997. To assess the performance of the networks we used them to predict the weekly closing SENSEX values for the two-year period beginning January 2002. The root mean square error (RMSE) and mean absolute error (MAE) are chosen as indicators of performance of the networks. ANN1 achieved an RMSE of 4.82 per cent and MAE of 3.93 per cent while ANN2 achieved an RMSE of 6.87 per cent and MAE of 5.52 per cent.

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The syndrome called COVID‐19 which was firstly spread in Wuhan, China has already been declared a globally “Pandemic.” To stymie the further spread of the virus at an early stage, detection needs to be done. Artificial Intelligence‐based deep learning models have gained much popularity in the detection of many diseases within the confines of biomedical sciences. In this paper, a deep neural network‐based “LiteCovidNet” model is proposed that detects COVID‐19 cases as the binary class (COVID‐19, Normal) and the multi‐class (COVID‐19, Normal, Pneumonia) bifurcated based on chest X‐ray images of the infected persons. An accuracy of 100% and 98.82% is achieved for binary and multi‐class classification respectively which is competitive performance as compared to the other recent related studies. Hence, our methodology can be used by health professionals to validate the detection of COVID‐19 infected patients at an early stage with convenient cost and better accuracy.