As vaccines against COVID-19 are becoming available in India, people have started forming perspectives about them. Hence in this paper, we conducted a survey to understand people’s perspective with regards to vaccines. Out of 200 participants we had 32 doctors participating in our survey, also 35 participants of our survey were vaccinated. The main objective of the paper was to analyse the response to the survey and draw conclusions with the help of data analysis techniques. In this paper, we have performed a Sentimental Analysis on participants’ response to identify what stops people from getting vaccinated. With the help of this paper, we aim that all the information provided in this paper will help the government of India to have an idea about what factors influence people the most when it comes to COVID-19 vaccination, thereby helping in eliminating vaccination myths from the society.
Understanding the effects of a pandemic on the public sentiment is an important challenge in the study of social dynamics during a global pandemic. This paper puts forward a case study that throws light on the psychological impact of the COVID-19 pandemic on the people living in the Indian subcontinent. The study is based on a pipeline that involves preprocessing, sentiment analysis, topic modelling, natural language processing and statistical analysis of Twitter data extracted in the form of tweets. The results demonstrate the effectiveness of this pipeline in understanding the temporal impact of the different lockdowns implemented in the span of the pandemic on the public sentiment, which can be useful for healthcare workers, authorities, and researchers.
The proliferation of data usage and insights gained from it has seen its applications manifold in the recent decades. One such application of data available is through analysis of the effect of news and historical data on the stock market. The paper has analysed the effect of historical prices and news on the Stock market using various models. Since news and historical data can gravely impact the Stock market, the paper uses Natural Language Processing (NLP) to analyse the news and several predictive analysis models to make use of the historical data to make predictions concerning the stock prices. The NLP algorithm utilized makes use of the text as input and tokenizes each sentence into words to make the process of analysis easier. Google’s Bidirectional Encoder Representations from Transformers (BERT) model is used for the analysis of the news. For the historical data analysis, the two models namely Auto-regressive Integrated Moving Average (ARIMA) and Long short-term memory (LSTM) have been compared and post evaluation it has been found that LSTM Recurrent Neural Networks (RNN) is a superior model. The data for these models, has been procured from the Internet to ensure relevancy and credibility. Both the models used in the analysis of news and historical data have been cross-validated several times which has yielded adequate accuracy.
Global meliorism of technologies brings down the equipment cost of renewable energy system especially solar photovoltaic system. Depleting resources as well as increasing energy demand make the people to look out for alternative energy sources. So, it becomes essential to increase the energy conversion efficiency of the panels. Technology that helps in increasing the energy efficiency is Internet of Things-IoT. In this digital era IoT evolved as a powerful enabler for it transforms the use of internet to provide solution to improve the living standards of the people. IoT with embedded technology allows sensing and communication devices to interact with each other in all ways to respond to the individual needs of the people, thus it merges digital and physical world. In monitoring and controlling the parameters of PV panel human intervention is replaced by machine-machine communication by IoT. IoT performs A3 that is at any time from any place collect data using sensors in the world and also analyze the data at any time and then process the data to make decisions and perform actions. IoT finds its application in all sectors such as health, industry, agriculture, energy etc. This paper discusses about the various methodologies involved in improving energy conversion efficiency of the PV panel using IoT.
In this paper, modified antenna design suitable for 5G communication systems with better return loss, increased BW and perfect impedance matching is successfully demonstrated. Proposed design contains partial ground plane and parasitic patch having a rectangular slot along with the pentagonal slot in radiating patch which is extended in transmission line. Slits are also inserted into the radiating patch near transmission line to attain perfect 50Ω impedance matching. Fabrication of proposed antenna can be done using FR4 epoxy as a substrate, having εr=4.4, tanδ=0.02 and dimensions 18 mm x 16 mm x 1.6 mm. The proposed antenna has been analysed using commercially available software Ansoft’s HFSS (High Frequency Structure Simulator) using the properties of FR4 substrate and with optimum dimensions of various antenna components. Results of the proposed design are analysed in terms of return loss (S11), radiation pattern, 3D gain plot, directivity, VSWR and BW at -10 dB. The proposed antenna has S11< -50 dB and -20 dB at 18.73 GHz and 23.21 GHz respectively, approximate BW of 3.9 GHz and VSWR< 2.5 suitable for K-band application in 5G.
