Road accidents have become a major concern for safety today. The increase in the number of vehicles has significantly increased the real-time road traffic, leading to many fatal accidents. Over the years, researchers have been trying to find solutions to enhance vehicular security in order to prevent these accidents or at least reduce their impact. This paper aims to contribute in this domain by studying the reaction time of vehicles in low visibility conditions and proposing a solution which can be implemented in the real world to prevent accidents in such scenarios. A novel algorithm is proposed which utilizes a modified form of AODV routing protocol in VANETS Cloud to alert the vehicles whenever a leading vehicle in the same lane slows down. This study can help the drivers by providing them sufficient time to react before any occurrence of an accident. The drivers would have a better idea about the vehicles in front, allowing them to make proper and informed decisions while driving in low visibility conditions.
The world witnessed the emergence of a deadly virus in December 2019, later named COVID-19. The virus was found to be highly contagious, and so people across the world were highly prone to be affected by the virus. Being a virus-borne disease, developing a vaccine was one of the most promising remedies. Thus, research organizations across the globe started working on developing the vaccine. However, it was later found by many researchers that a large number of people were hesitant to receive the vaccine. This paper aims to study the acceptance and hesitancy levels of people in India and compares them with the acceptance and hesitancy levels of people from the UK, the USA, and the rest of the world by analyzing their tweets on Twitter. For this study, 2,98,452 tweets were fetched from January 2020 to March 2022 from Twitter, and 1,84,720 tweets from 1,22,960 unique users were selected based on their country of origin. Machine learning based Sentiment analysis is then used to evaluate and analyze the tweets. The paper also proposes an NLP-based algorithm to perform opinion mining on Twitter data. The study found the public sentiment of the Indian population to be 63% positive, 28% neutral, and 9% negative. While the worldwide sentiment distribution is 45% positive, 34% neutral, and 21% negative, the USA has 42% positive, 34% neutral, and 23% negative and the UK has 50% positive, 29% neutral, and 21% negative. Also, sentiment analysis for individual vaccines in Indian context resulted in “Covaxin” with the highest positive sentiment at 43% followed by “Covishield” at 36%. The outcome of this work yields an insight into the public perception of the COVID-19 vaccine and thus can be used to formulate policies for existing and future vaccine campaigns. This study becomes more relevant as it is the consolidated opinion of Indian people, which is versatile in nature.
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