CS9.435.S22

  Introduction Deriving conclusions from historical datasets is one of the hardest problems historians have to solve. The problems include verifying information, deriving correlations, and defending conclusions. The complexity of the problem results in increased time for a solution.  Through this project, we have proposed an accurate and faster way of concluding new inferences from historical data. This methodology primarily has 4 parts 1. Collecting and preprocessing the dataset 2. Analysing data using modern machine learning techniques. 3. Validate results using existing Information 4. Derive conclusions To showcase the working of the proposed methodology we have designed an experiment, in which we have derived cultural and economical relationships between ancient cities of the Indus valley civilization. Project Description Experimentation Data Collection (https://github.com/sidgupta2205/CSS_Project/tree/descriptors/Data) In the first phase, we worked on research and knowled...

   Introduction Prediction of the stock market and crypto market performance is one of the most difficult things to do. There are so many aspects influencing the prediction – physical factors vs. psychological, behaviour, sentiments, etc. All these factors combine to make share prices volatile and very difficult to predict with a high degree of accuracy. In this project, we use sentiment analysis and machine learning to establish the correlation between “public sentiment” and “market sentiment”. We use data from twitter to predict public mood and use the predicted mood to forecast the stock market and crypto market movements. We access historical tweets using snscrape, along with daily historical stock data, which we use to incorporate while training the model to make it more robust. Problem Identified Researchers for a long time have been working on volatile market prediction. The Efficient Market Hypothesis (EMH) says that stock market prices are largely influenced by trend...

  CSS Team 19 : Fighting an Infodemic Problem statement description :  Social media is used by everyone nowadays for sharing information or getting news about the ongoing policies and protest in a India. Information spread faster than news channels through images and text shared on social media. Images are the most preceived social visual non-verbal information which speaks more about scenario as compared to verbal/textual information. Everywhere fake news are studied on social media. But images also have equal impact of how a news or protest is being shown on social media. Through this project we are trying to analyse, how a protest is being depicted on social media through images. We have seen in the past about the farmes protest, hijab protest etc. and how videos and images shape the prespective of people/viewers towards a protest. Images of the protest can be manipulated and a image of a protest an be taken far away from what it really is. Thats why our project aims to cl...

About CAA - NRC The Citizenship (Amendment) Act, 2019 aims to facilitate grant of citizenship to migrants belonging to Hindu, Sikh, Buddhist, Jain, Parsi or Christian communities from Pakistan, Bangladesh and Afghanistan who entered into India on or before 31st December, 2014 (1). It was enacted by the Government of India on 12 December, 2019. This particular move by the government of India sparked nationwide protests against CAA and the National Register of Citizens (NRC). The NRC is the official record of all the legal citizens of India. The main reason for the protests was the exclusion of Muslims and other communities who fled from the same or neighboring countries along with refugees from Sri Lankan Tamils in India, Rohingyas from Myanmar, and Tibetan refugees (4).  Due to the widespread national and overseas unrest from this act, it became a very important event in Indian history. Therefore, this led to an extensive media coverage in India. Furthermore, detailed accounts of t...