Christophe Chesneau

Nowadays, young people are spending the majority of their time on social media, using various platforms like WhatsApp, Facebook, Instagram, etc. It is a common perception that these media have become the new sources of information, which progressively converts into knowledge. It is interesting to check whether they use those sources to achieve knowledge or not. The main focal point of this study is to understand the political engagement, knowledge, and outlook of college-studying youngsters in India. It studies the factors influencing political awareness and examines the influence of politics and politicians on the involvement of youngsters in politics. The findings show that social media has a significant influence on political issues, but they are also more corrupted

Predicting financial markets has always been one of the most challenging issues, attracting the attention of many investors and researchers. In this regard, deep learning methods have been used a lot recently. Due to the desired results, such networks are always in development and progress. One of the networks that is being implemented in various fields is capsule network. The first time the classification capsule network was introduced, it was able to attract a lot of attention with its success on MNIST data 1 . In such networks, as in the other ones, the parameters are obtained by minimizing a loss function. In this paper, we first change the classification capsule network to a regression capsule network by modifying the last layer of the network. Then we use different information measures such as Kullnack-Leibler, Lin-Wang and Triangular information measures as a loss function, and compare their results with wellknown models including Artificial Neural Network (ANN), Convolutional Network (CNN) and Long Short-Term Memory (LSTM) as well as common used loss functions such as Mean Square Error (MSE) and Mean Absolute Percentage Error (MAPE). Using appropriate accuracy metrics, it is shown that the capsule network using triangular information measure is well able to predict the price of bitcoin for the medium and long term period including 10, 90 and 180 days.