Birla Institute of Technology and Science

About: Birla Institute of Technology and Science is a education organization based out in Pilāni, Rajasthan, India. It is known for research contribution in the topics: Computer science & Population. The organization has 8897 authors who have published 13947 publications receiving 170008 citations.

A Bird's Eye view on process and engineering aspects of hydrogen storage

Abstract: Hydrogen as a clean fuel is becoming vital in view of depleting fossil fuels and ever increasing energy demand. Hence hydrogen generation and storage gains immense importance. In this review article, different methods of hydrogen storage viz., physical, chemical and electrochemical in various forms like gas, liquid and solid have been discussed and compared in terms of their efficacy, capacity, operating conditions and other safety aspects. Modeling and simulation studies reported on various aspects of hydrogen storage have been presented and compared vis a vis governing equations, applications, assumptions, merits and demerits. These modeling studies would enable to understand the phenomenon better and to explore new avenues. Kinetic and thermodynamic aspects of hydrogen storage especially employing metal hydrides have been discussed too to understand the mechanism, rate controlling actors and energy aspects. Future challenges and prospects of all the aspects of the review were provided.

Sentiment Classification System of Twitter Data for US Airline Service Analysis

Abstract: The airline industry is a very competitive market which has grown rapidly in the past 2 decades. Airline companies resort to traditional customer feedback forms which in turn are very tedious and time consuming. This is where Twitter data serves as a good source to gather customer feedback tweets and perform a sentiment analysis. In this paper, we worked on a dataset comprising of tweets for 6 major US Airlines and performed a multi-class sentiment analysis. This approach starts off with pre-processing techniques used to clean the tweets and then representing these tweets as vectors using a deep learning concept (Doc2vec) to do a phrase-level analysis. The analysis was carried out using 7 different classification strategies: Decision Tree, Random Forest, SVM, K-Nearest Neighbors, Logistic Regression, Gaussian Naive Bayes and AdaBoost. The classifiers were trained using 80% of the data and tested using the remaining 20% data. The outcome of the test set is the tweet sentiment (positive/negative/neutral). Based on the results obtained, the accuracies were calculated to draw a comparison between each classification approach and the overall sentiment count was visualized combining all six airlines.