Christ University

About: Christ University is a education organization based out in Bengaluru, India. It is known for research contribution in the topics: Computer science & Convection. The organization has 2267 authors who have published 2715 publications receiving 14575 citations. The organization is also known as: Christ College & Christ University.

A framework for smart transportation using Big Data

Abstract: In the current era of information technology, data driven decision is widely recognized. It leads to involvement of the term “Big Data”. The use of IOT and ICT deployment is a key player of the smart city project in India. It leads to smart transportation systems with huge amounts of real time data that needs to be communicated, aggregated, interpreted, analyzed and maintained. These technologies enhance the effective usage of smart transportation systems, which is economical and has a high social impact. Social applications like transportation can be benefited by using IOT, ICT and big data analytics to give better prediction. In this paper, we present how big data analytics can be used to build a smart transportation system. Increasing traffic and frequent jams in today's scenario are becoming a routine, citizens are facing various health issues due to the bad transportation systems such as high blood pressure, stress, asthma due to air and noise pollution. In smart transportation mobility can be easily implemented as most of the citizens use smartphones. It can be easily linked to smart traffic signals to achieve the objective of smart transportation. Smart transportation is a key component to attract companies as it leads to better services, business planning, support beneficial environment and social behavior.

Sensitivity analysis of nonlinear radiated heat transport of hybrid nanoliquid in an annulus subjected to the nonlinear Boussinesq approximation

Abstract: The main emphasis of the current study is to analyze the novel feature of the quadratic convective and nonlinear radiative flow of MHD hybrid nanoliquid (Cu–Al2O3–H2O) in an annulus with sensitivity analysis. The significance of exponential space-related heat source, movement of annuli and a new radiation parameter corresponding to an asymptotic nature are also comprehended in the existing study. The dimensionless governing nonlinear equations are treated numerically by employing shooting technique. Impact of effective parameters on the flow and heat transport features has been scrutinized. The optimization procedure is implemented to analyze the influence of three effective parameters $$\left( {1.5 \le R_{\mathrm{f}} \le 5.5,\; 1 \le Q_{\mathrm{E}} \le 3\;{\mathrm{and}}\; 1\% \le \phi_{\mathrm{Cu}} \le 3\% } \right)$$ on skin friction and Nusselt number by utilizing response surface methodology and sensitivity analysis. The obtained results portray that the nonlinear convection parameter is more favorable for the skin friction coefficient. Further, a comparison of sensitivity depicts that the skin friction coefficient is more sensitive to $$R_{\mathrm{f}}$$ and $$Q_{\mathrm{E}}$$ , whereas Nusselt number is more sensitive to $$\phi_{\mathrm{Cu}}$$ .

Detecting the magnitude of depression in Twitter users using sentiment analysis

Abstract: Today the different social networking sites have enabled everyone to easily express and share their feelings with people around the world. A lot of people use text for communicating, which can be done through different social media messaging platforms available today such as Twitter, Facebook etc, as they find it easier to express their feelings through text instead of speaking them out. Many people who also suffer from stress find it easier to express their feelings on online platform, as over there they can express themselves very easily. So if they are alerted beforehand, there are ways to overcome the mental problems and stress they are suffering from. Depression stands out to be one of the most well known mental health disorders and a major issue for medical and mental health practitioners. Legitimate checking can help in its discovery, which could be useful to anticipate and prevent depression all-together.Hence there is a need for a system, which can cater to such issues and help the user. The purpose of this paper is to propose an efficient method that can detect the level of depression in Twitter users. Sentiment scores calculated can be combined with different emotions to provide a better method to calculate depression scores. This process will help underscore various aspects of depression that have not been understood previously. The main aim is to provide a sense of understanding regarding depression levels in different users and how the scores can be correlated to the main data.