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.

Non-Linear Convection in Chemically Reacting Fluid with an Induced Magnetic Field across a Vertical Porous Plate in the Presence of Heat Source/Sink

Abstract: An investigation is carried out to observe the impacts of non-linear convection and induced magnetic field in the flow of viscous fluid over a porous plate under the influence of chemical reaction and heat source/sink. The plate is subjected to a regular free stream velocity as well as a suction velocity. The subjected non-linear problem is non-dimensionalized and analytic solutions are presented via perturbation method. The graphs are plotted to analyze the effect of relevant parameters on velocity, induced magnetic field, heat and mass transfer fields as well as friction factor, current density, Nusselt and Sherwood numbers. It is established that nonlinear convection aspect is destructive for thermal field and its layer thickness. The magnetic field effect enhances the thermal field while it reduces the velocity field. Also, the nonlinear effect subsides heat transfer rate significantly.

A comprehensive novel model for network speech anomaly detection system using deep learning approach

Abstract: Network Intrusion Detection System (NIDS) is the key technology for information security, and it plays significant role for classifying various attacks in the networks accurately. An NIDS gains an understanding of normal and anomalous behavior by examining the network traffic and can identify unknown and new attacks. Analyzing and Identifying unfamiliar attacks are one of the big challenges in Network IDS research. A huge response has been given to deep learning over the past several years and novelty in deep learning techniques are also improved regularly. Deep learning based Network Intrusion Detection approach is highly essential for improved performance. Nowadays, Machine learning algorithms made a revolution in the area of human computer interaction and achieved significant advancement in imitating human brain exactly. Convolutional Neural Network (CNN) is a powerful learning algorithm in deep learning model for improving the machine learning ability in order to achieve high attack classification accuracy and low false alarm rate. In this article, an overview of deep learning methodologies for commonly used NIDS such as Auto Encoder (AE), Deep Belief Network (DBN), Deep Neural Network (DNN), Restricted Boltzmann Machine (RBN). Moreover, the article introduces the most recent work on network anomaly detection using deep learning techniques for better understanding to choose appropriate method while implementing NIDS through widespread literature analysis. The experimental results designate that the accuracy, false alarm rate, and timeliness of the proposed CNN-NIDS model are superior than the traditional algorithms.

Entrepreneurship Education: Innovations and Best Practices

Abstract: Entrepreneurship education has become a priority for policy-makers especially in developing countries. Such interventions in the education system are expected to create a culture of entrepreneurship in the society and thereby bring economic benefits through the enterprising behaviour of individuals resulting in better performance of existing organizations as well as creation of new ventures. While the process appears to be simple and straightforward, the experiences have often belied the expectations. The fact that it is rather difficult to assess the long-term impact of entrepreneurship education adds to the confusion and ambiguities. Educators therefore have been tinkering with various aspects of entrepreneurship education and training in the hope of arriving at the best design. Obviously, this has led to many innovations in the curriculum, pedagogy, target groups and institutions involved in entrepreneurship education. The present paper attempts to document these innovations and best practices under a ‘WHAT-HOW-WHO-WHERE’ framework to capture the four domains of activities involved. Based on a comprehensive review of the literature, we have developed a fairly comprehensive picture of what is happening in the field and proposed a theoretical model highlighting the dual role of entrepreneurship education, namely developing enterprising individuals in the society and providing knowledge and skills required for enterprise creation.

Predictability and herding of bourse volatility: an econophysics analogue

Abstract: Financial Reynolds number works as a proxy for volatility in stock markets. This piece of work helps to identify the predictability and herd behavior embedded in the financial Reynolds number (time series) series for both CNX Nifty Regular and CNX Nifty High Frequency Trading domains. Hurst exponent and fractal dimension have been used to carry out this work. Results confirm conclusive evidence of predictability and herd behavior for both the indices. However, it has been observed that CNX Nifty High Frequency Trading domain (represented by its corresponding financial Reynolds number) is more predictable and has traces of significant herd behavior. The pattern of the predictability has been found to follow a quadratic equation.