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.

AI enabled Applications towards Intelligent Transportation

Abstract: Artificial intelligence (AI) is the ability of a machine to perform cognitive functions like perceiving, reasoning, learning and problem-solving which humans are capable of performing at ease. AI has gained traction since the past two decades across the globe due to availability of huge volume of data generated through Internet. There has been a huge benefit to governments and businesses by processing this data using advanced algorithms in the recent past. The robust growth of machine learning algorithms supported by various technologies like Internet of Things, Robotic Process Automation, Computer Vision, Natural Language Processing have enabled the growth of AI. This article is a compilation of various issues plaguing Transport Industry classified under Intelligent Transportation Systems. Some of the sub-systems considered are related to Traffic Management, Public Transport, Safety Management, Manufacturing & Logistics from Intelligent Transportation Systems where AI benefits are put into use. The study takes up specific areas of concern in transport industry and its related issues that have possible solutions using AI. The approach involves a secondary study based on the country-wise data available from various sources. Further, discussions on AI solutions to resolve issues in transport industry across various countries in the globe and in Indian states is taken up.

Fusion model of wavelet transform and adaptive neuro fuzzy inference system for stock market prediction

Abstract: Stock market prediction is one of the most important financial subjects that have drawn researchers’ attention for many years. Several factors affecting the stock market make stock market forecasting highly complicated and a difficult task. The successful prediction of a stock market may promise attractive benefits. Various data mining methods such as artificial neural network (ANN), fuzzy system (FS), and adaptive neuro-fuzzy inference system (ANFIS) etc are being widely used for predicting stock prices. The goal of this paper is to find out an efficient soft computing technique for stock prediction. In this paper, time series prediction model of closing price via fusion of wavelet-adaptive network-based fuzzy inference system (WANFIS) is formulated, which is capable of predicting stock market. The data used in this study were collected from the internet sources. The fusion forecasting model uses the discrete wavelet transform (DWT) to decompose the financial time series data. The obtained approximation and detailed coefficients after decomposition of the original time series data are used as input variables of ANFIS to forecast the closing stock prices. The proposed model is applied on four different companies’ previous data such as opening price, lowest price, highest price and total volume share traded. The day end closing price of stock is the outcome of WANFIS model. Numerical illustration is provided to demonstrate the efficiency of the proposed model and is compared with the existing techniques namely ANN and hybrid of ANN and wavelet to prove its effectiveness. The experimental results reveal that the proposed fusion model achieves better forecasting accuracy than either of the models used separately. From the results, it is suggested that the fusion model WANFIS provides a promising alternative for stock market prediction and can be a useful tool for practitioners and economists dealing with the prediction of stock market.

A Reverse Vaccinology Approach for the Identification of Potential Vaccine Candidates from Leishmania spp

Abstract: Leishmaniasis is a group of diseases with a spectrum of clinical manifestations ranging from cutaneous ulcers to visceral leishmaniasis, which results from the bite of an infected sandfly to human. Attempts to develop an effective vaccine have been shown to be feasible but no vaccine is in active clinical use. This study adopts a Reverse Vaccinology approach to identify common vaccine candidates from both highly pathogenic species Leishmania major and Leishmania infantum. Total proteome of both species were compared to identify common proteins, which are further taken for sub-cellular localization and transmembrane helices prediction. Plasma membrane proteins having only one transmembrane helix were first identified and analyzed which are non-homologous in human and mouse in order to avoid molecular mimicry with other proteins. Selected proteins were analyzed for their binding efficiency to both major histocompatibility complex (MHC) class I and class II alleles. As a result, 19 potential epitopes are screened in this study using different approaches, which can be further verified through in vivo experiments in MHC compatible animal models. This study demonstrates that Reverse Vaccinology approach has potential in discovering various immunogenic antigens from in silico analysis of pathogen’s genome or proteome instead of culturing the whole organism by conventional methods.

Effect of nonlinear thermal radiation on double-diffusive mixed convection boundary layer flow of viscoelastic nanofluid over a stretching sheet

Abstract: The present exploration deliberates the effect of nonlinear thermal radiation on double diffusive free convective boundary layer flow of a viscoelastic nanofluid over a stretching sheet. Fluid is assumed to be electrically conducting in the presence of applied magnetic field. In this model, the Brownian motion and thermophoresis are classified as the main mechanisms which are responsible for the enhancement of convection features of the nanofluid. Entire different concept of nonlinear thermal radiation is utilized in the heat transfer process. Appropriate similarity transformations reduce the nonlinear partial differential system to ordinary differential system which is then solved numerically by using the Runge–Kutta–Fehlberg method with the help of shooting technique. Validation of the current method is proved by having compared with the preexisting results with limiting solution. The effect of pertinent parameters on the velocity, temperature, solute concentration and nano particles concentration profiles are depicted graphically with some relevant discussion and tabulated result. It is found that the effect of nanoparticle volume fraction and nonlinear thermal radiation stabilizes the thermal boundary layer growth. Also it was found that as the Brownian motion parameter increases, the local Nusselt number decreases, while the local friction factor coefficient and local Sherwood number increase.

Tailoring the CdS surface structure for photocatalytic applications

Abstract: Semiconductor mediated photocatalysis is envisaged as a promising approach to initiate the diverse redox reactions under the ambient conditions. Although titania still remains as benchmark photocatalyst, its wide band gap and rapid charge carrier recombination blights their utility under natural solar light. Thus, search of functional materials with narrow gap and suitable band edge potentials has drawn significant attention for photocatalytic applications. Towards this end, CdS have been impressive as prime nanomaterial which is mainly attributed to their visible light absorption capacity, more negative conduction band edge potential, and simplistic preparation with diverse morphologies and their proficiency to form stable heterostructure with variety of co-catalysts. However, photocorrosion vulnerability of CdS becomes the origin of intimidation for long term operations and massive charge carrier recombination constrains their performance. In this review article, surface structure of the CdS modified with various co-catalysts such as metal NPs, metal oxides, sulfides, phosphides, carbides, g-C3N4, polymers and carbon materials to overcome the aforementioned drawbacks is discussed. Besides, fundamental aspects concerning the relationship between the crystal structure and morphological effects of CdS on the photocatalytic property is emphasized. The preparative methods, charge carrier dynamics and performance of CdS-based binary and ternary composites benefitting the model reactions such as pollutant degradation, hydrogen evolution and organic functional group transformation is reviewed in detail. The literature survey concludes that the surface modifications with co-catalysts can be the constructive approach for exploring the CdS based nanomaterials for broader environmental applications.