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.

Significance of inclined magnetic field on nano-bioconvection with nonlinear thermal radiation and exponential space based heat source: a sensitivity analysis

Abstract: The characteristics of heat transport in nanoliquids under the influence of bio-convection (motile microorganism) have significant applications, since nanoliquids have greater capacity to improve heat transport properties than conventional liquids. With these incredible nanoliquid characteristics, the main objective of current research is to examine the impact of the exponential heat source linked to space and the inclined magnetic force on the nano-bioconvective flow between two turntables. The effect of nonlinear thermal radiation, variable thermal conductivity and viscosity aspects are also considered. The complicated nonlinear problem is treated numerically by using Finite difference method. Optimization procedure implemented via Response surface Methodology for the effective parameters thermophoresis parameter, Hartmann number and radiation parameter on the heat transfer rate. The axial velocity is a dwelling function of the inclined angle of the magnetic field, and the variable viscosity parameter. The temperature profile hikes with an exponential space-related heat source and thermal radiation aspects. Also, the heat transport rate is highly sensitive towards nonlinear thermal radiation parameter compared to the thermophoresis effect and Hartmann number.

Facial Pain Expression Recognition in Real-Time Videos

Abstract: Recognition of pain in patients who are incapable of expressing themselves allows for several possibilities of improved diagnosis and treatment. Despite the advancements that have already been made in this field, research is still lacking with respect to the detection of pain in live videos, especially under unfavourable conditions. To address this gap in existing research, the current study proposed a hybrid model that allowed for efficient pain recognition. The hybrid, which consisted of a combination of the Constrained Local Model (CLM), Active Appearance Model (AAM), and Patch-Based Model, was applied in conjunction with image algebra. This contributed to a system that enabled the successful detection of pain from a live stream, even with poor lighting and a low-resolution recording device. The final process and output allowed for memory for storage that was reduced up to 40%-55% and an improved processing time of 20%-25%. The experimental system met with success and was able to detect pain for the 22 analysed videos with an accuracy of 55.75%-100.00%. To increase the fidelity of the proposed technique, the hybrid model was tested on UNBC-McMaster Shoulder Pain Database as well.

Qutrit-Inspired Fully Self-Supervised Shallow Quantum Learning Network for Brain Tumor Segmentation

Abstract: Classical self-supervised networks suffer from convergence problems and reduced segmentation accuracy due to forceful termination. Qubits or bilevel quantum bits often describe quantum neural network models. In this article, a novel self-supervised shallow learning network model exploiting the sophisticated three-level qutrit-inspired quantum information system, referred to as quantum fully self-supervised neural network (QFS-Net), is presented for automated segmentation of brain magnetic resonance (MR) images. The QFS-Net model comprises a trinity of a layered structure of qutrits interconnected through parametric Hadamard gates using an eight-connected second-order neighborhood-based topology. The nonlinear transformation of the qutrit states allows the underlying quantum neural network model to encode the quantum states, thereby enabling a faster self-organized counterpropagation of these states between the layers without supervision. The suggested QFS-Net model is tailored and extensively validated on the Cancer Imaging Archive (TCIA) dataset collected from the Nature repository. The experimental results are also compared with state-of-the-art supervised (U-Net and URes-Net architectures) and the self-supervised QIS-Net model and its classical counterpart. Results shed promising segmented outcomes in detecting tumors in terms of dice similarity and accuracy with minimum human intervention and computational resources. The proposed QFS-Net is also investigated on natural gray-scale images from the Berkeley segmentation dataset and yields promising outcomes in segmentation, thereby demonstrating the robustness of the QFS-Net model.

Effect of Viscous Dissipation on Three Dimensional Flow of a Nanofluid by Considering a Gyrotactic Microorganism in the Presence of Convective Condition

Abstract: This article deals with the combined effects of viscous dissipation and convective condition on 3D flow, heat and mass transfer of a nanofluid over a stretching sheet by considering gyrotactic microorganism. Appropriate transformations yield the nonlinear ordinary differential systems. The resulting nonlinear system has been solved. Role of substantial parameters on flow fields as well as on heat, mass and microorganism transportation rates are determined and conferred in depth through graphs. It is found that, the larger values of bio-convection Schmidt number decreases the microorganisms profile.