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.

COVID-19 outbreak prediction using quantum neural networks

Abstract: Artificial intelligence has become an important tool in fight against COVID-19. Machine learning models for COVID-19 global pandemic predictions have shown a higher accuracy than the previously used statistical models used by epidemiologists. With the advent of quantum machine learning, we present a comparative analysis of continuous variable quantum neural networks (variational circuits) and quantum backpropagation multilayer perceptron (QBMLP). We analyze the convoluted and sporadic data of two affected countries, and hope that our study will help in effective modeling of outbreak while throwing a light on bright future of quantum machine learning.

Message Efficient Ring Leader Election in Distributed Systems

Abstract: Leader Election Algorithm, not only in distributed systems but in any communication network, is an essential matter for discussion. Tremendous amount of work are happening in the research community on election as network protocols are in need of co-ordinator process for the smooth running of the system. These so called Coordinator processes are responsible for the synchronization of the system otherwise, the system loses its reliability. Furthermore, if the leader process crashes, the new leader process should take the charge as early as possible. New leader is one among the currently running processes with the highest process id. In this paper we have presented a modified version of ring algorithm. Our work involves substantial modifications of the existing ring election algorithm and the comparison of message complexity with the original algorithm. Simulation results show that our algorithm minimizes the number of messages even in worst case scenario

Natural Disaster Prediction by Using Image Based Deep Learning and Machine Learning

Abstract: In recent years, diseases and disaster have become more unpredictable. The advent of technology has not only making our lives easier but also technology-dependent. Nevertheless, the natural disasters cause great adversity by disrupting considerable human lives. Also, the disasters obstruct and affect many industries and services either directly or indirectly. Hence, it is necessary to study and observe data patterns and warning signs that lead to a natural disaster, its potential risk and its ability to resolve management strategies, which can be implemented immediately to minimize the socio-economic loss. This article reviews the state-of-the-art research works and findings through a technological perspective on data analysis, natural disaster prediction, and the utilization of technology for deploying management strategy. Also, this paper focuses on investigating the today's Industry 4.0 that utilizes cognitive computing. The primary aim of this article is to review the research ideas that leverage big data and data mining to observe and track patterns, which can impelment predictive analysis to anticipate the forthcoming disasters. Furthermore, this research work analyzed the posed predictive models by specifically using ANN (Artificial Neural Networks), sentiment model, and smart disaster prediction application (SDPA) to predict the flash flood.

Nontoxic photoluminescent tin oxide nanoparticles for cell imaging: deep eutectic solvent mediated synthesis, tuning and mechanism

Abstract: Non-toxic and photoluminescent (PL) tin oxide nanoparticle synthesis in Deep Eutectic Solvents (DESs) is being reported herein. Both radiation (electron beam and γ radiation) and solvothermal methods were employed for the synthesis. An electron beam radiation technique proved to be more appropriate in tuning the size and morphology compared to the solvothermal process. Addition of any external oxido-reductive or stabilizing agent could be avoided by the use of Reline (choline chloride : urea; 1 : 2) as the host matrix. Detailed analysis of the PL behaviour of the nanoparticles is another important aspect of this study. The oxygen vacancies and tin interstitials responsible for photoluminescence have been identified from the de-convoluted PL spectra of the nanoparticles. Time dependent PL kinetics depicts PL decay at ∼1.2 ns due to near band edge emission and at ∼3.15 ns due to defect state emission. The synthetic process has been standardized focusing on the size of the particles by varying all possible experimental parameters such as the temperature, concentration of the precursors, reaction time, dose of irradiation and dose rate. Synthesized nanoparticles have been characterized using XRD, XPS and EDX. TEM images illustrate nanomorphological differences obtained in the two methods. The probable mechanism of synthesis (both radiation and thermal) has been proposed based on the results obtained from transient studies using electron pulses and FTIR experiments. Cytotoxicity data demonstrate that the nanoparticles are suitable for application in biological studies involving cells up to a concentration of 10 μM. Imaging experiments with these photoluminescent nanoparticles exhibit their ubiquitous distribution including the nucleus of the tumour cells, which signifies potential application of these NPs for targeted drug delivery in cancer chemotherapy. Furthermore, the nanoparticles exhibited excellent antioxidant properties in vitro. The findings herein can open up enormous possibilities for more advanced and dedicated research towards using this cheap and versatile nanomaterial in a variety of biomedical applications.