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Institution

Chandigarh University

EducationMohali, India
About: Chandigarh University is a education organization based out in Mohali, India. It is known for research contribution in the topics: Materials science & Computer science. The organization has 1358 authors who have published 2104 publications receiving 10050 citations.


Papers
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Journal ArticleDOI
TL;DR: In this article, the toxicity effect of silver nanoparticles on humans Health was reviewed and to which content the silver ion fraction contributes the toxicity to cells, however, the toxicity of green formation of Silver nanoparticles(AgNPs) can be reduced.

43 citations

Journal ArticleDOI
TL;DR: In this article, thermal spray coatings are a group of coating processes that enhance the performance of parts by adding functionality to surfaces, and they are used to enhance the functionality of parts.
Abstract: Thermal spray coatings are a group of coating processes that enhance the performance of parts by adding functionality to surfaces. In the past few decades, thermal spray coatings technology was use...

43 citations

Journal ArticleDOI
TL;DR: The prepared AgNPs showed clear cytotoxicity for HeLa cells and showcased a close relationship between activity and concentration as evidenced by the decrease in the percentage of metabolically active cells up to 25 µM–75 µM concentration of silver nanoparticles.
Abstract: In the present work, silver nanoparticles were prepared by using the extract of Camellia Sinensis. The extract contains phytochemicals which are mainly polyphenols acting as the natural reducing and stabilizing agents leading to the formation of uniformly dispersed and stabilized silver nanoparticles. The synthesis of silver nanoparticles was significantly influenced by the impact of the pH, as well as temperature conditions. It was found that at pH 5 and 25 °C, nanoparticles of different morphologies (spherical, polygonal, capsule) and sizes were formed. However, with the increase in temperature from 25 °C to 65 °C but at the same pH, these particles started attaining the spherical shape of different sizes owing to an increase in the reduction rate. Furthermore, for the reaction of the mixture at 65 °C, an increase in pH from 5 to 11 led to an increase in the monodispersity of spherically shaped nanoparticles, attributed to the hydroxide ions facilitated reduction. The prepared nanoparticles were investigated for their antibacterial activity using Nathan’s Agar Well-Diffusion method. It was found that AgNPs prepared at pH 9 and 65 °C demonstrated strong antibacterial activity against gram-negative Escherichia coli in contrast to gram-positive Staphylococcus aureus. In reference to the cytotoxic potency, the prepared AgNPs showed clear cytotoxicity for HeLa cells and showcased a close relationship between activity and concentration as evidenced by the decrease in the percentage (100 to 30%) of metabolically active cells up to 25 µM–75 µM concentration of silver nanoparticles.

43 citations

Journal ArticleDOI
TL;DR: DeTrAs: Deep Learning-based Internet of Health Framework for the Assistance of Alzheimer Patients depicts almost 10–20% improvement in terms of accuracy in contrast to the different existing machine learning algorithms.
Abstract: Healthcare 4.0 paradigm aims at realization of data-driven and patient-centric health systems wherein advanced sensors can be deployed to provide personalized assistance. Hence, extreme mentally affected patients from diseases like Alzheimer can be assisted using sophisticated algorithms and enabling technologies. Motivated from this fact, in this paper, DeTrAs: Deep Learning-based Internet of Health Framework for the Assistance of Alzheimer Patients is proposed. DeTrAs works in three phases: (1) A recurrent neural network-based Alzheimer prediction scheme is proposed which uses sensory movement data, (2) an ensemble approach for abnormality tracking for Alzheimer patients is designed which comprises two parts: (a) convolutional neural network-based emotion detection scheme and (b) timestamp window-based natural language processing scheme, and (3) an IoT-based assistance mechanism for the Alzheimer patients is also presented. The evaluation of DeTrAs depicts almost 10–20% improvement in terms of accuracy in contrast to the different existing machine learning algorithms.

43 citations

Journal ArticleDOI
TL;DR: A blockchain-based security mechanism for cyber-physical systems is proposed to ensure secure transfer of information among drones and the results obtained show the potential benefits of the proposed scheme.
Abstract: Drones are equipped with high-vision cameras, advanced sensors, and GPS receivers to deliver diverse services from high altitude thereby creating an airborne network. In this environment, physical things (drones, sensors, etc.,) are controlled using computational algorithms to form a cyber-physical system for the Internet of drones. Although the drones provide manifold benefits still there are many issues (security, privacy, and data integrity) which must be resolved before the usage of drones in smart cyber-physical systems. So, in this paper, a blockchain-based security mechanism for cyber-physical systems is proposed to ensure secure transfer of information among drones. In this mechanism, the miner node is selected using a deep learning-based approach, i.e., a deep Boltzmann machine, using features like computational resources, the available battery power, and flight time of the drone. The proposed mechanism is evaluated based on different performance metrics and the results obtained show the potential benefits of the proposed scheme.

43 citations


Authors

Showing all 1533 results

NameH-indexPapersCitations
Neeraj Kumar7658718575
Rupinder Singh424587452
Vijay Kumar331473811
Radha V. Jayaram321143100
Suneel Kumar321805358
Amanpreet Kaur323675713
Vikas Sharma311453720
Munish Kumar Gupta311923462
Vijay Kumar301132870
Shashi Kant291602990
Sunpreet Singh291532894
Gagangeet Singh Aujla281092437
Deepak Kumar282732957
Dilbag Singh27771723
Tejinder Singh271622931
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023116
2022182
2021893
2020373
2019233
2018174