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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, PACT@γ-Fe2O3 was synthesized via oxidative free radical polymerization of acrylamide monomer in presence of γ-Fe 2O3 nanoparticles as a filler by grafting with chitosan biopolymer.

45 citations

Journal ArticleDOI
TL;DR: A broad overview of the progress of immunotherapy-based treatments and discuss future opportunities for their use in triple negative breast cancers (TNBCs) is provided in this paper, where the authors also discuss the potential for using immunotherapy in TNBCs.

44 citations

Journal ArticleDOI
TL;DR: This paper proposes an approach that devises a new hybrid technique, which is a combination of Maximum Likelihood Estimation (MLE) and Self Cancellation (SC) techniques through wavelet implication, to enhance BER performance of the OFDM system.

44 citations

Journal ArticleDOI
01 Aug 2021
TL;DR: This article proposes energy efficient optimal parent selection in RPL (EEOPS‐RPL) using firefly optimization algorithm to extend the lifespan of the IoT network.
Abstract: Energy conservation is a major challenge in the Internet of Things (IoT) as the number of resource‐constrained devices is connected to the network. Routing plays a vital role in IoT to ext...

44 citations

Proceedings ArticleDOI
02 Jul 2020
TL;DR: To prove the effectiveness, K-NN algorithms and collaborative filtering are used to mainly focus on enhancing the accuracy of results as compared to content-based filtering, based on cosine similarity using k-nearest neighbor with the help of a collaborative filtering technique.
Abstract: Movies are one of the sources of entertainment, but the problem is in finding the desired content from the ever-increasing millions of content every year. However, recommendation systems come much handier in these situations. The aim of this paper is to improve the accuracy and performance of a regular filtering technique. Although varieties of methods are used to implement a recommendation system, Content-based filtering is the simplest method. Which takes input from the users, rechecks his/her history/past behavior, and recommends a list of similar movies. In this paper, to prove the effectiveness, K-NN algorithms and collaborative filtering are used to mainly focus on enhancing the accuracy of results as compared to content-based filtering. This approach is based on cosine similarity using k-nearest neighbor with the help of a collaborative filtering technique, at the same time removing the drawbacks of the content-based filtering. Although using Euclidean distance is preferred, cosine similarity is used as the accuracy of cosine angle and the equidistance of movies remain almost the same.

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
Network Information
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023116
2022182
2021893
2020373
2019233
2018174