M
Mallappa Kumara Swamy
Researcher at Concordia University
Publications - 865
Citations - 18487
Mallappa Kumara Swamy is an academic researcher from Concordia University. The author has contributed to research in topics: Adaptive filter & Wavelet. The author has an hindex of 54, co-authored 864 publications receiving 14508 citations. Previous affiliations of Mallappa Kumara Swamy include Universiti Putra Malaysia & Concordia University Wisconsin.
Papers
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Journal ArticleDOI
Nano based drug delivery systems: recent developments and future prospects
Jayanta Kumar Patra,Gitishree Das,Leonardo Fernandes Fraceto,Leonardo Fernandes Fraceto,Estefania Vangelie Ramos Campos,Estefania Vangelie Ramos Campos,Maria del Pilar Rodriguez-Torres,Laura Susana Acosta-Torres,Luis A. Diaz-Torres,Renato Grillo,Mallappa Kumara Swamy,Shivesh Sharma,Solomon Habtemariam,Han-Seung Shin +13 more
TL;DR: An updated summary of recent advances in the field of nanomedicines and nano based drug delivery systems through comprehensive scrutiny of the discovery and application of nanomaterials in improving both the efficacy of novel and old drugs and selective diagnosis through disease marker molecules is presented.
Journal ArticleDOI
Antimicrobial Properties of Plant Essential Oils against Human Pathogens and Their Mode of Action: An Updated Review.
TL;DR: This comprehensive review will benefit researchers who wish to explore the potential of essential oils in the development of novel broad-spectrum key molecules against a broad range of drug-resistant pathogenic microbes.
Book
Graphs: Theory and Algorithms
TL;DR: This book discusses Graphs and Vector Spaces, which are concerned with the construction of graphs, and some of the algorithms used to solve these problems.
Journal ArticleDOI
Nanoparticles: Alternatives Against Drug-Resistant Pathogenic Microbes.
TL;DR: The present review highlights the antimicrobial effects of various nanomaterials and their potential advantages, drawbacks, or side effects and may be useful in the discovery of broad-spectrum antimicrobial drugs for use against multi-drug-resistant microbial pathogens in the near future.
Journal ArticleDOI
Optimizing the kernel in the empirical feature space
TL;DR: It is shown that there exists a close relationship between the class separability measure introduced here and the alignment measure defined recently by Cristianini, and that the optimized kernel is more adaptive to the input data.