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Mallappa Kumara Swamy

Bio: 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
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.
Abstract: Nanomedicine and nano delivery systems are a relatively new but rapidly developing science where materials in the nanoscale range are employed to serve as means of diagnostic tools or to deliver therapeutic agents to specific targeted sites in a controlled manner Nanotechnology offers multiple benefits in treating chronic human diseases by site-specific, and target-oriented delivery of precise medicines Recently, there are a number of outstanding applications of the nanomedicine (chemotherapeutic agents, biological agents, immunotherapeutic agents etc) in the treatment of various diseases The current review, presents 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 (eg, natural products) and selective diagnosis through disease marker molecules The opportunities and challenges of nanomedicines in drug delivery from synthetic/natural sources to their clinical applications are also discussed In addition, we have included information regarding the trends and perspectives in nanomedicine area

3,112 citations

Journal ArticleDOI
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.
Abstract: A wide range of medicinal and aromatic plants (MAPs) have been explored for their essential oils in the past few decades. Essential oils are complex volatile compounds, synthesized naturally in different plant parts during the process of secondary metabolism. Essential oils have great potential in the field of biomedicine as they effectively destroy several bacterial, fungal, and viral pathogens. The presence of different types of aldehydes, phenolics, terpenes, and other antimicrobial compounds means that the essential oils are effective against a diverse range of pathogens. The reactivity of essential oil depends upon the nature, composition, and orientation of its functional groups. The aim of this article is to review the antimicrobial potential of essential oils secreted from MAPs and their possible mechanisms of action against human pathogens. 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.

568 citations

Book
01 Jan 1992
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.
Abstract: Basic Concepts. Trees, Cutsets, and Circuits. Eulerian and Hamiltonian Graphs. Graphs and Vector Spaces. Directed Graphs. Matrices of a Graph. Planarity and Duality. Connectivity and Matching. Covering and Coloring. Matroids. Graph Algorithms. Flows in Networks. Indexes.

500 citations

Journal ArticleDOI
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.
Abstract: Antimicrobial substances may be synthetic, semisynthetic, or of natural origin (i.e., from plants and animals). Antimicrobials are considered “miracle drugs” and can determine if an infected patient/animal recovers or dies. However, the misuse of antimicrobials has led to the development of multi-drug-resistant bacteria, which is one of the greatest challenges for healthcare practitioners and is a significant global threat. The major concern with the development of antimicrobial resistance is the spread of resistant organisms. The replacement of conventional antimicrobials by new technology to counteract antimicrobial resistance is ongoing. Nanotechnology-driven innovations provide hope for patients and practitioners in overcoming the problem of drug resistance. Nanomaterials have tremendous potential in both the medical and veterinary fields. Several nanostructures comprising metallic particles have been developed to counteract microbial pathogens. The effectiveness of nanoparticles (NPs) depends on the interaction between the microorganism and the NPs. The development of effective nanomaterials requires in-depth knowledge of the physicochemical properties of NPs and the biological aspects of microorganisms. However, the risks associated with using NPs in healthcare need to be addressed. The present review highlights the antimicrobial effects of various nanomaterials and their potential advantages, drawbacks, or side effects. In addition, this comprehensive information may be useful in the discovery of broad-spectrum antimicrobial drugs for use against multi-drug-resistant microbial pathogens in the near future.

365 citations

Journal ArticleDOI
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.
Abstract: In this paper, we present a method of kernel optimization by maximizing a measure of class separability in the empirical feature space, an Euclidean space in which the training data are embedded in such a way that the geometrical structure of the data in the feature space is preserved. Employing a data-dependent kernel, we derive an effective kernel optimization algorithm that maximizes the class separability of the data in the empirical feature space. It is shown that there exists a close relationship between the class separability measure introduced here and the alignment measure defined recently by Cristianini. Extensive simulations are carried out which show that the optimized kernel is more adaptive to the input data, and leads to a substantial, sometimes significant, improvement in the performance of various data classification algorithms.

320 citations


Cited by
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Journal ArticleDOI

[...]

08 Dec 2001-BMJ
TL;DR: There is, I think, something ethereal about i —the square root of minus one, which seems an odd beast at that time—an intruder hovering on the edge of reality.
Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

33,785 citations

Journal Article
TL;DR: This book by a teacher of statistics (as well as a consultant for "experimenters") is a comprehensive study of the philosophical background for the statistical design of experiment.
Abstract: THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

13,333 citations

Christopher M. Bishop1
01 Jan 2006
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

10,141 citations

Journal ArticleDOI
TL;DR: This work proposes a novel dimensionality reduction framework for reducing the distance between domains in a latent space for domain adaptation and proposes both unsupervised and semisupervised feature extraction approaches, which can dramatically reduce thedistance between domain distributions by projecting data onto the learned transfer components.
Abstract: Domain adaptation allows knowledge from a source domain to be transferred to a different but related target domain. Intuitively, discovering a good feature representation across domains is crucial. In this paper, we first propose to find such a representation through a new learning method, transfer component analysis (TCA), for domain adaptation. TCA tries to learn some transfer components across domains in a reproducing kernel Hilbert space using maximum mean miscrepancy. In the subspace spanned by these transfer components, data properties are preserved and data distributions in different domains are close to each other. As a result, with the new representations in this subspace, we can apply standard machine learning methods to train classifiers or regression models in the source domain for use in the target domain. Furthermore, in order to uncover the knowledge hidden in the relations between the data labels from the source and target domains, we extend TCA in a semisupervised learning setting, which encodes label information into transfer components learning. We call this extension semisupervised TCA. The main contribution of our work is that we propose a novel dimensionality reduction framework for reducing the distance between domains in a latent space for domain adaptation. We propose both unsupervised and semisupervised feature extraction approaches, which can dramatically reduce the distance between domain distributions by projecting data onto the learned transfer components. Finally, our approach can handle large datasets and naturally lead to out-of-sample generalization. The effectiveness and efficiency of our approach are verified by experiments on five toy datasets and two real-world applications: cross-domain indoor WiFi localization and cross-domain text classification.

3,195 citations