Publications by authors named "M K Saraswat"

Background: In India, the prevalence of Chlamydia Trachomatis (CT) studies in different groups are focused on high-risk populations - HIV-positive women and female sex workers - and have shown a variable prevalence rate ranging from 1.1 to 45%. One concern about comparing these studies is that the enzyme-linked immunosorbent assay (ELISA) test is estimated to be only 65-70% sensitive.

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In the common classification practices, feature selection is an important aspect that highly impacts the computation efficacy of the model, while implementing complex computer vision tasks. The metaheuristic optimization algorithms gain popularity to obtain optimal feature subset. However, the feature selection using metaheuristics suffers from two common stability problems, namely premature convergence and slow convergence rate.

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Understanding the structure and properties of heterocyclic radicals and their cations is crucial for elucidating reaction mechanisms as they serve as versatile synthetic intermediates. In this work, the -carbazolyl radical was generated via pyrolysis and characterized using photoion mass-selected threshold photoelectron spectroscopy coupled with tunable vacuum-ultraviolet synchrotron radiation. The -centered radical is classified as a π-radical (B), with the unpaired electron found to be delocalized over the central five-membered ring of the carbazole.

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Alkynyl radicals and cations are crucial reactive intermediates in chemistry, but often evade direct detection. Herein, we report the direct observation of the phenylethynyl radical (CHCC˙) and its cation (CHCC), which are two of the most reactive intermediates in organic chemistry. The radical is generated pyrolysis of (bromoethynyl)benzene at temperatures above 1500 K and is characterized by photoion mass-selected threshold photoelectron spectroscopy (ms-TPES).

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The ECG is a crucial tool in the medical field for recording the heartbeat signal over time, aiding in the identification of various cardiac diseases. Commonly, the interpretation of ECGs necessitates specialized knowledge. However, this paper explores the application of machine learning algorithms and deep learning algorithm to autonomously identify cardiac diseases in diabetic patients in the absence of expert intervention.

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