Article Per Year (5 Year)
p-Index From 2020 - 2025
0.444
P-Index
This Author published in this journals
All Journal Journal of Applied Pharmaceutical Research Journal of Soft Computing Exploration
Kumar, Mohit
Unknown Affiliation
Computer Science & IT Medicine & Pharmacology

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Unveiling unmasked faces: a novel model for improved mask detection using haar cascade technique Kumar, Sanjeev; Kumar, Mohit; Dubey, Kriti; Sharma, Kaushal
Journal of Soft Computing Exploration Vol. 4 No. 3 (2023): September 2023
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v4i3.179

Abstract

In response to the urgent need to enforce mask-wearing compliance during the COVID-19 pandemic, this "Face Mask Detection" project introduces a robust model for identifying individuals not wearing face masks in videos. Leveraging computer vision's Haar Cascade technique, the project achieves rapid face detection within video streams, facilitating accurate mask usage assessment. This initiative holds paramount importance due to the pivotal role of masks in curbing virus spread. The model finds practical applications in monitoring mask adherence in public settings, pinpointing potential COVID-19 hotspots through data analysis, and bolstering safety via integration into surveillance systems. By effectively addressing the intricate challenge of precise mask detection, this project makes significant contributions to public health endeavors and the mitigation of COVID-19 hazards. The proposed algorithm showcases remarkable performance across various metrics. With an impressive detection rate of 98.4%, it surpasses established methods such as CNN (91.26%), PCA+SVM (93.4%), and Adaboost (96.1%), signifying its potential to revolutionize mask detection technology.
Pharmacophore insights and molecular docking of ciprofloxacin analogues against 2XE1: strategies for reduced antibiotic resistance Katlaria, Sanjana; Chauhan, Ashish Singh; Kumar, Krishna; Kumar, Mohit; Chauhan, Bhumika; Jakhmola, Vikash
Journal of Applied Pharmaceutical Research Vol. 12 No. 6 (2024)
Publisher : Creative Pharma Assent

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69857/joapr.v12i6.660

Abstract

Background: Antibiotic resistance is a silent pandemic disease that is growing and causing a global threat. Existing antibiotics are less effective against infectious diseases, so we must discover more potent and effective drugs. The latest report from the World Health Organization (WHO) underscores the global nature of the situation, revealing that high levels of antibiotic resistance in bacteria worldwide lead to life-threatening bloodstream infections and resistance to treatment. Methods: This study focuses on the Molecular Docking and Pharmacophore Modeling of Ciprofloxacin and its analogs to explore ligand-protein interactions and identify potent drugs against AMR. Twenty ciprofloxacin analogs, designed using ChemDraw Pro12.0, were docked with the 2XE1 protein. Molecular docking assessed the binding affinity, with Arguslab 4.0 scoring the lowest docking scores to indicate strong interactions and biological activity. Pharmacophore modeling identified essential molecular features like HBA, HBD, and AI for optimal biological activity. Results: The computational screening identified several compounds with improved binding properties, showing greater affinity towards ALA129, TYR149, and PHE88 amino acids, essential for biological activity. Conclusion: The study identifies the best analog of ciprofloxacin, which can effectively combat antibiotic resistance. Compound 13 showed promising docking scores and relevant pharmacophoric features, outperforming the parent ciprofloxacin in binding affinity, suggesting it could be a potent drug candidate against AMR.
Co-Authors Chauhan, Ashish Singh Chauhan, Bhumika Dubey, Kriti Jakhmola, Vikash Katlaria, Sanjana Kumar, Krishna Kumar, Sanjeev Sharma, Kaushal