Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal International Journal of Public Health Science (IJPHS) Indonesian Journal of Electrical Engineering and Computer Science
Khan, Mudassir
Unknown Affiliation
Health Professions

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

Found 2 Documents
Search

 1 
An efficient method for privacy protection in big data analytics using oppositional fruit fly algorithm Kiran, Ajmeera; Elseed Ahmed, Alwalid Bashier Gism; Khan, Mudassir; Babu, J. Chinna; Kumar, B. P. Santosh
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 1: January 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i1.pp670-679

Abstract

This work employs anonymization techniques to safeguard privacy. Data plays a vital role in corporate decision-making in the current information-centric landscape. Various sectors, like banking and healthcare, gather confidential information on a daily basis. This information is disseminated by multiple sources through numerous methods. Securing sensitive data is of paramount importance for any data mining application. This study safeguarded confidential information using an anonymization technique. Several machine learning methodologies have a deficiency in accuracy. The study seeks to generate superior and more precise results compared to alternative methodologies. For large datasets, numerous solutions exhibit increased time complexity and memory use. For huge datasets, numerous solutions require more time and memory. The enhanced fuzzy C-means (FCM) algorithm surpasses existing approaches in terms of both accuracy and information preservation. This study provides a comprehensive analysis of data anonymization utilizing the oppositional fruit fly approach, a technique that enhances privacy. The clustering method being presented utilizes an enhanced version of the FCM algorithm. The secrecy of the recommended oppositional fruit fly algorithm is effective. The comparison demonstrated that the proposed research enhanced both accuracy and privacy in comparison to two existing methods. The existing strategy outperforms data anonymization-based privacy preservation by 82.17%, while the suggested method surpasses it by 94.17%.
Computational paradigm for advancing lung cancer drug discovery Sharma, Ochin; Ahmed, Alwalid Bashier Gism Elseed; Khan, Mudassir; Pradeep, Ghantasala Gnana Sudha; Vidyullatha, Pellakuri; Nezami, Mohammad Mazhar
International Journal of Public Health Science (IJPHS) Vol 14, No 3: September 2025
Publisher : Intelektual Pustaka Media Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijphs.v14i3.25783

Abstract

Lung cancer remainders one of the foremost causes of cancer-related impermanence worldwide. The availability of novel medicines for patients with lung cancer is restricted by the extremely lengthy timetables and high attrition rates of traditional drug discovery procedures. However, in silico drug discovery has emerged as a powerful and affordable way to identify potential treatments. This work offers well-structured paradigms for using virtual techniques to identify potential lung cancer treatments. The main concerns are virtual screening, target validation and identification, pharmacokinetic assessment, and molecular docking. The cost and time of drug development are reduced and a valuable platform for discovering novel drugs to treat lung cancer is produced by merging computational resources with proper methodologies. The current work explores the recent advancements, challenges, and possible future paths. Mann-Whitney U test says that the sampled data is different in distribution for molecular weight (MW), LogP, amount of H acceptors, and quantity of H donors for active and inactive molecules. Python tool has been utilized and identified that the CHEMBL4850929 (C31H31F2N7O4) molecule is a potential drug. It has pIC50 7.61, Lipinski values in terms of MW 603.63, LogP 3.36, amount of H donors 1, quantity of H acceptors 10.
Co-Authors Ahmed, Alwalid Bashier Gism Elseed Babu, J. Chinna Elseed Ahmed, Alwalid Bashier Gism Kiran, Ajmeera Kumar, B. P. Santosh Nezami, Mohammad Mazhar Pradeep, Ghantasala Gnana Sudha Sharma, Ochin Vidyullatha, Pellakuri