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All Journal International Journal of Electrical and Computer Engineering International Journal of Public Health Science (IJPHS)
Vidyullatha, Pellakuri
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Computer Science & IT Electrical & Electronics Engineering Health Professions

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Optimal shortest path selection using an evolutionary algorithm in wireless sensor networks Rajkumar, Dhamodharan Udaya Suriya; Karani, Krishna Prasad; Sathiyaraj, Rajendran; Vidyullatha, Pellakuri
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp6743-6752

Abstract

A wireless sensor network comprises of distributed independent devices, called sensors that monitor the physical conditions of the environment for various applications, such as tracking and observing environmental changes. Sensors have the ability to detect information, process it, and forward it to neighboring sensor nodes. Wireless sensor networks are facing many issues in terms of scalability, which necessitates numerous nodes and network range. The route chosen between the source node and the destination node with the shortest distance determines how well the network performs. In this paper, evolutionary algorithm based shortest path selection provides high end accessibility of path nodes for data transmission among source and destination. It employs the best fitness function methodology, which involves the replication of input, mutation, crossover, and mutation methods, to produce efficient outcomes that align with the best fitness function, thereby determining the shortest path. This is a probabilistic technique that receives input from learning models and provides the best results. The execution results are presented well compared with earlier methodologies in terms of path cost, function values, throughput, packet delivery ratio, and computation time.
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 Karani, Krishna Prasad Khan, Mudassir Nezami, Mohammad Mazhar Pradeep, Ghantasala Gnana Sudha Rajkumar, Dhamodharan Udaya Suriya Sathiyaraj, Rajendran Sharma, Ochin