1Department of Pharmacognosy, Parul University, Vadodara, Gujarat (India), 391760
2SGT College of Pharmacy, SGT University, Gurugram-Badli Road, Budhera, Gurugram, Haryana, India - 122505
*Corresponding Author: Komal Patel, Department of Pharmacognosy, Parul University, Vadodara, Gujarat (India), 391760
Internet of Medical Things (IoMT) continues to be increasingly integrated in modern healthcare systems as a form of real-time specialist healthcare along with data exchange among medical equipment. Nevertheless, the high rate of interconnected medical equipment development also preconditions the emergence of a range of cybersecurity threats that can alarm patient safety and confidentiality of their data. This study suggests a hybrid intrusion detection agenda to solve these concerns, a pool of machine learning and deep learning processes. The suggested system combines the feature selection based on a Grey Wolf Optimizer (GWO), a Random Forest classifier to determine known attacks, and an autoencoder model to detect unknown attacks or zero-day attacks. GWO algorithm eliminates redundant features in the network traffic dataset without losing significant information needed to do the accurate detection. Random Forest model is applied to classify known patterns of attack with categorized data, and the autoencoder is trained to learn the normal work of the IoMT traffic and identify anomalies without the necessity of labelled attack samples. On the whole, the hybrid framework advances the reliability and effectiveness of intrusion detection on the IoMT setting through the fusion of the feature optimization and machine learning and anomaly detection algorithms. The suggested method can assist in enhancing the security of healthcare networks and assist in safer disposition of IoMT systems.
Keywords: Pure, impure, excipients, UV spectrophotometric method, resveratrol
How to cite this article: Singh PK, Patel K, Prasad N., Ultraviolet-visible Spectrophotometric Method for Estimation of Resveratrol in Presence of Excipients as Impurities...Int J Drug Deliv Technol. 2026; 16(11s): 769-775; DOI: 10.25258/ijddt.16.11s.77
Source of support: Nil.
Conflict of interest: None