Title: An improved hybrid (HHO-FFO) algorithm for healthcare and secure data transmission

Authors: Basetty Mallikarjuna

Addresses: Department of Information Technology, School of Computing Science and Engineering, Institute of Aeronautical Engineering, Dundigal, 500043, India

Abstract: Support vector machines (SVM) and optimisation algorithms like hybrid harmony search optimisation (HHO) and firefly algorithm (FFO) have revolutionised machine learning and data optimisation techniques. This article delves into the novel approach of integrating SVM with the hybrid Harries Hawks-fruit fly optimisation (HHO-FFO) algorithm to enhance healthcare services and ensure secure data transmission over blockchain networks. By exploring the synergies between SVM, HHO-FFO, and blockchain technology, this article highlights the potential for improved predictive analytics in healthcare, robust data security measures, and efficient information exchange. This model is useful to data analysts who desire to train SVM classifiers to access encryption data via communication through comparable data provided by the SVM classifier parameter values enhanced by a hybrid HHO-FFO algorithm. Extensive experiments are performed to demonstrate that the method can safely train hybrid SVM classifiers with relatively high accuracy.

Keywords: privacy protection; support vector machine; SVM; encrypted IoT data; machine learning; blockchain; homomorphic cryptosystem; hybrid Harries Hawks-fruit fly optimisation; HHO-FFO.

DOI: 10.1504/IJSTM.2024.145234

International Journal of Services Technology and Management, 2024 Vol.29 No.2/3/4, pp.274 - 295

Received: 25 Aug 2023
Accepted: 22 Nov 2024
Published online: 28 Mar 2025 *