Nojood O Aljehane^*

Computer Systems Science and Engineering, Vol.47, No.3, pp. 3109-3126, 2023, DOI:10.32604/csse.2023.038042 - 09 November 2023

Abstract Medical image analysis is an active research topic, with thousands of studies published in the past few years. Transfer learning (TL) including convolutional neural networks (CNNs) focused to enhance efficiency on an innovative task using the knowledge of the same tasks learnt in advance. It has played a major role in medical image analysis since it solves the data scarcity issue along with that it saves hardware resources and time. This study develops an Enhanced Tunicate Swarm Optimization with Transfer Learning Enabled Medical Image Analysis System (ETSOTL-MIAS). The goal of the ETSOTL-MIAS technique lies in… More >

Gia Nhu Nguyen^1,2, Nin Ho Le Viet^1,2, Gyanendra Prasad Joshi³, Bhanu Shrestha^4,*

CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 551-562, 2021, DOI:10.32604/cmc.2020.012441 - 30 October 2020

Abstract Fog computing in the Internet of Health Things (IoHT) is promising owing to the increasing need for energy- and latency-optimized health sector provisioning. Additionally, clinical data (particularly, medical image data) are a delicate, highly protected resource that should be utilized in an effective and responsible manner to fulfil consumer needs. Herein, we propose an energy-effi- cient fog-based IoHT with a tunicate swarm-optimization-(TSO)-based lightweight Simon cipher to enhance the energy efficiency at the fog layer and the security of data stored at the cloud server. The proposed Simon cipher uses the TSO algorithm to select the More >