Title: Preschool education video image optimisation mechanism based on deep evolutionary learning in smart city

Authors: Junqing Fan

Addresses: Jiangmen Preschool Education College, Guangdong, China

Abstract: As an important stage of basic education, the richness and quality of teaching resources in preschool education directly affect the growth and development of children. In order to better optimise the processing of preschool education video images and improve their clarity, this paper proposes a deep evolutionary learning method based on the Improved Whale Optimisation Algorithm and Bi-directional Long-Short-Term Memory (IWOA-BiLSTM). BiLSTM utilises the temporal information between adjacent frames of preschool education video images to preserve the time series output in the feature map of the images. This can fully learn the information between adjacent frames of the images, making the optimised image contain richer information. IWOA is used to optimise the key parameters of BiLSTM and improve its optimisation performance. Finally, experiments show that IWOA-BiLSTM can effectively optimise preschool education video images in smart city.

Keywords: deep learning; image optimisation; evolutionary algorithms; preschool education video; smart city.

DOI: 10.1504/IJCAT.2025.150328

International Journal of Computer Applications in Technology, 2025 Vol.77 No.3/4, pp.215 - 225

Received: 01 Oct 2024
Accepted: 18 Jun 2025
Published online: 09 Dec 2025 *

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