Work

Implementation of Kolmogorov–Arnold Networks for Efficient Image Processing in Resource-Constrained Internet of Things Devices

Year: 2025

Type: article

Abstract: This research investigates the implementation of Kolmogorov–Arnold networks (KANs) for image processing in resource-constrained IoTs devices. KANs represent a novel neural network architecture that offers significant advantages over traditional deep learning approaches, particularly in applications where computational resources are limited. Our study demonstrates the efficiency of KAN-based solutions for image analysis tasks in IoTs environments, providing comparative performance metrics against conventional convolutional neural networks. The experimental results indicate substantial improvements in processing speed and memory utilization while maintaining competitive accuracy. This work contributes to the advancement of AI-driven IoTs applications by proposing optimized KAN-based implementations suitable for edge computing scenarios. The findings have important implications for IoTs deployment in smart infrastructure, environmental monitoring, and industrial automation where efficient image processing is critical.

Source: Technologies

DOI: 10.3390/technologies13040155

Authors: Anargul Shaushenova, Oleksandr Kuznetsov, Ardak Nurpeisova, Maral Ongarbayeva

Institutions: S.Seifullin Kazakh Agro Technical University, Università degli Studi eCampus, V. N. Karazin Kharkiv National University, International Taraz University named after Sherkhan Murtaza

Language: English

Topic: Advanced Neural Network Applications

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $1,930