[HTML][HTML] Adaptive K values and training subsets selection for optimal K-NN performance on FPGA

A El Bouazzaoui, N Jariri, O Mouhib… - Journal of King Saud …, 2024 - Elsevier
This study introduces an Adaptive K-Nearest Neighbors methodology designed for FPGA
platforms, offering substantial improvements over traditional K-Nearest Neighbors …

Modulation format identification in elastic optical networks using integrated photonic reservoir computing and untrained K-nearest neighbors algorithm

Q Li, L Pei, B Bai, J Wang, B Bai, X Zuo, J Sui… - Optics …, 2024 - opg.optica.org
In the next generation of Elastic Optical Networks, various modulation formats exhibit varying
degrees of sensitivity to channel impairments during transmission. To adopt appropriate …

Classification of Concrete Compressive Strength Using Machine Learning Methods

M Ozdemir, G Celik - International Conference on Cooperative Design …, 2024 - Springer
The compressive strength of concrete is critical for the design, safety, and durability of
structures. While traditional methods used to determine the compressive strength of concrete …

Revolutionizing Raisin Processing through Advanced Sorting and Visualization with a Deep Learning Based Support Vector Machine Model

J Agrawal, KS Gill, R Chauhan… - 2024 Asia Pacific …, 2024 - ieeexplore.ieee.org
By using a Deep Learning-Based Support Vector Machine (SVM) model, this work presents
a novel method for the sorting and visualisation of raisins. A vital component of the …

Classification of Concrete Compressive Strength

M Ozdemir¹, G Celik - … , CDVE 2024, Valencia, Spain, September 15–18 … - books.google.com
The compressive strength of concrete is critical for the design, safety, and durability of
structures. While traditional methods used to determine the compressive strength of concrete …