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Determination of wheat types using optimized extreme learning machine with metaheuristic algorithms

Neural Computing and Applications · Haziran 2023

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Determination of wheat types using optimized extreme learning machine with metaheuristic algorithms
NEURAL COMPUTING & APPLICATIONS · 2023 SCI-Expanded
ARAŞTIRMA GÖREVLİSİ MUSA DOĞAN →
Determination of wheat types using optimized extreme learning machine with metaheuristic algorithms
Neural Computing and Applications · 2023 SCI-Expanded
DOÇENT İLKER ALİ ÖZKAN →

Makale Bilgileri

DergiNeural Computing and Applications
Yayın TarihiHaziran 2023
Cilt / Sayfa35 · 12565-12581
Özet In order to increase the market value and quality of wheat, it is important to separate different types and determine the amount of foreign matter using the visual properties of durum and bread wheat. In this study, the extreme learning machine (ELM) algorithm, which is often preferred in real-time applications, was used to make classifications using features obtained from images containing the wheat kernel and foreign matter. The feature selection process was applied to remove the irrelevant ones from the obtained 236 features. In addition, the Harris hawks’ optimizer (HHO), a novel method in the literature, and the particle swarm optimizer (PSO), one of the well-known algorithms, were used to improve the ELM model. As part of this study, new models called HHO-ELM and PSO-ELM were created and compared with the original ELM model and other artificial neural networks (ANNs) studies published in the literature. As a result, in comparison with other models, the optimized ELM models demonstrated good stability and accuracy, having 99.32% in binary classification and 95.95% in multi-class classification.

Yazarlar (2)

1
Musa Dogan
2
Ilker Ali Ozkan

Anahtar Kelimeler

Extreme learning machine Harris hawks optimizer Optimization Particle swarm optimization Wheat classification

Kurumlar

Selçuk Üniversitesi
Selçuklu Turkey

Metrikler

9
Atıf
2
Yazar
5
Anahtar Kelime