Extreme Learning Machine Weight Optimization using Particle Swarm Optimization to Identify Sugar Cane Disease
| Autor: | Wayan Firdaus Mahmudy, Abdul Latief Abadi, Mukhammad Wildan Alauddin |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
biology
Computer science business.industry Sugar cane Particle swarm optimization 02 engineering and technology Machine learning computer.software_genre biology.organism_classification 01 natural sciences Expert system 010305 fluids & plasmas 0103 physical sciences lcsh:Technology (General) 0202 electrical engineering electronic engineering information engineering lcsh:T1-995 020201 artificial intelligence & image processing Artificial intelligence Cane business computer Extreme learning machine |
| Zdroj: | JITeCS (Journal of Information Technology and Computer Science), Vol 4, Iss 2, Pp 127-136 (2019) |
| ISSN: | 2540-9824 2540-9433 |
| Popis: | Sugar cane disease is a major factor in reducing sugar cane yields. The low intensity of experts to go into the field to check the condition of sugar cane causes the handling of sugarcane disease tends to be slow. This problem can be solved by instilling expert intelligence on sugar cane into an expert system. In this study the method of classification of sugar cane disease was proposed using Extreme Learning Machine (ELM). However, ELM alone is not enough to classify multilabel and multiclass disease case data in this study. Therefore, it is proposed to optimize the weight of hidden neurons in ELM using Particle Swarm Optimization (PSO). The experimental results show that the classification using ELM alone can reach an accuracy rate of 71%. After the weight of hidden neurons from ELM was optimized, the accuracy rate became 79.92% or an increase of 8.92%. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|