Extreme Learning Machine Weight Optimization using Particle Swarm Optimization to Identify Sugar Cane Disease

Alauddin, Mukhammad Wildan; Mahmudy, Wayan Firdaus; Abadi, Abdul Latief

doi:10.25126/jitecs.201942116

Cited by 5 publications

(3 citation statements)

References 17 publications

(17 reference statements)

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“…Neural Network (NN) is a model of information inspired by the biological nervous system. NN have been successfully implemented in several studies for prediction and classification [1], [7], [8]. NN gets knowledge through several learning processes.…”

Section: Neural Networkmentioning

confidence: 99%

Comparison of Neural Network and Recurrent Neural Network to Predict Rice Productivity in East Java

Hamdianah

Mahmudy

Widaryanto

2021

JITeCS

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Rice is the staple food for most of the population in Indonesia which is processed from rice plants. To meet the needs and food security in Indonesia, a prediction is required. The predictions are carried out to find out the annual yield of rice in an area. Weather factors greatly affect production results so that in this study using weather parameters as input parameters. The Input Parameters are used in the Recurrent Neural Network algorithm with the Backpropagation learning process. The results are compared with Neural Networks with Backpropagation learning to find out the most effective method. In this study, the Recurrent Neural Network has better prediction results compared to a Neural Network. Based on the computational experiments, it is found that the Recurrent Neural Network obtained a Means Square Error of 0.000878 and a Mean Absolute Percentage Error of 10,8832%, while the Neural Network obtained a Means Square Error of 0.00104 and a Mean Absolute Percentage Error of 10,3804.

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Section: Neural Networkmentioning

confidence: 99%

Comparison of Neural Network and Recurrent Neural Network to Predict Rice Productivity in East Java

Hamdianah

Mahmudy

Widaryanto

2021

JITeCS

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“…The work presented in [33] combines Particle Swarm Optimization (PSO) and Extreme Learning Machine (ELM) to forecast the inflation rate in Indonesia. It uses PSO to optimize weight in order to obtain the optimal input values in ELM.…”

Section: Related Workmentioning

confidence: 99%

A Modified Weight Optimization for Artificial Higher Order Neural Networks in Physical Time Series

Husaini¹,

Ghazali²,

Arbaiy³

et al. 2020

IJACSA

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Many methods and approaches have been proposed for analyzing and forecasting time series data. There are different Neural Network (NN) variations for specific tasks (e.g., Deep Learning, Recurrent Neural Networks, etc.). Time series forecasting are a crucial component of many important applications, from stock markets to energy load forecasts. Recently, Swarm Intelligence (SI) techniques including Cuckoo Search (CS) have been established as one of the most practical approaches in optimizing parameters for time series forecasting. Several modifications to the CS have been made, including Modified Cuckoo Search (MCS) that adjusts the parameters of the current CS, to improve algorithmic convergence rates. Therefore, motivated by the advantages of these MCSs, we use the enhanced MCS known as the Modified Cuckoo Search-Markov Chain Monté Carlo (MCS-MCMC) learning algorithm for weight optimization in Higher Order Neural Networks (HONN) models. The Lévy flight function in the MCS is replaced with Markov Chain Monté Carlo (MCMC) since it can reduce the complexity in generating the objective function. In order to prove that the MCS-MCMC is suitable for forecasting, its

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“…Optimization techniques are also implemented to increase the performance of classification methods such as modified simulated annealing and extreme learning machine which gets 69.7% accuracy [11], particle swarm optimization and extreme learning machine which gets 79.92% accuracy [12], particle swarm optimization and back-propagation neural network that achieves 96.2% accuracy [13], dempster-shafer optimization using genetic algorithm which gets 87.096% accuracy [14], and fuzzy inference systems optimization using Quasi-Newton and genetic algorithms which gets 94% accuracy [15]. One of drawback using hybrid methods is it tend to require higher computational time.…”

Section: Introductionmentioning

confidence: 99%

Kenaf plant pest and disease detection using faster regional based convolutional neural network

Rakhmandasari

Mahmudy

Yulianti³

2021

IJEECS

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<span>Kenaf plant is a fibre plant whose stem bark is taken to be used as raw material for making geo-textile, particleboard, pulp, fiber drain, fiber board, and paper. The presence of plant pests and diseases that attack causes crop production to decrease. The detection of pests and diseases by farmers may be a challenging task. The detection can be done using artificial intelligence-based method. Convolutional neural networks (CNNs) are one of the most popular neural network architectures and have been successfully implemented for image classification. However, the CNN method is still considered a long time in the process, so this method was developed into namely faster regional based convolution neural network (RCNN). As the selection of the input features largely determines the accuracy of the results, a pre-processing procedure is developed to transform the kenaf plant image into input features of faster RCNN. A computational experiment proves that the faster RCNN has a very short computation time by completing 10000 iterations in 3 hours compared to convolutional neural network (CNN) completing 100 iterations at the same time. Furthermore, Faster RCNN gets 77.50% detection accuracy and bounding box accuracy 96.74% while CNN gets 72.96% detection accuracy at 400 epochs. The results also prove that the selection of input features and its pre-processing procedure could produce a high accuracy of detection. </span>

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Extreme Learning Machine Weight Optimization using Particle Swarm Optimization to Identify Sugar Cane Disease

Cited by 5 publications

References 17 publications

Comparison of Neural Network and Recurrent Neural Network to Predict Rice Productivity in East Java

Comparison of Neural Network and Recurrent Neural Network to Predict Rice Productivity in East Java

A Modified Weight Optimization for Artificial Higher Order Neural Networks in Physical Time Series

Kenaf plant pest and disease detection using faster regional based convolutional neural network

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