PSO-ELM with Time-varying Inertia Weight for Classification of SMILES Codes

Ratnawati, Dian; Marjono, Marjono; Widodo, Widodo; Anam, Syaiful

doi:10.22266/ijies2020.1231.46

Cited by 4 publications

(3 citation statements)

References 24 publications

(39 reference statements)

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“…By giving each classifier 𝐶 a weight 𝑤 proportionally based on performance, it is important to estimate the strength of the classifier performance in the future. The positions and velocities of the ith particle on the twelfth sequence are given as [24] : 𝑥 = (𝑥 , 𝑥 , … , 𝑥 ), (8) 𝑣 = (𝑣 , 𝑣 , … , 𝑣 ).…”

Section: D3 Ensemble Weighted Votingmentioning

confidence: 99%

A Weighted Majority Voting Ensemble Model for Disease Prediction Boosted by PSO: The Case of Type 2 Diabetes

Muljono,

Adhipurna

2023

IJC

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Early detection of diabetes is critical to reducing the number of cases, which continues to rise year after year. Many approaches to diagnosis have been used, but they still have flaws in making clinical decisions that are more effective and efficient. The use of intelligent systems is very effective in assisting in data analysis and clinical decision support. The purpose of this article is to develop a model to predict diabetes mellitus using the Pima Indian Diabetes Dataset (PIDD). The ensemble method has shown to be quite effective at increasing accuracy, but it has the issue of determining the optimal weight. As a result, to improve prediction accuracy, this study employs PSO optimization in the selection of ensemble majority voting weights. The test results show that predictions for ensemble majoritarian voting using PSO perform well, with an accuracy rate of 0.9322, precision of 0.9412, recall of 0.8421, and F1-score of 0.8889. PSO accuracy is improved by 4% and 7%, respectively. This demonstrates that applying PSO to the ensemble model can improve accuracy.

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Section: D3 Ensemble Weighted Votingmentioning

confidence: 99%

A Weighted Majority Voting Ensemble Model for Disease Prediction Boosted by PSO: The Case of Type 2 Diabetes

Muljono,

Adhipurna

2023

IJC

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“…For the reason, PSO algorithm tackles the premature convergence problem of K-means algorithm. The PSO algorithm has been implemented for many applications, such as image segmentation [12,13], neural networks [14], parameter optimization [15,16], etc. However, the particle velocity of PSO algorithm influences the PSO algorithm performance.…”

Section: Introductionmentioning

confidence: 99%

Tomato Leaf Disease Segmentation Using Clustering Method Based on FATPSO with Multi Features

Anam¹,

Yanti²,

Fitriah³

et al. 2023

IJETAE

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Early blight is one of diseases that infects tomato leaves. This disease causes a decrease in the production of tomato plants. The early detection of this diseases is very important to maintain the tomato production. Monitoring tomato leaves health manually in large area is very time-consuming and inefficient. The drones and computer vision technology give an alternative in solving this problem. One of the important steps in detecting the tomato leaf disease based on computer vision is the segmentation area of the tomato leaf into the healthy and diseased tomato leaf. The K-means clustering offers an image segmentation method that is simple, fast and works unsupervised. However, the solutions of the K-means clustering often be trapped into the local optimum. The Particle Swarm Optimization (PSO) offers a solution of this problem. However, the performance of PSO depends on the particle velocity of the PSO, if the particle velocity is not determined precisely then the PSO will converge prematurely. Fuzzy Adaptive Turbulence Particle Swarm Optimization (FATPSO) is able to control minimum velocity the PSO particles adaptively for overcoming the premature convergence problem in PSO. The good features from image will increase the accuracy of machine learning algorithm. For this reason, these papers the tomato leaf segmentation based on the FATPSO clustering algorithm with multi features. The fitness function of FATPSO uses an objective function of K-means. The experiments use the image taken manually from garden tomatoes. The images have good quality but they have many varieties in size and color. The next research should be considered to use the image taken by drone to guarantee a robust method of image quality produced by drones. The experimental results show that the FATPSO clustering algorithm with multi features has a better performance than the PSO algorithm with multi feature in the tomato leaf disease segmentation

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“…So, that particular person has to visit the nearest hospital for scanning the brain. Regarding the scanning process, MRI is the best method to find out the clear picture of the brain as well as it has been built by some advance technologies [ 7 ]. For better understanding of MRI, we can use functional MRI due to the different types of activities that can be detected by changing with the blood vessels [ 8 , 9 ].…”

Section: Introductionmentioning

confidence: 99%

Segmentation and Classification of Encephalon Tumor by Applying Improved Fast and Robust FCM Algorithm with PSO-Based ELM Technique

Mohapatra

Sahu

Almotiri

et al. 2022

Computational Intelligence and Neuroscience

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Nowadays, so many people are living in world. If so many people are living, then the diseases are also increasing day by day due to adulterated and chemical content food. The people may suffer either from a small disease such as cold and cough or from a big disease such as cancer. In this work, we have discussed on the encephalon tumor or cancer which is a big problem nowadays. If we will consider about the whole world, then there are deficiency of clinical experts or doctors as compared to the encephalon tumor affected person. So, here, we have used an automatic classification of tumor by the help of particle swarm optimization (PSO)-based extreme learning machine (ELM) technique with the segmentation process by the help of improved fast and robust fuzzy C mean (IFRFCM) algorithm and most commonly feature reduction method used gray level co-occurrence matrix (GLCM) that may helpful to the clinical experts. Here, we have used the BraTs (“Multimodal Brain Tumor Segmentation Challenge 2020”) dataset for both the training and testing purpose. It has been monitored that our system has given better classification accuracy as an approximation of 99.47% which can be observed as a good outcome.

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PSO-ELM with Time-varying Inertia Weight for Classification of SMILES Codes

Cited by 4 publications

References 24 publications

A Weighted Majority Voting Ensemble Model for Disease Prediction Boosted by PSO: The Case of Type 2 Diabetes

A Weighted Majority Voting Ensemble Model for Disease Prediction Boosted by PSO: The Case of Type 2 Diabetes

Tomato Leaf Disease Segmentation Using Clustering Method Based on FATPSO with Multi Features

Segmentation and Classification of Encephalon Tumor by Applying Improved Fast and Robust FCM Algorithm with PSO-Based ELM Technique

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