Evolutionary warning system for COVID-19 severity: Colony predation algorithm enhanced extreme learning machine

Shi, Beibei; Ye, Hua; Zheng, Lei; Lyu, Juncheng; Chen, Cheng; Heidari, Ali Asghar; Hu, Zhongyi; Chen, Huiling; Wu, Peiliang

doi:10.1016/j.compbiomed.2021.104698

Cited by 59 publications

(24 citation statements)

References 140 publications

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“…The PSNR, SSIM index, and FSIM index is applied to further evaluate image segmentation quality (Liu et al, 2021b ; Shi et al, 2021 ; Zhang et al, 2021 ).…”

Section: Experiments and Resultsmentioning

confidence: 99%

Multi-Threshold Image Segmentation of Maize Diseases Based on Elite Comprehensive Particle Swarm Optimization and Otsu

et al. 2021

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Maize is a major global food crop and as one of the most productive grain crops, it can be eaten; it is also a good feed for the development of animal husbandry and essential raw material for light industry, chemical industry, medicine, and health. Diseases are the main factor limiting the high and stable yield of maize. Scientific and practical identification is a vital link to reduce the damage of diseases and accurate segmentation of disease spots is one of the fundamental techniques for disease identification. However, one single method cannot achieve a good segmentation effect to meet the diversity and complexity of disease spots. In order to solve the shortcomings of noise interference and oversegmentation in the Otsu segmentation method, a non-local mean filtered two-dimensional histogram was used to remove the noise in disease images and a new elite strategy improved comprehensive particle swarm optimization (PSO) method was used to find the optimal segmentation threshold of the objective function in this study. The experimental results of segmenting three kinds of maize foliar disease images show that the segmentation effect of this method is better than other similar algorithms and it has better convergence and stability.

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“…The PSNR, SSIM index, and FSIM index is applied to further evaluate image segmentation quality (Liu et al, 2021b ; Shi et al, 2021 ; Zhang et al, 2021 ).…”

Section: Experiments and Resultsmentioning

confidence: 99%

Multi-Threshold Image Segmentation of Maize Diseases Based on Elite Comprehensive Particle Swarm Optimization and Otsu

et al. 2021

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“…For example, the combination of Harris hawks optimization (HHO), cuckoo search (CS), and SVM method for drug design and discovery has achieved good results [ 83 ]. One study used an SVM model optimized by slime mould algorithm (SMA) in combination with random forest method to identify the severity of COVID-19 patients [ 84 ], and another study used colony predation algorithm (CPA) in combination with kernel extreme learning machine (KELM) to analyze the biochemical indicators and prognosis of COVID-19 patients [ 85 ], both showing high prediction accuracy and stability. Applying monarch butterfly optimization (MBO) to medical image recognition can significantly reduce mean square error (MSE), and the efficiency is better than the existing traditional technology [ 86 ].…”

Section: Discussionmentioning

confidence: 99%

Cross-Tissue Analysis Using Machine Learning to Identify Novel Biomarkers for Knee Osteoarthritis

Zhao

Kuang

et al. 2022

Computational and Mathematical Methods in Medicine

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Background. Knee osteoarthritis (KOA) is a common degenerative joint disease. In this study, we aimed to identify new biomarkers of KOA to improve the accuracy of diagnosis and treatment. Methods. GSE98918 and GSE51588 were downloaded from the Gene Expression Omnibus database as training sets, with a total of 74 samples. Gene differences were analyzed by Gene Ontology, Kyoto Encyclopedia of Genes and Genomes pathway, and Disease Ontology enrichment analyses for the differentially expressed genes (DEGs), and GSEA enrichment analysis was carried out for the training gene set. Through least absolute shrinkage and selection operator regression analysis, the support vector machine recursive feature elimination algorithm, and gene expression screening, the range of DEGs was further reduced. Immune infiltration analysis was carried out, and the prediction results of the combined biomarker logistic regression model were verified with GSE55457. Results. In total, 84 DEGs were identified through differential gene expression analysis. The five biomarkers that were screened further showed significant differences in cartilage, subchondral bone, and synovial tissue. The diagnostic accuracy of the model synthesized using five biomarkers through logistic regression was better than that of a single biomarker and significantly better than that of a single clinical trait. Conclusions. CX3CR1, SLC7A5, ARL4C, TLR7, and MTHFD2 might be used as novel biomarkers to improve the accuracy of KOA disease diagnosis, monitor disease progression, and improve the efficacy of clinical treatment.

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“…2 depicts the framework of KELM. A novel model to ELM in the kernel is newly extended [18]. The subsequent section provides a brief outline for KELM technique.…”

Section: Design Of Okelm Techniquementioning

confidence: 99%

Colliding Bodies Optimization with Machine Learning Based Parkinson’s Disease Diagnosis

Dutta¹,

Zakari²,

Albagory³

et al. 2023

Computer Systems Science and Engineering

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Parkinson's disease (PD) is one of the primary vital degenerative diseases that affect the Central Nervous System among elderly patients. It affect their quality of life drastically and millions of seniors are diagnosed with PD every year worldwide. Several models have been presented earlier to detect the PD using various types of measurement data like speech, gait patterns, etc. Early identification of PD is important owing to the fact that the patient can offer important details which helps in slowing down the progress of PD. The recently-emerging Deep Learning (DL) models can leverage the past data to detect and classify PD. With this motivation, the current study develops a novel Colliding Bodies Optimization Algorithm with Optimal Kernel Extreme Learning Machine (CBO-OKELM) for diagnosis and classification of PD. The goal of the proposed CBO-OKELM technique is to identify whether PD exists or not. CBO-OKELM technique involves the design of Colliding Bodies Optimization-based Feature Selection (CBO-FS) technique for optimal subset of features. In addition, Water Strider Algorithm (WSA) with Kernel Extreme Learning Machine (KELM) model is also developed for the classification of PD. CBO algorithm is used to elect the optimal set of features whereas WSA is utilized for parameter tuning of KELM model which altogether helps in accomplishing the maximum PD diagnostic performance. The experimental analysis was conducted for CBO-OKELM technique against four benchmark datasets and the model portrayed better performance such as 95.68%, 96.34%, 92.49%, and 92.36% on Speech PD, Voice PD, Hand PD Meander, and Hand PD Spiral datasets respectively.

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Evolutionary warning system for COVID-19 severity: Colony predation algorithm enhanced extreme learning machine

Cited by 59 publications

References 140 publications

Multi-Threshold Image Segmentation of Maize Diseases Based on Elite Comprehensive Particle Swarm Optimization and Otsu

Multi-Threshold Image Segmentation of Maize Diseases Based on Elite Comprehensive Particle Swarm Optimization and Otsu

Cross-Tissue Analysis Using Machine Learning to Identify Novel Biomarkers for Knee Osteoarthritis

Colliding Bodies Optimization with Machine Learning Based Parkinson’s Disease Diagnosis

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