Dynamic Levy Flight Chimp Optimization

Knowledge-Based Systems, Год журнала: 2021, Номер 235, С. 107625 - 107625

Опубликована: Окт. 22, 2021

Язык: Английский

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Improved deep convolutional neural networks using chimp optimization algorithm for Covid19 diagnosis from the X-ray images

Expert Systems with Applications, Год журнала: 2022, Номер 213, С. 119206 - 119206

Опубликована: Ноя. 4, 2022

Язык: Английский

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An enhanced and efficient approach for feature selection for chronic human disease prediction: A breast cancer study

Heliyon, Год журнала: 2024, Номер 10(5), С. e26799 - e26799

Опубликована: Фев. 28, 2024

Computer-aided diagnosis (CAD) systems play a vital role in modern research by effectively minimizing both time and costs. These support healthcare professionals like radiologists their decision-making process efficiently detecting abnormalities as well offering accurate dependable information. heavily depend on the efficient selection of features to accurately categorize high-dimensional biological data. can subsequently assist related medical conditions. The task identifying patterns biomedical data be quite challenging due presence numerous irrelevant or redundant features. Therefore, it is crucial propose then utilize feature (FS) order eliminate these primary goal FS approaches improve accuracy classification eliminating that are less informative. phase plays critical attaining optimal results machine learning (ML)-driven CAD systems. effectiveness ML models significantly enhanced incorporating during training phase. This empirical study presents methodology for using technique. proposed approach incorporates three soft computing-based optimization algorithms, namely Teaching Learning-Based Optimization (TLBO), Elephant Herding (EHO), hybrid algorithm two. algorithms were previously employed; however, addressing issues predicting human diseases has not been investigated. following evaluation focuses categorization benign malignant tumours publicly available Wisconsin Diagnostic Breast Cancer (WDBC) benchmark dataset. five-fold cross-validation technique employed mitigate risk over-fitting. approach's proficiency determined based several metrics, including sensitivity, specificity, precision, accuracy, area under receiver-operating characteristic curve (AUC), F1-score. best value computed through suggested 97.96%. clinical decision system demonstrates highly favourable performance outcome, making valuable tool practitioners secondary opinion reducing overburden expert practitioners.

Язык: Английский

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Neighbour adjusted dispersive flies optimization based deep hybrid sentiment analysis framework

Multimedia Tools and Applications, Год журнала: 2024, Номер 83(24), С. 64393 - 64416

Опубликована: Янв. 15, 2024

Язык: Английский

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AOAAO: The Hybrid Algorithm of Arithmetic Optimization Algorithm With Aquila Optimizer

IEEE Access, Год журнала: 2022, Номер 10, С. 10907 - 10933

Опубликована: Янв. 1, 2022

Many new algorithms have been proposed to solve the mathematical equations formulated describe real-world problems. But there still does not exist one algorithm that could problems all. And most of defects in some aspects, they need be improved application. In order find a more efficient optimization and inspired by better performance Arithmetic Optimization (AOA) Aquila Optimizer (AO), we hybridization them abbreviated AOAAO this paper. Considering Harris Hawk (HHO) algorithm, an energy parameter E was also introduced balance exploration exploitation procedures individuals swarms, furthermore, piecewise linear map decrease randomness parameter. Pseudo code presented, Simulation experiments were carried out on benchmark functions three classical engineering involved optimization. Nine popular well demonstrated included for comparison. Results confirmed would with faster convergence rate, higher accuracy.

Язык: Английский

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Intelligent computing on time-series data analysis and prediction of COVID-19 pandemics

Pattern Recognition Letters, Год журнала: 2021, Номер 151, С. 69 - 75

Опубликована: Авг. 14, 2021

Язык: Английский

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Advanced Ensemble Model for Solar Radiation Forecasting Using Sine Cosine Algorithm and Newton’s Laws

IEEE Access, Год журнала: 2021, Номер 9, С. 115750 - 115765

Опубликована: Янв. 1, 2021

As research in alternate energy sources is growing, solar radiation catching the eyes of community immensely. Since generation depends on uncontrollable natural variables, without proper forecasting, this source cannot be trusted. For use machine learning algorithms one best choices. This paper proposed an optimized forecasting ensemble model consisting pre-processing and training phases. The phase works advanced sine cosine algorithm (ASCA) using Newton's laws gravity motion for objects (agents). ASCA uses functions to update agent's position/velocity components by considering its mass. then developed k-nearest neighbors (KNN) regression. performance measured a dataset from Kaggle (Solar Radiation Prediction, Task NASA Hackathon). evaluated comparison with Particle Swarm Optimizer (PSO), Whale Optimization Algorithm (WOA), Genetic (GA), Grey Wolf (GWO), Squirrel Search (SSA), Harris Hawks (HHO), Hybrid Greedy Sine Cosine Differential Evolution (HGSCADE), Modified Cuckoo (HMSCACSA), Marine Predators (MPA), Chimp (ChOA), Slime Mould (SMA). Obtained results are compared those state-of-the-art models, significant superiority confirmed statistical analysis such as ANOVA Wilcoxon's rank-sum tests.

Язык: Английский

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Solving Optimization Problems of Metamaterial and Double T-Shape Antennas Using Advanced Meta-Heuristics Algorithms

IEEE Access, Год журнала: 2022, Номер 10, С. 74449 - 74471

Опубликована: Янв. 1, 2022

This study offers an adaptive dynamic sine cosine fitness grey wolf optimizer (ADSCFGWO) for optimizing the parameters of two types antennas. The antennas are metamaterial and double T-shape monopoles. ADSCFGWO algorithm is based on technique recently developed powerful optimization techniques: a modified (GWO) value (SCA). suggested approach utilizes capabilities both algorithms to balance better exploration exploitation responsibilities process while achieving rapid convergence. First, new feature selection proposed choose most significant features from dataset using ADSCFGWO-based ensemble model optimal performance. also optimizes bidirectional recurrent neural network (BRNN) estimate monopole antenna characteristics. Several experiments were undertaken demonstrate superiority by comparing their results those existing algorithms, selectors, regression models. In addition, statistical analysis offered evaluate algorithm's effectiveness stability. achieved findings efficacy method over numerous competing algorithms.

Язык: Английский

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Prognostic Nutritional Index, Controlling Nutritional Status (CONUT) Score, and Inflammatory Biomarkers as Predictors of Deep Vein Thrombosis, Acute Pulmonary Embolism, and Mortality in COVID-19 Patients

Diagnostics, Год журнала: 2022, Номер 12(11), С. 2757 - 2757

Опубликована: Ноя. 11, 2022

Background: Numerous tools, including nutritional and inflammatory markers, have been evaluated as the predictors of poor outcomes in COVID-19 patients. This study aims to verify predictive role prognostic index (PNI), CONUT Score, markers (monocyte lymphocyte ratio (MLR), neutrophil (NLR), platelet (PLR), systemic (SII), Systemic Inflammation Response Index (SIRI), Aggregate (AISI)) cases deep vein thrombosis (DVT) acute pulmonary embolism (APE) risk, well mortality, Methods: The present was designed an observational, analytical, retrospective cohort study, included 899 patients over age 18 who had a infection, confirmed through real time-polymerase chain reaction (RT-PCR), were admitted County Emergency Clinical Hospital Modular Intensive Care Unit UMFST “George Emil Palade” Targu Mures, Romania between January 2020 March 20212. Results: Non-Surviving associated with higher incidence chronic kidney disease (p = 0.01), cardiovascular (atrial fibrillation (AF) p 0.01; myocardial infarction (MI) 0.02; peripheral arterial (PAD) 0.0003), malignancy 0.0001), tobacco obesity dyslipidemia 0.004), malnutrition < 0.0001). Multivariate analysis showed that both high baseline value all independent adverse for enrolled (for presence PAD, malignancy, tobacco, also outcomes. Conclusions: According our findings, MLR, NLR, PLR, SII, SIRI, AISI, lower PNI values at admission strongly predict DVT APE mortality Moreover, predicted outcomes, while CKD predicts risk but not risk.

Язык: Английский

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Breast Cancer Diagnosis Using Support Vector Machines Optimized by Whale Optimization and Dragonfly Algorithms

IEEE Access, Год журнала: 2022, Номер 10, С. 69688 - 69699

Опубликована: Янв. 1, 2022

Breast Cancer (BC) has become a critical illness with high mortality rate during the previous decade. It is considered women's most common cancer. In this paper, we propose two optimum automated BC classification approaches based on hybridization of Whale Optimization Algorithm (WOA) and Dragonfly (DA), Radial Basis Function Kernel Support Vector Machines (RBF-SVM), to increase accuracy (CA) by determining SVM parameters. The effectiveness proposed WOA-SVM DA-SVM algorithms tested Wisconsin Diagnosis (WDBC) databases Database (WBCD). Various metric parameters such as CA, confusion matrix, area under ROC curve (AUC), sensitivity, specificity are utilized assess consider approaches. results compared not only optimizers, Particle Swarm (PSO) Genetic (GA), that used for training artificial neural networks (ANN) classifiers, but also other models. explored feature selection, their findings offered According experimental results, method outperforms WBCD dataset. On WDBC dataset, however, algorithm algorithms. Using typical datasets' partition, resultant CA 99.65% 100% WBCD, respectively. However, using 10-fold cross-validation mean 97.89% 99.27%,

Язык: Английский

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