Greylag goose optimization and multilayer perceptron for enhancing lung cancer classification

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Окт. 10, 2024

Lung cancer is an important global health problem, and it defined by abnormal growth of the cells in tissues lung, mostly leading to significant morbidity mortality. Its timely identification correct staging are very for proper therapy prognosis. Different computational methods have been used enhance precision lung classification, among which optimization algorithms such as Greylag Goose Optimization (GGO) employed. These purpose improving performance machine learning models that presented with a large amount complex data, selecting most features. As per data preparation one steps, contains operations scaling, normalization, handling gap factor ensure reasonable reliable input data. In this domain, use GGO includes refining feature selection, mainly focuses on enhancing classification accuracy compared other binary format algorithms, like bSC, bMVO, bPSO, bWOA, bGWO, bFOA. The efficiency bGGO algorithm choosing optimal features improved indicator possible application method field diagnosis. achieved highest MLP model at 98.4%. selection results were assessed using statistical analysis, utilized Wilcoxon signed-rank test ANOVA. also accompanied set graphical illustrations ensured adequacy adopted hybrid (GGO + MLP).

Язык: Английский

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Enhancing heart disease classification based on greylag goose optimization algorithm and long short-term memory

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Янв. 8, 2025

Heart disease is a category of various conditions that affect the heart, which includes multiple diseases influence its structure and operation. Such may consist coronary artery disease, characterized by narrowing or clotting arteries supply blood to heart muscle, with resulting threat attacks. rhythm disorders (arrhythmias), valve problems, congenital defects present at birth, muscle (cardiomyopathies) are other types disease. The objective this work introduce Greylag Goose Optimization (GGO) algorithm, seeks improve accuracy classification. GGO algorithm's binary format specifically intended choose most effective set features can classification when compared six optimization algorithms. bGGO algorithm for selecting optimal enhance accuracy. phase utilizes many classifiers, findings indicated Long Short-Term Memory (LSTM) emerged as classifier, achieving an rate 91.79%. hyperparameter LSTM model tuned using GGO, outcome alternative optimizers. obtained highest performance, 99.58%. statistical analysis employed Wilcoxon signed-rank test ANOVA assess feature selection outcomes. Furthermore, visual representations results was provided confirm robustness effectiveness proposed hybrid approach (GGO + LSTM).

Язык: Английский

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Metaheuristic optimization algorithms for multi-area economic dispatch of power systems: part II—a comparative study

Artificial Intelligence Review, Год журнала: 2025, Номер 58(5)

Опубликована: Фев. 14, 2025

Язык: Английский

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Solving Engineering Optimization Problems Based on Multi-Strategy Particle Swarm Optimization Hybrid Dandelion Optimization Algorithm

Biomimetics, Год журнала: 2024, Номер 9(5), С. 298 - 298

Опубликована: Май 17, 2024

In recent years, swarm intelligence optimization methods have been increasingly applied in many fields such as mechanical design, microgrid scheduling, drone technology, neural network training, and multi-objective optimization. this paper, a multi-strategy particle hybrid dandelion algorithm (PSODO) is proposed, which based on the problems of slow speed being easily susceptible to falling into local extremum ability algorithm. This makes whole more diverse by introducing strong global search unique individual update rules (i.e., rising, landing). The ascending descending stages also help introduce changes explorations space, thus better balancing search. experimental results show that compared with other algorithms, proposed PSODO greatly improves optimal value ability, convergence speed. effectiveness feasibility are verified solving 22 benchmark functions three engineering design different complexities CEC 2005 comparing it algorithms.

Язык: Английский

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Applications of artificial intelligence and computational intelligence in hydraulic optimization of centrifugal pumps: a comprehensive review

Engineering Applications of Computational Fluid Mechanics, Год журнала: 2025, Номер 19(1)

Опубликована: Март 10, 2025

Язык: Английский

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3-D gravity inversion for the basement relief reconstruction through modified success-history-based adaptive differential evolution

Geophysical Journal International, Год журнала: 2023, Номер 235(1), С. 377 - 400

Опубликована: Май 25, 2023

SUMMARY A gravity inversion procedure using the success-history-based adaptive differential evolution (SHADE) algorithm is presented to reconstruct 3-D basement relief geometry in sedimentary basins. We introduced exponential population size (number) reduction (EPSR) reduce computational cost and used self-adaptive control parameters solve this highly nonlinear inverse problem. Model parametrization was carried out by discretizing cover via juxtaposed right prisms, each placed below observation point. Resolvability characteristics of problem were revealed through some function topography landscapes. The fine-tuned parameter namely, number allowed us get best benefit from algorithm. Additionally, a stabilizing as relative constraint avoid undesired effects originated ill-posedness In synthetic data cases, strategy we propose outperformed linear which has won various IEEE–CEC competitions so far. Thorough uncertainty assessments probability density principal component analysis demonstrated solidity obtained models. real case, residual anomalies two well-known major grabens Aegean Graben System (Türkiye), calculated thanks finite element method, inverted. It determined that solutions for these reliefs, whose depths are still controversial, statistically reliable. Moreover, found be less than reported most previous studies. conclude SHADE EPSR powerful alternative tool geophysical problems.

Язык: Английский

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Best-worst individuals driven multiple-layered differential evolution

Information Sciences, Год журнала: 2023, Номер 655, С. 119889 - 119889

Опубликована: Ноя. 10, 2023

Язык: Английский

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Generalized predictive control application scheme for nonlinear hydro-turbine regulation system: Based on a precise novel control structure

Energy, Год журнала: 2024, Номер 296, С. 130916 - 130916

Опубликована: Апрель 6, 2024

Язык: Английский

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An Optimized Extreme Learning Machine Composite Framework for Point, Probabilistic, and Quantile Regression Forecasting of Carbon Price

Process Safety and Environmental Protection, Год журнала: 2025, Номер unknown, С. 106772 - 106772

Опубликована: Янв. 1, 2025

Язык: Английский

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A Novel Digital Twin Framework for Aeroengine Performance Diagnosis

Aerospace, Год журнала: 2023, Номер 10(9), С. 789 - 789

Опубликована: Сен. 8, 2023

Aeroengine performance diagnosis technology is essential for ensuring flight safety and reliability. The complexity of engine the strong coupling fault characteristics make it challenging to develop accurate efficient gas path methods. To address these issues, this study proposes a novel digital twin framework aeroengines that achieves digitalization physical systems. mechanism model constructed at component level. data-driven built using particle swarm optimization–extreme gradient boosting algorithm (PSO-XGBoost). These two models are fused low-rank multimodal fusion method (LWF) combined with sparse stacked autoencoder (SSAE) form diagnosis. Compared methods solely based on or data, proposed can effectively use data information improve accuracy research results show has an error rate 0.125% in predicting parameters 98.6%. Considering degradation cost typical mission only one aircraft after 3000 cycles approximately USD 209.5, good economic efficiency. This be used reliability, availability, efficiency, significant value engineering applications.

Язык: Английский

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