Hybrid CNN and XGBoost Model Tuned by Modified Arithmetic Optimization Algorithm for COVID-19 Early Diagnostics from X-ray Images

Electronics, Journal Year: 2022, Volume and Issue: 11(22), P. 3798 - 3798

Published: Nov. 18, 2022

Developing countries have had numerous obstacles in diagnosing the COVID-19 worldwide pandemic since its emergence. One of most important ways to control spread this disease begins with early detection, which allows that isolation and treatment could perhaps be started. According recent results, chest X-ray scans provide information about onset infection, may evaluated so diagnosis can begin sooner. This is where artificial intelligence collides skilled clinicians’ diagnostic abilities. The suggested study’s goal make a contribution battling epidemic by using simple convolutional neural network (CNN) model construct an automated image analysis framework for recognizing afflicted data. To improve classification accuracy, fully connected layers CNN were replaced efficient extreme gradient boosting (XGBoost) classifier, used categorize extracted features layers. Additionally, hybrid version arithmetic optimization algorithm (AOA), also developed facilitate proposed research, tune XGBoost hyperparameters images. Reported experimental data showed approach outperforms other state-of-the-art methods, including cutting-edge metaheuristics algorithms, tested same framework. For validation purposes, balanced images dataset 12,000 observations, belonging normal, viral pneumonia classes, was used. method, tuned introduced AOA, superior performance, achieving accuracy approximately 99.39% weighted average precision, recall F1-score 0.993889, 0.993887 0.993887, respectively.

Language: Английский

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A Literature Review and Critical Analysis of Metaheuristics Recently Developed

Archives of Computational Methods in Engineering, Journal Year: 2023, Volume and Issue: 31(1), P. 125 - 146

Published: July 22, 2023

Abstract Metaheuristic algorithms have applicability in various fields where it is necessary to solve optimization problems. It has been a common practice this field for several years propose new that take inspiration from natural and physical processes. The exponential increase of controversial issue researchers criticized. However, their efforts point out multiple issues involved these practices insufficient since the number existing metaheuristics continues yearly. To know current state problem, paper analyzes sample 111 recent studies so-called new, hybrid, or improved are proposed. Throughout document, topics reviewed will be addressed general perspective specific aspects. Among study’s findings, observed only 43% analyzed papers make some mention No Free Lunch (NFL) theorem, being significant result ignored by most presented. Of studies, 65% present an version established algorithm, which reveals trend no longer based on analogies. Additionally, compilation solutions found engineering problems commonly used verify performance state-of-the-art demonstrate with low level innovation can erroneously considered as frameworks years, known Black Widow Optimization Coral Reef analyzed. study its components they do not any innovation. Instead, just deficient mixtures different evolutionary operators. This applies extension recently proposed versions.

Language: Английский

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Hyperparameter Optimizer with Deep Learning-Based Decision-Support Systems for Histopathological Breast Cancer Diagnosis

Cancers, Journal Year: 2023, Volume and Issue: 15(3), P. 885 - 885

Published: Jan. 31, 2023

Histopathological images are commonly used imaging modalities for breast cancer. As manual analysis of histopathological is difficult, automated tools utilizing artificial intelligence (AI) and deep learning (DL) methods should be modelled. The recent advancements in DL approaches will helpful establishing maximal image classification performance numerous application zones. This study develops an arithmetic optimization algorithm with deep-learning-based cancer (AOADL-HBCC) technique healthcare decision making. AOADL-HBCC employs noise removal based on median filtering (MF) a contrast enhancement process. In addition, the presented applies AOA SqueezeNet model to derive feature vectors. Finally, belief network (DBN) classifier Adamax hyperparameter optimizer applied order exhibit enhanced results methodology, this comparative states that displays better than other methodologies, maximum accuracy 96.77%.

Language: Английский

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Image Registration Using the Arithmetic Optimization Algorithm for Robotic Visual Servoing

International Journal of Computational Intelligence Systems, Journal Year: 2025, Volume and Issue: 18(1)

Published: Jan. 8, 2025

Visual servoing using image registration is a method employed in robotics to control the movement of system visual information. In this context, we propose new intensity-based algorithm (IBIR) that uses information derived from images acquired at different times or views determine parameters geometric transformations needed align these images. The Arithmetic Optimization Algorithm (AOA) used optimize parameters, minimizing difference between be aligned. proposed algorithm, Intensity-Based Image Registration via Optimisation (IBIRAOA), robust data fluctuations and perturbations can avoid local optima. Simulation results prove importance efficiency terms computation time similarity aligned compared other methods based on various metaheuristics. addition, our confirm significant improvement trajectory wheeled mobile robot, thus reinforcing overall effectiveness practical navigation robotic applications.

Language: Английский

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Improved arithmetic optimization algorithm and its application to carbon fiber reinforced polymer-steel bond strength estimation

Composite Structures, Journal Year: 2022, Volume and Issue: 306, P. 116599 - 116599

Published: Dec. 15, 2022

Language: Английский

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A collaboration-based hybrid GWO-SCA optimizer for engineering optimization problems

Expert Systems with Applications, Journal Year: 2022, Volume and Issue: 213, P. 119017 - 119017

Published: Oct. 14, 2022

Language: Английский

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Deep Learning LSTM Recurrent Neural Network Model for Prediction of Electric Vehicle Charging Demand

Sustainability, Journal Year: 2022, Volume and Issue: 14(16), P. 10207 - 10207

Published: Aug. 17, 2022

The immense growth and penetration of electric vehicles has become a major component smart transport systems; thereby decreasing the greenhouse gas emissions that pollute environment. With increased volumes (EV) in past few years, charging demand these also an immediate requirement. Due to which, prediction vehicle is key importance so it minimizes burden on grids offers reduced costs charging. In this research study, attempt made develop novel deep learning (DL)-based long-short term memory (LSTM) recurrent neural network predictor model carry out forecasting demand. parameters new (DLSTM) are tuned for its optimal values using classic arithmetic optimization algorithm (AOA) input time series data decomposed as maintain their features empirical mode decomposition (EMD). EMD—AOA—DLSTM modeled study overcomes vanishing exploding gradients basic tested superiority EV dataset Georgia Tech, Atlanta, USA. At simulation, best results 97.14% accuracy with mean absolute error 0.1083 root square 2.0628 × 10−5 attained. Furthermore, was evaluated be pertaining 4.25516 10−10. prove efficacy metrics computed LSTM considered comparison previous techniques from existing works.

Language: Английский

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E-FEERP: Enhanced Fuzzy Based Energy Efficient Routing Protocol for Wireless Sensor Network

Wireless Personal Communications, Journal Year: 2023, Volume and Issue: 131(1), P. 371 - 398

Published: April 20, 2023

Language: Английский

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A reinforcement learning-based hybrid Aquila Optimizer and improved Arithmetic Optimization Algorithm for global optimization

Expert Systems with Applications, Journal Year: 2023, Volume and Issue: 224, P. 119898 - 119898

Published: March 21, 2023

Language: Английский

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Lévy Arithmetic Algorithm: An enhanced metaheuristic algorithm and its application to engineering optimization

Expert Systems with Applications, Journal Year: 2023, Volume and Issue: 241, P. 122335 - 122335

Published: Nov. 3, 2023

Language: Английский

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