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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Multi-input CNN based vibro-acoustic fusion for accurate fault diagnosis of induction motor

Engineering Applications of Artificial Intelligence, Journal Year: 2023, Volume and Issue: 120, P. 105872 - 105872

Published: Jan. 28, 2023

Language: Английский

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Metaheuristic-Based Hyperparameter Tuning for Recurrent Deep Learning: Application to the Prediction of Solar Energy Generation

Axioms, Journal Year: 2023, Volume and Issue: 12(3), P. 266 - 266

Published: March 4, 2023

As solar energy generation has become more and important for the economies of numerous countries in last couple decades, it is highly to build accurate models forecasting amount green that will be produced. Numerous recurrent deep learning approaches, mainly based on long short-term memory (LSTM), are proposed dealing with such problems, but most may differ from one test case another respect architecture hyperparameters. In current study, use an LSTM a bidirectional (BiLSTM) data collection that, besides time series values denoting generation, also comprises corresponding information about weather. The research additionally endows hyperparameter tuning by means enhanced version recently metaheuristic, reptile search algorithm (RSA). output tuned neural network compared ones several other state-of-the-art metaheuristic optimization approaches applied same task, using experimental setup, obtained results indicate approach as better alternative. Moreover, best model achieved R2 0.604, normalized MSE value 0.014, which yields improvement around 13% over traditional machine models.

Language: Английский

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Breast cancer diagnosis using support vector machine optimized by improved quantum inspired grey wolf optimization

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: May 10, 2024

Abstract A prompt diagnosis of breast cancer in its earliest phases is necessary for effective treatment. While Computer-Aided Diagnosis systems play a crucial role automated mammography image processing, interpretation, grading, and early detection cancer, existing approaches face limitations achieving optimal accuracy. This study addresses these by hybridizing the improved quantum-inspired binary Grey Wolf Optimizer with Support Vector Machines Radial Basis Function Kernel. hybrid approach aims to enhance accuracy classification determining Machine parameters. The motivation this hybridization lies need performance compared optimizers such as Particle Swarm Optimization Genetic Algorithm. Evaluate efficacy proposed IQI-BGWO-SVM on MIAS dataset, considering various metric parameters, including accuracy, sensitivity, specificity. Furthermore, application feature selection will be explored, results compared. Experimental findings demonstrate that suggested technique outperforms state-of-the-art methods resulting mean specificity 99.25%, 98.96%, 100%, respectively, using tenfold cross-validation datasets partition.

Language: Английский

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Optimizing long-short-term memory models via metaheuristics for decomposition aided wind energy generation forecasting

Artificial Intelligence Review, Journal Year: 2024, Volume and Issue: 57(3)

Published: Feb. 12, 2024

Abstract Power supply from renewable energy is an important part of modern power grids. Robust methods for predicting production are required to balance and demand avoid losses. This study proposed approach that incorporates signal decomposition techniques with Long Short-Term Memory (LSTM) neural networks tuned via a modified metaheuristic algorithm used wind generation forecasting. LSTM perform notably well when addressing time-series prediction, further hyperparameter tuning by version the reptile search (RSA) can help improve performance. The RSA was first evaluated against standard CEC2019 benchmark instances before being applied practical challenge. model has been tested two datasets hourly resolutions. predictions were executed without one, two, three steps ahead. Simulation outcomes have compared other cutting-edge metaheuristics. It observed introduced methodology exceed contenders, as later confirmed statistical analysis. Finally, this also provides interpretations best-performing models on both datasets, accompanied analysis importance impact each feature predictions.

Language: Английский

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Augmented weighted K-means grey wolf optimizer: An enhanced metaheuristic algorithm for data clustering problems

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: March 5, 2024

Abstract This study presents the K-means clustering-based grey wolf optimizer, a new algorithm intended to improve optimization capabilities of conventional optimizer in order address problem data clustering. The process that groups similar items within dataset into non-overlapping groups. Grey hunting behaviour served as model for however, it frequently lacks exploration and exploitation are essential efficient work mainly focuses on enhancing using weight factor concepts increase variety avoid premature convergence. Using partitional clustering-inspired fitness function, was extensively evaluated ten numerical functions multiple real-world datasets with varying levels complexity dimensionality. methodology is based incorporating concept purpose refining initial solutions adding diversity during phase. results show performs much better than standard discovering optimal clustering solutions, indicating higher capacity effective solution space. found able produce high-quality cluster centres fewer iterations, demonstrating its efficacy efficiency various datasets. Finally, demonstrates robustness dependability resolving issues, which represents significant advancement over techniques. In addition addressing shortcomings algorithm, incorporation innovative establishes further metaheuristic algorithms. performance around 34% original both test problems problems.

Language: Английский

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Forecasting bitcoin: Decomposition aided long short-term memory based time series modeling and its explanation with Shapley values

Knowledge-Based Systems, Journal Year: 2024, Volume and Issue: 299, P. 112026 - 112026

Published: June 6, 2024

Bitcoin price volatility fascinates both researchers and investors, studying features that influence its movement. This paper expends on previous research examines time series data of various exogenous endogenous factors: Bitcoin, Ethereum, S&P 500, VIX closing prices; exchange rates the Euro GPB to USD; number Bitcoin-related tweets per day. A period three years (from September 2019 2022) is covered by dataset. two-layer framework introduced tasked with accurately forecasting price. In first layer, account for complexities in analyzed data, variational mode decomposition (VMD) extracts trends from series. second Long short-term memory hybrid Bidirectional long networks were used forecast prices several steps ahead. work also an enhanced variant sine cosine algorithm tune control parameters VMD neural attaining best possible performance. The main focus combining modified metaheuristics improve cryptocurrency value forecast. Two sets experiments conducted, without VMD. results have been contrasted models tuned seven other cutting-edge optimizers. Extensive experimental outcomes indicate can be forecasted great accuracy using selected decomposition. Additionally, model was analyzed, Shapley values indicated such as EUR/USD rates, Ethereum prices, GBP/USD a significant impact forecasts.

Language: Английский

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The explainable potential of coupling hybridized metaheuristics, XGBoost, and SHAP in revealing toluene behavior in the atmosphere

The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 929, P. 172195 - 172195

Published: April 15, 2024

Language: Английский

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Application of Natural Language Processing and Machine Learning Boosted with Swarm Intelligence for Spam Email Filtering

Mathematics, Journal Year: 2022, Volume and Issue: 10(22), P. 4173 - 4173

Published: Nov. 8, 2022

Spam represents a genuine irritation for email users, since it often disturbs them during their work or free time. Machine learning approaches are commonly utilized as the engine of spam detection solutions, they efficient and usually exhibit high degree classification accuracy. Nevertheless, sometimes happens that good messages labeled and, more often, some emails enter into inbox ones. This manuscript proposes novel approach by combining machine models with an enhanced sine cosine swarm intelligence algorithm to counter deficiencies existing techniques. The introduced was adopted training logistic regression tuning XGBoost part hybrid learning-metaheuristics framework. developed framework has been validated on two public high-dimensional benchmark datasets (CSDMC2010 TurkishEmail), extensive experiments conducted have shown model successfully deals high-degree data. comparative analysis other cutting-edge models, also based metaheuristics, proposed method obtains superior performance in terms accuracy, precision, recall, f1 score, relevant metrics. Additionally, empirically established superiority is using rigid statistical tests.

Language: Английский

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Software defects prediction by metaheuristics tuned extreme gradient boosting and analysis based on Shapley Additive Explanations

Applied Soft Computing, Journal Year: 2023, Volume and Issue: 146, P. 110659 - 110659

Published: July 29, 2023

Language: Английский

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