Binary Artificial Algae Algorithm for feature selection

Applied Soft Computing, Год журнала: 2022, Номер 120, С. 108630 - 108630

Опубликована: Фев. 23, 2022

Язык: Английский

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A multi-objective quantum-inspired genetic algorithm for workflow healthcare application scheduling with hard and soft deadline constraints in hybrid clouds

Applied Soft Computing, Год журнала: 2022, Номер 128, С. 109440 - 109440

Опубликована: Авг. 3, 2022

Язык: Английский

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Addressing feature selection and extreme learning machine tuning by diversity-oriented social network search: an application for phishing websites detection

Complex & Intelligent Systems, Год журнала: 2023, Номер 9(6), С. 7269 - 7304

Опубликована: Июнь 28, 2023

Abstract Feature selection and hyper-parameters optimization (tuning) are two of the most important challenging tasks in machine learning. To achieve satisfying performance, every learning model has to be adjusted for a specific problem, as efficient universal approach does not exist. In addition, data sets contain irrelevant redundant features that can even have negative influence on model’s performance. Machine applied almost everywhere; however, due high risks involved with growing number malicious, phishing websites world wide web, feature tuning this research addressed particular problem. Notwithstanding many metaheuristics been devised both challenges, there is still much space improvements. Therefore, exhibited manuscript tries improve website detection by extreme utilizes relevant subset features. accomplish goal, novel diversity-oriented social network search algorithm developed incorporated into two-level cooperative framework. The proposed compared six other cutting-edge algorithms, were also implemented framework tested under same experimental conditions. All employed level 1 perform task. best-obtained then used input 2, where all algorithms machine. Tuning referring neurons hidden layers weights biases initialization. For evaluation purposes, three different sizes classes, retrieved from UCI Kaggle repositories, methods terms classification error, separately 2 over several independent runs, detailed metrics final outcomes (output layer 2), including precision, recall, f1 score, receiver operating characteristics precision–recall area curves. Furthermore, an additional experiment conducted, only used, establish performance features, which represents large-scale NP-hard global challenge. Finally, according results statistical tests, findings suggest average obtains better achievements than competitors challenges sets. SHapley Additive exPlanations analysis best-performing was determine influential

Язык: Английский

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Machine learning tuning by diversity oriented firefly metaheuristics for Industry 4.0

Expert Systems, Год журнала: 2023, Номер 41(2)

Опубликована: Март 30, 2023

Abstract The progress of Industrial Revolution 4.0 has been supported by recent advances in several domains, and one the main contributors is Internet Things. Smart factories healthcare have both benefited terms leveraged quality service productivity rate. However, there always a trade‐off some largest concerns include security, intrusion, failure detection, due to high dependence on Things devices. To overcome these other challenges, artificial intelligence, especially machine learning algorithms, are employed for fault prediction, intrusion computer‐aided diagnostics, so forth. efficiency models heavily depend feature selection, predetermined values hyper‐parameters training deliver desired result. This paper proposes swarm intelligence‐based approach tune models. A novel version firefly algorithm, that overcomes known deficiencies original method employing diversification‐based mechanism, proposed applied selection hyper‐parameter optimization two models—XGBoost extreme machine. tested four real‐world Industry data sets, namely distributed transformer monitoring, elderly fall BoT‐IoT, UNSW‐NB 15. Achieved results compared eight cutting‐edge metaheuristics, implemented under same conditions. experimental outcomes strongly indicate significantly outperformed all competitor metaheuristics convergence speed results' measured with standard metrics—accuracy, precision, recall, f1‐score.

Язык: Английский

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Optimizing process parameters of in-situ laser assisted cutting of glass–ceramic by applying hybrid machine learning models

Advanced Engineering Informatics, Год журнала: 2024, Номер 62, С. 102590 - 102590

Опубликована: Май 13, 2024

Язык: Английский

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