Timely detection of skin cancer: An AI-based approach on the basis of the integration of Echo State Network and adapted Seasons Optimization Algorithm

Biomedical Signal Processing and Control, Год журнала: 2024, Номер 94, С. 106324 - 106324

Опубликована: Апрель 22, 2024

Язык: Английский

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Machine learning-assisted intelligent interpretation of distributed fiber optic sensor data for automated monitoring of pipeline corrosion

Measurement, Год журнала: 2024, Номер 226, С. 114190 - 114190

Опубликована: Янв. 19, 2024

Язык: Английский

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Monitoring of pipelines subjected to interactive bending and dent using distributed fiber optic sensors

Automation in Construction, Год журнала: 2024, Номер 160, С. 105306 - 105306

Опубликована: Фев. 3, 2024

Язык: Английский

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A novel efficient energy optimization in smart urban buildings based on optimal demand side management

Energy Strategy Reviews, Год журнала: 2024, Номер 54, С. 101461 - 101461

Опубликована: Июль 1, 2024

Increasing electrical energy consumption during peak hours leads to increased losses and the spread of environmental pollution. For this reason, demand-side management programs have been introduced reduce hours. This study proposes an efficient optimization in Smart Urban Buildings (SUBs) based on Improved Sine Cosine Algorithm (ISCA) that uses load-shifting technique for as a way improve patterns SUBs. The proposed system's goal is optimize SUBs appliances order effectively regulate load demand, with end result being reduction average ratio (PAR) consequent minimization electricity costs. accomplished while also keeping user comfort priority. system evaluated by comparing it Grasshopper Optimization (GOA) unscheduled cases. Without applying algorithm, total cost, carbon emission, PAR waiting time are equal 1703.576 ID, 34.16664 (kW), 413.5864s respectively RTP. While, after GOA, improved 1469.72 21.17 355.772s ISCA Improves PAR, 1206.748 16.5648 268.525384s respectively. Where 13.72 %, 38.00 13.97 % And method, 29.16 51.51 35.07 According results, created algorithm performed better than case GOA scheduling situations terms stated objectives was advantageous both utilities consumers. Furthermore, has presented novel two-stage stochastic model Moth-Flame (MFOA) co-optimization capacity planning systems storage would be incorporated grid connected smart urban buildings.

Язык: Английский

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SOFC stack modeling: a hybrid RBF-ANN and flexible Al-Biruni Earth radius optimization approach

International Journal of Low-Carbon Technologies, Год журнала: 2024, Номер 19, С. 1337 - 1350

Опубликована: Янв. 1, 2024

Abstract This study introduces a novel hybrid methodology for model identification of solid oxide fuel cell (SOFC) stacks by integrating radial basis function-based artificial neural network (RBF-ANN) with flexible Al-Biruni Earth radius optimizer (FA-BERO). The primary objective the proposed method is to augment precision and efficiency SOFC stack modeling considering advantages both RBF-ANN FA-BERO algorithms. main purpose using these two methods optimize structure based on suggested algorithm. other contribution this improving (A-BERO) applying improvements it, including constriction factor elimination phase increase exploration exploitation strength basic A-BERO. To validate effectiveness model, it compared some state-of-the-art models in field, such as multi-armed bandit algorithm (ANN/MABA) rotor Hopfield grey wolf optimization (RHNN/GWO). Furthermore, validated experimental data, final results demonstrate efficacy approach accurately representing intricate behavior stacks. achieves lower error rates (ERs) root mean squared errors (RMSEs) than comparative across different arrangements temperature conditions. show that, instance, 2/12/1 arrangement at 900°C, attains an ER 6.69% RMSE 2.13, while ANN/MABA RHNN/GWO obtain ERs 9.67% 8.54%, well values 24.48 9.23, respectively. also exhibits superior accuracy convergence speed methods, shown current–voltage curves analysis. Consequently, offers valuable tool researchers engineers working domain technology, enabling them better understand performance.

Язык: Английский

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Applications of machine learning methods for design and characterization of high-performance fiber-reinforced cementitious composite (HPFRCC): a review

Journal of Sustainable Cement-Based Materials, Год журнала: 2025, Номер unknown, С. 1 - 24

Опубликована: Фев. 6, 2025

Язык: Английский

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Boosted dipper throated optimization algorithm-based Xception neural network for skin cancer diagnosis: An optimal approach

Heliyon, Год журнала: 2024, Номер 10(5), С. e26415 - e26415

Опубликована: Фев. 18, 2024

Skin cancer is a prevalent form of that necessitates prompt and precise detection. However, current diagnostic methods for skin are either invasive, time-consuming, or unreliable. Consequently, there demand an innovative efficient approach to diagnose utilizes non-invasive automated techniques. In this study, unique method has been proposed diagnosing by employing Xception neural network optimized using Boosted Dipper Throated Optimization (BDTO) algorithm. The deep learning model capable extracting high-level features from dermoscopy images, while the BDTO algorithm bio-inspired optimization technique can determine optimal parameters weights network. To enhance quality diversity ISIC dataset utilized, widely accepted benchmark system diagnosis, various image preprocessing data augmentation techniques were implemented. By comparing with several contemporary approaches, it demonstrated outperforms others in detecting cancer. achieves average precision 94.936%, accuracy 94.206%, recall 97.092% surpassing performance alternative methods. Additionally, 5-fold ROC curve error have presented validation showcase superiority robustness method.

Язык: Английский

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Basketball technical action recognition based on a combination of capsule neural network and augmented red panda optimizer

Egyptian Informatics Journal, Год журнала: 2025, Номер 29, С. 100603 - 100603

Опубликована: Янв. 10, 2025

Язык: Английский

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Fast and accurate estimation of PEMFCs model parameters using a dimension learning-based modified grey wolf metaheuristic algorithm

Measurement, Год журнала: 2025, Номер unknown, С. 116917 - 116917

Опубликована: Фев. 1, 2025

Язык: Английский

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Advancements in rice disease detection through convolutional neural networks: A comprehensive review

Heliyon, Год журнала: 2024, Номер 10(12), С. e33328 - e33328

Опубликована: Июнь 1, 2024

This review paper addresses the critical need for advanced rice disease detection methods by integrating artificial intelligence, specifically convolutional neural networks (CNNs). Rice, being a staple food large part of global population, is susceptible to various diseases that threaten security and agricultural sustainability. research significant as it leverages technological advancements tackle these challenges effectively. Drawing upon diverse datasets collected across regions including India, Bangladesh, Türkiye, China, Pakistan, this offers comprehensive analysis efforts in using CNNs. While some are universally prevalent, many vary significantly growing region due differences climate, soil conditions, practices. The primary objective explore application AI, particularly CNNs, precise early identification diseases. literature includes detailed examination data sources, datasets, preprocessing strategies, shedding light on geographic distribution collection profiles contributing researchers. Additionally, synthesizes information algorithms models employed detection, highlighting their effectiveness addressing complexities. thoroughly evaluates hyperparameter optimization techniques impact model performance, emphasizing importance fine-tuning optimal results. Performance metrics such accuracy, precision, recall, F1 score rigorously analyzed assess effectiveness. Furthermore, discussion section critically examines associated with current methodologies, identifies opportunities improvement, outlines future directions at intersection machine learning detection. review, analyzing total 121 papers, underscores significance ongoing interdisciplinary meet evolving technology needs enhance security.

Язык: Английский

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