E3H2O-LE-TDR Algorithm for Optimizing Solar PV Cell Models and Solving Real-World Engineering Problems DOI Creative Commons

Dalia T. Akl,

Mahmoud M. Saafan,

Amira Y. Haikal

и другие.

Research Square (Research Square), Год журнала: 2023, Номер unknown

Опубликована: Авг. 3, 2023

Abstract Engineering and science have increasingly used metaheuristic algorithms to solve actual optimization problems. One of the challenging problems is proper selection parameters photovoltaic cells since these are a great source clean energy. For such difficult situations, Harris Hawks Optimization method can be useful tool. However, HHO susceptible local minimum. This study suggests novel optimizer called Enhanced Exploration Exploitation using Logarithms, Exponentials, Travelled Distance Rate (E 3 H 2 O-LE-TDR) algorithm, which modified version HHO. The algorithm proposed in this emphasizes utilization random location-based habitats during exploration phase implementation strategies 1, 3, 4 exploitation phase. In hawks wild will change their perch strategy chasing pattern according updates both phases. Therefore, cons original been solved. Furthermore, E O-LE-TDR was also tested across multiple benchmarks prove its credibility efficacy. approach on CEC2017, CEC2019, CEC2020, 27 other benchmark functions with different modalities. suggested evaluated six traditional real-world engineering situations. compared state-of-the-art algorithms, as well modifications numerical results show that outperforms all competitors, visually proven convergence curves. mean Friedman rank statistical test proved superiority algorithm. for single double diode pv cell model, presented best performance indicated by absolute error current power values operating conditions.

Язык: Английский

Prostate cancer grading framework based on deep transfer learning and Aquila optimizer DOI Creative Commons
Hossam Magdy Balaha,

Ahmed Osama Shaban,

Eman M. El-Gendy

и другие.

Neural Computing and Applications, Год журнала: 2024, Номер 36(14), С. 7877 - 7902

Опубликована: Фев. 22, 2024

Abstract Prostate cancer is the one of most dominant among males. It represents leading death causes worldwide. Due to current evolution artificial intelligence in medical imaging, deep learning has been successfully applied diseases diagnosis. However, recent studies prostate classification suffers from either low accuracy or lack data. Therefore, present work introduces a hybrid framework for early and accurate segmentation using learning. The proposed consists two stages, namely stage stage. In stage, 8 pretrained convolutional neural networks were fine-tuned Aquila optimizer used classify patients normal ones. If patient diagnosed with cancer, segmenting cancerous spot overall image U-Net can help diagnosis, here comes importance trained on 3 different datasets order generalize framework. best reported accuracies are 88.91% MobileNet “ISUP Grade-wise Cancer” dataset 100% ResNet152 “Transverse Plane Dataset” precisions 89.22% 100%, respectively. model gives an average AUC 98.46% 0.9778, respectively, “PANDA: Resized Train Data (512 × 512)” dataset. results give indicator acceptable performance

Язык: Английский

Процитировано

15
IHHO: an improved Harris Hawks optimization algorithm for solving engineering problems DOI Creative Commons

Dalia T. Akl,

Mahmoud M. Saafan,

Amira Y. Haikal

и другие.

Neural Computing and Applications, Год журнала: 2024, Номер 36(20), С. 12185 - 12298

Опубликована: Апрель 20, 2024

Abstract Harris Hawks optimization (HHO) algorithm was a powerful metaheuristic for solving complex problems. However, HHO could easily fall within the local minimum. In this paper, we proposed an improved (IHHO) different engineering tasks. The focused on random location-based habitats during exploration phase and strategies 1, 3, 4 exploitation phase. modified hawks in wild would change their perch strategy chasing pattern according to updates both phases. To avoid being stuck solution, values were generated using logarithms exponentials explore new regions more quickly locations. evaluate performance of algorithm, IHHO compared other five recent algorithms [grey wolf optimization, BAT teaching–learning-based moth-flame whale algorithm] as well three modifications (BHHO, LogHHO, MHHO). These optimizers had been applied benchmarks, namely standard CEC2017, CEC2019, CEC2020, 52 benchmark functions. Moreover, six classical real-world problems tested against prove efficiency algorithm. numerical results showed superiority algorithms, which proved visually convergence curves. Friedman's mean rank statistical test also inducted calculate algorithms. Friedman indicated that ranked first HHO.

Язык: Английский

Процитировано

9
AutYOLO-ATT: an attention-based YOLOv8 algorithm for early autism diagnosis through facial expression recognition DOI Creative Commons

Reham Hosney,

Fatma M. Talaat, Eman M. El-Gendy

и другие.

Neural Computing and Applications, Год журнала: 2024, Номер 36(27), С. 17199 - 17219

Опубликована: Июнь 6, 2024

Abstract Autism Spectrum Disorder (ASD) is a developmental condition resulting from abnormalities in brain structure and function, which can manifest as communication social interaction difficulties. Conventional methods for diagnosing ASD may not be effective the early stages of disorder. Hence, diagnosis crucial to improving patient's overall health well-being. One alternative method autism facial expression recognition since autistic children typically exhibit distinct expressions that aid distinguishing them other children. This paper provides deep convolutional neural network (DCNN)-based real-time emotion system kids. The proposed designed identify six emotions, including surprise, delight, sadness, fear, joy, natural, assist medical professionals families recognizing intervention. In this study, an attention-based YOLOv8 (AutYOLO-ATT) algorithm proposed, enhances model's performance by integrating attention mechanism. outperforms all classifiers metrics, achieving precision 93.97%, recall 97.5%, F1-score 92.99%, accuracy 97.2%. These results highlight potential real-world applications, particularly fields where high essential.

Язык: Английский

Процитировано

6
Enhanced handwriting recognition through hybrid UNet-based architecture with global classical features DOI
Xiaofei Liu

Journal of Ambient Intelligence and Humanized Computing, Год журнала: 2025, Номер unknown

Опубликована: Янв. 18, 2025

Язык: Английский

Процитировано

0
MCHIAO: a modified coronavirus herd immunity-Aquila optimization algorithm based on chaotic behavior for solving engineering problems DOI Creative Commons
Heba M. Selim,

Amira Y. Haikal,

Labib M. Labib

и другие.

Neural Computing and Applications, Год журнала: 2024, Номер 36(22), С. 13381 - 13465

Опубликована: Апрель 20, 2024

Abstract This paper proposes a hybrid Modified Coronavirus Herd Immunity Aquila Optimization Algorithm (MCHIAO) that compiles the Enhanced Optimizer (ECHIO) algorithm and (AO). As one of competitive human-based optimization algorithms, (CHIO) exceeds some other biological-inspired algorithms. Compared to CHIO showed good results. However, gets confined local optima, accuracy large-scale global problems is decreased. On hand, although AO has significant exploitation capabilities, its exploration capabilities are insufficient. Subsequently, novel metaheuristic optimizer, (MCHIAO), presented overcome these restrictions adapt it solve feature selection challenges. In this paper, MCHIAO proposed with three main enhancements issues reach higher optimal results which cases categorizing, enhancing new genes’ value equation using chaotic system as inspired by behavior coronavirus generating formula switch between expanded narrowed exploitation. demonstrates it’s worth contra ten well-known state-of-the-art algorithms (GOA, MFO, MPA, GWO, HHO, SSA, WOA, IAO, NOA, NGO) in addition CHIO. Friedman average rank Wilcoxon statistical analysis ( p -value) conducted on all testing 23 benchmark functions. test well 29 CEC2017 Moreover, tests 10 CEC2019 Six real-world used validate against same twelve classical functions, including 24 unimodal 44 multimodal respectively, exploitative explorative evaluated. The significance technique for functions demonstrated -values calculated rank-sum test, found be less than 0.05.

Язык: Английский

Процитировано

3
A Novel Hybrid Algorithm Based on Beluga Whale Optimization and Harris Hawks Optimization for Optimizing Multi-Reservoir Operation DOI

Xiaohui Shen,

Yonggang Wu, Lingxi Li

и другие.

Water Resources Management, Год журнала: 2024, Номер 38(12), С. 4883 - 4909

Опубликована: Июнь 19, 2024

Язык: Английский

Процитировано

3
A Hybrid Algorithm Based on Multi-Strategy Elite Learning for Global Optimization DOI Open Access

Xuhua Zhao,

Chao Yang, Donglin Zhu

и другие.

Electronics, Год журнала: 2024, Номер 13(14), С. 2839 - 2839

Опубликована: Июль 18, 2024

To improve the performance of sparrow search algorithm in solving complex optimization problems, this study proposes a novel variant called Improved Beetle Antennae Search-Based Sparrow Search Algorithm (IBSSA). A new elite dynamic opposite learning strategy is proposed population initialization stage to enhance diversity. In update discoverer, staged inertia weight guidance mechanism used formula promote information exchange between individuals, and algorithm’s ability optimize on global level. After follower’s position updated, logarithmic spiral opposition-based introduced disturb initial individual beetle antennae obtain more purposeful solution. address issue decreased diversity susceptibility local optima during later stages, improved are combined using greedy strategy. This integration aims convergence accuracy. On 20 benchmark test functions CEC2017 Test suite, IBSSA performed better than other advanced algorithms. Moreover, six engineering problems were demonstrate effectiveness feasibility.

Язык: Английский

Процитировано

3
Potential of digital chest radiography-based deep learning in screening and diagnosing pneumoconiosis: An observational study DOI Creative Commons
Y S Zhang, Bowen Zheng,

Fengxia Zeng

и другие.

Medicine, Год журнала: 2024, Номер 103(25), С. e38478 - e38478

Опубликована: Июнь 21, 2024

The diagnosis of pneumoconiosis is complex and subjective, leading to inevitable variability in readings. This especially true for inexperienced doctors. To improve accuracy, a computer-assisted system used more effective diagnoses. Three models (Resnet50, Resnet101, DenseNet) were classification based on 1250 chest X-ray images. experienced highly qualified physicians read the collected digital radiography images classified them from category 0 III double-blinded manner. results 3 agreement considered relative gold standards. Subsequently, train test these their performance was evaluated using multi-class metrics. We kappa values accuracy evaluate consistency reliability optimal model with clinical typing. showed that ResNet101 among convolutional neural networks. AUC 1.0, 0.9, 0.89, 0.94 detecting categories 0, I, II, III, respectively. micro-average macro-average mean 0.93 0.94, Kappa 0.72 0.7111 quadruple 0.98 0.955 dichotomous classification, respectively, compared standard clinic. study develops deep learning screening staging radiographs. performed relatively better classifying than radiologists. displayed outstanding performance, thereby indicating feasibility techniques screening.

Язык: Английский

Процитировано

2
Compound improved Harris hawks optimization for global and engineering optimization DOI
Chengtian Ouyang, Liao Chang, Donglin Zhu

и другие.

Cluster Computing, Год журнала: 2024, Номер 27(7), С. 9509 - 9568

Опубликована: Апрель 24, 2024

Язык: Английский

Процитировано

1
$$D_MD_RDF$$: diabetes mellitus and retinopathy detection framework using artificial intelligence and feature selection DOI Creative Commons
Hossam Magdy Balaha, Eman M. El-Gendy, Mahmoud M. Saafan

и другие.

Soft Computing, Год журнала: 2024, Номер 28(19), С. 11393 - 11420

Опубликована: Авг. 5, 2024

Abstract Diabetes mellitus is one of the most common diseases affecting patients different ages. can be controlled if diagnosed as early possible. One serious complications diabetes retina diabetic retinopathy. If not early, it lead to blindness. Our purpose propose a novel framework, named $$D_MD_RDF$$ DMDRDF , for and accurate diagnosis The framework consists two phases, detection (DMD) other retinopathy (DRD). novelty DMD phase concerned in contributions. Firstly, feature selection approach called Advanced Aquila Optimizer Feature Selection ( $$A^2OFS$$ xmlns:mml="http://www.w3.org/1998/Math/MathML">A2OFS ) introduced choose promising features diagnosing diabetes. This extracts required from results laboratory tests while ignoring useless features. Secondly, classification (CA) using five modified machine learning (ML) algorithms used. modification ML proposed automatically select parameters these Grid Search (GS) algorithm. DRD lies 7 CNNs reported concerning datasets shows that AO reports best performance metrics process with help classifiers. achieved accuracy 98.65% GS-ERTC model max-absolute scaling on “Early Stage Risk Prediction Dataset” dataset. Also, datasets, AOMobileNet considered suitable this problem outperforms CNN models 95.80% “The SUSTech-SYSU dataset”

Язык: Английский

Процитировано

1