Solving the maximum cut problem using Harris Hawk Optimization algorithm DOI Creative Commons
Md. Rafiqul Islam, Md. Shahidul Islam, Pritam Khan Boni

et al.

PLoS ONE, Journal Year: 2024, Volume and Issue: 19(12), P. e0315842 - e0315842

Published: Dec. 30, 2024

The objective of the max-cut problem is to cut any graph in such a way that total weight edges are off maximum both subsets vertices divided due edges. Although it an elementary partitioning problem, one most challenging combinatorial optimization-based problems, and tons application areas make this highly admissible. Due its admissibility, solved using Harris Hawk Optimization algorithm (HHO). Though HHO effectively some engineering optimization sensitive parameter settings may converge slowly, potentially getting trapped local optima. Thus, additional operators used solve problem. Crossover refinement modify fitness hawk they can provide precise results. A mutation mechanism along with adjustment operator has improvised outcome obtained from updated hawk. To accept potential result, acceptance criterion been used, then repair applied proposed approach. system provided comparatively better outcomes on G-set dataset than other state-of-the-art algorithms. It 533 cuts more discrete cuckoo search 9 instances, 1036 PSO-EDA 14 1021 TSHEA instances. But for four lower TSHEA. Besides, statistical significance also tested Wilcoxon signed rank test proof superior performance method. In terms solution quality, MC-HHO produce quite competitive when compared related

Language: Английский

An aseptic approach towards skin lesion localization and grading using deep learning and harris hawks optimization DOI Creative Commons
Hossam Magdy Balaha, Asmaa El-Sayed Hassan, Eman M. El-Gendy

et al.

Multimedia Tools and Applications, Journal Year: 2023, Volume and Issue: 83(7), P. 19787 - 19815

Published: July 28, 2023

Abstract Skin cancer is the most common form of cancer. It predicted that total number cases will double in next fifty years. an expensive procedure to discover skin types early stages. Additionally, survival rate reduces as progresses. The current study proposes aseptic approach toward lesion detection, classification, and segmentation using deep learning Harris Hawks Optimization Algorithm (HHO). utilizes manual automatic approaches. used when dataset has no masks use while used, U-Net models, build adaptive model. meta-heuristic HHO optimizer utilized achieve optimization hyperparameters 5 pre-trained CNN namely VGG16, VGG19, DenseNet169, DenseNet201, MobileNet. Two datasets are "Melanoma Cancer Dataset 10000 Images" "Skin ISIC" from two publicly available sources for variety purpose. For segmentation, best-reported scores 0.15908, 91.95%, 0.08864, 0.04313, 0.02072, 0.20767 terms loss, accuracy, Mean Absolute Error, Squared Logarithmic Root respectively. dataset, applied experiments, best reported 97.08%, 98.50%, 95.38%, 98.65%, 96.92% overall precision, sensitivity, specificity, F1-score, respectively by DenseNet169 96.06%, 83.05%, 81.05%, 97.93%, 82.03% MobileNet After computing results, suggested compared with 9 related studies. results comparison proves efficiency proposed framework.

Language: Английский

Citations

13
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

et al.

Neural Computing and Applications, Journal Year: 2024, Volume and Issue: 36(22), P. 13381 - 13465

Published: April 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.

Language: Английский

Citations

4
Precise Prostate Cancer Assessment Using IVIM-Based Parametric Estimation of Blood Diffusion from DW-MRI DOI Creative Commons
Hossam Magdy Balaha, Sarah M. Ayyad, Ahmed Alksas

et al.

Bioengineering, Journal Year: 2024, Volume and Issue: 11(6), P. 629 - 629

Published: June 19, 2024

Prostate cancer is a significant health concern with high mortality rates and substantial economic impact. Early detection plays crucial role in improving patient outcomes. This study introduces non-invasive computer-aided diagnosis (CAD) system that leverages intravoxel incoherent motion (IVIM) parameters for the of prostate (PCa). IVIM imaging enables differentiation water molecule diffusion within capillaries outside vessels, offering valuable insights into tumor characteristics. The proposed approach utilizes two-step segmentation through use three U-Net architectures extracting tumor-containing regions interest (ROIs) from segmented images. performance CAD thoroughly evaluated, considering optimal classifier comparing diagnostic value commonly used apparent coefficient (ADC). results demonstrate combination central zone (CZ) peripheral (PZ) features Random Forest Classifier (RFC) yields best performance. achieves an accuracy 84.08% balanced 82.60%. showcases sensitivity (93.24%) reasonable specificity (71.96%), along good precision (81.48%) F1 score (86.96%). These findings highlight effectiveness accurately segmenting diagnosing PCa. represents advancement methods early PCa, showcasing potential machine learning techniques. developed solution has to revolutionize PCa diagnosis, leading improved outcomes reduced healthcare costs.

Language: Английский

Citations

4
Advancements in AI for cardiac arrhythmia detection: A comprehensive overview DOI Creative Commons
Jagdeep Rahul, Lakhan Dev Sharma

Computer Science Review, Journal Year: 2025, Volume and Issue: 56, P. 100719 - 100719

Published: Jan. 5, 2025

Language: Английский

Citations

0
Comprehensive multimodal approach for Parkinson’s disease classification using artificial intelligence: insights and model explainability DOI
Hossam Magdy Balaha, Asmaa El-Sayed Hassan,

Ranaa Ahmed

et al.

Soft Computing, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 15, 2025

Language: Английский

Citations

0
Transformative Approaches in Breast Cancer Detection: Integrating Transformers into Computer-Aided Diagnosis for Histopathological Classification DOI Creative Commons
Majed Alwateer, Amna Bamaqa, Mohammad Farsi

et al.

Bioengineering, Journal Year: 2025, Volume and Issue: 12(3), P. 212 - 212

Published: Feb. 20, 2025

Breast cancer (BC) remains a leading cause of cancer-related mortality among women worldwide, necessitating advancements in diagnostic methodologies to improve early detection and treatment outcomes. This study proposes novel twin-stream approach for histopathological image classification, utilizing both histopathologically inherited vision-based features enhance precision. The first stream utilizes Virchow2, deep learning model designed extract high-level features, while the second employs Nomic, transformer model, capture spatial contextual information. fusion these streams ensures comprehensive feature representation, enabling achieve state-of-the-art performance on BACH dataset. Experimental results demonstrate superiority approach, with mean accuracy 98.60% specificity 99.07%, significantly outperforming single-stream methods related studies. Statistical analyses, including paired t-tests, ANOVA, correlation studies, confirm robustness reliability model. proposed not only improves but also offers scalable efficient solution clinical applications, addressing challenges resource constraints increasing demands.

Language: Английский

Citations

0
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

et al.

Medicine, Journal Year: 2024, Volume and Issue: 103(25), P. e38478 - e38478

Published: June 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.

Language: Английский

Citations

2
A Novel Machine Learning-Based Classification Framework for Age-Related Macular Degeneration (AMD) Diagnosis from Fundus Images DOI

Aya A. Abd El-Khalek,

Hossam Magdy Balaha, Ali Mahmoud

et al.

Published: May 27, 2024

Language: Английский

Citations

2
Early detection of monkeypox: Analysis and optimization of pretrained deep learning models using the Sparrow Search Algorithm DOI Creative Commons
Amna Bamaqa, Waleed M. Bahgat, Yousry AbdulAzeem

et al.

Results in Engineering, Journal Year: 2024, Volume and Issue: 24, P. 102985 - 102985

Published: Sept. 30, 2024

Language: Английский

Citations

2
An Ensemble Feature Optimization for an Effective Heart Disease Prediction Model DOI Open Access

Kavitha Chandrashekar,

Anitha Narayanreddy

International journal of intelligent engineering and systems, Journal Year: 2023, Volume and Issue: 16(2), P. 517 - 525

Published: Feb. 25, 2023

The use of machine learning (ML) within medical field is on the rise, notably as a means to enhance both speed and precision diagnosis.Through evaluating large volumes patient information, able provide disease prediction, giving patients doctors more control over their health.Predicting preventing heart has become major area study in data processing result increased expense therapy.Since there are so many factors that come into play, estimating one's risk manually challenging task.Moreover, very few methods which better accuracy for prediction disease.Hence, by using openly accessible cleveland dataset, this research aims design evaluate several advanced technologies constructed employing leaning algorithms diagnosing if an individual going get or not.In paper, we propose ensemble feature optimized (EFO) method uses enhanced extreme gradient boosting tree level cross validation scheme effective (EHD) prediction.The presented EFO algorithm other existing have been used diseases.The performance (XGB-based, hyper optimization (ETHO), MLP-PSO) proposed evaluated classification metrics.When compared with XGB-based, MLP-PSO algorithm, attained 98.61%.The provides predict efficiently effectively.

Language: Английский

Citations

6