IHHO: an improved Harris Hawks optimization algorithm for solving engineering problems

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

---

A novel skin cancer detection model using modified finch deep CNN classifier model

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Май 16, 2024

Abstract Skin cancer is one of the most life-threatening diseases caused by abnormal growth skin cells, when exposed to ultraviolet radiation. Early detection seems be more crucial for reducing aberrant cell proliferation because mortality rate rapidly rising. Although multiple researches are available based on detection, there still exists challenges in improving accuracy, computational time and so on. In this research, a novel performed using modified falcon finch deep Convolutional neural network classifier (Modified Falcon CNN) that efficiently detects disease with higher efficiency. The usage CNN effectively analyzed information relevant errors also minimized. inclusion optimization necessary efficient parameter tuning. This tuning enhanced robustness boosted convergence less stipulated time. achieved sensitivity, specificity values 93.59%, 92.14%, 95.22% regarding k-fold 96.52%, 96.69%, 96.54% training percentage, proving effective than literary works.

Язык: Английский

---

A Comprehensive Review of AI Diagnosis Strategies for Age-Related Macular Degeneration (AMD)

Bioengineering, Год журнала: 2024, Номер 11(7), С. 711 - 711

Опубликована: Июль 13, 2024

The rapid advancement of computational infrastructure has led to unprecedented growth in machine learning, deep and computer vision, fundamentally transforming the analysis retinal images. By utilizing a wide array visual cues extracted from fundus images, sophisticated artificial intelligence models have been developed diagnose various disorders. This paper concentrates on detection Age-Related Macular Degeneration (AMD), significant condition, by offering an exhaustive examination recent learning methodologies. Additionally, it discusses potential obstacles constraints associated with implementing this technology field ophthalmology. Through systematic review, research aims assess efficacy techniques discerning AMD different modalities as they shown promise disorders diagnosis. Organized around prevalent datasets imaging techniques, initially outlines assessment criteria, image preprocessing methodologies, frameworks before conducting thorough investigation diverse approaches for detection. Drawing insights more than 30 selected studies, conclusion underscores current trajectories, major challenges, future prospects diagnosis, providing valuable resource both scholars practitioners domain.

Язык: Английский

---

Optimizing Wind Power Forecasting with RNN-LSTM Models through Grid Search Cross-validation

Sustainable Computing Informatics and Systems, Год журнала: 2024, Номер unknown, С. 101054 - 101054

Опубликована: Ноя. 1, 2024

Язык: Английский

---

A Vision Transformer Approach for Breast Cancer Classification in Histopathology

Опубликована: Май 27, 2024

Язык: Английский

---

Early detection of monkeypox: Analysis and optimization of pretrained deep learning models using the Sparrow Search Algorithm

Results in Engineering, Год журнала: 2024, Номер 24, С. 102985 - 102985

Опубликована: Сен. 30, 2024

Язык: Английский

---

Enhanced handwriting recognition through hybrid UNet-based architecture with global classical features

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

Опубликована: Янв. 18, 2025

Язык: Английский

---

DeepMelaNet: Advancing Melanoma Stage Classification in Skin Cancer Diagnosis

Engineering Technology & Applied Science Research, Год журнала: 2025, Номер 15(1), С. 19627 - 19635

Опубликована: Фев. 1, 2025

Melanoma skin cancer is a global public health threat due to its increasing rates and the possibility of severe outcomes if not adequately addressed. caused by ultraviolet radiation and, among two stages, malignant more dangerous than benign. The diagnosis melanoma typically based on visual inspection manual methods carried out experienced physicians. However, this method usually slow has high probability error. Deep-learning-based models can offer better low-cost treatments for people with melanoma. This study aimed develop deep-learning model classify in early stages. presents modified model, named DeepMelaNet, correctly images as benign or malignant. proposed classifier achieved an accuracy 93.40%, precision 98%, recall 94%, F1 score 93% dataset 10,000 images, offering practical solution that help healthcare professionals prediction.

Язык: Английский

---

AOA-guided hyperparameter refinement for precise medical image segmentation

Alexandria Engineering Journal, Год журнала: 2025, Номер 120, С. 547 - 560

Опубликована: Фев. 24, 2025

Язык: Английский

---

$$D_MD_RDF$$: diabetes mellitus and retinopathy detection framework using artificial intelligence and feature selection

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

---