EHMFFL: Ensemble Heuristic-Metaheuristic Feature Fusion Learning Algorithm for Heart Disease Diagnosis DOI Open Access
Mohammad Shokouhifar, Mohamad Hasanvand, Elaheh Moharamkhani

и другие.

Опубликована: Окт. 30, 2023

Heart disease is a global health concern of paramount importance, causing significant number fatalities and disabilities. Precise timely diagnosis heart pivotal in pre-venting adverse outcomes improving patient well-being, thereby creating growing demand for intelligent approaches to predict effectively. This paper introduces an Ensemble Heuristic-Metaheuristic Feature Fusion Learning (EHMFFL) algorithm diagnosis. Within the EHMFFL algorithm, diverse ensemble learning model crafted, featuring different feature subsets each heterogeneous base learner, including support vector machine, K-nearest neighbors, logistic regression, random forest, naive bayes, decision tree, XGBoost. The primary objective identify most pertinent features leveraging combined heuristic-metaheuristic approach that integrates heuristic knowledge Pearson correlation coefficient with metaheuristic-driven grey wolf optimizer. second aggregate various learners through learning, aimed at constructing robust prediction model. performance rigorously assessed using Cleveland Statlog datasets yielding remarkable results accuracy 91.8% 88.9%, respectively, surpassing state-of-the-art machine selection techniques These findings underscore potential enhancing diagnostic providing valuable clinicians making more informed decisions regarding care.

Язык: Английский

Enhancing Brain Tumor Classification with Transfer Learning across Multiple Classes: An In-Depth Analysis DOI Creative Commons
Syed Ahmmed, Prajoy Podder, M. Rubaiyat Hossain Mondal

и другие.

BioMedInformatics, Год журнала: 2023, Номер 3(4), С. 1124 - 1144

Опубликована: Дек. 6, 2023

This study focuses on leveraging data-driven techniques to diagnose brain tumors through magnetic resonance imaging (MRI) images. Utilizing the rule of deep learning (DL), we introduce and fine-tune two robust frameworks, ResNet 50 Inception V3, specifically designed for classification MRI Building upon previous success V3 in classifying other medical datasets, our investigation encompasses datasets with distinct characteristics, including one four classes another two. The primary contribution research lies meticulous curation these paired datasets. We have also integrated essential techniques, Early Stopping ReduceLROnPlateau, refine model hyperparameter optimization. involved adding extra layers, experimenting various loss functions rates, incorporating dropout layers regularization ensure convergence predictions. Furthermore, strategic enhancements, such as customized pooling significantly elevated accuracy models, resulting remarkable accuracy. Notably, pairing Nadam optimizer yields extraordinary reaching 99.34% gliomas, 93.52% meningiomas, 98.68% non-tumorous images, 97.70% pituitary tumors. These results underscore transformative potential custom-made approach, achieving an aggregate testing 97.68% classes. In a two-class dataset, Resnet Adam excels, demonstrating better precision, recall, F1 score, overall 99.84%. Moreover, it attains perfect per-class 99.62% ‘Tumor Positive’ 100% Negative’, underscoring advancement realm tumor categorization. underscores innovative possibilities DL models specialized optimization methods domain diagnosing cancer from

Язык: Английский

Процитировано

31
TSFIS-GWO: Metaheuristic-driven takagi-sugeno fuzzy system for adaptive real-time routing in WBANs DOI Creative Commons

Saeideh Memarian,

Navid Behmanesh-Fard, Pouya Aryai

и другие.

Applied Soft Computing, Год журнала: 2024, Номер 155, С. 111427 - 111427

Опубликована: Фев. 24, 2024

Wireless body area network (WBAN) is an internet-of-things technology that facilitates remote patient monitoring and enables medical staff to administer timely treatments. One of the main challenges in designing WBANs routing problem, which complicated due dynamic changes topology limited resources nodes. Several heuristic metaheuristic methods have been presented solve problem WBANs. Although metaheuristics outperform heuristics by producing higher-quality solutions, they cannot respond real-time requests. This paper introduces a reactive protocol for combines fuzzy with learning model. It utilizes Takagi-Sugeno Fuzzy Inference System conjunction Grey Wolf Optimizer (named TSFIS-GWO). The objective simultaneously benefit from advantages both approaches, namely, effectiveness offline hyperparameter tuning quickness routing. At every round, tuned system takes multiple parameters current state nodes links construct multi-hop tree under IEEE 802.15.6. To optimize performance each WBAN, rules TSFIS model are automatically adjusted through method based on GWO. done accordance specific requirements application, process place once before applied. Simulation results three applications demonstrate proposed TSFIS-GWO capable providing solutions while outperforming existing terms application-specific measures.

Язык: Английский

Процитировано

14
Ensemble Heuristic–Metaheuristic Feature Fusion Learning for Heart Disease Diagnosis Using Tabular Data DOI Creative Commons
Mohammad Shokouhifar, Mohamad Hasanvand, Elaheh Moharamkhani

и другие.

Algorithms, Год журнала: 2024, Номер 17(1), С. 34 - 34

Опубликована: Янв. 14, 2024

Heart disease is a global health concern of paramount importance, causing significant number fatalities and disabilities. Precise timely diagnosis heart pivotal in preventing adverse outcomes improving patient well-being, thereby creating growing demand for intelligent approaches to predict effectively. This paper introduces an ensemble heuristic–metaheuristic feature fusion learning (EHMFFL) algorithm using tabular data. Within the EHMFFL algorithm, diverse model crafted, featuring different subsets each heterogeneous base learner, including support vector machine, K-nearest neighbors, logistic regression, random forest, naive bayes, decision tree, XGBoost techniques. The primary objective identify most pertinent features leveraging combined approach that integrates heuristic knowledge Pearson correlation coefficient with metaheuristic-driven grey wolf optimizer. second aggregate various learners through learning. performance rigorously assessed Cleveland Statlog datasets, yielding remarkable results accuracy 91.8% 88.9%, respectively, surpassing state-of-the-art techniques diagnosis. These findings underscore potential enhancing diagnostic providing valuable clinicians making more informed decisions regarding care.

Язык: Английский

Процитировано

11
Combining State-of-the-Art Pre-Trained Deep Learning Models: A Noble Approach for Skin Cancer Detection Using Max Voting Ensemble DOI Creative Commons
Md Hossain, Md. Moazzem Hossain, Most. Binoee Arefin

и другие.

Diagnostics, Год журнала: 2023, Номер 14(1), С. 89 - 89

Опубликована: Дек. 30, 2023

Skin cancer poses a significant healthcare challenge, requiring precise and prompt diagnosis for effective treatment. While recent advances in deep learning have dramatically improved medical image analysis, including skin classification, ensemble methods offer pathway further enhancing diagnostic accuracy. This study introduces cutting-edge approach employing the Max Voting Ensemble Technique robust classification on ISIC 2018: Task 1-2 dataset. We incorporate range of cutting-edge, pre-trained neural networks, MobileNetV2, AlexNet, VGG16, ResNet50, DenseNet201, DenseNet121, InceptionV3, ResNet50V2, InceptionResNetV2, Xception. These models been extensively trained datasets, achieving individual accuracies ranging from 77.20% to 91.90%. Our method leverages synergistic capabilities these by combining their complementary features elevate performance further. In our approach, input images undergo preprocessing model compatibility. The integrates with architectures weights preserved. For each lesion under examination, every produces prediction. are subsequently aggregated using max voting technique yield final majority-voted class serving as conclusive Through comprehensive testing diverse dataset, outperformed models, attaining an accuracy 93.18% AUC score 0.9320, thus demonstrating superior reliability evaluated effectiveness proposed HAM10000 dataset ensure its generalizability. delivers robust, reliable, tool cancer. By utilizing power advanced we aim assist professionals timely accurate diagnoses, ultimately reducing mortality rates patient outcomes.

Язык: Английский

Процитировано

15
EfficientPolypSeg: Efficient Polyp Segmentation in colonoscopy images using EfficientNet-B5 with dilated blocks and attention mechanisms DOI

P. Lijin,

Mohib Ullah, Anuja Vats

и другие.

Biomedical Signal Processing and Control, Год журнала: 2024, Номер 93, С. 106210 - 106210

Опубликована: Март 16, 2024

Язык: Английский

Процитировано

6
Advancing Skin Cancer Prediction Using Ensemble Models DOI Creative Commons
Priya Natha, P. Rajarajeswari

Computers, Год журнала: 2024, Номер 13(7), С. 157 - 157

Опубликована: Июнь 21, 2024

There are many different kinds of skin cancer, and an early precise diagnosis is crucial because cancer both frequent deadly. The key to effective treatment accurately classifying the various cancers, which have unique traits. Dermoscopy other advanced imaging techniques enhanced detection by providing detailed images lesions. However, interpreting these distinguish between benign malignant tumors remains a difficult task. Improved predictive modeling necessary due occurrence erroneous inconsistent outcomes in present diagnostic processes. Machine learning (ML) models become essential field dermatology for automated identification categorization lesions using image data. aim this work develop improved predictions ensemble models, combine numerous machine approaches maximize their combined strengths reduce individual shortcomings. This paper proposes fresh special approach model optimization classification: Max Voting method. We trained assessed five ISIC 2018 HAM10000 datasets: AdaBoost, CatBoost, Random Forest, Gradient Boosting, Extra Trees. Their enhance overall performance with Moreover, were fed feature vectors that optimally generated from data genetic algorithm (GA). show that, accuracy 95.80%, significantly improves when compared individually. Obtaining best results F1-measure, recall, precision, method turned out be most dependable robust. novel aspect more robustly reliably classified technique. Several pre-trained models’ benefits approach.

Язык: Английский

Процитировано

4
Pediatric Brain Tissue Segmentation Using a Snapshot Hyperspectral Imaging (sHSI) Camera and Machine Learning Classifier DOI Creative Commons

Naomi Kifle,

Saige Teti,

Bo Ning

и другие.

Bioengineering, Год журнала: 2023, Номер 10(10), С. 1190 - 1190

Опубликована: Окт. 13, 2023

Pediatric brain tumors are the second most common type of cancer, accounting for one in four childhood cancer types. Brain tumor resection surgery remains treatment option cancer. While assessing margins intraoperatively, surgeons must send tissue samples biopsy, which can be time-consuming and not always accurate or helpful. Snapshot hyperspectral imaging (sHSI) cameras capture scenes beyond human visual spectrum provide real-time guidance where we aim to segment healthy tissues from lesions on pediatric patients undergoing resection. With institutional research board approval, Pro00011028, 139 red-green-blue (RGB), 279 visible, 85 infrared sHSI data were collected subjects with system integrated into an operating microscope. A random forest classifier was used analysis. The RGB, sHSI, visible models achieved average intersection unions (IoUs) 0.76, 0.59, 0.57, respectively, while segmentation a specificity 0.996, followed by HSI at 0.93 0.91, respectively. Despite small dataset considering cases, our leveraged technology successfully segmented high during procedures.

Язык: Английский

Процитировано

9
Impacts of digital technologies and social media platforms on advocating environmental sustainability in sports sector DOI Creative Commons

Vishal Mehra,

Salil Bharany, Prabhsimran Singh

и другие.

Discover Sustainability, Год журнала: 2025, Номер 6(1)

Опубликована: Фев. 20, 2025

Язык: Английский

Процитировано

0
Classification of Load Balancing Optimization Algorithms in Cloud Computing: A Survey Based on Methodology DOI
Elaheh Moharamkhani, Reyhaneh Babaei Garmaroodi, Mehdi Darbandi

и другие.

Wireless Personal Communications, Год журнала: 2024, Номер 136(4), С. 2069 - 2103

Опубликована: Июнь 1, 2024

Язык: Английский

Процитировано

2
A New Parallel Cuckoo Flower Search Algorithm for Training Multi-Layer Perceptron DOI Creative Commons
Rohit Salgotra, Nitin Mittal, Vikas Mittal

и другие.

Mathematics, Год журнала: 2023, Номер 11(14), С. 3080 - 3080

Опубликована: Июль 12, 2023

This paper introduces a parallel meta-heuristic algorithm called Cuckoo Flower Search (CFS). combines the Pollination Algorithm (FPA) and (CS) to train Multi-Layer Perceptron (MLP) models. The is evaluated on standard benchmark problems its competitiveness demonstrated against other state-of-the-art algorithms. Multiple datasets are utilized assess performance of CFS for MLP training. experimental results compared with various algorithms such as Genetic (GA), Grey Wolf Optimization (GWO), Particle Swarm (PSO), Evolutionary (ES), Ant Colony (ACO), Population-based Incremental Learning (PBIL). Statistical tests conducted validate superiority in finding global optimum solutions. indicate that achieves significantly better outcomes higher convergence rate when tested. highlights effectiveness solving optimization potential competitive field.

Язык: Английский

Процитировано

5