HXAI-ML: A Hybrid Explainable Artificial Intelligence Based Machine Learning Model For Cardiovascular Heart Disease Detection

Results in Engineering, Год журнала: 2025, Номер unknown, С. 104370 - 104370

Опубликована: Фев. 1, 2025

Язык: Английский

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ITD-YOLOv8: An Infrared Target Detection Model Based on YOLOv8 for Unmanned Aerial Vehicles

Drones, Год журнала: 2024, Номер 8(4), С. 161 - 161

Опубликована: Апрель 19, 2024

A UAV infrared target detection model ITD-YOLOv8 based on YOLOv8 is proposed to address the issues of missed and false detections caused by complex ground background uneven scale in aerial image detection, as well high computational complexity. Firstly, an improved backbone feature extraction network designed lightweight GhostHGNetV2. It can effectively capture information at different scales, improving accuracy environments while remaining lightweight. Secondly, VoVGSCSP improves perceptual abilities referencing global contextual multiscale features enhance neck structure. At same time, a convolutional operation called AXConv introduced replace regular module. Replacing traditional fixed-size convolution kernels with sizes reduces complexity model. Then, further optimize reduce during object CoordAtt attention mechanism weight channel dimensions map, allowing pay more important information, thereby robustness detection. Finally, implementation XIoU loss function for boundary boxes enhances precision localization. The experimental findings demonstrate that ITD-YOLOv8, comparison YOLOv8n, rate detecting multi-scale small targets backgrounds. Additionally, it achieves 41.9% reduction parameters 25.9% decrease floating-point operations. Moreover, mean (mAP) attains impressive 93.5%, confirming model’s applicability unmanned vehicles (UAVs).

Язык: Английский

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G-YOLO: A Lightweight Infrared Aerial Remote Sensing Target Detection Model for UAVs Based on YOLOv8

Drones, Год журнала: 2024, Номер 8(9), С. 495 - 495

Опубликована: Сен. 18, 2024

A lightweight infrared target detection model, G-YOLO, based on an unmanned aerial vehicle (UAV) is proposed to address the issues of low accuracy in UAV images complex ground scenarios and large network models that are difficult apply mobile or embedded platforms. Firstly, YOLOv8 backbone feature extraction improved designed network, GhostBottleneckV2, remaining part adopts depth-separable convolution, DWConv, replace standard which effectively retains effect model while greatly reducing number parameters calculations. Secondly, neck structure by ODConv module, adaptive convolutional adaptively adjust kernel size step size, allows for more effective targets at different scales. At same time, further optimized using attention mechanism, SEAttention, improve model’s ability learn global information input maps, then applied each channel map enhance useful a specific performance. Finally, introduction SlideLoss loss function enables calculate differences between predicted actual truth bounding boxes during training process, these efficiency object detection. The experimental results show compared with YOLOv8n, G-YOLO reduces missed false rates small backgrounds. reduced 74.2%, computational floats 54.3%, FPS 71, improves average (mAP) reaches 91.4%, verifies validity UAV-based Furthermore, 556, it will be suitable wider task such as targets, long-distance other scenes.

Язык: Английский

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HFSA: hybrid feature selection approach to improve medical diagnostic system

PeerJ Computer Science, Год журнала: 2025, Номер 11, С. e2764 - e2764

Опубликована: Май 6, 2025

Thanks to the presence of artificial intelligence methods, diagnosis patients can be done quickly and accurately. This article introduces a new diagnostic system (DS) that includes three main layers called rejection layer (RL), selection (SL), (DL) accurately diagnose cases suffering from various diseases. In RL, outliers removed using genetic algorithm (GA). At same time, best features selected by feature method hybrid approach (HFSA) in SL. next step, filtered data is passed naive Bayes (NB) classifier DL give accurate diagnoses. this work, contribution represented introducing HFSA as composed two stages; fast stage (FS) (AS). FS, chi-square, filtering methodology, applied select while Hybrid Optimization Algorithm (HOA), wrapper AS features. It concluded better than other methods based on experimental results because enable different classifiers NB, K-nearest neighbors (KNN), neural network (ANN) provide maximum accuracy, precision, recall values minimum error value. Additionally, proved DS, including GA an outlier method, selection, NB mode, outperformed models.

Язык: Английский

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EODA: A three-stage efficient outlier detection approach using Boruta-RF feature selection and enhanced KNN-based clustering algorithm

PLoS ONE, Год журнала: 2025, Номер 20(5), С. e0322738 - e0322738

Опубликована: Май 30, 2025

Outlier detection is essential for identifying unusual patterns or observations that significantly deviate from the normal behavior of a dataset. With rapid growth data science, prevalence anomalies and outliers has increased, which can disrupt system modeling parameter estimation, leading to inaccurate results. Recently, deep learning-based outlier methods have gained significant attention, but their performance often limited by challenges in selection nearest neighbor search. To overcome these limitations, we propose three-stage Efficient Detection Approach (named EODA), not only detects with high accuracy also emphasizes dataset characteristics. In first stage, apply feature algorithm based on Boruta method Random Forest reduce size selecting most relevant attributes calculating highest Z-score shadow features. second improve K-nearest neighbors enhance identification clustering phase. Finally, third stage efficiently identifies within clustered datasets. We evaluate proposed EODA across eight UCI machine-learning repository The results demonstrate effectiveness our approach, achieving Precision 63.07%, Recall 82.49%, an F1-Score 64.53%, outperforming existing techniques field.

Язык: Английский

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Classification of Load Balancing Optimization Algorithms in Cloud Computing: A Survey Based on Methodology

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

Опубликована: Июнь 1, 2024

Язык: Английский

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A Lightweight Real-Time Infrared Object Detection Model Based on YOLOv8 for Unmanned Aerial Vehicles

Drones, Год журнала: 2024, Номер 8(9), С. 479 - 479

Опубликована: Сен. 12, 2024

Deploying target detection models on edge devices such as UAVs is challenging due to their limited size and computational capacity, while typically require significant resources. To address this issue, study proposes a lightweight real-time infrared object model named LRI-YOLO (Lightweight Real-time Infrared YOLO), which based YOLOv8n. The improves the C2f module’s Bottleneck structure by integrating Partial Convolution (PConv) with Pointwise (PWConv), achieving more design. Furthermore, during feature fusion stage, original downsampling ordinary convolution replaced combination of max pooling regular convolution. This modification retains map information. model’s further optimized redesigning decoupled head Group (GConv) instead convolution, significantly enhancing speed. Additionally, BCELoss EMASlideLoss, newly developed classification loss function introduced in study. allows focus hard samples, thereby improving its capability. Compared YOLOv8n algorithm, lightweight, parameters reduced 46.7% floating-point operations (FLOPs) 53.1%. Moreover, mean average precision (mAP) reached 94.1%. Notably, moderate power that only have Central Processing Unit (CPU), speed 42 frames per second (FPS), surpassing most mainstream models. indicates offers novel solution for drones.

Язык: Английский

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Performance evaluation of optimal ensemble learning approaches with PCA and LDA-based feature extraction for heart disease prediction

Biomedical Signal Processing and Control, Год журнала: 2024, Номер 101, С. 107138 - 107138

Опубликована: Ноя. 16, 2024

Язык: Английский

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A Communication-Efficient Federated Learning Framework for Sustainable Development Using Lemurs Optimizer

Algorithms, Год журнала: 2024, Номер 17(4), С. 160 - 160

Опубликована: Апрель 15, 2024

The pressing need for sustainable development solutions necessitates innovative data-driven tools. Machine learning (ML) offers significant potential, but faces challenges in centralized approaches, particularly concerning data privacy and resource constraints geographically dispersed settings. Federated (FL) emerges as a transformative paradigm by decentralizing ML training to edge devices. However, communication bottlenecks hinder its scalability sustainability. This paper introduces an FL framework that enhances efficiency. proposed addresses the bottleneck harnessing power of Lemurs optimizer (LO), nature-inspired metaheuristic algorithm. Inspired cooperative foraging behavior lemurs, LO strategically selects most relevant model updates communication, significantly reducing overhead. was rigorously evaluated on CIFAR-10, MNIST, rice leaf disease, waste recycling plant datasets representing various areas development. Experimental results demonstrate reduces overhead over 15% average compared baseline while maintaining high accuracy. breakthrough extends applicability resource-constrained environments, paving way more scalable real-world initiatives.

Язык: Английский

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Heart Disease Prediction using Optimized Feature Set and Classifiers

Опубликована: Июнь 28, 2024

Язык: Английский

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