Cited by An efficient intrusion detection system for IoT security using CNN decision forest

An efficient intrusion detection system for IoT security using CNN decision forest DOI

и другие.

PeerJ Computer Science, Год журнала: 2024, Номер 10, С. e2290 - e2290

Опубликована: Сен. 9, 2024

The adoption and integration of the Internet Things (IoT) have become essential for advancement many industries, unlocking purposeful connections between objects. However, surge in IoT has also made it a prime target malicious attacks. Consequently, ensuring security systems ecosystems emerged as crucial research area. Notably, advancements addressing these threats include implementation intrusion detection (IDS), garnering considerable attention within community. In this study, aim to enhance network anomaly detection, we present novel approach: Deep Neural Decision Forest-based IDS (DNDF-IDS). DNDF-IDS incorporates an improved decision forest model coupled with neural networks achieve heightened accuracy (ACC). Employing four distinct feature selection methods separately, namely principal component analysis (PCA), LASSO regression (LR), SelectKBest, Random Forest Feature Importance (RFFI), our objective is streamline training prediction processes, overall performance, identify most correlated features. Evaluation on three diverse datasets (NSL-KDD, CICIDS2017, UNSW-NB15) reveals impressive ACC values ranging from 94.09% 98.84%, depending dataset method. achieves remarkable time 0.1 ms per record. Comparative analyses other recent random Convolutional Networks (CNN) based models indicate that performs similarly or even outperforms them certain instances, particularly when utilizing top 10 One key advantage lies its ability make accurate predictions only few features, showcasing efficient utilization computational resources.

Язык: Английский

Cloud computing load prediction by decomposition reinforced attention long short-term memory network optimized by modified particle swarm optimization algorithm DOI

Nebojša Bačanin, Vladimir Šimić, Miodrag Živković

и другие.

Annals of Operations Research, Год журнала: 2023, Номер unknown

Опубликована: Дек. 15, 2023

Язык: Английский

Процитировано

An improved U-net and attention mechanism-based model for sugar beet and weed segmentation DOI

Yadong Li, Ruinan Guo,

Rujia Li

и другие.

Frontiers in Plant Science, Год журнала: 2025, Номер 15

Опубликована: Янв. 13, 2025

Introduction Weeds are a major factor affecting crop yield and quality. Accurate identification localization of crops weeds essential for achieving automated weed management in precision agriculture, especially given the challenges recognition accuracy real-time processing complex field environments. To address this issue, paper proposes an efficient crop-weed segmentation model based on improved UNet architecture attention mechanisms to enhance both speed. Methods The adopts encoder-decoder structure UNet, utilizing MaxViT (Multi-Axis Vision Transformer) as encoder capture global local features within images. Additionally, CBAM (Convolutional Block Attention Module) is incorporated into decoder multi-scale feature fusion module, adaptively adjusting map weights enable focus more accurately edges textures weeds. Results discussion Experimental results show that proposed achieved 84.28% mIoU 88.59% mPA sugar beet dataset, representing improvements 3.08% 3.15% over baseline model, respectively, outperforming mainstream models such FCN, PSPNet, SegFormer, DeepLabv3+, HRNet. Moreover, model’s inference time only 0.0559 seconds, reducing computational overhead while maintaining high accuracy. Its performance sunflower dataset further verifies generalizability robustness. This study, therefore, provides accurate solution segmentation, laying foundation future research identification.

Язык: Английский

Процитировано

Hybrid voting-GA ensemble learning for multi-class fault detection in digital twin-driven IIoT systems DOI

Ezz El‐Din Hemdan,

Samar M. Zayed,

Gamal Attiya

и другие.

Computing, Год журнала: 2025, Номер 107(2)

Опубликована: Янв. 18, 2025

Язык: Английский

Процитировано

Computer-Vision Unmanned Aerial Vehicle Detection System Using YOLOv8 Architectures DOI

Aleksandar Petrović, Nebojša Bačanin, Luka Jovanovic

и другие.

International Journal of Robotics and Automation Technology, Год журнала: 2024, Номер 11, С. 1 - 12

Опубликована: Май 22, 2024

Abstract: This work aims to test the performance of you only look once version 8 (YOLOv8) model for problem drone detection. Drones are very slightly regulated and standards need be established. With a robust system detecting drones possibilities regulating their usage becoming realistic. Five different sizes were tested determine best architecture size this problem. The results indicate high across all models that each is used specific case. Smaller suited lightweight approaches where some false identification tolerable, while largest with stationary systems require precision.

Язык: Английский

Процитировано

Advancing skeleton-based human behavior recognition: multi-stream fusion spatiotemporal graph convolutional networks DOI

Fenglin Liu, Chenyu Wang, Zhiqiang Tian

и другие.

Complex & Intelligent Systems, Год журнала: 2024, Номер 11(1)

Опубликована: Дек. 28, 2024

Язык: Английский

Процитировано

Interpretable artificial intelligence for advancing slope stability assessment techniques with Technosols DOI

Jiazhou Li, Mengjie Huang, M Ma

и другие.

Soil Use and Management, Год журнала: 2025, Номер 41(1)

Опубликована: Янв. 1, 2025

Abstract Slope stability is a critical factor in ensuring the safety and longevity of infrastructure, especially areas prone to landslides soil erosion. Traditional methods slope assessment, while widely used, often struggle provide accurate results when applied Technosols—soils modified by human activities composed waste materials. This study proposes novel approach that combines artificial intelligence techniques improve precision predictions these complex types. The method utilizes model based on neural networks, trained large dataset factors. Unlike conventional techniques, proposed integrates multiple environmental material properties more assessment compared other models. model's performance demonstrated R 2 values .999975 for test datasets, which significantly better than similar work statistical analysis. Moreover, incorporating Shapley Additive Explanations (SHAP), we clear understanding impact various parameters stability. findings suggest machine learning‐based offers reliable tool evaluation Technosols, making it valuable addition field.

Язык: Английский

Процитировано

Optimizing XGBoost hyperparameters using the dragonfly algorithm for enhanced cyber attack detection in the internet of healthcare things (IoHT) DOI

SURBHI SURBHI,

Nupa Ram Chauhan,

Neeraj Dahiya

и другие.

Cluster Computing, Год журнала: 2025, Номер 28(4)

Опубликована: Фев. 25, 2025

Язык: Английский

Процитировано

Heterogeneous graph neural networks with post-hoc explanations for multi-modal and explainable land use inference DOI

Xuehao Zhai, Junqi Jiang, Adam Dejl

и другие.

Information Fusion, Год журнала: 2025, Номер unknown, С. 103057 - 103057

Опубликована: Март 1, 2025

Язык: Английский

Процитировано

Waste classification using convolutional neural networks tuned by modified metaheuristics algorithm DOI

Ana Tasic,

Luka Jovanovic, M. Popović

и другие.

Elsevier eBooks, Год журнала: 2025, Номер unknown, С. 237 - 261

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

Brain Tumor Segmentation using Optimized Depth Wise Separable Convolutional Neural Network with Dense U-Net DOI

K.G. Revathi,

M. Shirley,

S. Sreethar

и другие.

Knowledge-Based Systems, Год журнала: 2025, Номер unknown, С. 113678 - 113678

Опубликована: Май 1, 2025

Язык: Английский

Процитировано