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361
Morphological and functional characteristics of Trichinella sp. larvae in bears and badgers in the Kirov Region
Published 2022-03-01“…For postmortem diagnosis of trichinellosis in the obtained bears and badgers, we can use trichinelloscopy and peptolysis methods which are aimed at detecting infection sources and preventing zoonosis in humans.…”
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362
Advancing precision oncology with AI-powered genomic analysis
Published 2025-04-01Get full text
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363
YOLO-SRMX: A Lightweight Model for Real-Time Object Detection on Unmanned Aerial Vehicles
Published 2025-07-01“…Unmanned Aerial Vehicles (UAVs) face a significant challenge in balancing high accuracy and high efficiency when performing real-time object detection tasks, especially amidst intricate backgrounds, diverse target scales, and stringent onboard computational resource constraints. …”
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364
Surface Defect Detection for Small Samples of Particleboard Based on Improved Proximal Policy Optimization
Published 2025-04-01“…The method integrates the variable action space and the composite reward function and achieves the balanced optimization of different types of defect detection performance by adjusting the scaling and translation amplitude of the detection region. …”
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365
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366
MSSA: multi-stage semantic-aware neural network for binary code similarity detection
Published 2025-01-01“…Binary code similarity detection (BCSD) aims to identify whether a pair of binary code snippets is similar, which is widely used for tasks such as malware analysis, patch analysis, and clone detection. …”
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367
FsDAOD: Few-shot domain adaptation object detection for heterogeneous SAR image
Published 2025-06-01“…Heterogeneous Synthetic Aperture Radar (SAR) image object detection task with inconsistent joint probability distributions is occurring more and more frequently in practical applications. …”
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368
Network security traffic detection and legal supervision based on adaptive metric learning algorithm
Published 2025-09-01Get full text
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369
App-DDoS detection method using partial binary tree based SVM algorithm
Published 2018-03-01“…As it ignored the detection of ramp-up and pulsing type of application layer DDoS (App-DDoS) attacks in existing flow-based App-DDoS detection methods,an effective detection method for multi-type App-DDoS was proposed.Firstly,in order to fast count the number of HTTP GET for users and further support the calculation of feature parameters applied in detection method,the indexes of source IP address in multiple time windows were constructed by the approach of Hash function.Then the feature parameters by combining SVM classifiers with the structure of partial binary tree were trained hierarchically,and the App-DDoS detection method was proposed with the idea of traversing binary tree and feedback learning to distinguish non-burst normal flow,burst normal flow and multi-type App-DDoS flows.The experimental results show that compared with the conventional SVM-based and naïve-Bayes-based detection methods,the proposed method has more excellent detection performance and can distinguish specific App-DDoS types through subdividing attack types and training detection model layer by layer.…”
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370
Cellulose-Based Colorimetric Test Strips for SARS-CoV-2 Antibody Detection
Published 2025-06-01Get full text
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371
Enhancing Drone Detection via Transformer Neural Network and Positive–Negative Momentum Optimizers
Published 2025-06-01“…The developed algorithms for training NN architectures improved the accuracy of drone detection by achieving the global extremum of the loss function in fewer epochs using positive–negative pulse-based optimization algorithms. …”
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372
A lightweight UAV target detection algorithm based on improved YOLOv8s model
Published 2025-05-01“…Furthermore, the original loss function is replaced with SIoU to enhance detection accuracy. …”
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373
A Lightweight Network for UAV Multi-Scale Feature Fusion-Based Object Detection
Published 2025-03-01“…To tackle the issues of small target sizes, missed detections, and false alarms in aerial drone imagery, alongside the constraints posed by limited hardware resources during model deployment, a streamlined object detection approach is proposed to enhance the performance of YOLOv8s. …”
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374
Research on underwater disease target detection method of inland waterway based on deep learning
Published 2025-04-01“…Abstract Aiming at the problems of low detection accuracy and poor generalization ability of underwater disease targets in inland waterways, an underwater disease target detection algorithm for inland waterways based on improved YOLOv5 is designed, which is denoted as YOLOv5-GBCE. …”
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375
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376
Self-Powered Microsystem for Ultra-Fast Crash Detection via Prestressed Triboelectric Sensing
Published 2025-01-01“…We further developed a self-powered, compact (<4.5 cm3) microsystem that integrates the shock sensor, a signal processing module, airbag triggering circuitry, and a high-g-resistant supercapacitor as a backup power source. The microsystem achieves ultra-fast shock detection and airbag activation with a delay of less than 0.2 ms. …”
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377
STAR-YOLO: A High-Accuracy and Ultra-Lightweight Method for Brain Tumor Detection
Published 2025-01-01“…STAR-YOLO accomplishes a lightweight design while guaranteeing high detection accuracy, prominently demonstrating its immense potential in the diagnosis of clinical brain tumors, particularly in circumstances with constrained computing resources.…”
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378
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379
YOLOv9-GDV: A Power Pylon Detection Model for Remote Sensing Images
Published 2025-06-01“…Finally, the Variable Minimum Point Distance Intersection over Union (VMPDIoU) loss is proposed to optimize the model’s loss function. This method employs variable input parameters to directly calculate key point distances between predicted and ground-truth boxes, more accurately reflecting positional differences between detection results and reference targets, thus effectively improving the model’s mean Average Precision (mAP). …”
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380
GSF-YOLOv8: A Novel Approach for Fire Detection Using Gather-Distribute Mechanism and SimAM Attention
Published 2025-01-01“…To address the current challenges in fire detection algorithms, including insufficient feature extraction, high computational complexity, limited deployment on resource-constrained devices, missed detections, false detections, and low accuracy, we developed a high-precision algorithm named GSF-YOLOv8. …”
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