Comparative Analysis of Improved YOLO v5 Models for Corrosion Detection in Coastal Environments by Qifeng Yu, Yudong Han, Xinjia Gao, Wuguang Lin, Yi Han

“…YOLO v5, known for its speed, accuracy, and ease of deployment, has been employed for the rapid detection and identification of marine corrosion. However, corrosion images often feature complex characteristics and high variability in detection targets, presenting significant challenges for YOLO v5 in recognizing and extracting corrosion features. …”
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Improved RT-DETR for Infrared Ship Detection Based on Multi-Attention and Feature Fusion by Chun Liu, Yuanliang Zhang, Jingfu Shen, Feiyue Liu

“…However, the broad spectral range of the infrared band makes it susceptible to environmental interference, which can reduce the contrast between the target and the background. As a result, detecting infrared targets in complex marine environments remains challenging. …”
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An improved multi‐scale YOLOv8 for apple leaf dense lesion detection and recognition by Shixin Huo, Na Duan, Zhizheng Xu

“…Abstract Apple leaf lesions present a challenge for their detection and recognition because of their wide variety of species, morphologies, uneven sizes, and complex backgrounds. …”
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LEAD-YOLO: A Lightweight and Accurate Network for Small Object Detection in Autonomous Driving by Yunchuan Yang, Shubin Yang, Qiqing Chan

“…These components synergistically enhance small object perception and environmental context understanding without compromising network efficiency. Second, the neck features a hierarchical feature fusion module (HFFM) that establishes guided feature aggregation paths through hierarchical structuring, facilitating collaborative modeling between local structural information and global semantics for robust multi-scale object detection in complex traffic scenarios. …”
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Trade-off between toxicity and signal detection orchestrated by frequency- and density-dependent genes. by Laury Arthaud, Selim Ben Rokia-Mille, Hussein Raad, Aviv Dombrovsky, Nicolas Prevost, Maria Capovilla, Alain Robichon

“…In our current report, we show that alleles of for encoding different cGMP-dependent kinase isoforms influence the oxidation of aldehyde groups of aromatic molecules emitted by plants via Aldh-III and a phosphorylatable adaptor. The enhanced efficiency of oxidation of aldehyde odorants into carboxyl groups by the action of for lessens their action and toxicity, which should facilitate exploration and guidance in a complex odor environment. …”
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Use of Business Intelligence as a Strategic Information Technology in Banking: Farud Discovery & Detection by علی محقر, کارو لوکس, ، فرید حسینی, آصف علی منشی

“…It also explains how BI can be effective technology in industries like banking to overcome critical issue like fraud discovery and detection. The methodology, based on BI, in the form of model named BI model for fraud discovery and detection has been devised and suggested at the end of paper along with it its details for overcoming the above mentioned issue in more effective and efficient manner.…”
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Residual-enhanced graph convolutional networks with hypersphere mapping for anomaly detection in attributed networks by Wasim Khan, Afsaruddin Mohd, Mohammad Suaib, Mohammad Ishrat, Anwar Ahamed Shaikh, Syed Mohd Faisal

“…This study introduces an innovative approach that synergizes the strengths of graph convolutional networks with advanced deep residual learning and a unique residual-based attention mechanism, thereby creating a more nuanced and efficient method for anomaly detection in complex networks. …”
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Enhanced YOLOv5s Model for Improved Multi-Sized Object Detection in Road Scenes by Sangavi Sivanandham, Dharanibai Gunaseelan

“…Detecting objects in complex driving environments is crucial for autonomous vehicles to navigate safely. …”
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Cotton Leaf Disease Detection Using LLM-Synthetic Data and DEMM-YOLO Model by Lijun Gao, Tiantian Ran, Hua Zou, Huanhuan Wu

“…It plays a crucial role in enhancing cotton yield and quality while promoting the advancement of intelligent agriculture and efficient crop harvesting. This study proposes a novel method for detecting cotton leaf diseases based on large language model (LLM)-generated image synthesis and an improved DEMM-YOLO model, which is enhanced from the YOLOv11 model. …”
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Hybrid Optimization Method for Social Internet of Things Service Provision Based on Community Detection by Bahar Allakaram Tawfeeq, Amir Masoud Rahmani, Abbas Koochari, Nima Jafari Navimipour

“…The average response time of IGBSA‐CD presents that it is efficient in all three tasks is 0.04 s. It also has a consistently lower response time, even when the task is complex. …”
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LN-DETR: cross-scale feature fusion and re-weighting for lung nodule detection by Dibin Zhou, Honggang Xu, Wenhao Liu, Fuchang Liu

“…However, challenges remain in deploying these methods in complex real-world scenarios. This paper introduces an enhanced lung nodule detection algorithm base on RT-DETR, called LN-DETR. …”
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Research on Target Detection Algorithm for Solder Joint Defects Based on the Improved YOLOv8 by Chengkai Zhang, Xiaocui Feng, Pujun Mao, Fuyan Lv

“…Aiming at the problem of low accuracy in solder joint defect detection caused by the complex background and difficult to extract defect features of circuit boards using through-hole technology (THT), an improved YOLOv8 solder joint defect target detection algorithm was proposed. …”
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LSTM-JSO framework for privacy preserving adaptive intrusion detection in federated IoT networks by Shaymaa E. Sorour, Mohammed Aljaafari, Amany M. Shaker, Ahmed E. Amin

“…This research introduces a novel framework that enhances intrusion detection in IoT systems through a decentralized approach. …”
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Detect+Track: robust and flexible software tools for improved tracking and behavioural analysis of fish by Abhishek Dutta, Natalia Pérez-Campanero, Graham K. Taylor, Andrew Zisserman, Cait Newport

“…The modular workflow is adaptable to new experimental designs, supports manual correction and retraining for new object classes and allows efficient large-scale batch processing. By addressing key limitations of existing tracking tools, Detect+Track provides a flexible and generalizable solution for researchers studying movement and decision-making in complex environments. …”
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Characterising Payload Entropy in Packet Flows—Baseline Entropy Analysis for Network Anomaly Detection by Anthony Kenyon, Lipika Deka, David Elizondo

“…A key technique in early detection is the classification of unusual patterns of network behaviour, often hidden as low-frequency events within complex time-series packet flows. …”
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YOLO-Act: Unified Spatiotemporal Detection of Human Actions Across Multi-Frame Sequences by Nada Alzahrani, Ouiem Bchir, Mohamed Maher Ben Ismail

“…This paper introduces YOLO-Act, a novel spatiotemporal action detection model that enhances the object detection capabilities of YOLOv8 to efficiently manage complex action dynamics within video sequences. …”
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CoroYOLO: a novel colorectal cancer detection method based on the Mamba framework by Wenfei Chen, Fengrui Hou, Yika Shen

“…However, CNNs have limitations in capturing global contextual information, and while transformers can handle long-range dependencies, their high computational complexity limits their efficiency in practical applications. …”
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Research on the detection of obstacles in front of unmanned vehicles in opencast mines based on binocular vision by Shunling RUAN, Huiguo ZHANG, Qinghua GU, Caiwu LU, Di LIU, Jing MAO

“…Driverless cars in the complex environment of the open pit mining area encountered falling rocks, puddles, pedestrians and other obstacles have great safety hazards, easy to cause vehicle rollover, stuck in the car, resulting in the risk of loss of property or pose a threat to the safety of personnel, therefore, the complex and changing terrain environment on the road of the open pit mine as an important problem solving of the open pit mine unmanned vehicles in the mining intelligence, the need to measure the depth of obstacles in front of the car of the while guaranteeing the accuracy and speed of obstacle detection. …”
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Unsupervised Hybrid VAE-Based Anomaly Detection for Vehicle Onboard LiDAR Sensors by Nourhen Sboui, Hakim Ghazzai, Mohamed Hadded, Mourad Elhadef, Gianluca Setti

“…The model strikes a good balance between complexity and efficiency, achieving a 10% accuracy improvement compared to existing models, with an accuracy of 95.1% and an F1-score of 82.6%. …”
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YOLO-WAS: A Lightweight Apple Target Detection Method Based on Improved YOLO11 by Xinwu Du, Xiaoxuan Zhang, Tingting Li, Xiangyu Chen, Xiufang Yu, Heng Wang

“…To overcome the limitations of existing apple target detection methods, including low recognition accuracy of multi-species apples in complex orchard environments and a complex network architecture that occupies large memory, a lightweight apple recognition model based on the improved YOLO11 model was proposed, named YOLO-WAS model. …”
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