Real-Time Runway Detection Using Dual-Modal Fusion of Visible and Infrared Data by Lichun Yang, Jianghao Wu, Hongguang Li, Chunlei Liu, Shize Wei

“…This study proposes a salient object detection (SOD) method that integrates visible and infrared sensors for robust airport runway detection in complex environments. …”
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Detection of Apple Leaf Gray Spot Disease Based on Improved YOLOv8 Network by Siyi Zhou, Wenjie Yin, Yinghao He, Xu Kan, Xin Li

“…The details are as follows: (1) we introduce Dynamic Residual Blocks (DRBs) to boost the model’s ability to extract lesion features, thereby improving detection accuracy; (2) add a Self-Balancing Attention Mechanism (SBAY) to optimize the feature fusion and improve the ability to deal with complex backgrounds; and (3) incorporate an ultra-small detection head and simplify the computational model to reduce the complexity of the YOLOv8 network while maintaining the high precision of detection. …”
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Adaptive Symmetry Self-Matching for 3D Point Cloud Completion of Occluded Tomato Fruits in Complex Canopy Environments by Wenqin Wang, Chengda Lin, Haiyu Shui, Ke Zhang, Ruifang Zhai

“…It dynamically adjusts symmetry planes by detecting defect region characteristics in real time, implements point cloud completion under multi-symmetry constraints and constructs a triple-orthogonal symmetry plane system to adapt to multi-directional heterogeneous structures under complex occlusion. …”
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MSDNet: A Multi-Scale Feature Representation Network Model for Tunnel Bolt Detection by Xiufeng Wu, Xueli Li, Guangxing Cao, Chao Guo, Wei Chang, Zhao Wang, Shuliang Liu

“…To address these issues, we introduces a novel multi-scale feature extraction detection network (MSDNet) designed to improve tunnel bolt maintenance by reducing false positives and missed detections. …”
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InvMOE: MOEs Based Invariant Representation Learning for Fault Detection in Converter Stations by Hao Sun, Shaosen Li, Hao Li, Jianxiang Huang, Zhuqiao Qiao, Jialei Wang, Xincui Tian

“…Converter stations are pivotal in high-voltage direct current (HVDC) systems, enabling power conversion between an alternating current (AC) and a direct current (DC) while ensuring efficient and stable energy transmission. Fault detection in converter stations is crucial for maintaining their reliability and operational safety. …”
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The Scalable Detection and Resolution of Data Clumps Using a Modular Pipeline with ChatGPT by Nils Baumgartner, Padma Iyenghar, Timo Schoemaker, Elke Pulvermüller

“…This paper explores a modular pipeline architecture that integrates ChatGPT, a Large Language Model (LLM), to automate the detection and refactoring of data clumps—a prevalent type of code smell that complicates software maintainability. …”
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YOLOv11-GSF: an optimized deep learning model for strawberry ripeness detection in agriculture by Haoran Ma, Qian Zhao, Runqing Zhang, Chunxu Hao, Wenhui Dong, Xiaoying Zhang, Fuzhong Li, Xiaoqin Xue, Gongqing Sun

“…The challenge of efficiently detecting ripe and unripe strawberries in complex environments like greenhouses, marked by dense clusters of strawberries, frequent occlusions, overlaps, and fluctuating lighting conditions, presents significant hurdles for existing detection methodologies. …”
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Enhancing image quality in circular-view photoacoustic tomography using randomized detection points by Soheil Hakakzadeh, Praveenbalaji Rajendran, Zahra Kavehvash, Manojit Pramanik

“…Circular-view (circular scan) photoacoustic computed tomography (PACT) with low-density detection points (DPs) is an efficient, high-speed, and inexpensive modality with numerous (pre-) clinical applications. …”
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Radio Frequency Interference Detection Using Swin Transformer Embedding U2-Net by Shengdi Chen, Bo Liang

“…The main encoder enhances the feature representation through the efficient multiscale attention (EMA) mechanism, reorganizes the channel information, captures pixel-level relationships, and improves the detection accuracy in complex backgrounds. …”
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YOLOv8-LSW: A Lightweight Bitter Melon Leaf Disease Detection Model by Shuang Liu, Haobin Xu, Ying Deng, Yixin Cai, Yongjie Wu, Xiaohao Zhong, Jingyuan Zheng, Zhiqiang Lin, Miaohong Ruan, Jianqing Chen, Fengxiang Zhang, Huiying Li, Fenglin Zhong

“…Bitter melon, an important medicinal and edible economic crop, is often threatened by diseases such as downy mildew, powdery mildew, viral diseases, anthracnose, and blight during its growth. Efficient and accurate disease detection is of significant importance for achieving sustainable disease management in bitter melon cultivation. …”
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Comparative Analysis of Input Image Characteristics in Convolutional Neural Network-based Signature Detection by M. Adamec, M. Turcanik

“…The utilization of the NxN scalable format for machine code instruction representation results in enhanced accuracy, accelerated training, and a considerable reduction in pixel usage, indicating a promising avenue for optimizing the efficiency of malware detection.…”
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MMYFnet: Multi-Modality YOLO Fusion Network for Object Detection in Remote Sensing Images by Huinan Guo, Congying Sun, Jing Zhang, Wuxia Zhang, Nengshuang Zhang

“…To overcome the limitations of existing fusion methods in terms of complexity and insufficient information utilization, we innovatively propose a Cosine Similarity-based Image Feature Fusion (CSIFF) module and integrate it into a dual-branch YOLOv8 network, constructing a lightweight and efficient target detection network called Multi-Modality YOLO Fusion Network (MMYFNet). …”
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E-DoH: elegantly detecting the depths of open DoH service on the internet by Cong Dong, Jiahai Yang, Yun Li, Haoran Jiao, Chenglong Li, Xia Yin, Yuling Liu

“…Through experiments, our approach demonstrates a remarkable 80% improvement in time efficiency and only requires 4–20% traffic volume to complete the detection task. …”
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DSF-YOLO for weld defect detection in X-ray images with dynamic staged fusion by Meng Zhang, Yanzhu Hu, Binbin Xu, Lisha Luo, Song Wang

“…These results establish DSF-YOLO as an efficient and accurate solution addressing critical challenges in industrial weld defect detection with significant practical value.…”
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Research on insider threat detection based on personalized federated learning and behavior log analysis by Xiaoyun Ye, Faqin Luo, Huangrongbin Cui, Jinlong Wang, Xiaoyun Xiong, Wencui Zhang, Jiawei Yu, Wenhao Zhao

“…While machine learning methods offer certain advantages, they still face challenges in complex data scenarios. This study focuses on the application of Federated Learning in insider threat detection. …”
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Intelligent back-to-back testing with denoising autoencoder-based fault detection and DBSCAN clustering by Mohammad Abboush, Christoph Knieke, Andreas Rausch

“…The approach is capable of automatically detecting and clustering the faults in an efficient manner considering the noisy conditions. …”
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Deep Learning–Based Enhanced Optimization for Automated Rice Plant Disease Detection and Classification by P. Preethi, R. Swathika, S. Kaliraj, R. Premkumar, J. Yogapriya

“…ABSTRACT Ensuring global food security necessitates innovative solutions for early detection and precise classification of diseases in staple crops like rice. …”
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Sentiment analysis for depression detection: A stacking ensemble-based deep learning approach by Kinza Noor, Mariam Rehman, Maria Anjum, Afzaal Hussain, Rabia Saleem

“…Due to the widespread prevalence of this disorder in society, novel and efficient methods must be developed for effective detection and treatment. …”
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Anomaly detection method for cyber physical power system based on bilateral data fusion by Tianlei Zang, Shijun Wang, Chuangzhi Li, Yunfei Liu, Yujian Xiao, Zian Wang, Xueying Yu

“…It is a challenge to fully quantify the complex characteristics of CPPS. A cyber-physical bilateral data-driven composite model is proposed in this paper to achieve efficient and accurate anomaly detection of CPPS. …”
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ASAD: A Meta Learning-Based Auto-Selective Approach and Tool for Anomaly Detection by Nadia Rashid, Rashid Mehmood, Fahad Alqurashi, Saad Alqahtany, Juan M. Corchado

“…By automating the selection process, the method aims to reduce the reliance on trial-and-error methods, streamline the anomaly detection workflow, and lead to more robust, adaptable, and efficient anomaly detection systems. …”
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