YOLOv10n-Based Defect Detection in Power Insulators: Attention Enhancement and Feature Fusion Optimization by Zhihao Wei, Yan Wei

“…In modern power systems, insulators, as key components of transmission lines, are crucial for defect detection for the safe operation of power grids. Aiming at the problems of low efficiency of traditional manual detection, the vulnerability of traditional image processing methods to environmental interference, and the insufficient ability of existing deep learning models to detect small target defects under complex backgrounds, this paper proposes an improved target detection model based on YOLOv10n, which is the first time to integrate the spatial channel attention mechanism (SEAttention) with the up-sampling expansion operation (Patch Expanding) in the Neck part of the model. …”
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A Review of Machine Learning and Transfer Learning Strategies for Intrusion Detection Systems in 5G and Beyond by Kinzah Noor, Agbotiname Lucky Imoize, Chun-Ta Li, Chi-Yao Weng

“…Naive Bayes (NB) stands out for its computational efficiency despite moderate performance in other areas. …”
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A Full-Scale Shadow Detection Network Based on Multiple Attention Mechanisms for Remote-Sensing Images by Lei Zhang, Qing Zhang, Yu Wu, Yanfeng Zhang, Shan Xiang, Donghai Xie, Zeyu Wang

“…Therefore, this model offers an efficient solution for shadow detection in aerial imagery.…”
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VMMCD: VMamba-Based Multi-Scale Feature Guiding Fusion Network for Remote Sensing Change Detection by Zhong Chen, Hanruo Chen, Junsong Leng, Xiaolei Zhang, Qi Gao, Weiyu Dong

“…Moreover, the former also leads to a reduction in speed. To guarantee the efficiency of change detection, encompassing both speed and accuracy, a VMamba-based Multi-scale Feature Guiding Fusion Network (VMMCD) is proposed. …”
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Sustainable phytoprotection: a smart monitoring and recommendation framework using Puma Optimization for potato pathogen detection by Amal H. Alharbi, Faris H. Rizk, Khaled Sh. Gaber, Marwa M. Eid, Marwa M. Eid, El-Sayed M. El-kenawy, El-Sayed M. El-kenawy, Pushan Kumar Dutta, Doaa Sami Khafaga

“…In contrast, convergence analysis demonstrated PO-RBM’s computational efficiency relative to PSO, GWO, and GA alternatives. …”
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Enhanced Performance of Asymmetrically Clipped DC-Biased Optical OFDM Systems Using Adjacent Symbol Detection by Wei-Wen Hu

“…Compared to the direct current (DC) biased optical OFDM (DCO-OFDM) and asymmetrically clipped optical orthogonal frequency division multiplexing (ACO-OFDM) schemes, the asymmetrically clipped DC biased optical OFDM (ADO-OFDM) has demonstrated a better balance between optical power efficiency and spectrum efficiency. However, both the traditional ADO-OFDM and ADO-OFDM with iteration receiver suffer from an error floor issue when a lower DC bias is allocated to the DCO-OFDM branch. …”
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A Real-Time Cotton Boll Disease Detection Model Based on Enhanced YOLOv11n by Lei Yang, Wenhao Cui, Jingqian Li, Guotao Han, Qi Zhou, Yubin Lan, Jing Zhao, Yongliang Qiao

“…To mitigate false negatives and false positives encountered by the original YOLOv11n model during detection, the EMA (efficient multi-scale attention) mechanism is introduced to enhance the weights of important features and suppress irrelevant regions, thereby improving the detection accuracy of the model. …”
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Detection and functional assessment of structural variants using whole-genome re-sequencing data in Nellore cattle by Natalia A. Marín-Garzón, Lucio F. M. Mota, Giovana Vargas, Leonardo M. Arikawa, Larissa F. S. Fonseca, Gerardo A. Fernandes Júnior, Roberto Carvalheiro, Lucia G. Albuquerque

“…Abstract Ongoing advances in genome sequencing technologies have enabled the identification of numerous structural variants (SVs) in livestock genomes, which are the main determinants of complex traits due to their impact on gene expression. …”
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LDDFSF-YOLO11: A Lightweight Insulator Defect Detection Method Focusing on Small-Sized Features by Peng Shen, Keyu Mei, Huiqiong Cao, Yongxiang Zhao, Guoqing Zhang

“…This study proposes a lightweight insulator defect detection method (LDDFSF-YOLO11) that focuses on small-sized features to efficiently detect and identify insulator defects. …”
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Research on multi class pests identification and detection based on fusion attention mechanism with Mask-RCNN-CBAM by Xingwang Wang, Xingwang Wang, Xingwang Wang, Can Hu, Xufeng Wang, Hainie Zha, Xueyong Chen, Shanshan Yuan, Jing Zhang, Jianfeng Liao, Zhangying Ye

“…This study addresses challenges in agricultural pest detection, such as false positives and missed detections in complex environments, by proposing an enhanced Mask-RCNN model integrated with a Convolutional Block Attention Module (CBAM). …”
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Detection and Segmentation of Glioma Tumors Using an Improved Visual Geometry Group (IVGG) Deep Learning Structure by Parameswari Alagarsamy, Vinoth Kumar Kalimuthu, Bhavani Sridharan

“…Analysis of the performance efficiency has been done in relation to the Jaccard index, recall, precision, and detection rate.…”
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Improved Building Information Modeling Based Method for Prioritizing Clash Detection in the Building Construction Design Phase by Iman Bitaraf, Ali Salimpour, Pedram Elmi, Ali Akbar Shirzadi Javid

“…The proposed method substantially improves clash detection and prioritization efficiency and accuracy, yielding considerable benefits in project management and resource allocation.…”
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SAG-YOLO: A Lightweight Real-Time One-Day-Old Chick Gender Detection Method by Yulong Chang, Rongqian Sun, Zheng Yang, Shijun Li, Qiaohua Wang

“…This study proposes an improved SAG-YOLO method for chick sex detection. Firstly, the model reduces both parameter size and computational complexity by replacing the original feature extraction with the StarNet lightweight Backbone. …”
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A multi-scale small object detection algorithm SMA-YOLO for UAV remote sensing images by Shilong Zhou, Haijin Zhou, Lei Qian

“…Abstract Detecting small objects in complex remote sensing environments presents significant challenges, including insufficient extraction of local spatial information, rigid feature fusion, and limited global feature representation. …”
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An optimized anomaly detection framework in industrial control systems through grey wolf optimizer and autoencoder integration by Muhammad Muzamil Aslam, Liyanage Chandratilak De Silva, Rosyzie Anna Awg Haji Mohd Apong, Ali Tufail

“…Abstract Ensuring reliable Internet connectivity in Industrial Control Systems is critical for real-time monitoring and anomaly detection. Existing methods, however, struggle with high computational complexity, limited applicability to specific datasets, and elevated false-positive rates. …”
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A novel lightweight YOLOv8-PSS model for obstacle detection on the path of unmanned agricultural vehicles by Zhijian Chen, Yijun Fang, Jianjun Yin, Shiyu Lv, Farhan Sheikh Muhammad, Lu Liu

“…PConv significantly reduces processing load during convolution operations, enhancing the model's real-time detection performance. Second, a Slim-neck lightweight neck network is introduced, replacing the original neck network's conventional convolution with GSConv, to further improve detection efficiency and accuracy. …”
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Real-Time Human Group Detection and Clustering in Crowded Environments Using Enhanced Multi-Object Tracking by Hyunmin Lee, Donggoo Kang, Hasil Park, Sangwoo Park, Dasol Jeong, Joonki Paik

“…Group detection is a critical yet challenging task in video-based applications such as surveillance analysis, especially in crowded and dynamic environments where complex pedestrian interactions occur. …”
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Machine Learning-Based Attack Detection and Mitigation with Multi-Controller Placement Optimization over SDN Environment by Binod Sapkota, Arjun Ray, Manish Kumar Yadav, Babu R. Dawadi, Shashidhar R. Joshi

“…Additionally, the IDS decouples from the controllers, preserves controller resources, and allows for efficient near-real-time attack detection and mitigation. …”
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LPCF-YOLO: A YOLO-Based Lightweight Algorithm for Pedestrian Anomaly Detection with Parallel Cross-Fusion by Peiyi Jia, Hu Sheng, Shijie Jia

“…To address the issue of high complexity in current pedestrian anomaly detection network models, which hinders real-world deployment, this paper proposes a lightweight anomaly detection network called LPCF-YOLO (Lightweight Parallel Cross-Fusion YOLO) based on the YOLOv8n model. …”
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An Improved Deep Learning Unsupervised Approach for MRI Tissue Segmentation for Alzheimer’s Disease Detection by Karan Kumar, Isha Suwalka, Adaora Uche-Ezennia, Celestine Iwendi, Cresantus N. Biamba

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