A Deep Learning-Based Algorithm for Ceramic Product Defect Detection by Junxiang Diao, Hua Wei, Yawei Zhou, Zhihua Diao

“…In the field of ceramic product defect detection, traditional manual visual inspection methods suffer from low efficiency and high subjectivity, while existing deep learning algorithms are limited in detection efficiency due to their high complexity. …”
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An annotated Dataset and Benchmark for Detecting Floating Debris in Inland Waters by Guangchao Qiao, Mingxiang Yang, Hao Wang

“…However, complex light conditions in the water, small size objects and other factors pose a huge challenge for floating object detection. …”
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Coral reef detection using ICESat-2 and machine learning by Gabrielle A. Trudeau, Kim Lowell, Jennifer A. Dijkstra

“…This research establishes a new application of ICESat-2 data combined with advanced machine learning techniques as a promising method for efficient and cost-effective coral reef monitoring. …”
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Robust Resilience Blocks Detection Problem in Dynamic Social Networks by Yuxin Gao, Jianming Zhu, Peikun Ni, Guoqing Wang

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Research on Laser Radar Inspection Station Planning of Vehicle Body-In-White (BIW) with Complex Constraints by Lijuan Li, Siyi Wang, Jichao Ma, Xiaobing Gao

“…Focusing on the spatially constrained industrial environments and complex measurement specifications, the work reformulates Laser Radar inspection planning as a multi-constrained optimization problem challenge. …”
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Ensemble Voting for Enhanced Robustness in DarkNet Traffic Detection by Varun Shinde, Kartik Singhal, Ahmad Almogren, Vineet Dhanawat, Vishal Karande, Ateeq Ur Rehman

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Deep Learning Methods and UAV Technologies for Crop Disease Detection by S. G. Mudarisov, I. R. Miftakhov

“…It focuses on evaluating deep learning techniques and unmanned aerial vehicles for crop disease detection. (Research purpose) The study aims to review and systemize scientific literature on the application of unmanned aerial vehicles, remote sensing technologies and deep learning 24 methods for the early detection and prediction of crop diseases. …”
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Lightweight detection algorithms for small targets on unmanned mining trucks by Shuoqi CHENG, Yilihamu·YAERMAIMAITI, Lirong XIE, Xiyu LI, Ying MA

“…It enhances multi-scale feature fusion capability via weighted feature fusion, significantly reducing parameter count while improving small target detection capability. Additionally, it designs a detection decoupling head with a multi-head attention mechanism to improve the issue of network complexity caused by convolutional layer redundancy, processes spatial dimensions to focus on capturing target features, reduces interference from irrelevant backgrounds, and enhances the accuracy of occluded target recognition.urthermore, it constructs a lightweight neural network with dual convolution (CDC), enhancing inter-channel information flow, improving model feature expression capability, and reducing model complexity. …”
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Explainable correlation-based anomaly detection for Industrial Control Systems by Ermiyas Birihanu, Imre Lendák

“…Anomaly detection is vital for enhancing the safety of Industrial Control Systems (ICS). …”
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Daxx and HIRA go viral – How chromatin remodeling complexes affect DNA virus infection by Julia Mai, Masih Nazari, Thomas Stamminger, Sabrina Schreiner

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Remote Sensing Change Detection by Pyramid Sequential Processing With Mamba by Jiancong Ma, Bo Li, Hanxi Li, Siying Meng, Ruitao Lu, Shaohui Mei

“…Change detection (CD) in remote sensing imagery is crucial for monitoring environmental variations over time. …”
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Detection of hydrophobicity grade of insulators based on AHC-YOLO algorithm by Shaotong Pei, Weiqi Wang, Peng Wu, Chenlong Hu, Haichao Sun, Keyu Li, Mianxiao Wu, Bo Lan

“…Abstract Thanks to the rapid development of image processing technology, the efficiency and accuracy of power inspection have been enhanced through deep learning techniques. …”
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Smart deep learning model for enhanced IoT intrusion detection by Faisal S. Alsubaei

“…This paper addresses these limitations with large preprocessing steps followed by hyperparameter tuning of machine learning XGBoost and deep learning Sequential Neural Network (OSNN) algorithms through Grid Search for their best values to improve multiclass intrusion detection across varied datasets. These deep models were then augmented with a variety of various filters, kernels, activation functions, and regularization techniques in an attempt to boost them in detecting complex, multiclass intrusion patterns. …”
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Exploration of machine learning approaches for automated crop disease detection by Annu Singla, Ashima Nehra, Kamaldeep Joshi, Ajit Kumar, Narendra Tuteja, Rajeev K. Varshney, Sarvajeet Singh Gill, Ritu Gill

“…Recent advancements in machine learning (ML) offer promising alternatives by automating the disease detection processes with high precision and efficiency. …”
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Network-based intrusion detection using deep learning technique by Muhammad Farhan, Hafiz Waheed ud din, Saadat Ullah, Muhammad Sajjad Hussain, Muhammad Amir Khan, Tehseen Mazhar, Umar Farooq Khattak, Ines Hilali Jaghdam

“…Abstract A high growth rate in network traffic and the complexity of cyber threats have made it necessary to create more effective and flexible intrusion detection systems. …”
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YOLO-MECD: Citrus Detection Algorithm Based on YOLOv11 by Yue Liao, Lerong Li, Huiqiang Xiao, Feijian Xu, Bochen Shan, Hua Yin

“…The experimental results and comparative analysis with similar network models indicate that the YOLO-MECD model has achieved significant improvements in both detection performance and computational efficiency. …”
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A lightweight personnel detection method for underground coal mines by Shuai WANG, Wei YANG, Yuxiang LI, Jiaqi WU, Wei YANG

“…The underground environment of coal mines is complex and has more safety hazards. Personnel detection is an important part of ensuring safe production in coal mines and building smart mines. …”
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Spatial Identification and Distribution Pattern of the Complexity of Rural Poverty in China Using Multisource Spatial Data by Zhenyu Qi, Jinghu Pan, Yaya Feng

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Intrusion Detection System to Advance Internet of Things Infrastructure-Based Deep Learning Algorithms by Hasan Alkahtani, Theyazn H. H. Aldhyani

“…The results of comparative predictions between the proposed framework and existing systems showed that the proposed system more efficiently and effectively enhanced the security of the IoT environment from attacks. …”
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A Frequency Domain-Enhanced Transformer for Nighttime Object Detection by Yaru Li, Li Shen

“…Our approach integrates physics-prior enhancement to improve the visibility of objects in low-light conditions, frequency domain feature extraction to capture structural information potentially lost in the spatial domain, and window cross-attention fusion that efficiently combines complementary features while reducing computational complexity, significantly improving detection performance without increasing the parameter count. …”
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