OE-YOLO: An EfficientNet-Based YOLO Network for Rice Panicle Detection by Hongqing Wu, Maoxue Guan, Jiannan Chen, Yue Pan, Jiayu Zheng, Zichen Jin, Hai Li, Suiyan Tan

“…Third, a C3k2_DConv module improved by dynamic convolution is introduced, enabling input-adaptive kernel fusion to amplify discriminative features while suppressing background interference. …”
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Improving Solar Radiation Forecasting in Cloudy Conditions by Integrating Satellite Observations by Qiangsheng Bu, Shuyi Zhuang, Fei Luo, Zhigang Ye, Yubo Yuan, Tianrui Ma, Tao Da

“…Forecast errors are related to cloud regimes, of which the cloud amount leads to a maximum relative RMSE difference of about 50% with an additional 5% from cloud variability. …”
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Robust Road Surface Classification Using Time Series Augmented Intelligent Tire Sensor Data and 1-D CNN by Seokchan Kim, Yeong-Jae Kim, Dongwook Lee, Hanmin Lee

“…The robustness to different tires and driving conditions makes the proposed algorithm practical for estimating road surface conditions in real vehicles.…”
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Segmentation of dermatoscopic images of skin lesions. Comparison of methods by A. F. Smalyuk, M. S. Dzeshka, I. D. Kupchykava

“…The work discusses three methods of segmentation: thresholding using Otsu's method to calculate the threshold value, a convolutional neural network built on the U-net architecture, and a similar convolutional neural network with an added attention mechanism. …”
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A review of plant leaf disease identification by deep learning algorithms by Junmin Zhao, Laixiang Xu, Zizhen Ma, Juncai Li, Xiaowei Wang, Yunchang Liu, Xiaojie Du

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DPD-v2: Generalised deep particle diffusometry for varied particle shapes and experimental conditions by Pranshul Sardana, Steven T. Wereley

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Multi-scale Information Aggregation for Spoofing Detection by Changtao Li, Yi Wan, Feiran Yang, Jun Yang

“…The unique topology of DLA makes possible compounding of speech features from convolution layers at different depths, and therefore the local and the global speech representations can be incorporated simultaneously. …”
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Fine-Tuning-Based Transfer Learning for Building Extraction from Off-Nadir Remote Sensing Images by Bipul Neupane, Jagannath Aryal, Abbas Rajabifard

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Introducing an ensemble method for the early detection of Alzheimer's disease through the analysis of PET scan images by Arezoo Borji, Taha-Hossein Hejazi, Abbas Seifi

“…In this paper, three deep-learning models, namely VGG16 and AlexNet, and a custom Convolutional Neural Network (CNN) with 8-fold cross-validation, have been used for classification. …”
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Efficient Method of Road Outlier Recognition Using Deep Learning Coupled with Data Augmentation Approach by Sarfaraz Natha, Fareed Ahmed Jokhio, Muhammad Shafique, Naeem Ahmed, Danish Munir Arain

“…The proposed study investigated different types of Convolutional Neural Network (CNN) pre-trained models with the Data Augmentation (DA) approach to address the frame variance problem in real-time videos. …”
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A Novel CNN-Based Framework for Detection and Classification of Power Quality Disturbances: Exploring Multi-Class Versus Multi-Label Classification by Aleksandra Zlatkova, Dimitar Taskovski

“…In this paper, we propose a novel model based on a deep convolutional neural network (DCNN) for the feature extraction and classification of PQ disturbances. …”
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Individual Contribution-Based Spatial-Temporal Attention on Skeleton Sequences for Human Interaction Recognition by Xing Liu, Bo Gao

“…First, variations in camera positions and viewpoints can cause significant differences in skeletal data for actions of the same type. …”
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Tissue Puncture Event Detection in Needle Procedures using Vibroacoustic Signals - ResNet optimised Phantom Results by Serwatka Witold, Rzepka Dominik, Oran Hamza, Steeg Katharina, Krombach Gabriele, Stefanski Juliusz, Heryan Katarzyna, Friebe Michael

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Integrated CNN‐LSTM for Photovoltaic Power Prediction based on Spatio‐Temporal Feature Fusion by Junwei Ma, Meiru Huo, Jinfeng Han, Yunfeng Liu, Shunfa Lu, Xiaokun Yu

“…This paper proposes a convolutional neural network‐long short‐term memory (CNN‐LSTM) network integration model based on spatio‐temporal feature fusion. …”
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Multi-Harmonic Nonlinear Ultrasonic Fusion with Deep Learning for Subtle Parameter Identification of Micro-Crack Groups by Qi Lin, Xiaoyang Bi, Xiangyan Ding, Bo Yang, Bingxi Liu, Xiao Yang, Jie Xue, Mingxi Deng, Ning Hu

“…First, we trained a one-dimensional convolutional neural network (1D CNN) with various time-domain signals obtained from finite element method (FEM) models and analyzed the sensitivity of different harmonic nonlinear responses to various subtle parameters of micro-crack groups. …”
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Node Classification Based on Kolmogorov-Arnold Networks by YUAN Lining, FENG Wengang, LIU Zhao

“…In the node classification task, G-KAN outperforms the currently advanced baselines and raises Micro-F1 and Macro-F1 by 50.42 and 52.84 percentage points respectively compared with graph convolutional networks (GCN) on BlogCatalog. In activation comparison experiments, the method introduced by KAN outperforms the variants that use different activation functions and demonstrates stronger generalization across various datasets. …”
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Classifying sex with volume-matched brain MRI by Matthis Ebel, Martin Domin, Nicola Neumann, Carsten Oliver Schmidt, Martin Lotze, Mario Stanke

“…Sex differences in the size of specific brain structures have been extensively studied, but careful and reproducible statistical hypothesis testing to identify them produced overall small effect sizes and differences in brains of males and females. …”
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Automatic segmentation and volume measurement of anterior visual pathway in brain 3D-T1WI using deep learning by Yongliang Han, Haixiang Wang, Qi Luo, Jingjie Wang, Chun Zeng, Qiao Zheng, Linquan Dai, Yiqiu Wei, Qiyuan Zhu, Wenlong Lin, Shaoguo Cui, Yongmei Li

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A study on Chinese language Cross-Modal pedestrian image information retrieval by Yan Xie, Jian Qu

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Research on Predictive Analysis Method of Building Energy Consumption Based on TCN-BiGru-Attention by Sijia Fu, Rui Zhu, Feiyang Yu

“…Although there are currently a large number of energy consumption research methods, each method has different applicability and advantages and disadvantages. …”
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