DMSF-YOLO: Cow Behavior Recognition Algorithm Based on Dynamic Mechanism and Multi-Scale Feature Fusion by Changfeng Wu, Jiandong Fang, Xiuling Wang, Yudong Zhao

“…For the problem in multi-scale behavior changes of dairy cows, a multi-scale convolution module (MSFConv) is designed, and some C3k2 modules of the backbone network and neck network are replaced with MSFConv, which can extract cow behavior information of different scales and perform multi-scale feature fusion. …”
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Contributions of lifestyle, education, and cardiovascular risk factors to the brain age gap by Kostas Stoitsas, Pieter Bakx, Trudy Voortman, Jing Yu, Gennady Roshchupkin, Daniel Bos

“…The brain age gap is the difference between chronological age and the age predicted from Magnetic Resonance Imaging (MRI) brain scans. …”
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Pixels relationship analysis for extracting building footprints by A. Emelyanov, A. Emelyanov, V. Knyaz, V. Knyaz, V. Kniaz, V. Kniaz, D. Artist

“…The main difference from existing methods is a new regularization method based on compiling a neighborhood matrix for each point belonging to the “building” class. …”
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Respiratory Rate Estimation from Thermal Video Data Using Spatio-Temporal Deep Learning by Mohsen Mozafari, Andrew J. Law, Rafik A. Goubran, James R. Green

“…A respiratory signal is estimated from a dynamically cropped thermal video using 3D convolutional neural networks and bi-directional long short-term memory stages. …”
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Hybrid deep learning model for density and growth rate estimation on weed image dataset by Anand Muni Mishra, Mukund Pratap Singh, Prabhishek Singh, Manoj Diwakar, Indrajeet Gupta, Anchit Bijalwan

“…This research has proposed proposes hybrid Convolutional Neural Network models (HCNN) which have amalgamated the feature of the SegNet and U-Net CNN model for weed image segmentation. …”
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Winter Wheat Yield Prediction Using Satellite Remote Sensing Data and Deep Learning Models by Hongkun Fu, Jian Lu, Jian Li, Wenlong Zou, Xuhui Tang, Xiangyu Ning, Yue Sun

“…By adjusting the key parameters of the Convolutional Neural Network (CNN) with IGWO, the prediction accuracy is significantly enhanced. …”
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A novel multi-scale and fine-grained network for large choroidal vessels segmentation in OCT by Wei Huang, Qifeng Yan, Lei Mou, Yitian Zhao, Wei Chen, Wei Chen, Wei Chen

“…To address the problem of low contrast and blurred boundaries of choroidal vessels in OCT images, we developed a large kernel and multi-scale attention module, which can improve the features of the target area through multi-scale convolution kernels, channel mixing and feature refinement. …”
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Estimating Subsurface Geostatistical Properties from GPR Reflection Data Using a Supervised Deep Learning Approach by Yu Liu, James Irving, Klaus Holliger

“…The proposed approach uses a convolutional neural network (CNN), which is trained on a vast database of autocorrelations obtained from synthetic GPR images for a comprehensive range of stochastic subsurface models. …”
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Monitoring of vegetation chlorophyll content in photovoltaic areas using UAV-mounted multispectral imaging by Ming Li, Weiyi Wang, Haoran Li, Zekun Yang, Jianjun Li

“…The results indicated that both vegetation indices and texture features exhibited significant correlations with chlorophyll content, with the strongest correlation observed between the green normalized difference vegetation index (GNDVI) and the NIR_Mean (Pearson coefficients of 0.82 and 0.65, respectively). …”
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Modeling Terrestrial Net Ecosystem Exchange Based on Deep Learning in China by Zeqiang Chen, Lei Wu, Nengcheng Chen, Ke Wan

“…The results show that the normalized difference vegetation index, the enhanced vegetation index, and the leaf area index play a dominant role at most sites. …”
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Development and validation of CNN-MLP models for predicting anti-VEGF therapy outcomes in diabetic macular edema by Xiangjie Leng, Ruijie Shi, Zhaorui Xu, Hai Zhang, Wenxuan Xu, Keyin Zhu, Xuejing Lu

“…In this study, both the training set and the validation set exhibited a consistent decreasing trend in MAE, MSE, and MSLE. No statistical difference was found between the actual and predicted values in all clinical indicators. …”
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Adoption of Compound Echocardiography under Artificial Intelligence Algorithm in Fetal Congenial Heart Disease Screening during Gestation by Guowei Han, Tianliang Jin, Li Zhang, Chen Guo, Hua Gui, Risu Na, Xuesong Wang, Haihua Bai

“…The abnormal distribution of fetal heart and the difference of cardiac Z score between group II and group I were analyzed, and the diagnostic value of group C and group W for CHD was compared. …”
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Numerical solution of a spatio-temporal gender-structured model for hantavirus infection in rodents by Raimund BÜrger, Gerardo Chowell, Elvis GavilÁn, Pep Mulet, Luis M. Villada

“…The numerical results demonstrate significant differences in the spatio-temporal behavior predicted by the different models, which suggest future research directions.…”
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Defect Diagnosis of Photovoltaic Module Visible Light Images Under Imbalanced Sample Conditions by Huiqing Rao, Qiong Li, Long Chen, Sha Jin, Yong Lu, Zhiguang Li

“…Firstly, in the DCGAN model, fully connected layers are incorporated into both the generator and the discriminator, and the transpose convolutional layers and convolutional layers are replaced with residual blocks. …”
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Unraveling Cyberbullying Dynamis: A Computational Framework Empowered by Artificial Intelligence by Liliana Ibeth Barbosa-Santillán, Bertha Patricia Guzman-Velazquez, Ma. Teresa Orozco-Aguilera, Leticia Flores-Pulido

“…This study introduces a computational framework to identify such content using convolutional neural networks of weapon-related images. …”
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CP-YOLO: An Algorithm for Cigarette Pack Defects Detection Based on CCD Images by Peng Dong, Weihua Feng, Rui Wang, Mingyan Zhang, Qunye Hong, Yongsheng Wang, Di Wang, Guohao Zong

“…The algorithm integrates multi-scale aggregate convolution into the BottleneckCSP architecture to form the C3MSAC module. …”
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Applying artificial neural networks to solve the inverse problem of evaluating concentrations in multianalyte mixtures from biosensor signals by Ignas Dapšys, Raimondas Čiegis, Vadimas Starikovičius

“…To solve the problem, we employ feed-forward and convolutional neural networks. Computational experiments were performed with different levels of additive and multiplicative noises for the batch and flow injection analysis modes of the biosensor. …”
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Network and Dataset for Multiscale Remote Sensing Image Change Detection by Shenbo Liu, Dongxue Zhao, Yuheng Zhou, Ying Tan, Huang He, Zhao Zhang, Lijun Tang

“…To address this issue, a multiscale remote sensing change detection network (MSNet) and a multiscale RSCD dataset (MSRS-CD) are proposed. A multiscale convolution module (MSCM) is investigated, and combined with MSCM, an encoder capable of capturing features of different sizes is designed to efficiently extract multiscale semantic change features. …”
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High Precision Piston Error Sensing of Segmented Telescope Based on Frequency Domain Filtering by Dequan Li, Dong Wang

“…The representation of feature image that reflects each submirror's piston error which obtained by this method is the same.Therefore, regardless of the number or the arrangement of submirrors, the single shallow convolutional neural network trained by any of the extracted submirror interference image dataset can be used to achieve high-precision detection of different submirror piston errors.Finally, simulation experiment results show the effectiveness of the proposed method.…”
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Multiple orbital angular momentum modes conversion with transmission metasurface by Sitong He, Qiang Feng, Qingle Tu, Haixia Liu, Jiaqi Han, Yan Shi, Long Li

“…The orthogonal properties between different orbital angular momentum (OAM) modes of vortex beams have made it a great potential research area in recent years. …”
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