Vehicle detection method based on multi-layer selective feature for UAV aerial images by Yinbao Ma, Yuyu Meng, Jiuyuan Huo

“…However, this task remains challenging due to variable high-altitude viewpoints, complex environmental interference, and limitations in algorithmic efficiency. …”
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Lightweight detection of cotton leaf diseases using StyleGAN2-ADA and decoupled focused self-attention by Henghui Mo, Linjing Wei

“…Current models face challenges like diverse disease traits, variable stages, small target detection, uneven lighting, and occlusions, resulting in low accuracy and adaptability. …”
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An intelligent prediction method for rock core integrity based on deep learning by Zhaoxia Hu, Hua Mei, Lei Yu

“…In IDA-RCF, a two-branch feature extraction network is firstly proposed, in which branch one is used to fully extract the complex and variable local detail fissure features by Deformable convolution, and branch two is used to capture the global context information of the rock core images by EfficientViT network based on the self-attention. …”
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Toward Spatio‐Temporally Consistent Multi‐Site Fire Danger Downscaling With Explainable Deep Learning by Óscar Mirones, Jorge Baño‐Medina, Swen Brands, Joaquín Bedia

“…Abstract This study introduces a novel Convolutional Long Short‐Term Memory neural networks (ConvLSTM)‐based multi‐site downscaling approach for fire danger prediction, that leverages the properties of Long‐Short Term Memory (LSTM) Recursive Neural Networks and Convolutional Neural Networks (CNNs) by learning daily Multivariate‐Gaussian distributions conditioned on large‐scale atmospheric predictors. …”
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YOLOv9-GDV: A Power Pylon Detection Model for Remote Sensing Images by Ke Zhang, Ningxuan Zhang, Chaojun Shi, Qiaochu Lu, Xian Zheng, Yujie Cao, Xiaoyun Zhang, Jiyuan Yang

“…Finally, the Variable Minimum Point Distance Intersection over Union (VMPDIoU) loss is proposed to optimize the model’s loss function. …”
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Improving the Parameterization of Complex Subsurface Flow Properties With Style‐Based Generative Adversarial Network (StyleGAN) by Wei Ling, Behnam Jafarpour

“…Deep learning techniques, such as Variational Autoencoders (VAE) and Generative Adversarial Networks (GAN), have recently been proposed to address this difficulty by learning complex spatial patterns from prior training images and synthesizing similar realizations using low‐dimensional latent variables with Gaussian distributions. The resulting Gaussian latent variables lend themselves to calibration with the ensemble Kalman filter‐based updating schemes that are suitable for parameters with Gaussian distribution. …”
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3D-SCUMamba: An Abdominal Tumor Segmentation Model by Juwita, Ghulam Mubashar Hassan, Amitava Datta

“…Identification and segmentation of tumors from CT scans are essential for early detection and effective treatment but they remain challenging due to imaging artifacts and significant variability in tumor location, size, and morphology. …”
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A survey: Breast Cancer Classification by Using Machine Learning Techniques by Ruaa Hassan Mohammed Ameen, Nasseer Moyasser Basheer, Ahmed Khazal Younis

“…This paper focuses on various statistical and machine learning studies of mammography datasets for enhancing the accuracy of breast cancer diagnosis and classification based on various variables. The Naïve Bayes, the K-nearest neighbors (KNN), the Support Vector Machine (SVM), the Random Forest, the Logistic Regression, Multilayer Perceptron (MLP), fuzzy classifier, and Convolutional Neural Network (CNN) classifiers, are the most widely used technologies in this field. …”
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Detection of Tomato Leaf Pesticide Residues Based on Fluorescence Spectrum and Hyper-Spectrum by Jiayu Gao, Xuhui Yang, Simo Liu, Yufeng Liu, Xiaofeng Ning

“…The data in the spectral raw bands were optimized using convolutional smoothing (S-G), standard normal variable transformation (SNV), multiplicative scatter correction (MSC), and baseline calibration (baseline) algorithms, respectively. …”
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Development of a river dissolved oxygen prediction model integrating spatial effects and multiple deep learning algorithm by Yubo Zhao, Mo Chen

“…To address the nonlinear, complex, and periodic nature of DO time series, a novel prediction framework is proposed, in which Wavelet Convolution (WTConv), a technique traditionally used in image processing, is applied for the first time to DO forecasting. …”
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COMQ: A Backpropagation-Free Algorithm for Post-Training Quantization by Aozhong Zhang, Zi Yang, Naigang Wang, Yingyong Qi, Jack Xin, Xin Li, Penghang Yin

“…Within a fixed layer, COMQ treats all the scaling factor(s) and bit-codes as the variables of the reconstruction error. Every iteration improves this error along a single coordinate while keeping all other variables constant. …”
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ABDviaMSIFAT: Abnormal Crowd Behavior Detection Utilizing a Multi-Source Information Fusion Technique by Ali Ahmad Hamid, S. Amirhassan Monadjemi, Bijan Shoushtarian

“…In the first pipeline, we utilize a depth-wise Separable Convolutional Neural Network (DWS-CNN) that provides reduced filtering compared to standard CNNs. …”
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Optimization of a multi-environmental detection model for tomato growth point buds based on multi-strategy improved YOLOv8 by Jiang Liu, Jingxin Yu, Changfu Zhang, Huankang Cui, Jinpeng Zhao, Wengang Zheng, Fan Xu, Xiaoming Wei

“…Three key innovations address YOLOv8’s limitations: (1) an SE attention module boosts feature representation in cluttered environments, (2) GhostConv replaces standard convolution to reduce computational load by 19% while preserving feature discrimination, and (3) a scale-adaptive WIoU_v2 loss function optimizes gradient allocation for variable-quality data. …”
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Addressing spatial imprecision in deep learning for satellite imagery-based socioeconomic predictions by Heather Baier, Dan Runfola

“…In cases where the exact location at which a measurement was taken is unknown (i.e. household income), the SIA approach (a) samples multiple potential candidates in an adaptable-size buffer region, (b) extracts activations from the fully connected (FC) layers of convolutional-based models for each candidate; and (c) applies a Random Forest (RF) model to each candidate’s activations to generate a single prediction of the target variable. …”
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Learning a Robust Hybrid Descriptor for Robot Visual Localization by Qingwu Shi, Junjun Wu, Zeqin Lin, Ningwei Qin

“…However, semantic segmentation images will be more stable than the original images against considerable drastically variable environments; therefore, to make full use of the advantages of both semantic segmentation image and its original image, this paper solves the above problems with the latest work of semantic segmentation and proposes the novel hybrid descriptor for long-term visual localization, which is generated by combining a semantic image descriptor extracted from segmentation images and an image descriptor extracted from RGB images with a certain weight, and then trained by a convolutional neural network. …”
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Enhancing Tomato Detection in Complex Field Environments using Faster R-CNN Deep Learning Model for Autonomous Picking Robots by Pandey Devras, Lalmawipuii R.

“…However, accurately detecting tomatoes in dynamic and complex field environments remains a challenge due to issues such as high false positive rates, missed detections, variable illumination, occlusion, and heterogeneous foliage. …”
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Dynamic spatiotemporal graph network for traffic accident risk prediction by Pengcheng Zhang, Wen Yi, Yongze Song, Penggao Yan, Peng Wu, Ammar Shemery, Keith Hampson, Albert P. C. Chan

“…Our model uses channel-wise convolutional neural networks to detect spatial accident patterns across weekly, daily, and hourly time scales with automatic weight learning, simultaneously employing graph convolutional networks to process road network features, population feature while integrating external data like weather and dates. …”
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FD²-YOLO: A Frequency-Domain Dual-Stream Network Based on YOLO for Crack Detection by Junwen Zhu, Jinbao Sheng, Qian Cai

“…However, most existing methods use multi-scale and attention mechanisms to improve on a single backbone, and this single backbone network is often ineffective in detecting slender or variable cracks in complex scenarios. We propose a novel network, FD²-YOLO, based on frequency-domain dual-stream YOLO, for accurate and efficient detection of cement cracks. …”
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Few-shot bearing fault diagnosis method based on an EEMD parallel neural network and a relation network by Cunsheng Zhao, Bo Tong, Chao Zhou, Qingrong Fan

“…Original signal decomposition, STFT transformation and splicing effectively improve the randomness and blindness of convolution operations, improve the accuracy of fault feature extraction in RN, and thus improve the overall diagnostic performance of the model. …”
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Toward accurate and scalable rainfall estimation using surveillance camera data and a hybrid deep-learning framework by Fiallos-Salguero Manuel, Soon-Thiam Khu, Jingyu Guan, Mingna Wang

“…The second module integrates depthwise separable convolution (DSC) layers with gated recurrent units (GRU) in a regression model to accurately estimate rainfall intensity using these ROIs. …”
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