Broiler Behavior Detection and Tracking Method Based on Lightweight Transformer by Haixia Qi, Zihong Chen, Guangsheng Liang, Riyao Chen, Jinzhuo Jiang, Xiwen Luo

“…The FasterNet network based on partial convolution (PConv) was used to replace the Resnet18 backbone network to reduce the computational complexity of the model and to improve the speed of model detection. …”
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A comprehensive model for concrete strength prediction using advanced learning techniques by Sagar Dhengare, Udaykumar Waghe, Ganesh Yenurkar, Anjana Shyamala

“…The main ingredient analysis (PCA) was used to reduce the dimension, while random forest regression (RFR), support vector regression (SVR), and the Convolutional Neural Network (CNN) were applied for the forecast. …”
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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

“…In order to improve the operating rate of discrimination, a continuous projection algorithm (SPA) was used to extract the characteristic wavelengths of the fluorescence spectra and hyperspectral data of pesticide residues, and algorithms such as the least-squares support vector machine (LSSVM) algorithm and least partial squares regression (PLSR) were used to build a quantitative model, while algorithms such as the convolutional neural network (BPNN) algorithm and decision tree algorithm (CART) were used to build a qualitative model. …”
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Enhancement and evaluation for deep learning-based classification of volumetric neuroimaging with 3D-to-2D knowledge distillation by Hyemin Yoon, Do-Young Kang, Sangjin Kim

“…Abstract The application of deep learning techniques for the analysis of neuroimaging has been increasing recently. The 3D Convolutional Neural Network (CNN) technology, which is commonly adopted to encode volumetric information, requires a large number of datasets. …”
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Revolutionizing sleep disorder diagnosis: A Multi-Task learning approach optimized with genetic and Q-Learning techniques by Soraya Khanmohmmadi, Toktam Khatibi, Golnaz Tajeddin, Elham Akhondzadeh, Amir Shojaee

“…The study proposes an innovative multi-task learning convolutional neural network with a partially shared structure that uses frequency-time images generated from EEG signals to address these limitations. …”
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A Lightweight Fault Diagnosis Method Based on Knowledge Distillation Under Time-Varying Rotational Speeds by Xiwang Yang, Yarong Wang, Lele Gao, Jia Luo, Licheng Jing, Jinying Huang, Guangpu Liu, Chenfeng Yang

“…First, TL-ResPConv based on transfer learning and partial convolution was designed as a teacher network model to train a lightweight student network model for fault diagnosis based on TL-ResPConv-KD. …”
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Advances in Online Grain Quality Assessment: Near-Infrared Spectroscopic Modeling and Transfer Strategies by CUI Chen-hao, FAN Chen

“…This review summarizes the application of NIR spectroscopy in online grain quality inspection, systematically outlining the development from traditional linear modeling (e.g., partial least squares regression), nonlinear modeling (e.g., support vector machines, artificial neural networks) to deep learning methods (e.g., convolutional neural networks). …”

Multibranch semantic image segmentation model based on edge optimization and category perception. by Zhuolin Yang, Zhen Cao, Jianfang Cao, Zhiqiang Chen, Cunhe Peng

“…This may cause some effective information to be mistaken for redundant information and discarded, which in turn causes object segmentation confusion. As a convolutional layer deepens, the loss of spatial detail information makes the segmentation effect achieved at the object boundary insufficiently accurate. …”
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A Copula-Driven CNN-LSTM Framework for Estimating Heterogeneous Treatment Effects in Multivariate Outcomes by Jong-Min Kim

“…In this study, we propose a novel deep learning framework integrating empirical copula transformations with a CNN-LSTM (Convolutional Neural Networks and Long Short-Term Memory networks) architecture to capture nonlinear dependencies and temporal dynamics in multivariate treatment effect estimation. …”
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Optimizing Personalized Recommender Systems for Teachers’ Digital Learning Models Using Deep Learning Algorithms by Jun Zhong, Wenjuan Zhang

“…Additionally, a graph convolutional neural network (GCN) is employed, leveraging the sequential relationships between subject knowledge points to automatically capture semantic information from the higher-order structure of knowledge points, thereby enabling personalized recommendations. …”
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A Reconfigurable Coarse-to-Fine Approach for the Execution of CNN Inference Models in Low-Power Edge Devices by Auangkun Rangsikunpum, Sam Amiri, Luciano Ost

“…Convolutional neural networks (CNNs) have evolved into essential components for a wide range of embedded applications due to their outstanding efficiency and performance. …”
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ViPeR: Vision-Based Surgical Phase Recognition by Soumyadeep Chandra, Sayeed Shafayet Chowdhury, Courtney Yong, Ginnie Jeng, Ashorne Mahenthiran, Kostantinos E. Morris, Harrison L. Love, Chandru P. Sundaram, Kaushik Roy

“…Our model incorporates hierarchical dilated temporal convolution layers and inter-layer residual connections to capture temporal correlations at both fine and coarse granularities. …”
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Deep learning-enabled exploration of global spectral features for photosynthetic capacity estimation by Xianzhi Deng, Xiaolong Hu, Liangsheng Shi, Chenye Su, Jinmin Li, Shuai Du, Shenji Li

“…However, vegetation indices-based linear regression exhibits insufficient utilization of spectral information, while full spectra-based traditional machine learning has limited representational capacity (partial least squares regression) or uninterpretable (convolution). …”
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Edge-YOLO: Lightweight Multi-Scale Feature Extraction for Industrial Surface Inspection by Wang Guiqiang, Chen Junbao, Li Chengzhang, Lu Shuo

“…The Edge-backbone systematically strengthens edge feature extraction and retention through three synergistic components: an Edge-Sensitive (EdgeS) module for optimized feature initialization, a Cross-Stage-Partial Edge Enhancement (C3E2) module that integrates edge information across network stages, and a Multi-Scale Dilated Convolution (MSDC) module that efficiently fuses multi-scale features through weight-sharing. …”
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Graph Learning-Based Power System Health Assessment Model by Koji Yamashita, Nanpeng Yu, Evangelos Farantatos, Lin Zhu

“…The proposed framework leverages a physics-informed graph convolution network and graph attention network with ordinal encoders, which are benchmarked with multi-layer perceptron models. …”
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An efficient algorithm for pedestrian fall detection in various image degradation scenarios based on YOLOv8n by Jianhui Xun, Xuefeng Wang, Xiufang Wang, Xiaoliang Fan, Peishuai Yang, Zhifei Zhang

“…Additionally, the Cross Stage Partial Bottleneck with 2 Convolution Block (C2f) was optimized to reduce computational load and parameter count without compromising performance, while the Inner Extended Intersection over Union (Inner-EIoU) loss function was employed to improve bounding box regression accuracy and speed. …”
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Physics-Guided Self-Supervised Learning Full Waveform Inversion with Pretraining on Simultaneous Source by Qiqi Zheng, Meng Li, Bangyu Wu

“…The inversion network is a partial convolution attention modified UNet (PCAMUNet), which combines local feature extraction with global information integration to achieve high-resolution velocity model estimation from seismic shot gathers. …”
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AI-Driven Integration of Deep Learning With Lung Imaging, Functional Analysis, and Blood Gas Metrics for Perioperative Hypoxemia Prediction by Kecheng Huang, Chujun Wu, Rongpeng Pi, Jieyu Fang

“…Perioperative hypoxemia, defined as arterial oxygen partial pressure <60 mmHg or oxygen saturation <90%, poses significant risks of delayed recovery and organ dysfunction. …”
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Evaluation of Shelf Life Prediction for Broccoli Based on Multispectral Imaging and Multi-Feature Data Fusion by Xiaoshuo Cui, Xiaoxue Sun, Shuxin Xuan, Jinyu Liu, Dongfang Zhang, Jun Zhang, Xiaofei Fan, Xuesong Suo

“…Savitzky–Golay (SG) convolution smoothing and standard normal variate (SNV) and normalization (Norm) preprocessing methods were employed to preprocess the original spectral data and textural features, while a successive projection algorithm (SPA) was used to extract relevant feature bands. …”
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FCDNet: A Lightweight Network for Real-Time Wildfire Core Detection in Drone Thermal Imaging by Linfeng Wang, Oualid Doukhi, Deok Jin Lee

“…It includes an Efficient Processing (EP) module based on the novel Partial Depthwise Convolution (PDWConv) and the lightweight feature-sharing decoupled detection head (Fast Head), achieving low-size and low-computation wildfire detection. …”
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