Leveraging moisture elimination and hybrid deep learning models for soil organic carbon mapping with multi-modal remote sensing data by Yilin Bao, Xiangtian Meng, Weimin Ruan, Huanjun Liu, Mingchang Wang, Abdul Mounem Mouazen

“…Next, a hybrid deep learning model, Multimodal Transformer Mechanism-Convolutional Neural Network-Convolutional Long Short-Term Memory (MT-CNN-ConvLSTM, MCCL), is constructed to enhance predictive accuracy and generalizability. …”
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Spectral Data-Driven Prediction of Soil Properties Using LSTM-CNN-Attention Model by Yiqiang Liu, Luming Shen, Xinghui Zhu, Yangfan Xie, Shaofang He

“…The Long Short-Term Memory (LSTM) component captures temporal dependencies, the Convolutional Neural Network (CNN) extracts spatial features, and the attention mechanism highlights critical information within the data. …”
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Discretization-independent surrogate modeling of physical fields around variable geometries using coordinate-based networks by James Duvall, Karthik Duraisamy

“…Numerical solutions of partial differential equations require expensive simulations, limiting their application in design optimization, model-based control, and large-scale inverse problems. …”
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Optical OTFS waveform PAPR analysis for high order modulation employing CNN, DNN, and AE machine learning algorithms under a variety of channel scenarios by Arun Kumar, Nishant Gaur, Aziz Nanthaamornphong

“…Power Spectral Density (PSD) analysis verifies that ML-based techniques, such as deep neural networks (DNN), convolutional neural networks (CNN), and autoencoders (AE), are spectrally efficient with negligible out-of-band radiation of -1070 and -1470 for 256QAM with diverse channel conditions. …”
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Portable optical spectroscopy and machine learning techniques for quantification of the biochemical content of raw food materials by Cosimo Ricci, Agata Gadaleta, Annamaria Gerardino, Angelo Didonna, Giuseppe Ferrara, Francesca Romana Bertani

“…Results Considering the specific samples, the obtained results of the classification models indicate a validation mean absolute error of 0.8% (percentage of total protein content in dry matter) for two species of wheat using Convolutional Neural Network following normalization procedures and 0.32% using Partial Least Square (PLS) analysis applied to Tritordeum samples; visible reflectance spectra have been used to discriminate the two cereal species. …”
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YOLOv10-CBRC: A high-precision document image layout analysis model by Zhenjie Wu, Weilan Wang, Hongrui Li

“…Building upon YOLOv10 as the baseline model, the proposed model implements three key improvements: (1) the replacement of the Partial Self-Attention (PSA) module with the Convolutional Block Attention Module (CBAM), which enhances perception of channel-wise information and spatial localization of objects,(2) the adoption of dual-branch Re-upsample and Re-SCDown modules (2Re), which facilitates more effective utilization of multi-scale information,(3) the design of a novel classification feature processor, CIBwithResidualCV3 (CRCV3), which improves performance in classification tasks.Experimental results demonstrate that YOLOv10-CBRC achieves a mAP $$_{50\text {-}95}$$ 50 - 95 of 77.6% on the AbaTND, while YOLOv10-RC reaches mAP $$_{50\text {-}95}$$ 50 - 95 scores of 70.6% and 74.6% on the $$D^4LA$$ D 4 L A and IIIT-AR-13K datasets, respectively, significantly outperforming baseline model. …”
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Multi-CNN Deep Feature Fusion and Stacking Ensemble Classifier for Breast Ultrasound Lesion Classification by Kemal PANÇ, Sümeyye SEKMEN

“…A multi-step feature selection process involving principal component analysis, recursive feature elimination with LightGBM, and partial least squares discriminant analysis was applied. …”
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Advances in weed identification using hyperspectral imaging: A comprehensive review of platform sensors and deep learning techniques by Bright Mensah, Nitin Rai, Kelvin Betitame, Xin Sun

“…Techniques like image calibration, standard normal variate, multiplicative scatter correction, Savitsky-Golay smoothing, derivatives, and features selection are among the most used techniques, (d) traditional machine learning models namely support vector machines (SVM), partial least square discriminant analysis (PLS-DA), maximum likelihood classifiers (MLC), and random forest (RF) are the widely employed classifiers for weed identification, (e) the application of deep learning technique, namely convolutional neural networks (CNNs) are limited, but its application demonstrated superior performance accuracies compared to traditional machine learning models. …”
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On the effectiveness of neural operators at zero-shot weather downscaling by Saumya Sinha, Brandon Benton, Patrick Emami

“…Neural operators, which learn solution operators for a family of partial differential equations, have shown great success in scientific ML applications involving physics-driven datasets. …”
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Fractional-Order Adaptive P-Laplace Equation-Based Art Image Edge Detection by Ming Chen

“…The experimental results demonstrate that the algorithm can remove the noise while preserving the texture and details of the image. A fractional-order partial differential equation image edge detection model with a fractional-order fidelity term is proposed for Gaussian noise. …”
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GDS-YOLOv7: A High-Performance Model for Water-Surface Obstacle Detection Using Optimized Receptive Field and Attention Mechanisms by Xu Yang, Lei Huang, Fuyang Ke, Chao Liu, Ruixue Yang, Shicheng Xie

“…The proposed system implements three key innovations: (1) Architectural optimization through replacement of the Spatial Pyramid Pooling Cross Stage Partial Connections (SPPCSPC) module with GhostSPPCSPC for expanded receptive field representation. (2) Integration of a parameter-free attention mechanism (SimAM) with refined pooling configurations to boost multi-scale detection sensitivity, and (3) Strategic deployment of depthwise separable convolutions (DSC) to reduce computational complexity while maintaining detection fidelity. …”
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Real-Time Polyp Detection From Endoscopic Images Using YOLOv8 With YOLO-Score Metrics for Enhanced Suitability Assessment by Zahid Farooq Khan, Muhammad Ramzan, Mudassar Raza, Muhammad Attique Khan, Areej Alasiry, Mehrez Marzougui, Jungpil Shin

“…The proposed detector uses multiple convolutional, C2f (Faster Cross Stage Partial Bottleneck with 2 convolutions), and SPPF (Spatial Pyramid Pooling-Fast) blocks. …”
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Selective Feature Sets Based Fake News Detection for COVID-19 to Manage Infodemic by Manideep Narra, Muhammad Umer, Saima Sadiq, Ala' Abdulmajid Eshmawi, Hanen Karamti, Abdullah Mohamed, Imran Ashraf

“…Models tend to yield poor results if no preprocessing or partial processing is carried out. Convolutional neural network, long short term memory network, residual neural network (ResNet), and InceptionV3 show marginally lower performance than the extra tree classifier. …”
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Evaluation and Early Detection of Downy Mildew of Lettuce Using Hyperspectral Imagery by Songtao Ban, Minglu Tian, Dong Hu, Mengyuan Xu, Tao Yuan, Xiuguo Zheng, Linyi Li, Shiwei Wei

“…Moreover, regression models developed using Partial Least Squares (PLS), Random Forest (RF), and Convolutional Neural Network (CNN) algorithms demonstrated high accuracy and reliability in predicting DI, flavonoids, and anthocyanins, with the highest R² of 0.857, 0.910, and 0.963, respectively. …”
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FIDC-YOLO: Improved YOLO for Detecting Pine Wilt Disease in UAV Remote Sensing Images via Feature Interaction and Dependency Capturing by Zekun Xu, Yipeng Zhou, Shiting Wen, Weipeng Jing

“…Although current PWD detection methods use the attention mechanisms to improve the recognition of infected targets, the limitations of convolutional neural networks (CNNs) in capturing long-range dependencies hinder their ability to separate targets from the background. …”
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Segmentation-Assisted Fusion-Based Classification for Automated CXR Image Analysis by Shilu Kang, Dongfang Li, Jiaxin Xu, Aokun Mei, Hua Huo

“…The method involves two stages: first, we use a lightweight segmentation model, Partial Convolutional Segmentation Network (PCSNet) designed based on an encoder–decoder architecture, to accurately obtain lung masks from CXR images. …”
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LD-Det: Lightweight Ship Target Detection Method in SAR Images via Dual Domain Feature Fusion by Hang Yu, Bingzong Liu, Lei Wang, Teng Li

“…This model designs three effective modules, including the following: (1) a wavelet transform method for image compression and the frequency domain feature extraction; (2) a lightweight partial convolutional module for channel feature extraction; and (3) an improved multidimensional attention module to realize the weight assignment of different dimensional features. …”
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Improving drug-induced liver injury prediction using graph neural networks with augmented graph features from molecular optimisation by Taeyeub Lee, Joram M. Posma

“…Methods We evaluated several GNN architectures, including Graph Convolutional Networks (GCNs), Graph Attention Networks (GATs), Graph Sample and Aggregation (GraphSAGE), and Graph Isomorphism Networks (GINs), using the latest FDA DILI dataset and other molecular property prediction datasets. …”
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A Model for Diagnosing Mild Nutrient Stress in Facility-Grown Tomatoes Throughout the Entire Growth Cycle by Yunpeng Yuan, Guoxiang Sun, Guangyu Chen, Qihua Zhang, Lingwei Liang

“…The study compares the diagnostic performance of Random Forest (RF), Support Vector Machine (SVM), Partial Least Squares (PLS), Convolutional Neural Networks (CNNs), and CNN + Long Short-Term Memory (LSTM) models for detecting mild nutrient stress in facility-grown tomatoes. …”
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LC-MS-based metabolomics for detecting adulteration in Tribulus terrestris-derived dietary supplements by Dejan Gođevac, Jovana Stanković Jeremić, Mirjana Cvetković, Katarina Simić, Ivana Sofrenić, Jovana Ljujić, Lazar Popović, Uroš Gašić, Yen-Nhi Hoang, Tao Huan, Stefan Ivanović

“…Authentic plant materials, simulated adulterated samples, and commercial products were analyzed using principal component analysis (PCA), orthogonal partial least squares discriminant analysis (OPLS-DA), and a convolutional neural network tool. …”
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