Crop Recommendation Systems Based on Soil and Environmental Factors Using Graph Convolution Neural Network: A Systematic Literature Review by P. Ayesha Barvin, T. Sampradeepraj

“…Based on a broad variety of environmental variables, this research compares two graph-based crop recommendation algorithms, GCN and GNN. …”
Get full text

A topology-guided high-quality solution learning framework for security-constraint unit commitment based on graph convolutional network by Liqian Gao, Lishen Wei, Shichang Cui, Jiakun Fang, Xiaomeng Ai, Wei Yao, Jinyu Wen

“…In this sense, this paper proposes a topology-guided high-quality solution learning framework based on graph convolutional network (GCN) and neighborhood search (NS). …”
Get full text

Estimation of Strawberry Canopy Volume in Unmanned Aerial Vehicle RGB Imagery Using an Object Detection-Based Convolutional Neural Network by Min-Seok Gang, Thanyachanok Sutthanonkul, Won Suk Lee, Shiyu Liu, Hak-Jin Kim

“…Therefore, this study evaluated the spatial variability of strawberry canopy volumes using a ResNet50V2-based convolutional neural network (CNN) model trained with RGB images acquired through manual unmanned aerial vehicle (UAV) flights equipped with a digital color camera. …”
Get full text

STFDSGCN: Spatio-Temporal Fusion Graph Neural Network Based on Dynamic Sparse Graph Convolution GRU for Traffic Flow Forecast by Jiahao Chang, Jiali Yin, Yanrong Hao, Chengxin Gao

“…The dynamic sparse graph convolution gated recurrent unit (DSGCN-GRU) in this model is a novel component that integrates adaptive dynamic sparse graph convolution into the gated recurrent network to simulate the diffusion of information within a dynamic spatial structure. …”
Get full text

A Bootstrapping Convolutional Neural Network Technique for Optimizing Automated Detection of Equatorial Plasma Bubbles by Optical All‐Sky Imagers by Daniel Okoh, Claudio Cesaroni, Babatunde Rabiu, Kazuo Shiokawa, Yuichi Otsuka, Samuel Ogunjo, Aderonke Akerele, John Bosco Habarulema, Bruno Nava, Yenca Migoya‐Orué, Punyawi Jamjareegulgarn, Adeniran Seun, Ogechi Adama, George Ochieng, James Ameh, Adero Awuor, Paul Baki

“…This study presents a novel bootstrapping convolutional neural network (CNN) approach to optimize automated EPB detection on ASI images for operational space weather monitoring applications, and overcoming challenges related to image variability and imbalanced data sets. …”
Get full text

HD-MVCNN: High-density ECG signal based diabetic prediction and classification using multi-view convolutional neural network by D. Santhakumar, K. Dhana Shree, M. Buvanesvari, A. Saran Kumar, Ayodeji Olalekan Salau

“…This study explores the effects of diabetes on the heart, focusing on heart rate variability (HRV) signals, which can offer valuable information about the existence and seriousness of diabetes through the evaluation of diabetes-related heart problems. …”
Get full text

Comparison of Image Preprocessing Strategies for Convolutional Neural Network-Based Growth Stage Classification of Butterhead Lettuce in Industrial Plant Factories by Jung-Sun Gloria Kim, Soo Chung, Myungjin Ko, Jihoon Song, Soo Hyun Shin

Get full text

Identifying a Pattern of Predictable Decadal North Pacific SST Variability in Historical Observations by Emily M. Gordon, Noah S. Diffenbaugh

“…Abstract Improving predictions of decadal climate variability is critical for reducing uncertainty in near‐term climate change. …”
Get full text

Global surface eddy mixing ellipses: spatio-temporal variability and machine learning prediction by Tian Jing, Ru Chen, Chuanyu Liu, Chunhua Qiu, Chunhua Qiu, Cuicui Zhang, Mei Hong

“…Using satellite altimetry data and the Lagrangian single-particle method, we estimate eddy mixing ellipses across the global surface ocean, revealing substantial spatio-temporal variability. Notably, large mixing ellipses predominantly occur in eddy-rich and energetic ocean regions. …”
Get full text

Adaptive deep SVM for detecting early heart disease among cardiac patients by S. N. Netra, N. N. Srinidhi, E. Naresh

Subjects: “…Adaptive multiscale convolution capsule network…”
Get full text

Rolling Bearing Fault Diagnosis Model Based on Multi-Scale Depthwise Separable Convolutional Neural Network Integrated with Spatial Attention Mechanism by Zhixin Jin, Xudong Hu, Hongli Wang, Shengyu Guan, Kaiman Liu, Zhiwen Fang, Hongwei Wang, Xuesong Wang, Lijie Wang, Qun Zhang

“…In response to the challenges posed by complex and variable operating conditions of rolling bearings and the limited availability of labeled data, both of which hinder the effective extraction of key fault features and reduce diagnostic accuracy, this study introduces a model that combines a spatial attention (SA) mechanism with a multi-scale depthwise separable convolution module. …”
Get full text

Nonlinear Grey Prediction Model with Convolution Integral NGMC (1,n) and Its Application to the Forecasting of China’s Industrial SO2 Emissions by Zheng-Xin Wang

“…The grey prediction model with convolution integral GMC (1, n) is a multiple grey model with exact solutions. …”
Get full text

Evaluating pedestrian crossing safety: Implementing and evaluating a convolutional neural network model trained on paired aerial and subjective perspective images by Dylan Russon, Antoine Guennec, Juan Naredo-Turrado, Binbin Xu, Cédric Boussuge, Valérie Battaglia, Benoit Hiron, Emmanuel Lagarde

“…The analysis reveals that the ConvNextV2 model, in particular, demonstrates superior performance across most tasks, despite challenges such as data imbalance and the complex nature of variables like visibility and parking proximity.The findings highlight the potential of convolutional neural networks in improving pedestrian safety by enabling scalable and objective evaluations of crossings. …”
Get full text

An Adaptive Convolutional Neural Network With Spatio-Temporal Attention and Dynamic Pathways (ACNN-STADP) for Robust EEG-Based Motor Imagery Classification by Aaqib Raza, Mohd Zuki Yusoff

“…However, existing classification models face limitations such as inter-subject variability, lack of generalizability, high computational demands, low signal-to-noise ratios, and inefficient feature extraction, which impede their robustness and accuracy. …”
Get full text

Enhanced estimation of reference evapotranspiration using hybrid deep learning models and remote sensing variables by Tze Ying Fong, Yuk Feng Huang, Ren Jie Chin, Chai Hoon Koo

“…This study aims to develop ETo estimation models using deep learning algorithms with remote sensing variables as the input variables at Pulau Langkawi and Kuantan stations, located in Peninsular Malaysia. …”
Get full text

Bearing Fault Diagnosis under Transient Conditions: Using Variational Mode Decomposition and the Symmetrized Dot Pattern-Based Convolutional Neural Network Model by Jide Jia, Jianmin Mei, Chuang Sun, Fengjuan Yang

“…An effective bearing fault diagnosis method for gearbox applications under variable operating conditions is proposed, utilizing variational mode decomposition (VMD) for feature extraction, symmetrized dot pattern (SDP) for visual representation, and convolutional neural network (CNN) for deep feature extraction and classification. …”
Get full text

Improving Climate Bias and Variability via CNN‐Based State‐Dependent Model‐Error Corrections by William E. Chapman, Judith Berner

“…Abstract We develop an approach to correct biases in the atmospheric component of the Community Earth System Model using convolutional neural networks (CNNs) to create a corrective model parameterization for online bias reduction. …”
Get full text

Algae-Mamba: A Spatially Variable Mamba for Algae Extraction From Remote Sensing Images by Yaoteng Zhang, Shuaipeng Wang, Yanlong Chen, Shiqing Wei, Mingming Xu, Shanwei Liu

Get full text

Dynamics of Lemon Crop Production in Tambo Grande, Piura: Implementation of Convolutional Neural Networks and Analysis of Risk Management Associated with Thermal Climatic Phenomena by Luis Meneses, Mirtha Ortega, Misael Rivas, Piero Fernández, Antonio Angulo

“…This study uses satellite indices (NDVI and NDWI) and convolutional neural networks (CNNs, specifically AlexNet) to analyze crop dynamics and health during extreme events. …”
Get full text

Fault Diagnosis under Variable Working Conditions Based on STFT and Transfer Deep Residual Network by Yan Du, Aiming Wang, Shuai Wang, Baomei He, Guoying Meng

“…However, fault diagnosis under variable working conditions has been a significant challenge due to the domain discrepancy problem. …”
Get full text