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

“…Secondly, an adaptive threshold-based method is designed to fix binary variables to achieve model reduction. Thirdly, a customized prediction-based NS is developed to restore the feasibility of the predicted commitment. …”
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

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

Defending Data Poisoning Attack Through Watermarked Friendly Noise Luminosity Activated Dense Layered UNet for Classification of Lung Disease by M. Shyamala Devi, S. Priya

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

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

“…In 1998, El Niño reduced yield to 9.2 tons/ha, compared to 14 tons/ha in 2014. These variables provide key information for pruning, irrigation, and fertilization decisions, improving crop productivity.…”
Get full text

HCMMA-Net: A Hybrid Convolutional Multi-Modal Attention Network for Human Activity Recognition in Smart Homes Using Wearable Sensor Data by Nazish Ashfaq, Zeeshan Aziz, Muhammad Hassan Khan, Muhammad Adeel Nisar, Adnan Khalid

“…However, integrating these modalities poses challenges due to sensor heterogeneity and variability in placement. This study examines the role of multi-modalities in HAR using a hybrid convolutional multi-modal attention network (HCMMA-Net), designed to exploit spatial and temporal dependencies in sensor data. …”
Get full text

Melt Density Monitoring of Extruder Extrusion Process Based on Multi-source Data Fusion and Convolutional Long Short-term Memory Neural Network by Binbin ZHANG, Zhuyun CHEN, Fei ZHANG, Gang JIN

“…In addition, the proposed method exhibits robustness in handling the inherent complexities and variabilities in polymer extrusion processes, thus offering a reliable solution for ensuring product quality and process efficiency. …”
Get full text

Breast Tumor-Like-Masses Segmentation From Scattering Images Obtained With an Ultrahigh-Sensitivity Talbot-Lau Interferometer Using Convolutional Neural Networks by Ionut-Cristian Ciobanu, Nicoleta Safca, Elena Anghel, Dan Popescu

“…However, significant fluctuations were observed in validation accuracy, indicating sensitivity to dataset variability. U-Net demonstrated the most stable performance with an accuracy of 86.34% and an F1-score of 90.2%, making it the most reliable model for tumor segmentation in scattering images. …”
Get full text

Impact of training data composition on the generalizability of convolutional neural network aortic cross-section segmentation in four-dimensional magnetic resonance flow imaging by Chiara Manini, Markus Hüllebrand, Lars Walczak, Sarah Nordmeyer, Lina Jarmatz, Titus Kuehne, Heiko Stern, Christian Meierhofer, Andreas Harloff, Jennifer Erley, Sebastian Kelle, Peter Bannas, Ralf Felix Trauzeddel, Jeanette Schulz-Menger, Anja Hennemuth

“…However, the manual or semi-automatic segmentation of aortic vessel boundaries in 4D flow data introduces variability and limits the reproducibility of aortic hemodynamics visualization and quantitative flow-related parameter computation. …”
Get full text

Improved crop row detection by employing attention-based vision transformers and convolutional neural networks with integrated depth modeling for precise spatial accuracy by Hassan Afzaal, Derek Rude, Aitazaz A. Farooque, Gurjit S. Randhawa, Arnold W. Schumann, Nicholas Krouglicof

“…The depth maps were analyzed to examine GSD variability across fifteen clusters of field images, revealing a spectrum of GSD values ranging from 0.5 to 2.0 mm/pixel for most clusters. …”
Get full text

Dynamic Feature Extraction and Semi-Supervised Soft Sensor Model Based on SCINet for Industrial and Transportation Processes by Jun Wang, Changjian Qi, Xing Luo, Shihao Deng, Qi Lei

“…Meanwhile, the inconsistency of sensor sampling rates often leads to the problem of mismatch between process variables and quality variables. This paper proposes a semi-supervised soft sensor modeling method based on sample convolution and interactive networks (SCINet). …”
Get full text