Training Sample Formation for Convolution Neural Networks to Person Re-Identification from Video by S. A. Ihnatsyeva, R. P. Bohush

“…Therefore, the people images in the created set are characterized by the variability of the background, brightness and color characteristics. …”
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Identification method for wheel/rail tread defects based on integrated partial convolutional network by CHENG Xiang, HE Jing, ZHANG Changfan, JIA Lin

“…To address this challenge, an integrated partial convolutional network (I-PCNet) method was proposed for identifying wheel-rail tread defects. …”
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An XAI Approach to Melanoma Diagnosis: Explaining the Output of Convolutional Neural Networks with Feature Injection by Flavia Grignaffini, Enrico De Santis, Fabrizio Frezza, Antonello Rizzi

“…Computer-aided diagnosis (CAD) systems, which combine medical image processing with artificial intelligence (AI) to support experts in diagnosing various diseases, emerged from the need to solve some of the problems associated with medical diagnosis, such as long timelines and operator-related variability. The most explored medical application is cancer detection, for which several CAD systems have been proposed. …”
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Detecting ear lesions in slaughtered pigs through open-source convolutional neural networks by Matteo D’Angelo, Domenico Sciota, Anastasia Romano, Alfonso Rosamilia, Chiara Guarnieri, Chiara Cecchini, Alberto Olivastri, Giuseppe Marruchella

“…Results A total of 3,140 pictures were employed to train and test open-source convolutional neural networks. Investigations were carried out by three veterinarians, who agreed to assess porcine ears using a simplified method, to minimize inter-observers’ variability and to facilitate the convolutional neural networks’ training: a) healthy auricles (label 0); deformed auricles displaying alterations in their contour due to real lesions (label 1); postmortem artefacts due to slaughtering (label 2). …”
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Enhanced neurological anomaly detection in MRI images using deep convolutional neural networks by Ahmed Mateen Buttar, Zubair Shaheen, Abdu H. Gumaei, Mogeeb A. A. Mosleh, Mogeeb A. A. Mosleh, Indrajeet Gupta, Samah M. Alzanin, Muhammad Azeem Akbar

“…This study introduces a deep learning framework designed to automate neuro-diagnostics, addressing the limitations of current manual interpretation methods, which are often time-consuming and prone to variability.MethodsWe propose a specialized deep convolutional neural network (DCNN) framework aimed at detecting and classifying neurological anomalies in MRI data. …”
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Robust Long-Term Hand Grasp Recognition With Raw Electromyographic Signals Using Multidimensional Uncertainty-Aware Models by Yuzhou Lin, Ramaswamy Palaniappan, Philippe De Wilde, Ling Li

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Spatiotemporal Flood Hazard Classification in Bangkok Using Graph Convolutional Network and Temporal Fusion Transformer by Pakpoom Chaimook, Nirattaya Khamsemanan, Cholwich Nattee, Alice Sharp

“…To address this problem, this study proposes a hybrid deep learning framework combining Graph Convolution Network (GCN) and the Temporal Fusion Transformer (TFT) for predicting flood hazard levels in 50 Bangkok districts. …”
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An Efficient Aerial Image Detection with Variable Receptive Fields by Wenbin Liu, Liangren Shi, Guocheng An

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Finding the <i>q</i>-Appell Convolution of Certain Polynomials Within the Context of Quantum Calculus by Waseem Ahmad Khan, Khidir Shaib Mohamed, Francesco Aldo Costabile, Can Kızılateş, Cheon Seoung Ryoo

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Prediction of Effective Width of Varying Depth Box-Girder Bridges Using Convolutional Neural Networks by Kejian Hu, Xiaoguang Wu

“…The simplified formula for the effective flange width of box girder bridges of variable depth in existing codes and studies may not be conservative, and accurate methods, such as the finite element method, are time-consuming. …”
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Efficient Gearbox Fault Diagnosis Based on Improved Multi-Scale CNN with Lightweight Convolutional Attention by Bin Yuan, Yaoqi Li, Suifan Chen

“…The framework extracts the multi-band features of vibration signals through the improved multi-scale convolutional neural network, which significantly enhances adaptability to complex working conditions (variable rotational speed, strong noise); at the same time, the lightweight convolutional attention mechanism is used to replace the multi-attention of the traditional Transformer, which greatly reduces computational complexity while guaranteeing accuracy and realizes highly efficient, lightweight local–global feature modeling. …”
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Deep Convolutional Neural Network-Based Structural Damage Localization and Quantification Using Transmissibility Data by Sergio Cofre-Martel, Philip Kobrich, Enrique Lopez Droguett, Viviana Meruane

“…However, when dealing with massive data, manual feature extraction is not always a suitable approach as it is labor intensive requiring the intervention of domain experts with knowledge about the relevant variables that govern the system and their impact on its degradation process. …”
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Enhanced convolutional neural network methodology for solid waste classification utilizing data augmentation techniques by Daniel Hogan Itam, Ekwueme Chimeme Martin, Ibiba Taiwo Horsfall

“…The increasing volume of solid waste generated globally necessitates efficient classification systems to enhance recycling and waste management processes. Convolutional Neural Networks (CNNs) have emerged as a powerful tool for image classification tasks, including solid waste identification. …”
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Topological Attention-Based Convolution Neural Networks in Analyzing and Predicting Particulate Matter Pollution Level by Zixin Lin, Nur Fariha Syaqina Zulkepli, Mohd Shareduwan Mohd Kasihmuddin, R. U. Gobithaasan

“…Objective To improve the prediction of hourly PM10 pollution levels by integrating topological data analysis (TDA) with attention-based convolutional neural networks (ABCNNs), focusing on classifying air quality into eight severity levels. …”
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Exploring a minimal Convolutional Linear-Regression Model for Urban Land Surface Temperature estimation by Matteo Piccardo, Emanuele Massaro, Luca Caporaso, Alessandro Cescatti, Grégory Duveiller

“…In response, we introduce the Convolutional Linear-Regression Model (CLRM), a minimal complexity approach that focuses on two key assumptions: (i) correlations between LST at different times and spatial resolutions are considered without additional variables, and (ii) these correlations are modelled using linear relationships. …”
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ARCUNet: enhancing skin lesion segmentation with residual convolutions and attention mechanisms for improved accuracy and robustness by Tanishq Soni, Sheifali Gupta, Ahmad Almogren, Ayman Altameem, Ateeq Ur Rehman, Seada Hussen, Salil bharany

“…Abstract Skin lesion segmentation presents significant challenges due to the high variability in lesion size, shape, color, and texture and the presence of artifacts like hair, shadows, and reflections, which complicate accurate boundary delineation. …”
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Convolutional Neural Network-Based Approach for Cobb Angle Measurement Using Mask R-CNN by Marcos Villar García, José-Benito Bouza-Rodríguez, Alberto Comesaña-Campos

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Relapse prediction using wearable data through convolutional autoencoders and clustering for patients with psychotic disorders by April Yujie Yan, Traci Jenelle Speed, Casey Overby Taylor

“…We created 2-dimensional multivariate time-series profiles containing activity and heart rate variability metrics, extracted latent features via convolutional autoencoders, and identified relapse clusters. …”
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Efficient remote sensing image classification using the novel STConvNeXt convolutional network by Bo Liu, Chenmei Zhan, Cheng Guo, Xiaobo Liu, Shufen Ruan

“…Abstract Remote sensing images present formidable classification challenges due to their complex spatial organization, high inter-class similarity, and significant intra-class variability. To address the balance between computational efficiency and feature extraction capability in existing methods, this paper innovatively proposes a lightweight convolutional network, STConvNeXt. …”
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Improved leukocyte classification in bone marrow cytology using convolutional neural network with contrast enhancement by Shahid Mehmood, Tariq Shahzad, Muhammad Zubair, Farman Matloob Khan, Muhammad Adnan Khan, Khmaies Ouahada, Amir H. Gandomi

“…Image contrast enhancement techniques, particularly CLAHE, improve the convolution neural network (CNN) model’s performance. …”
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