Color image hybrid noise filtering algorithm based on deep convolution neural network by Yongfei Yu, Yuanjian Yan

“…To solve the problems of the classical color image hybrid noise filtering method, a deep convolutional neural network improved by evolutionary strategy and jump connection is proposed and applied to the filtering noise reduction of color images. …”
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Convolutional neural network-based deep learning for landslide susceptibility mapping in the Bakhtegan watershed by Li Feng, Maosheng Zhang, Yimin Mao, Hao Liu, Chuanbo Yang, Ying Dong, Yaser A. Nanehkaran

“…Fifteen key conditioning factors—including topographical, geological, hydrological, and climatological variables—were incorporated into the model. While traditional statistical methods often fail to extract spatial hierarchies, the CNN model effectively processes multi-dimensional geospatial data, learning intricate patterns influencing slope instability. …”
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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

“…However, the lack of standardized sampling equipment and the variability of sampling environments often result in detection datasets that contain underexposed images, which hinder the effective identification of minor tread damages. …”
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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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SpaSEG: unsupervised deep learning for multi-task analysis of spatially resolved transcriptomics by Yong Bai, Xiangyu Guo, Keyin Liu, Bingjie Zheng, Yilin Wei, Yingyue Wang, Wenxi Zhang, Qiuhong Luo, Jianhua Yin, Liang Wu, Yuxiang Li, Yong Zhang, Ao Chen, Xiangdong Wang, Xun Xu, Chuanyu Liu, Xin Jin

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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

“…Interpretability is achieved using attention-based variable importance and regime shift detection. The predicted flood hazard levels are mapped into a high-resolution map produced through QGIS that indicates a strong correlation with flood-prone areas. …”
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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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MFF-Net: A Lightweight Multi-Frequency Network for Measuring Heart Rhythm from Facial Videos by Wenqin Yan, Jialiang Zhuang, Yuheng Chen, Yun Zhang, Xiujuan Zheng

“…Many well-established deep learning models can provide highly accurate and robust results in measuring heart rate (HR) and heart rate variability (HRV). However, these methods are unable to effectively eliminate illumination variation and motion artifact disturbances, and their substantial computational resource requirements significantly limit their applicability in real-world scenarios. …”
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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

“…However, difficult external variables including changes in illumination, occlusion, and background clutter can have a big impact on CNN performance. …”
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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

“…Methods The proposed framework combines CNNs, self-attention mechanisms, and persistent homology-derived topological features from three key environmental variables. PM10 category labels were predicted 6, 12, and 24 hours ahead. …”
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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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