Vowel classification with combining pitch detection and one‐dimensional convolutional neural network based classifier for gender identification by Chia‐Hung Lin, Hsiang‐Yueh Lai, Ping‐Tzan Huang, Pi‐Yun Chen, Chien‐Ming Li

“…For IVR and ASR applications, speaker classification into different ages and gender groups can be applied in human–machine interaction or computer‐based interaction systems for customised advertisement, translation (text generation), machine dialog systems, or self‐service applications. …”
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Dual-Branch Attention Convolution Spectral–Spatial Feature Extraction Networks for Hyperspectral Image Classification by Genyun Sun, Cheng Jing, Aizhu Zhang, Xiaolin Chen, Hang Fu, Zhaojie Pan, Ji Cheng

“…DFEM dynamically extracts different scales features from both spatial and spectral branches by combining attention convolution and spectral–spatial attention mechanisms. …”
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Petrographic image classification of complex carbonate rocks from the Brazilian pre-salt using convolutional neural networks by Mateus Basso, João Paulo da Ponte Souza, Guilherme Furlan Chinelatto, Luis Augusto Antoniossi Mansini, Alexandre Campane Vidal

“…Among these techniques, Convolutional Neural Networks (CNNs) have emerged as powerful tools for image classification in various geoscientific applications. …”
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Predicting standardized uptake value of brown adipose tissue from CT scans using convolutional neural networks by Ertunc Erdil, Anton S. Becker, Moritz Schwyzer, Borja Martinez-Tellez, Jonatan R. Ruiz, Thomas Sartoretti, H. Alberto Vargas, A. Irene Burger, Alin Chirindel, Damian Wild, Nicola Zamboni, Bart Deplancke, Vincent Gardeux, Claudia Irene Maushart, Matthias Johannes Betz, Christian Wolfrum, Ender Konukoglu

“…Using the Attention U-Net architecture, we perform experiments on datasets from four different cohorts, the largest study to date. We segment BAT regions using predicted [18F]-FDG uptake values, achieving 23% to 40% better accuracy than conventional CT thresholding. …”
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WideConvNet: A Novel Wide Convolutional Neural Network Applied to ERP-Based Classification of Alcoholics by Abdul Baseer Buriro, Attaullah Buriro, Aziz Altaf Khuwaja, Abdul Aziz Memon, Nabeel Siddiqui, Stephen J. Weddell, Richard D. Jones

“…This paper presents WideConvNet, a novel convolutional neural network (ConvNet or CNN) that outperforms three established end-to-end models – EEGNet, Shallow Convolutional Neural Network (ShallowConvNet), and Deep Convolutional Neural Network (DeepConvNet) – in classifying event-related potentials (ERPs) from electroencephalogram (EEG) signals. …”
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Diagnosis of Sarcopenia Using Convolutional Neural Network Models Based on Muscle Ultrasound Images: Prospective Multicenter Study by Zi-Tong Chen, Xiao-Long Li, Feng-Shan Jin, Yi-Lei Shi, Lei Zhang, Hao-Hao Yin, Yu-Li Zhu, Xin-Yi Tang, Xi-Yuan Lin, Bei-Lei Lu, Qun Wang, Li-Ping Sun, Xiao-Xiang Zhu, Li Qiu, Hui-Xiong Xu, Le-Hang Guo

“…The contribution ratios of different data types were further analyzed for the multimodal model. …”
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Convolutional neural network (CNN) configuration using a learning automaton model for neonatal brain image segmentation. by Iran Sarafraz, Hamed Agahi, Azar Mahmoodzadeh

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Identifying Barong Tagalog Textile Using Convolutional Neural Network and Support Vector Machine with Structural Pattern Segmentation by Jeff B. Totesora, Edward C. Torralba, Cyrel O. Manlises

“…Although these textiles share similar hues, their patterns and textures differ significantly, leading to potential misidentification by individuals. …”
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Design of an Optimal Convolutional Neural Network Architecture for MRI Brain Tumor Classification by Exploiting Particle Swarm Optimization by Sofia El Amoury, Youssef Smili, Youssef Fakhri

“…The classification of brain tumors using MRI scans is critical for accurate diagnosis and effective treatment planning, though it poses significant challenges due to the complex and varied characteristics of tumors, including irregular shapes, diverse sizes, and subtle textural differences. Traditional convolutional neural network (CNN) models, whether handcrafted or pretrained, frequently fall short in capturing these intricate details comprehensively. …”
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Convolutional neural networks for sea surface data assimilation in operational ocean models: test case in the Gulf of Mexico by O. Zavala-Romero, O. Zavala-Romero, A. Bozec, E. P. Chassignet, J. R. Miranda, J. R. Miranda

“…This work explores the application of convolutional neural networks (CNNs) in data assimilation within the context of the HYbrid Coordinate Ocean Model (HYCOM) in the Gulf of Mexico. …”
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Predicting Index Trend Using Hybrid Neural Networks with a Focus on Multi-Scale Temporal Feature Extraction in the Tehran Stock Exchange by Mohammad Osoolian, Ali Nikmaram, Mahdi Karimi

“…To address the limitations identified, future research endeavors could explore alternative model configurations, ensemble methods, or hybrid architectures that combine the strengths of different predictive models. Additionally, incorporating additional market indicators and exploring intraday data sources could further enhance prediction accuracy and robustness. …”
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Convolutional Neural Network-Based Fiber Optic Channel Emulator and Its Application to Fiber-Longitudinal Power Profile Estimation by Daobin Wang, Kun Wen, Tiantian Bai, Ruiyang Xia, Zanshan Zhao, Guanjun Gao

“…The first network treats different polarization streams identically and is denoted as CNN. …”
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Weighted Feature Fusion Network Based on Large Kernel Convolution and Transformer for Multi-Modal Remote Sensing Image Segmentation by Jianxia Wang, Shaozu Qiu, Jia Cai, Xiaoming Zhang

“…Finally, a Difference information Feature Fusion Module (DFFM) leveraging attention to differential regions is used to achieve cross-level feature fusion and enhance small object detection. …”
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Spatial Modeling of Airborne Particles (PM2.5 and PM10) in Tehran city Using Convolutional Neural Network. by Abed Bashar Doost, Mohammad Saadi Mesgari

“…The evaluation of the model was done using different evaluation criteria, and the findings showed that the R-squared (R2) values in this model for PM2.5 and PM10 pollutants are 0.889 and 0.972, respectively. …”
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A High-Efficient Method for Synthesizing Multiple Antenna Array Radiation Patterns Simultaneously Based on Convolutional Neural Network by Shiyuan Zhang, Chuan Shi, Ming Bai

“…During training, the cost function is designed to represent the difference between each synthesized radiation pattern and the corresponding target radiation pattern, guiding self-learning. …”
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A Comparative Analysis of Convolutional Neural Network (CNN): MobileNetV2 and Xception for Butterfly Species Classification by Mehta Pradnyatama, Christy Atika Sari, Eko Hari Rachmawanto, Hussain Md Mehedul Islam

“…This study aims to compare the effectiveness and efficiency of two convolutional neural network architectures, MobileNetV2 and Xception, for automated butterfly species classification. …”
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Static Early Fusion Techniques for Visible and Thermal Images to Enhance Convolutional Neural Network Detection: A Performance Analysis by Enrique Heredia-Aguado, Juan José Cabrera, Luis Miguel Jiménez, David Valiente, Arturo Gil

“…This paper presents a comparison of different image fusion methods for matching visible-spectrum images with thermal-spectrum (far-infrared) images, aimed at enhancing person detection using convolutional neural networks (CNNs). …”
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Modified Multiresolution Convolutional Neural Network for Quasi-Periodic Noise Reduction in Phase Shifting Profilometry for 3D Reconstruction by Osmar Antonio Espinosa-Bernal, Jesús Carlos Pedraza-Ortega, Marco Antonio Aceves-Fernandez, Juan Manuel Ramos-Arreguín, Saul Tovar-Arriaga, Efrén Gorrostieta-Hurtado

“…Therefore, a model of convolutional neural network along with four different patterns of frequencies projected in the three-step technique is researched in this work. …”
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TDP-SAR: Task-Driven Pruning Method for Synthetic Aperture Radar Target Recognition Convolutional Neural Network Model by Tong Zheng, Qing Wu, Chongchong Yu

“…Unlike conventional pruning techniques that rely on generic parameter importance metrics, our approach implements frequency domain analysis of convolutional kernels across different processing stages of SAR target recognition models. …”
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Research on Multi-Scale Spatio-Temporal Graph Convolutional Human Behavior Recognition Method Incorporating Multi-Granularity Features by Yulin Wang, Tao Song, Yichen Yang, Zheng Hong

“…Firstly, a skeleton fine-grained partitioning strategy is proposed, which initializes the skeleton data into data streams of different granularities. An adaptive cross-scale feature fusion layer is designed using a normalized Gaussian function to perform feature fusion among different granularities, guiding the model to focus on discriminative feature representations among similar behaviors through fine-grained features. …”
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