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

“…The use of ML enables the analysis of large datasets, the identification of complex patterns, and can save time and reduce costs compared to conventional approaches. Among these techniques, Convolutional Neural Networks (CNNs) have emerged as powerful tools for image classification in various geoscientific applications. …”
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Determination of Sequential Well Placements Using a Multi-Modal Convolutional Neural Network Integrated with Evolutionary Optimization by Seoyoon Kwon, Minsoo Ji, Min Kim, Juliana Y. Leung, Baehyun Min

“…It achieves prediction accuracy within a 3% relative error margin, while significantly reducing computational costs to just 11.18% of those associated with full-physics reservoir simulations. …”
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Evaluation of Neural Networks for Improved Computational Cost in Carbon Nanotubes Geometric Optimization by Luis Josimar Vences-Reynoso, Daniel Villanueva-Vasquez, Roberto Alejo-Eleuterio, Federico Del Razo-López, Sonia Mireya Martínez-Gallegos, Everardo Efrén Granda-Gutiérrez

“…This work highlights the potential of integrating deep learning techniques into materials science; it also offers a transformative approach to reducing computational costs in optimizing CNTs and presents a way for accelerated research in molecular systems.…”
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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

“…The main framework of the method is a convolutional neural network, where the convolutional layer is used to reduce the expansion of input parameters due to the simultaneous input of multiple mask matrices. …”
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Induction of Convolutional Decision Trees for Semantic Segmentation of Color Images Using Differential Evolution and Time and Memory Reduction Techniques by Adriana-Laura López-Lobato, Héctor-Gabriel Acosta-Mesa, Efrén Mezura-Montes

“…Convolutional Decision Trees (CDTs) are machine learning models utilized as interpretable methods for image segmentation. …”
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RE-YOLO: An apple picking detection algorithm fusing receptive-field attention convolution and efficient multi-scale attention. by Jinxue Sui, Li Liu, Zuoxun Wang, Li Yang

“…First, this paper innovatively introduces Receptive-Field Attention Convolution (RFAConv) to improve the backbone and neck network of YOLOv8. …”
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Real-Time Dolphin Whistle Detection on Raspberry Pi Zero 2 W with a TFLite Convolutional Neural Network by Rocco De Marco, Francesco Di Nardo, Alessandro Rongoni, Laura Screpanti, David Scaradozzi

“…These advancements enable low-cost devices for real-time cetacean presence detection, offering transformative potential for bycatch reduction and adaptive deterrence systems. …”
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Integrating Multiscale Spatial–Spectral Shuffling Convolution With 3-D Lightweight Transformer for Hyperspectral Image Classification by Qinggang Wu, Mengkun He, Qiqiang Chen, Le Sun, Chao Ma

“…However, these accuracy improvements come at the cost of significant demands on storage resources, computational overhead, and extensive training samples. …”
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CMDMamba: dual-layer Mamba architecture with dual convolutional feed-forward networks for efficient financial time series forecasting by Zhenkai Qin, Zhenkai Qin, Zhenkai Qin, Baozhong Wei, Baozhong Wei, Yujia Zhai, Ziqian Lin, Xiaochuan Yu, Xiaochuan Yu, Jingxuan Jiang

“…This significantly enhances the real-time data processing capability and reduces the deployment costs for risk management systems. The CMDMamba model employs a dual-layer Mamba structure that effectively captures price fluctuations at both the micro- and macrolevels in financial markets and integrates an innovative Dual Convolutional Feedforward Network (DconvFFN) module. …”
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Golden Chip-Free Hardware Trojan Detection Using Attention-Based Non-Local Convolution With Simple Recurrent Unit by Rama Devi Maddineni, Deepak Ch

“…In this approach, a non-local convolutional neural network embedded with an attention module plays a crucial role in efficiently extracting global spatial features from relevant regions. …”
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A Multi-Kernel Mode Using a Local Binary Pattern and Random Patch Convolution for Hyperspectral Image Classification by Wei Huang, Yao Huang, Zebin Wu, Junru Yin, Qiqiang Chen

“…The convolution kernel for the convolution operation is obtained from the original image using a random strategy without training. …”
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Acceleration of Urdu Optical Character Recognition on Zynq UltraScale+ MPSoC Using Deep Convolutional Neural Network by Fauzia Yasir, Majida Kazmi

“…Deploying deep learning–based optical character recognition (OCR) systems for low-resource, complex-script languages like Urdu remains a major challenge due to high computational costs, lack of annotated datasets, and limited hardware support for real-time applications. …”
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A Joint Estimation Method of Distribution Network Topology and Line Parameters Based on Power Flow Graph Convolutional Networks by Yu Wang, Xiaodong Shen, Xisheng Tang, Junyong Liu

“…An innovative joint estimation method for distribution network topology and line parameters is presented, utilizing a power flow graph convolutional network (PFGCN). This approach addresses the limitations of traditional methods that rely on costly voltage phase angle measurements. …”
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3D Reconstruction of Asphalt Pavement Macro-Texture Based on Convolutional Neural Network and Monocular Image Depth Estimation by Xinliang Liu, Chao Yin

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Automatic melanoma and non-melanoma skin cancer diagnosis using advanced adaptive fine-tuned convolution neural networks by Muhammad Amir Khan, Tehseen Mazhar, Muhammad Danish Ali, Umar Farooq Khattak, Tariq Shahzad, Mamoon M. Saeed, Habib Hamam

“…Traditionally approaches have High computational costs, a lack of interpretability, deal with numerous hyperparameters and spatial variation have always been problems with machine learning (ML) and DL. …”
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Multi-View Stereo Using Perspective-Aware Features and Metadata to Improve Cost Volume by Zongcheng Zuo, Yuanxiang Li, Yu Zhou, Fan Mo

“…This paper proposes PAC-MVSNet, which integrates perspective-aware convolution (PAC) and metadata-enhanced cost volumes to address the challenges in reflective and texture-less regions. …”
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Research on foreign object intrusion detection in railway tracks based on MSL-YOLO by Hongxia Niu, Dingchao Feng, Tao Hou

“…Specifically, a Multi-scale Shared Convolution Module (MSCM) is designed to replace SPPF, enhancing feature extraction while reducing parameters and computational cost. …”
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HCVNet: Binocular Stereo Matching via Hybrid Cost Volume Computation Module With Attention by Chenglin Dai, Qingling Chang, Tian Qiu, Xinglin Liu, Yan Cui

“…Finally, we adopt the Hybrid Cost Volume Computation Module (HCVCM) to construct and aggregate cost volume. …”
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A Weight Assignment-Enhanced Convolutional Neural Network (WACNN) for Freight Volume Prediction of Sea–Rail Intermodal Container Systems by Yuhonghao Wang, Wenxin Li, Xingmin Qi, Yinzhang Yu

“…In order to integrate the use of transportation resources, develop a reasonable sea–rail intermodal container transportation plan, and achieve cost reduction and efficiency improvement of the multimodal transportation system, a method for predicting the daily freight volume of sea–rail intermodal transportation based on a convolutional neural network (CNN) algorithm is proposed and a new feature processing method is used: weight assignment (WA). …”
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Efficient intelligent fault diagnosis method and graphical user interface development based on fusion of convolutional networks and vision transformers characteristics by Chaoquan Mo, Ke Huang, Houxin Ji, Wenhan Li, Kaibo Xu

“…Vision Transformers, by leveraging self-attention mechanisms to capture global dependencies, have shown excellent performance in many visual tasks, but often come with high computational costs. Therefore, this paper proposes a lightweight and efficient intelligent fault diagnosis method based on the fusion of Convolutional Network and Vision Transformer features (FCNVT). …”
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