Triplet-Style Dynamic Graph Network With Transformer Encoder for Scam Detection in Cryptocurrency Transactions by Min-Woo Nam, Hyeon-Ju Lee, Seok-Jun Buu

“…To address these intertwined challenges, we introduce the Triplet-style Dynamic Graph Convolutional Network (TD-GCN). TD-GCN is specifically engineered to model the dynamic nature of cryptocurrency transaction networks and to disentangle subtle distinctions between scam and benign transaction patterns. …”
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High sensitivity in spontaneous intracranial hemorrhage detection from emergency head CT scans using ensemble-learning approach by Juuso Takala, Heikki Peura, Riku Pirinen, Katri Väätäinen, Sergei Terjajev, Ziyuan Lin, Rahul Raj, Miikka Korja

“…The DL solution included four base convolutional neural networks (CNNs), which were trained using 300 head CT scans. …”
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SLG-Net: Small-Large-Global Feature-Based Multilevel Feature Extraction Network for Ultrasound Image Segmentation by Xinya Fan, Jianwen Hu, Kai Hu

“…Specifically, the CNN encoder improves the representation and interaction of fine feature and large-scale context feature for targets of different sizes by large-small kernel attention (LSKA) modules. …”
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Assessment of a Smartphone-Based Neural Network Application for the Risk Assessment of Skin Lesions under Real-World Conditions by Teresa Maria Kränke, Philipp Efferl, Katharina Tripolt-Droschl, Rainer Hofmann-Wellenhof

“… Introduction: The diagnostic performance of convolutional neural networks (CNNs) in diagnosing different types of skin cancer has been quite promising. …”
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Application of Hyperspectral Imaging for Identification of Melon Seed Variety Using Deep Learning by Zhiqi Hong, Chu Zhang, Wenjian Song, Xiangbo Nie, Hongxia Ye, Yong He

“…Logistic Regression (LR), Support Vector Classification (SVC), and Extreme Gradient Boosting (XGBoost) were used to establish classification models using spectral features and the manually extracted image features. Convolutional Neural Network (CNN) models were established using the five datasets. …”
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A Machine Learning Free Energy Functional for the 1D Reference Interaction Site Model: Towards Prediction of Solvation Free Energy for All Solvent Systems by Jonathan G. M. Conn, Abdullah Ahmad, David S. Palmer

“…In this work, we show that a single machine learning free energy functional for RISM can accurately model solvation thermodynamics in multiple solvents. A convolutional neural network is trained on solvation free energy density functions calculated by RISM for small organic molecules in approximately 100 different solvent systems. …”
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PolyNet: A self-attention based CNN model for classifying the colon polyp from colonoscopy image by Khaled Eabne Delowar, Mohammed Borhan Uddin, Md Khaliluzzaman, Riadul Islam Rabbi, Md Jakir Hossen, M. Moazzam Hossen

“…This study provides a sensitivity analysis to demonstrate how slight modifications in the network's architecture can impact the balance between accuracy and performance. We examined different CNN architectures and developed a good convolutional neural network (CNN) model for correctly predicting colon polyps using the Kvasir dataset. …”
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Image Classification Model Based on Contrastive Learning With Dynamic Adaptive Loss by Quandeng Gou, Jingxuan Zhou, Zi Li, Fangrui Zhang, Yuheng Ren

“…Most existing mainstream image classification models use the Convolutional Neural Network (CNN), the Transformer, or a combination of both as the backbone. …”
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ANF-Net: A Refined Segmentation Network for Road Scenes with Multiple Noises and Various Morphologies of Cracks by Xiao Hu, Qihao Chen, Xiuguo Liu, Gang Deng, Cheng Chi, Bin Wang

“…On the other hand, a constrained multi-morphological convolution structure is constructed by imposing learnable continuous constraints on the deformation offsets of convolutional kernels, allowing the network to adaptively fit different crack shapes. …”
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Hand Gesture Recognition From Wrist-Worn Camera for Human–Machine Interaction by Hong-Quan Nguyen, Trung-Hieu Le, Trung-Kien Tran, Hoang-Nhat Tran, Thanh-Hai Tran, Thi-Lan Le, Hai Vu, Cuong Pham, Thanh Phuong Nguyen, Huu Thanh Nguyen

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A Deep Learning–Based Multimodal F10.7 Prediction with Mamba by Dong Zhao, Fengping Dou, Xueshang Feng, Xin Huang, Sai Ma, Long Xu

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Future variation and uncertainty source decomposition in deep learning bias-corrected CMIP6 global extreme precipitation historical simulation by Xiaohua Xiang, Yongxuan Li, Xiaoling Wu, Zhu Liu, Lei Wu, Biqiong Wu, Chuanxin Jin, Zhiqiang Zeng

“…This study explores a bias correction approach based on convolutional neural networks (CNNs) to improve the accuracy of Expert Team on Climate Change Detection and Indices (ETCCDI) extreme precipitation indices calculated from the Coupled Model Intercomparison Project Phase Six (CMIP6) daily predictions. …”
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Research on Fault Prediction of Power Devices in Rod Control Power Cabinets Based on BiTCN-Attention Transfer Learning Model by Zhi Chen, Liqi Ye, Yifan Jian, Meiyuan Chen, Yuan Min

“…A transfer learning (TL) model based on a bidirectional time convolutional network (BiTCN) combined with attention was proposed to solve the problem of low accuracy of cross-operating fault prediction in a multi-source domain. …”
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A regional site response analysis of the Petobo area after the 2018.09.28 Palu–Donggala Indonesia earthquake using an equivalent-linear model by Rini Kusumawardani, Muhsiung Chang, Togani Cahyadi Upomo, Fernando Lopez-Caballero, Dadang Permana

“…Abstract This paper presents a site-specific seismic ground response evaluation through convolution–deconvolution analysis in the Balaroa–Petobo area during the 2018 Palu–Donggala Indonesia earthquake. …”
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Anterior Cruciate Ligament (ACL) Tear Detection Using Hybrid CNN Transformer by Suthir Sriram, Deependra K. Singh, D. V. Sairam, Nivethitha Vijayaraj, Thangavel Murugan

“…However, this also poses some difficulties due to differences in formats of medical images and standard images. …”
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A Predictive Method for Unplanned Postoperative Readmission Risk Based on Heterogeneous Data by Kai YU, Xiaofeng DONG, Zhenming YUAN, Zhaojian CUI, Weibin LUO

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On the Synergy of Optimizers and Activation Functions: A CNN Benchmarking Study by Khuraman Aziz Sayın, Necla Kırcalı Gürsoy, Türkay Yolcu, Arif Gürsoy

“…In this study, we present a comparative analysis of gradient descent-based optimizers frequently used in Convolutional Neural Networks (CNNs), including SGD, mSGD, RMSprop, Adadelta, Nadam, Adamax, Adam, and the recent EVE optimizer. …”
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Identifying Canopy Snow in Subalpine Forests: A Comparative Study of Methods by Natasha Harvey, Sean P. Burns, Keith N. Musselman, Holly Barnard, Peter D. Blanken

“…Timelapse photography images were analyzed using thresholding analysis and used to train a Convolutional Neural Network (CNN) model to estimate canopy snow presence. …”
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Establishing an AI-based diagnostic framework for pulmonary nodules in computed tomography by Ruiting Jia, Baozhi Liu, Mohsin Ali

“…Method The proposed deep learning framework used convolutional neural networks, and the image database totaled 1,056 3D-DICOM CT images. …”
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Towards an Energy Consumption Index for Deep Learning Models: A Comparative Analysis of Architectures, GPUs, and Measurement Tools by Sergio Aquino-Brítez, Pablo García-Sánchez, Andrés Ortiz, Diego Aquino-Brítez

“…Furthermore, the inclusion of the Swin Transformer, a state-of-the-art and modern non-convolutional model, highlights the adaptability of the framework to diverse architectural paradigms. …”
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