Domain Adversarial Convolutional Neural Network Improves the Accuracy and Generalizability of Wearable Sleep Assessment Technology by Adonay S. Nunes, Matthew R. Patterson, Dawid Gerstel, Sheraz Khan, Christine C. Guo, Ali Neishabouri

“…This model generalized well to another dataset based on different wearable devices and activity counts, achieving an accuracy of 80.1% (sensitivity 84% and specificity 58%). …”
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Node-Based Graph Convolutional Network With SLIC Method for Breast Cancer Ultrasound Images Classification by Kien Trang, Fung Fung Ting, Bao Quoc Vuong, Chee-Ming Ting

“…This research presents a novel node-based Graph Convolutional Network (GCN) approach for the classification of breast cancer from ultrasound images. …”
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Classification Modeling Method for Near-Infrared Spectroscopy of Tobacco Based on Multimodal Convolution Neural Networks by Lei Zhang, Xiangqian Ding, Ruichun Hou

“…Finally, the influences of different network structure parameters on model identification performance are studied, and the optimal CNN models are selected and compared. …”
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Urban Spatiotemporal Event Prediction Using Convolutional Neural Network and Road Feature Fusion Network by Yirui Jiang, Shan Zhao, Hongwei Li, Huijing Wu, Wenjie Zhu

“…However, current methods fail to consider the impact of road information on the distribution of cases and the fusion of information at different scales. In order to solve the above problems, an urban spatiotemporal event prediction method based on a convolutional neural network (CNN) and road feature fusion network (FFN) named CNN-rFFN is proposed in this paper. …”
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Deep convolutional neural network for quantification of tortuosity factor of solid oxide fuel cell anode by Masashi KISHIMOTO, Yodai MATSUI, Hiroshi IWAI

“…A deep convolutional neural network model (DCNN) is developed to quantify the tortuosity factor of porous electrodes of solid oxide fuel cells (SOFCs). …”
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Ensemble-based sesame disease detection and classification using deep convolutional neural networks (CNN) by Abenet Alazar Hailu, Banchalem Chebudie Kassa, Esubalew Asmare Desta, Fikadu Berie Adugna, Ayodeji Olalekan Salau

“…Abstract This study presents an ensemble-based approach for detecting and classifying sesame diseases using deep convolutional neural networks (CNNs). Sesame is a crucial oilseed crop that faces significant challenges from various diseases, including phyllody and bacterial blight, which adversely affect crop yield and quality. …”
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A DNA-Level Convolutional Neural Network Based on Strand Displacement Reaction for Image Recognition by Shiyin Li, Zhixiang Yin, Chunlin Chen, Jing Yang, Zhen Tang

“…Firstly, we integrated DNA-based weighted-sum module, subtraction activation module, and reporter module using SDR to implement CNN, and designed the weighted shared boxes to perform the function of convolutional kernel. The feasibility and parallelism of the proposed DNA-level CNN were verified by simultaneous recognition of three different categories of images, including handwritten numbers, letters and Chinese characters. …”
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Liver Tumor Prediction using Attention-Guided Convolutional Neural Networks and Genomic Feature Analysis by S. Edwin Raja, J. Sutha, P. Elamparithi, K. Jaya Deepthi, S.D. Lalitha

“…More considerably, the proposed methods outperform all the other methods in different datasets in terms of recall, precision, and Specificity by up to 10 percent than all other methods including CELM, CAGS, DM-ML, and so on. • Utilization of Attention-Guided Convolutional Neural Networks (AG-CNN) enhances tumor region focus and segmentation accuracy. • Integration of Genomic Feature Analysis (GFAM) identifies molecular markers for subtype-specific tumor classification.…”
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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

“…Traditional flood prediction models often fail to capture spatial correlations across districts and the temporal patterns within different types of features. 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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Testing convolutional neural network based deep learning systems: a statistical metamorphic approach by Faqeer ur Rehman, Clemente Izurieta

“…We propose seven MRs combined with different statistical methods to statistically verify whether the program under test adheres to the relation(s) specified in the MR(s). …”
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Hypothalamic atrophy in primary lateral sclerosis, assessed by convolutional neural network-based automatic segmentation by Jan Kassubek, Francesco Roselli, Simon Witzel, Johannes Dorst, Albert C. Ludolph, Volker Rasche, Ina Vernikouskaya, Hans-Peter Müller

“…Recently, we have introduced automatic hypothalamic quantification method based on the use of convolutional neural network (CNN) to reduce human variability and enhance analysis robustness. …”
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Classifying early-stage soybean fungal diseases on hyperspectral images using convolutional neural networks by Chieh Fu Hsiao, Georg Feyrer, Anthony Stein

“…To this end, in this study, hyperspectral imaging (HSI) data are combined with deep learning models to test the classification ability of two soybean fungal diseases: Asian soybean rust (Phakopsora pachyhizi) and soybean stem rust (Sclerotinia scleroriorum). Different CNNs employing 2D, 3D convolution, and hybrid approaches are compared. …”
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PolSAR-SFCGN: An End-to-End PolSAR Superpixel Fully Convolutional Generation Network by Mengxuan Zhang, Jingyuan Shi, Long Liu, Wenbo Zhang, Jie Feng, Jin Zhu, Boce Chu

“…Addressing the above issues, this study proposes an end-to-end fully convolutional superpixel generation network for PolSAR images. …”
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Condition Monitoring of Chain Sprocket Drive System Based on IoT Device and Convolutional Neural Network by Sang Kwon Lee, Jiseon Back, Kanghyun An, Sunwon Kim, Changho Lee, Pungil Kim

“…Multiple-classification performance of the trained network was tested using 100 image samples. Feature maps for different fault types were obtained from the final CNN convolution layer. …”
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VR-Aided Ankle Rehabilitation Decision-Making Based on Convolutional Gated Recurrent Neural Network by Hu Zhang, Yujia Liao, Chang Zhu, Wei Meng, Quan Liu, Sheng Q. Xie

“…This allows for the simulation of five stages of rehabilitation based on the Brunnstrom staging scale, providing tailored control parameters for virtual training scenarios suited to patients at different stages of recovery. Experiments comparing the classification performance of convolutional neural networks and long short-term memory networks were conducted. …”
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The GAN Spatiotemporal Fusion Model Based on Multiscale Convolution and Attention Mechanism for Remote Sensing Images by Youping Xie, Jun Hu, Kang He, Li Cao, Kaijun Yang, Luo Chen

“…This article introduces a new generative adversarial network (GAN) spatiotemporal fusion model based on multiscale convolution and attention mechanism for remote sensing images (MSCAM-GAN), to generate high-resolution fused images. …”
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Segmentation of pores in carbon fiber reinforced polymers using the U-Net convolutional neural network by Miroslav Yosifov, Patrick Weinberger, Bernhard Plank, Bernhard Fröhler, Markus Hoeglinger, Johann Kastner, Christoph Heinzl

“…The proposed workflow is designed to generate efficient segmentation models with reasonable execution time, applicable even for users using consumer-grade GPU systems. First, U-Net, a convolutional neural network, is modified to handle the segmentation of XCT datasets. …”
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OCSCNet-Tracker: Hyperspectral Video Tracker Based on Octave Convolution and Spatial–Spectral Capsule Network by Dong Zhao, Mengyuan Wang, Kunpeng Huang, Weixiang Zhong, Pattathal V. Arun, Yunpeng Li, Yuta Asano, Li Wu, Huixin Zhou

“…To address this challenge, the current study explores the application of capsule networks in HVT and proposes an approach based on octave convolution and a spatial–spectral capsule network (OCSCNet). …”
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Enhancing Neurodegenerative Disease Diagnosis Through Confidence-Driven Dynamic Spatio-Temporal Convolutional Network by Ning Yuan, Donghai Guan, Shengrong Li, Li Zhang, Qi Zhu

“…To address these limitations, we propose a novel method called Confidence-Driven Dynamic Spatio-Temporal Convolutional Network (CD-DSTCN). First, our proposed method employs a spatio-temporal convolutional network integrated with a temporal attention mechanism to extract spatio-temporal features within each window. …”
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Semantic Segmentation Method for High-Resolution Tomato Seedling Point Clouds Based on Sparse Convolution by Shizhao Li, Zhichao Yan, Boxiang Ma, Shaoru Guo, Hongxia Song

“…In order to reduce the number of parameters so as to further improve the inference speed, the SpConv module is designed to function through the residual concatenation of the skeleton convolution kernel and the regular convolution kernel. …”
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