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

“…The ensemble approach achieved an impressive overall accuracy of 96.83%, demonstrating superior performance in accurately classifying the different leaf conditions. The results highlight the effectiveness of combining multiple deep learning models, which allows for the extraction of diverse feature representations and decision-making strategies. …”
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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

“…This unbiased CNN-based hypothalamus volume quantification study demonstrated similarly reduced hypothalamus volume in PLS and ALS patients, despite the clinical phenotypic differences.…”
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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

“…The experimental results on various PolSAR datasets show that the proposed method can achieve impressive superpixel segmentation by fitting the real boundaries of different types of ground objects effectively and efficiently. …”
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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

“…Employing an encoder–decoder architecture, the generator effectively extracts multilevel features, accommodating significant resolution differences between high-resolution and low-resolution 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 model performs well on datasets with both high and low resolution, and even works reasonably for barely visible pores with different shapes and size. In our experiments, we could show that U-Net is suitable for pore segmentation. …”
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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

“…The approach enhances separability and establishes relationships between different components and targets at various scales. …”
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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

“…This confidence score serves as a weight to assess the relative importance of different time windows. Finally, the confidence-weighted fused features are passed through a multilayer perceptron (MLP) for final classification. …”
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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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Exploring Applications of Convolutional Neural Networks in Analyzing Multispectral Satellite Imagery: A Systematic Review by Antonia Ivanda, Ljiljana Šerić, Maja Braović

“…This review addresses three Research Questions (RQ): RQ1: “In which application domains different CNN models have been successfully applied for processing MSI data?”…”
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OR-FCOS: an enhanced fully convolutional one-stage approach for growth stage identification of Oudemansiella raphanipes by Runze Fang, Huamao Huang, Nuoyan Guo, Haichuan Wei, Shiyi Wang, Haiying Hu, Ming Liu

“…We constructed the ORaph8K dataset, containing 8,000 images of Oudemansiella raphanipes at different growth stages, used for training and validation. …”
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IFNet: An Interactive Frequency Convolutional Neural Network for Enhancing Motor Imagery Decoding From EEG by Jiaheng Wang, Lin Yao, Yueming Wang

“…Methods: Inspired by the concept of cross-frequency coupling and its correlation with different behavioral tasks, this paper proposes a lightweight Interactive Frequency Convolutional Neural Network (IFNet) to explore cross-frequency interactions for enhancing representation of MI characteristics. …”
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Leveraging ensemble convolutional neural networks and metaheuristic strategies for advanced kidney disease screening and classification by Abeer saber, Esraa Hassan, Samar Elbedwehy, Wael A. Awad, Tamer Z. Emara

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