Automated learning of glaucomatous visual fields from OCT images using a comprehensive, segmentation-free 3D convolutional neural network model by Makoto Koyama, Yuta Ueno, Yoshikazu Ito, Tetsuro Oshika, Masaki Tanito

“…Further validation in external datasets and exploration in different clinical settings are warranted.…”
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Deep convolutional neural network based archimedes optimization algorithm for heart disease prediction based on secured IoT enabled health care monitoring system by Sureshkumar S, Santhosh Babu A. V, Joseph James S, Maranco M

“…The implementation work of the proposed model is simulated using JAVA software with different performance measures. Various performance metrics with state-of-art methods validate the effectiveness of the proposed model. …”
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Dynamic Path Flow Estimation Using Automatic Vehicle Identification and Probe Vehicle Trajectory Data: A 3D Convolutional Neural Network Model by Can Chen, Yumin Cao, Keshuang Tang, Keping Li

“…To fuse the two data sources belonging to different detection ways at the data level, the virtual AVI points, analogous to the real AVI points (turning movements at nodes with AVI detectors), are defined and selected to statically observe the dynamic movement of the probe vehicles. …”
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Cloud based real-time multivariate multi-step prediction of systolic blood pressure and heart rate using temporal convolutional network and Apache Spark by Hager Saleh, Nora El-Rashidy, Sherif Mostafa, Abdulaziz AlMohimeed, Shaker El-Sappagh, Zainab H. Ali

“…During the offline model development, we explore the single-task and multi-task modeling. Different optimization steps have been explored. The single task includes forecasting HR and SBP in multi-step heads using Temporal Convolutional Networks (TCN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). …”
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Accuracy of Diabetic Retinopathy Staging with a Deep Convolutional Neural Network Using Ultra-Wide-Field Fundus Ophthalmoscopy and Optical Coherence Tomography Angiography by Toshihiko Nagasawa, Hitoshi Tabuchi, Hiroki Masumoto, Shoji Morita, Masanori Niki, Zaigen Ohara, Yuki Yoshizumi, Yoshinori Mitamura

“…The present study aimed to compare the accuracy of diabetic retinopathy (DR) staging with a deep convolutional neural network (DCNN) using two different types of fundus cameras and composite images. …”
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WSDC-ViT: a novel transformer network for pneumonia image classification based on windows scalable attention and dynamic rectified linear unit convolutional modules by Yu Gu, Haotian Bai, Meng Chen, Lidong Yang, Baohua Zhang, Jing Wang, Xiaoqi Lu, Jianjun Li, Xin Liu, Dahua Yu, Ying Zhao, Siyuan Tang, Qun He

“…Abstract Accurate differential diagnosis of pneumonia remains a challenging task, as different types of pneumonia require distinct treatment strategies. …”
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EEG-Based Multi-Level Mental State Classification Using Partial Directed Coherence and Graph Convolutional Networks: Impact of Binaural Beats on Stress Mitigation by Yara Badr, Fares Al-Shargie, M. N. Afzal Khan, Nour Faris Ali, Usman Tariq, Fadwa Almughairbi, Fabio Babiloni, Hasan Al-Nashash

“…This study addresses limitations in EEG-based stress detection research by developing a novel approach to differentiate multiple mental states in different stress baseline population samples. Utilizing EEG signals, graph convolutional neural networks (GCNs), and binaural beats stimulation (BBs), the research investigates stress detection and reduction in two population sample groups with distinct baselines (group 1: low daily baseline, and group 2: stressed daily baseline). …”
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Brittleness evaluation of main coal seams in Permian Taiyuan-Shanxi formations, Baode block, Ordos Basin: based on a convolutional neural network method by Qingfeng ZHANG, Ziling LI, Jikun ZHANG, Shuai HAO, Xiaoguang SUN, Yanjie SHANG, Yun ZUO

“…However, the productivity varies greatly among wells, mainly attributed to the strong heterogeneity caused by regional differences in reservoir brittleness. Rock mechanical parameter method is commonly used to evaluate reservoir brittleness. …”
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Mesoscale Cellular Convection Detection and Classification Using Convolutional Neural Networks: Insights From Long‐Term Observations at ARM Eastern North Atlantic Site by Jingjing Tian, Jennifer Comstock, Andrew Geiss, Peng Wu, Israel Silber, Damao Zhang, Parvathi Kooloth, Ya‐ Chien Feng

“…The analysis of the MCC cases shows clear differences between closed and open MCCs: Closed MCC clouds are characterized by lower cloud tops and bases, shallower cloud geometrical depth, weaker horizontal wind speeds, stronger atmospheric stability, and a more homogeneous liquid water path than open MCCs. …”
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sEMG-based gesture recognition using multi-stream adaptive CNNs with integrated residual modules by Yutong Xia, Dawei Qiu, Cheng Zhang, Jing Liu

“…IntroductionConvolutional neural networks are widely used in gesture recognition research, which employs surface electromyography. …”
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A lip reading method based on adaptive pooling attention Transformer by YAO Yun, HU Zhenxiao, DENG Tao, WANG Xiao

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The lip reading method based on Adaptive Pooling Attention Transformer by YAO Yun, HU Zhenxiao, DENG Tao, WANG Xiao

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Restoration of Out-of-Focus Fluorescence Microscopy Images Using Learning-Based Depth-Variant Deconvolution by Da He, De Cai, Jiasheng Zhou, Jiajia Luo, Sung-Liang Chen

“…As for a wide-field image, there exist different DV-PSFs within the two-dimensional fluorescence image due to the surface undulation. …”
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Synthesis of MR fingerprinting information from magnitude-only MR imaging data using a parallelized, multi network U-Net convolutional neural network by Kiaran P. McGee, Yi Sui, Robert J. Witte, Ananya Panda, Norbert G. Campeau, Thomaz R. Mostardeiro, Nahil Sobh, Umberto Ravaioli, Shuyue (Lucia) Zhang, Kianoush Falahkheirkhah, Nicholas B. Larson, Christopher G. Schwarz, Jeffrey L. Gunter

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Predictive modelling employing machine learning, convolutional neural networks (CNNs), and smartphone RGB images for non-destructive biomass estimation of pearl millet (Pennisetum... by Faten Dhawi, Abdul Ghafoor, Norah Almousa, Sakinah Ali, Sara Alqanbar

“…The SHAP analysis identified Normalized Green-Red Difference Index (NGRDI) and plant height as the most influential features for AGB estimation. …”
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PIC2O-Sim: A physics-inspired causality-aware dynamic convolutional neural operator for ultra-fast photonic device time-domain simulation by Pingchuan Ma, Haoyu Yang, Zhengqi Gao, Duane S. Boning, Jiaqi Gu

“…Optical simulation plays an important role in photonic hardware design flow. The finite-difference time-domain (FDTD) method is widely adopted to solve time-domain Maxwell equations. …”
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Myoelectric-Based Estimation of Vertical Ground Reaction Force During Unconstrained Walking by a Stacked One-Dimensional Convolutional Long Short-Term Memory Model by Alessandro Mengarelli, Andrea Tigrini, Mara Scattolini, Rami Mobarak, Laura Burattini, Sandro Fioretti, Federica Verdini

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Application of YOLO11 Model with Spatial Pyramid Dilation Convolution (SPD-Conv) and Effective Squeeze-Excitation (EffectiveSE) Fusion in Rail Track Defect Detection by Weigang Zhu, Xingjiang Han, Kehua Zhang, Siyi Lin, Jian Jin

“…First, the conventional convolutional layers in the YOLO (You Only Look Once) 11backbone network were substituted with the SPD-Conv (Spatial Pyramid Dilation Convolution) module to enhance the model’s detection performance on low-resolution images and small objects. …”
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Individualized spatial network predictions using Siamese convolutional neural networks: A resting-state fMRI study of over 11,000 unaffected individuals. by Reihaneh Hassanzadeh, Rogers F Silva, Anees Abrol, Mustafa Salman, Anna Bonkhoff, Yuhui Du, Zening Fu, Thomas DeRamus, Eswar Damaraju, Bradley Baker, Vince D Calhoun

“…The proposed framework evaluates whether pairs of spatial networks (e.g., visual network and auditory network) are capable of subject identification and assesses the spatial variability in different network pairs' predictive power in an extensive whole-brain analysis. …”
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Multimodal feature fusion-based graph convolutional networks for Alzheimer's disease stage classification using F-18 florbetaben brain PET images and clinical indicators. by Gyu-Bin Lee, Young-Jin Jeong, Do-Young Kang, Hyun-Jin Yun, Min Yoon

“…The usage ratio of these different modal data and edge assignment threshold were tuned by setting them as hyperparameters. …”
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