Enhancing human–robot collaboration with thermal images and deep neural networks: the unique thermal industrial dataset WLRI-HRC and evaluation of convolutional neural networks by S. Süme, K.-M. Ponomarjova, T. M. Wendt, S. J. Rupitsch

“…In this research, the dataset is evaluated for implementation by different convolutional neural networks: first, one-stage methods, i.e., You Only Look Once (YOLO v5, v8, v9 and v10) in different model sizes and, secondly, two-stage methods with Faster R-CNN with three variants of backbone structures (ResNet18, ResNet50 and VGG16). …”
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Novel non-destructive authentication of nine Dendrobium species using residual convolutional neural network relying on plant images and FT-NIR spectral information by Yulin Xu, Lian Li, Yuanzhong Wang, Qiang Hu

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Deep Learning-Based Glaucoma Detection Using Clinical Notes: A Comparative Study of Long Short-Term Memory and Convolutional Neural Network Models by Ali Mohammadjafari, Maohua Lin, Min Shi

“…This study aims to investigate the capability of deep learning approaches to detect glaucoma from clinical notes based on a real-world dataset including 10,000 patients. Different popular models are explored to predict the binary glaucomatous status defined from a comprehensive vision function assessment. …”
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A GPSR-Based Sparse ECG Signal Recovery Approach for Hybrid Compressive Sensing Framework and Its Classification Using Deep Convolution Neural Network by Subramanyam Shashi Kumar, Prakash Ramachandran

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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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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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Multi-view Fusion 3D Model Classification by GAO Yuan, DING Bo, HE Yong-jun

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Discrimination of Types of Seizure Using Brain Rhythms Based on Markov Transition Field and Deep Learning by Anand Shankar, Samarendra Dandapat, Shovan Barma

“…For this purpose, the Markov transition field transformation technique has been employed for 2D image construction by preserving statistical dynamics characteristics of EEG signals, which are very important during the discrimination of different types of seizures. And, a convolution neural network (CNN) has been used for classification. …”
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Soil Porosity Detection Method Based on Ultrasound and Multi-Scale Feature Extraction by Hang Xing, Zeyang Zhong, Wenhao Zhang, Yu Jiang, Xinyu Jiang, Xiuli Yang, Weizi Cai, Shuanglong Wu, Long Qi

“…Since the collected ultrasonic signals belong to long-time series data and there are different frequency and sequence features, this study constructs a multi-scale CNN-LSTM deep neural network model using large convolution kernels based on the idea of multi-scale feature extraction, which uses multiple large convolution kernels of different sizes to downsize the collected ultra-long time series data and extract local features in the sequences, and combining the ability of LSTM to capture global and long-term dependent features enhances the feature expression ability of the model. …”
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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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Research on real-time monitoring method of mine personnel protective equipment with improved YOLOv8 by Lei ZHANG, Zhipeng SUN, Hongjing TAO, Shangkai HAO, Qianru YAN, Xiwei LI

“…Making convolution deformable, when sampling, it can more closely detect the true shape and size of the object, more robust, It effectively improves its feature acquisition ability for targets of different scales. …”
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