A temporal-spectral graph convolutional neural network model for EEG emotion recognition within and across subjects by Rui Li, Xuanwen Yang, Jun Lou, Junsong Zhang

“…To address these challenges in emotion recognition, we propose a novel neural network model named Temporal-Spectral Graph Convolutional Network (TSGCN). To capture high-level information distributed in time, spatial, and frequency domains, TSGCN considers both neural oscillation changes in different time windows and topological structures between different brain regions. …”
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Domain-Adaptive Direction of Arrival (DOA) Estimation in Complex Indoor Environments Based on Convolutional Autoencoder and Transfer Learning by Lingyu Shen, Jianfeng Li, Jingjing Pan, Junpeng Shi, Rui Xu, Hao Wang, Weiming Deng

“…By integrating Gradient Reversal Layer (GRL) and Maximum Mean Discrepancy (MMD) loss functions, the model effectively reduces distributional differences between the source and target domains. The CAE-DANN enables transfer learning between data with similar features from different domains. …”
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Modified AlexNet Convolution Neural Network For Covid-19 Detection Using Chest X-ray Images by Shadman Q. Salih, Hawre Kh. Abdulla, Zanear Sh. Ahmed, Nigar M. Shafiq Surameery, Rasper Dh. Rashid

“…Moreover, the modified AlexNet architecture is proposed in different scenarios were differing from each other in terms of the type of the pooling layers and/or the number of the neurons that have used in the second fully connected layer. …”
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Recognizing Digital Ink Chinese Characters Written by International Students Using a Residual Network with 1-Dimensional Dilated Convolution by Huafen Xu, Xiwen Zhang

“…The 1-D ResNetDC not only utilizes multi-scale convolution kernels, but also employs different dilation rates on a single-scale convolution kernel to obtain information from various ranges. …”
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Prediction of Residual Life of Rolling Bearings Based on Multi-Scale Improved Temporal Convolutional Network (MITCN) Model by Keru Xia, Qi Li, Luyuan Han, Zhaohui Ren, Hengfa Luo

“…By introducing a multi-scale expanded causal convolution residual structure, improved temporal convolutional network (ITCN) modules with different expansion factors capture information on different time scales and combine soft threshold functions and channel attention mechanisms to adaptively generate thresholds and eliminate redundant information. …”
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Real-Time Series Arc Fault Detection and Appliances Classification in AC Networks Based on Competing Convolutional Kernels by Francesco Ferracuti, Riccardo Felicetti, Luca Cavanini, Patrick Schweitzer, Andrea Monteriu

“…The proposed method is validated using a public database, where data from 13 different types of loads is collected according to the IEC 62606 standard. …”
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Detection of degraded forests in Guinea, West Africa, using convolutional neural networks and Sentinel-2 time series by An Vo Quang, An Vo Quang, Nicolas Delbart, Gabriel Jaffrain, Camille Pinet

“…Altogether, the results show that the method is transferable and applicable across different years and among the different Guinean forest regions, such as the Ziama, Diécké, and Nimba massifs. …”
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Convolutional Neural Networks Trained on Internal Variability Predict Forced Response of TOA Radiation by Learning the Pattern Effect by Maria Rugenstein, Senne VanLoon, Elizabeth A. Barnes

“…Trained on internal variability alone, the nonlinear CNN can predict radiation under strong climate change, outperforms a regularized linear regression approach, and works within and across different climate models. We show with explainable artificial intelligence methods that the CNN draws predictive skill from physically meaningful regions but at much smaller spatial scales than currently assumed.…”
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Multi-step prediction of coal mine adit deformation based on time convolutional long short-term memory by Wenli JI, Xin DAN, Chenyang MA, Jing CHAI, Yuyi WU, Fengqi QIU, Wentao LIU, Wulin LEI, Yongliang LIU

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Using a Region-Based Convolutional Neural Network (R-CNN) for Potato Segmentation in a Sorting Process by Jaka Verk, Jernej Hernavs, Simon Klančnik

“…Specifically, Mask R-CNN is implemented and evaluated based on its performance with different parameters in order to achieve the best segmentation results. …”
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Development of an EEG signal analysis application through a convolution of a complex Morlet wavelet: preliminary results by José Humberto Trueba Perdomo, Ignacio Herrera Aguilar, Francesca Gasparini

“…As a final result, the application shows the signal power value and a spectrogram of the convoluted signal. Moreover, the created application compares different EEG channels at the same time, in a fast and straightforward way, through a time and frequency analysis. …”
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Fatigue life prediction of composite materials using strain distribution images and a deep convolution neural network by Yuta Mizuno, Atsushi Hosoi, Hiroyuki Koshita, Dai Tsunoda, Hiroyuki Kawada

“…High prediction accuracy was obtained when training and testing were performed on the same SFRP specimens. In contrast, using different specimens for training and testing resulted in lower accuracy. …”
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Pavement condition detection using acceleration data collected by smartphones based on 1D convolutional neural network by Yudong Han, Zhaobo Li, Jiaqi Li

“…Five types of 1D-CNN with different activation functions and network structures were designed to classify the data and were compared with machine learning algorithms, including support vector machine (SVM) and radial basis function (RBF) neural networks. …”
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Adaptive spatial-channel feature fusion and self-calibrated convolution for early maize seedlings counting in UAV images by Zhenyuan Sun, Zhenyuan Sun, Zhenyuan Sun, Zhi Yang, Zhi Yang, Yimin Ding, Yimin Ding, Boyan Sun, Boyan Sun, Saiju Li, Saiju Li, Zhen Guo, Zhen Guo, Lei Zhu, Lei Zhu

“…The ASCFF module enhances the discriminability of early maize seedlings by adaptively fusing feature maps extracted from different layers of the backbone network. Additionally, transfer learning was employed to integrate pre-trained weights with RSCconv, facilitating faster convergence and improved accuracy. …”
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Rolling bearing fault identification with acoustic emission signal based on variable-pooling multiscale convolutional neural networks by Yue Zhang, Yang Yu, Zheng Yang, Qiang Liu

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Optimizing forest stand aggregation in fragmented stands using graph convolutional networks: A case study in Japan by YangYu You, Hyun Bae Kim, Takuyuki Yoshioka

“…Using forestry data from the Nishikawa area, two types of stand connectivity models—selective and full connection—were constructed to represent different spatial contexts. Results show that the selective model emphasizes road accessibility, while the full model better maintains age-class continuity and internal cohesion. …”
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A targeted one dimensional fully convolutional autoencoder network for intelligent compression of magnetic flux leakage data by Wenbo Xuan, Pengchao Chen, Rui Li, Fuxiang Wang, Kuan Fu, Zhitao Wen

“…Secondly, a data block classification algorithm is developed to calculate peak values for segmented differential data, and based on a predefined targeted threshold, distinguish different types of MFL data. Subsequently, based on the distinct data types, targeted one-dimensional fully convolutional autoencoder models are constructed to effectively achieve dimensionality reduction compression and reconstruction of the MFL data. …”
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Rolling Bearing Fault Diagnosis Using a Deep Convolutional Autoencoding Network and Improved Gustafson–Kessel Clustering by Yaochun Wu, Rongzhen Zhao, Wuyin Jin, Linfeng Deng, Tianjing He, Sencai Ma

“…Training deep neural networks, such as convolutional neural networks (CNNs), require plenty of labeled samples. …”
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Fetal-Net: enhancing Maternal-Fetal ultrasound interpretation through Multi-Scale convolutional neural networks and Transformers by Umar Islam, Yasser A. Ali, Muna Al-Razgan, Hanif Ullah, Mohmmed Amin Almaiah, Zeeshan Tariq, Khalid Mohammad Wazir

“…The model was trained on a large, expertly annotated set of more than 12,000 ultrasound images across different anatomical planes for effective identification of fetal structures and anomaly detection. …”
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Breast tumor segmentation in ultrasound using distance-adapted fuzzy connectedness, convolutional neural network, and active contour by Marta Biesok, Jan Juszczyk, Pawel Badura

“…We produce different weight combinations to determine connectivity maps driven by particular image specifics. …”
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