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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Wavelet kernel and convolution neural network based accurate detection of incipient stator and rotor faults of induction motor by Sudeep Samanta, Jitendra Nath Bera, Amitava Biswas

“…This technique leverages the deep structures of CNNs to autonomously learn features from current signals, achieving a notable accuracy of above 97% in tests with both simulated model and two different hardware motor setup. The experimental result shows that it is capable of detecting as low as 1–2% of stator interturn fault with varying impedance in short circuit path as well as one broken rotor bar fault. …”
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LKDA-Net: Hierarchical transformer with large Kernel depthwise convolution attention for 3D medical image segmentation. by Ming Li, Jingang Ma, Jing Zhao

“…Firstly, inspired by the Swin Transformer module, we investigate different-sized large-kernel convolution attention mechanisms to obtain larger global receptive fields, and replace the MLP in the Swin Transformer with the Inverted Bottleneck with Depthwise Convolutional Augmentation to reduce channel redundancy and enhance feature expression and segmentation performance. …”
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Radio Frequency Signal-Based Drone Classification with Frequency Domain Gramian Angular Field and Convolutional Neural Network by Yuanhua Fu, Zhiming He

“…Moreover, to further improve the recognition performance, the images obtained from different channels are fused to serve as the input of a CNN classifier. …”
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Investigating the Impact of the Stationarity Hypothesis on Heart Failure Detection using Deep Convolutional Scattering Networks and Machine Learning by Mohamed Elmehdi Ait Bourkha, Dounia Nasir

“…In this paper, many contributions have been developed with the aim of enhancing automated detection of CVDs under the inter-patient paradigm, including using WSN in conjunction with different Machine Learning (ML) models and the stationarity hypothesis of ECG signals. …”
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Magnetic resonance imaging reconstruction algorithm under complex convolutional neural network in diagnosis and prognosis of cerebral infarction. by Jie Dong, Shujun Zhao, Yun Meng, Yong Zhang, Suxiao Li

“…The MRI images of patients with cerebral infarction in the dataset were undertaken as the data source, the average diffusion coefficient (DCavg) and apparent diffusion coefficient (ADC) values of different parts of the MRI image were measured, respectively. …”
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A Lightweight and Efficient Plant Disease Detection Method Integrating Knowledge Distillation and Dual-Scale Weighted Convolutions by Xiong Yang, Hao Wang, Qi Zhou, Lei Lu, Lijuan Zhang, Changming Sun, Guilu Wu

“…Subsequently, we developed the DSConv module—a novel convolutional structure employing double-scale weighted convolutions that dynamically adjust to different scale perceptions and optimize attention allocation. …”
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Classification of Chicken Carcass Breast Blood-Related Defects Using Hyperspectral Imaging Combined with Convolutional Neural Networks by Liukui Duan, Juanfang Bao, Hao Yang, Liuqian Gao, Xu Zhang, Shengjie Li, Huihui Wang

“…The combination of hyperspectral data and CNN can effectively accomplish the classification of CBDs, although different model architectures emphasize classification speed and accuracy differently. …”
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Analysis and Classification of Distress on Flexible Pavements Using Convolutional Neural Networks: A Case Study in Benin Republic by Crespin Prudence Yabi, Godfree F. Gbehoun, Bio Chéissou Koto Tamou, Eric Alamou, Mohamed Gibigaye, Ehsan Noroozinejad Farsangi

“…The accuracy achieved by the different models varies from 94.6% to 97.3%. This accuracy is promising compared to the literature models. …”
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LSTA-CNN: A Lightweight Spatiotemporal Attention-Based Convolutional Neural Network for ASD Diagnosis Using EEG by Jing Li, Xiangwei Jia, Xinghan Chen, Gongfa Li, Gaoxiang Ouyang

“…However, as a kind of neural electrophysiological signal, EEG contains different types of temporal and spatial information. …”
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