Research on Wind Turbine Unbalance Fault Diagnosis Based on Wavelet Transform and Convolutional Neural Network by Zhenling Li, Yukun Gao

“…Wavelet transform is performed on the collected signals, and the 2-dimensional time-frequency map is obtained as the object dataset for the classification. Thirdly, a convolutional neural network is used to classify rotor imbalances of different magnitudes, and different convolution kernels and activation functions are tested. …”
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Finger Vein Recognition Based on Unsupervised Spiking Convolutional Neural Network with Adaptive Firing Threshold by Li Yang, Qiong Yao, Xiang Xu

“…Initially, Gabor and difference of Gaussian (DoG) filters are employed to convert image pixel intensities into spike latency encodings. …”
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Blink Detection Using 3D Convolutional Neural Architectures and Analysis of Accumulated Frame Predictions by George Nousias, Konstantinos K. Delibasis, Georgios Labiris

“…The cropped eye regions are organized as three-dimensional (3D) input with the third dimension spanning time of 300 ms. Two different 3D convolutional neural networks are utilized (a simple 3D CNN and 3D ResNet), as well as a 3D autoencoder combined with a classifier coupled to the latent space. …”
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Marine heatwaves in the Mediterranean Sea: a convolutional neural network study for extreme event prediction by A. Parasyris, V. Metheniti, N. Kampanis, S. Darmaraki

“…To ensure robust performance, we explore various configurations, including different forecast horizons and U-Net architectures, number of input days, features, and different subset splits of train–test datasets. …”
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Hybrid transformer and convolution iteratively optimized pyramid network for brain large deformation image registration by Xinxin Cui, Yuee Zhou, Caihong Wei, Guodong Suo, Fengqing Jin, Jianlan Yang

“…Secondly, the Swin-Transformer module is combined with the convolution iterative strategy, and each layer of the decoder is carefully designed according to the semantic information characteristics of different decoding layers. …”
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BDNet: Bengali Handwritten Numeral Digit Recognition based on Densely connected Convolutional Neural Networks by Abu Sufian, Anirudha Ghosh, Avijit Naskar, Farhana Sultana, Jaya Sil, M.M. Hafizur Rahman

“…Images of handwritten digits are different from natural images as the orientation of a digit, as well as similarity of features of different digits, makes confusion. …”
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Prediction of Effective Width of Varying Depth Box-Girder Bridges Using Convolutional Neural Networks by Kejian Hu, Xiaoguang Wu

“…In addition, the impact of different architectures is also studied. The proposed method makes real-time analysis possible and has a wide range of applications in the analysis and design of box-girder bridges at different depths.…”
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Convolutional neural networks-based red blood cell detection and tracking with automatic data generation by Kohei TOYAMA, Tomoki MIZUNO, Takuto ARAKI, Toru HYAKUTAKE

“…Moreover, we used the difference image between consecutive frames, along with RBC images, as inputs for CNNs. …”
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Automated Recognition of Submerged Body-like Objects in Sonar Images Using Convolutional Neural Networks by Yan Zun Nga, Zuhayr Rymansaib, Alfie Anthony Treloar, Alan Hunter

“…Due to the large class imbalance in the dataset, CNN models were trained with six different imbalance ratios. Two different pre-trained models (ResNet-50 and Xception) were compared, and trained via transfer learning. …”
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Improvement of Classification Results of Convolutional Neural Networks Using Various Gan-Based Augmentation Techniques by Kališková Lenka, Butka Peter

“…In the presented work, we focus on image augmentation with the use of several variations of GAN to improve the classification of convolutional neural network. Accordingly, to prove the advantage of GAN-based image augmentation in comparison with methods of classical augmentation, we used specifically three different degrees of image rotation and compared classification results of convolutional neural network that use images from these augmentation methods. …”
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A Lightweight Convolutional Neural Network for Classification of Brain Tumors Using Magnetic Resonance Imaging by Mahir Kaya, Alper Özatılgan

“…A public data set consisting of 4 different classes (Meningioma, Glioma, Pituitary and Normal) obtained for use in the training of CNN models was trained and tested with 50 different deep learning models designed, and a better result was obtained when compared with the existing studies in the literature with 99.47% accuracy and 99.44% F1 score values.…”
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Metaheuristic Algorithms for Optimization and Feature Selection in Cloud Data Classification Using Convolutional Neural Network by Nandita Goyal, Munesh Chandra Trivedi

“…But the truth is that everything has a price and cloud computing is no different. With Cloud computing there comes a number of security concerns which need to be addressed. …”
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Classification of Pulmonary Nodules Using Multimodal Feature‐Driven Graph Convolutional Networks with Specificity Proficiency by Renjie Xu, Zhanlue Liang, Dan Wang, Rui Zhang, Jiayi Li, Lingfeng Bi, Kai Zhang, Weimin Li

“…Graph neural networks could compare the difference among all samples (nodes in graph) and transmit the interrelationship among them to obtain a global landscape. …”
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Assessing the Generalization Capacity of Convolutional Neural Networks and Vision Transformers for Deforestation Detection in Tropical Biomes by P. J. Soto Vega, D. Lobo Torres, G. X. Andrade-Miranda, G. A. O. P. da Costa, R. Q. Feitosa

“…However, not enough attention has been paid to the domain shift issue, which affects classification performance when pre-trained models are used in areas with different forest covers and deforestation practices. …”
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Recognition of multi-symptomatic rice leaf blast in dual scenarios by using convolutional neural networks by Huiru Zhou, Dingzhou Cai, Lijie Lin, Dong Huang, Bo-Ming Wu

“…Firstly, the impact of different training methods on imbalanced datasets was compared. …”
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Audio copy-move forgery detection with decreasing convolutional kernel neural network and spectrogram fusion by Canghong Shi, Xin Qiu, Min Wu, Xianhua Niu, Xiaojie Li, Sani M. Abdullahi

“…We solve the problem of different sensitivities by sequentially lowering the parameters of the convolutional layers in the four convolutional groups, thus obtaining high accuracy in audio classification. …”
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3DVT: Hyperspectral Image Classification Using 3D Dilated Convolution and Mean Transformer by Xinling Su, Jingbo Shao

“…Hyperspectral imaging and laser technology both rely on different wavelengths of light to analyze the characteristics of materials, revealing their composition, state, or structure through precise spectral data. …”
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Exploring a minimal Convolutional Linear-Regression Model for Urban Land Surface Temperature estimation by Matteo Piccardo, Emanuele Massaro, Luca Caporaso, Alessandro Cescatti, Grégory Duveiller

“…In response, we introduce the Convolutional Linear-Regression Model (CLRM), a minimal complexity approach that focuses on two key assumptions: (i) correlations between LST at different times and spatial resolutions are considered without additional variables, and (ii) these correlations are modelled using linear relationships. …”
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Spotting Leaders in Organizations with Graph Convolutional Networks, Explainable Artificial Intelligence, and Automated Machine Learning by Yunbo Xie, Jose D. Meisel, Carlos A. Meisel, Juan Jose Betancourt, Jianqi Yan, Roberto Bugiolacchi

“…Our study investigates five and six types of social networks frequently observed in different organizations. This study is conducted using datasets we collected from an IT company and public datasets collected from a manufacturing company for the thorough evaluation of prediction performance. …”
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Convolutional Neural Network Training for RGBN Camera Color Restoration Using Generated Image Pairs by Zhenghao Han, Li Li, Weiqi Jin, Xia Wang, Gangcheng Jiao, Hailin Wang

“…The color correction matrix model widely used in current commercial color digital cameras cannot handle the complicated mapping function between biased color and ground truth color. Convolutional neural networks (CNNs) are good at fitting such complicated relationships, but they require a large quantity of training image pairs of different scenes. …”
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