Efficient and Effective NDVI Time-Series Reconstruction by Combining Deep Learning and Tensor Completion by Ang Li, Menghui Jiang, Dong Chu, Xiaobin Guan, Jie Li, Huanfeng Shen

“…Reconstruction of normalized difference vegetation index (NDVI) time series plays an imperative part in the inference of vegetation dynamics. …”
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Prediction of Fractal Dimension in Shale CT and its Robustness to Interference Based on Convolutional Neural Networks by SUN Dingwei, WANG Lei, YANG Dong, HUANG Xudong, JIA Yichao

“…The results demonstrate a high degree of similarity between the predicted fractal dimensions of shale CT images by using the convolutional neural network and those computed through the box-counting method, with a difference of approximately 0.01. …”
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Feasibility of virtual T2-weighted fat-saturated breast MRI images by convolutional neural networks by Andrzej Liebert, Dominique Hadler, Chris Ehring, Hannes Schreiter, Luise Brock, Lorenz A. Kapsner, Jessica Eberle, Ramona Erber, Julius Emons, Frederik B. Laun, Michael Uder, Evelyn Wenkel, Sabine Ohlmeyer, Sebastian Bickelhaupt

“…The U-Net was trained using different input protocols consisting of T1-weighted, diffusion-weighted, and dynamic contrast-enhanced sequences to generate VirtuT2. …”
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All-optical convolutional neural network based on phase change materials in silicon photonics platform by Samaneh Amiri, Mehdi Miri

“…The individual optical elements, network layers and the overall convolution network are simulated using finite-difference time-domain method, coupled mode theory and Python programming, respectively. …”
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A simplified approach for simulating pollutant transport in small rivers with dead zones using convolution by Szymkiewicz Romuald

“…This approach valid for solution of the transport equation with constant coefficients is extended for piecewise constant coefficients. Convolution approach does not produce any numerical dissipation and dispersion errors typically generated by the methods based on the finite difference technique. …”
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Fault Line Selection Method Based on Transfer Learning Depthwise Separable Convolutional Neural Network by Haixia Zhang, Wenao Cheng

“…It also has good adaptability under different sampling frequencies, different noise environments, and different distribution network topologies; the line selection accuracy can reach more than 97.43%.…”
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Improving person re-identification based on two-stage training of convolutional neural networks and augmentation by S. A. Ihnatsyeva, R. P. Bohush

“…At the first stage, training is carried out on augmented data, at the second stage, fine tuning of the CNN is performed on the original images, which allows minimizing the losses and increasing model efficiency. The use of different data at different training stages does not allow the CNN to remember training examples, thereby preventing overfitting.Proposed method as expanding the training sample differs as it combines an image pixels cyclic shift, color  exclusion and fragment replacement with a reduced copy of another image. …”
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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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