A Deep Learning-Based Echo Extrapolation Method by Fusing Radar Mosaic and RMAPS-NOW Data by Shanhao Wang, Zhiqun Hu, Fuzeng Wang, Ruiting Liu, Lirong Wang, Jiexin Chen

“…However, most of these extrapolation network architectures are built upon convolutional neural networks, using radar echo images as input. …”
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Identification of Pneumonia in Chest X-Ray Image Based on Transformer by Yongjun Ma, Wei Lv

“…The research of application models based on traditional convolutional neural networks has gradually entered the bottleneck period of performance improvement, and the improvement of chest X-ray image models has gradually become a difficult problem in the study. …”
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Analyzing the dynamics between crude oil spot prices and futures prices by maturity terms: Deep learning approaches to futures-based forecasting by Jeonghoe Lee, Bingjiang Xia

“…This study employs multiple deep learning algorithms, including Multilayer Perceptron (MLP), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolutional Neural Network (CNN), and Temporal Convolutional Neural Network (TCN), to forecast crude oil spot prices. …”
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Hybrid face recognition under adverse conditions using appearance‐based and dynamic features of smile expression by Murat Taskiran, Nihan Kahraman, Cigdem Eroglu Erdem

“…We evaluated the performances of three different state‐of‐the‐art pre‐trained deep convolutional neural networks (DCNNs) under a variety of severe image distortions with different parameters. …”
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DFPF-Net: Dynamically Focused Progressive Fusion Network for Remote Sensing Change Detection by Chengming Wang, Peng Duan, Jinjiang Li

“…In recent years, various CD methods represented by convolutional neural network (CNN) and transformer have achieved significant success in effectively detecting difference areas in bitemporal remote sensing images. …”
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Implementation of MF block in CNN for advanced REB fault diagnosis by M. Pandiyan, Narendiranath Babu T.

“…Using the Balance Cross-Entropy (BCE) loss function for training has helped the model optimize prediction accuracy by minimizing the difference between the actual class and the predicted class probabilities. …”
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MOD3NN: A Framework for Automatic Signal Modulation Detection Using 3D CNN by Vishal Perekadan, Chaity Banerjee, Tathagata Mukherjee, Eduardo Pasiliao, Hovannes Kulhandjian, Michel Kulhandjian

“…In this work, we present an application of a three-dimensional convolutional neural network for the task of automatic modulation recognition from raw I/Q signal data.  …”
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Fusing Events and Frames with Coordinate Attention Gated Recurrent Unit for Monocular Depth Estimation by Huimei Duan, Chenggang Guo, Yuan Ou

“…Unlike the conventional ConvGRUs, our CAGRU abandons the conventional practice of using convolutional layers for all the gates and innovatively designs the coordinate attention as an attention gate and combines it with the convolutional gate. …”
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FAFTransformer: Multivariate time series prediction method based on multi‐period feature recombination by WenChang Zhang, LuZhi Yuan, Yun Sha, LingLin Yang, XueJun Liu, Yong Yan

“…The temporal dependencies within sequences are then captured using convolution based on different periods, and the correlations between sequences are learned by combining the multivariate attention mechanism to obtain the intra‐sequence and inter‐sequence correlations under the same period. …”
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ED-SA-ConvLSTM: A Novel Spatiotemporal Prediction Model and Its Application in Ionospheric TEC Prediction by Yalan Li, Haiming Deng, Jian Xiao, Bin Li, Tao Han, Jianquan Huang, Haijun Liu

“…Existing work based on Convolutional Long Short-Term Memory (ConvLSTM) primarily relies on convolutional operations for spatial feature extraction, which are effective at capturing local spatial correlations, but struggle to model long-range dependencies, limiting their predictive performance. …”
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Fault Detection of a Wheelset Bearing Based on Appropriately Sparse Impulse Extraction by Jianming Ding, Fenglin Li, Jianhui Lin, Bingrong Miao, Lu Liu

“…Convolution sparse representation (CSR) is a novel compressive sensing technique proposed in 2016 and provides an excellent framework for extracting the impulses induced by bearing faults and the unevenness of wheel tread. …”
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Prediction of Alzheimer’s Disease Based on Multi-Modal Domain Adaptation by Binbin Fu, Changsong Shen, Shuzu Liao, Fangxiang Wu, Bo Liao

“…However, the structure and semantics of different modal data are different, and the distribution between different datasets is prone to the problem of domain shift. …”
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Deep Learning-Aided Acoustic Source Localization in Thin-Walled Waveguides by Giacomo Donati, Federica Zonzini, Stefano Mariani, Denis Bogomolov, Luca De Marchi

“…Our approach is based on the combination of Convolutional Neural Networks (CNNs) and a dispersion compensation operator, the Warped Frequency Transform (WFT). …”
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Joint vibrotactile coding for machine recognition and human perception by Ying FANG, Yiwen XU, Tiesong ZHAO

“…In order to accurately transmit the content meaning of vibrotactile signals and achieve intelligent recognition and signal reconstruction, a joint vibrotactile coding scheme for machine recognition and human perception was proposed.At the encoding end, the original three-dimensional vibrotactile signals were converted into one-dimensional signals.Then the semantic information of the signals was extracted using a short-time Fourier transform before being effectively compressed and transmitted.At the decoding end, a fully convolutional neural network was used to intelligently recognize based on the semantic information.The difference between the original signals and the reconstructed signals based on semantic information was used as compensation for the semantic information, and the quality of the reconstructed signals was gradually improved to meet human perceptual needs.The experimental results show that the proposed scheme achieve tactile recognition with semantic information at a lower bit rate while improving the compression efficiency of tactile data, thus satisfying human perceptual needs.…”
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Semantic-aware multi-task learning for image aesthetic quality assessment by Weiliang Yan, Yuqing Li, Huan Yang, Baoxiang Huang, Zhenkuan Pan

“…However, automatically assessing aesthetic quality of an image is a challenging task, because image aesthetic is affected by various factors, and the criteria for judging the aesthetic of images with diverse semantic information are different. To this end, a Semantic-Aware Multi-task convolution neural network (SAM-CNN) for evaluating image aesthetic quality is proposed in this paper. …”
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Application of deconvolutional networks for feature interpretability in epilepsy detection by Sihao Shao, Yu Zhou, Ruiheng Wu, Aiping Yang, Qiang Li

“…Even automated detection algorithms are already available to assist clinicians in reviewing EEG data, many algorithms used for seizure detection in epilepsy fail to account for the contributions of different channels. The Fully Convolutional Network (FCN) can provide the model’s interpretability but has not been applied in seizure detection.MethodsTo address these challenges, a novel convolutional neural network (CNN) model, combining SE (Squeeze-and-Excitation) modules, was proposed on top of the FCN. …”
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Remote sensing semantic segmentation based on multimodal feature alignment and fusion by B. Chang, T. Balz

“…However, the majority of these models employ convolutional neural networks (CNNs) or visual transformers (ViTs) for fusion operations, which results in inadequate modelling and representation of local-global context. …”
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Breath Detection from a Microphone Using Machine Learning by Tomasz Sankowski, Piotr Sulewski, Jan Walczak, Aleksandra Bruska

“…Spectrogram Classification Using Convolutional Neural Networks: This approach involves classifying spectrograms of half-second or quarter-second audio segments using a convolutional neural network adapted for image classification tasks. 3. …”
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Unveiling the Efficacy of AI-based Algorithms in Phishing Attack Detection by Tajamul Shahzad, Kashif Aman

“…The increased usage of the Internet has resulted in the emergence of a different kind of theft referred to as cybercrime. …”
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Research on Pedestrians’ Visual Perception Characteristics Based on USGGS: A Case Study of the Six Inner-City Districts of Tianjin by Hanwen YANG, Shang’en TSOU, Yike HU

“…The model increases the sensory field of the convolution kernel by Atrous convolution technique and null convolution, allowing the model to capture image details at different resolutions and providing more accurate feature recognition support for subsequent SVI segmentation tasks. …”
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