Enhancement of Underwater Images through Parallel Fusion of Transformer and CNN by Xiangyong Liu, Zhixin Chen, Zhiqiang Xu, Ziwei Zheng, Fengshuang Ma, Yunjie Wang

“…Subsequently, to extract global features, both temporal and frequency domain features are incorporated to construct the convolutional neural network. Finally, the image’s high and low frequency information are utilized to fuse different features. …”
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Research on the identification and simulation of the earth-rock dam leakage inversion based on a high-density electrical method by Yang Duo, Huang Xixi, Zheng Jianguo, Sun Hao, Zhang Dingfei, Wang Shilin, Liu Tao

“…The law was summarized according to the inversion results, and the detection ability of the device for leakage regions of different sizes, quantities and distances was analyzed. …”
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A Non-Contact AI-Based Approach to Multi-Failure Detection in Avionic Systems by Chengxin Liu, Michele Ferlauto, Haiwen Yuan

“…The proposed method combines a self-attention mechanism with an adaptive graph convolutional neural network to enhance diagnostic precision. …”
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Fault Diagnosis Method for UHVDC Transmission Based on Deep Learning under Cloud-Edge Architecture by Shihao Zhou, Benren Pan, Dongbin Lu, Yiming Zhong, Guannan Wang

“…It has the highest diagnosis accuracy under different fault types, and its performance is better than the other three comparison methods.…”
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A Rolling-Bearing-Fault Diagnosis Method Based on a Dual Multi-Scale Mechanism Applicable to Noisy-Variable Operating Conditions by Jing Kang, Taiyong Wang, Ye Wei, Usman Haladu Garba, Ying Tian

“…Ultimately, the signal that has been denoised is utilized as input for the DWMFCNN model to recognize different kinds of rolling-bearing faults. Results from the experiments show that the suggested approach shows an improved denoising performance and a greater adaptability to changing working conditions.…”
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Identification of cucumber leaf diseases using deep learning and small sample size for agricultural Internet of Things by Jingyao Zhang, Yuan Rao, Chao Man, Zhaohui Jiang, Shaowen Li

“…To overcome this shortcoming, one approach, based on small sample size and deep convolutional neural network, was proposed for conducting the recognition of cucumber leaf diseases under field conditions. …”
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An Efficient and Low-Complexity Transformer-Based Deep Learning Framework for High-Dynamic-Range Image Reconstruction by Josue Lopez-Cabrejos, Thuanne Paixão, Ana Beatriz Alvarez, Diodomiro Baldomero Luque

“…In this context, various architectures with different approaches exist, such as convolutional neural networks, diffusion networks, generative adversarial networks, and Transformer-based architectures, with the latter offering the best quality but at a high computational cost. …”
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Investigating the accuracy of neural networks for blood pressure prediction in the ICU by Charles J. Gillan, Bartosz Gorecki

“…The physiological state of an ICU patient is therefore quite different to a hyper or hypotensive patient outside hospital, suggesting that predicting blood pressure in this environment is more challenging The work investigates whether building neural network architectures with multivariate input data is capable of predicting blood pressures in this environment. …”
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Dense-TNT: Efficient Vehicle Type Classification Neural Network Using Satellite Imagery by Ruikang Luo, Yaofeng Song, Longfei Ye, Rong Su

“…Vehicle data for three regions under four different weather conditions were deployed to evaluate the recognition capability. …”
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A New and Tested Ionospheric TEC Prediction Method Based on SegED-ConvLSTM by Yuanhang Liu, Yingkui Gong, Hao Zhang, Ziyue Hu, Guang Yang, Hong Yuan

“…We also examined the effect of using different numbers of space weather feature values in these models. …”
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Simulation and Recognition of Concrete Lining Infiltration Degree via an Indoor Experiment by Dongsheng Wang, Jun Feng, Xinpeng Zhao, Yeping Bai, Yujie Wang, Xuezeng Liu

“…To solve this problem, we propose a recognition method by using a deep convolutional neural network. We carry out laboratory tests, prepare cement mortar specimens with different saturation levels, simulate different degrees of infiltration of tunnel concrete linings, and establish an infrared thermal image data set with different degrees of infiltration. …”
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Detection of Soluble Solid Content in Citrus Fruits Using Hyperspectral Imaging with Machine and Deep Learning: A Comparative Study of Two Citrus Cultivars by Yuxin Xiao, Yuanning Zhai, Lei Zhou, Yiming Yin, Hengnian Qi, Chu Zhang

“…Combined models that integrated different deep learning architectures were also explored. …”
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Time series image coding classification theory based on Lagrange multiplier method by Wentao Jiang, Ming Zhao, Hongbo Li

“…To address this gap, our paper integrates time series image encoding with algebraic techniques, utilizing Gramian Angular Summation Fields (GASF) and Gramian Angle Difference Fields (GADF) as effective encoding methods. …”
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A Method of Trackside Kilometer Post Identification Combined with YOLOv3 Model by QIU Xinhua, WANG Wenkun, JI Yuwen, LI Jia

“…Therefore, an image recognition method based on YOLOv3 was proposed, which could still ensure good recognition accuracy in the face of different illumination, complex background and different forms of image. …”
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Multi-camera video collaborative analysis method based on edge computing by Zhibo QI, Lei DU, Ru HUO, Fan YANG, Tao HUANG

“…In order to reduce the processing volume of multi-camera real-time video data in smart city scenarios, a video collaborative analysis method based on machine learning algorithms at the edge was proposed.Firstly, for the important objects detected by each camera, different key windows were designed to filter the region of interest (RoI) in the video, reduce the video data volume and extract its features.Then, based on the extracted data features, the same objects in the videos from different cameras were annotated, and a strategy for calculating the association degree value between cameras was designed for further reducing the video data volume.Finally, the GC-ReID algorithm based on graph convolutional network (GCN) and re-identification (ReID) was proposed, aiming at achieving the collaborative analysis of multi-camera videos.The experimental results show that proposed method can effectively reduce the system latency and improve the video compression rate while ensuring the high accuracy, compared with the existing video analysis methods.…”
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Automatic Quantification of Atmospheric Turbulence Intensity in Space-Time Domain by Damián Gulich, Myrian Tebaldi, Daniel Sierra-Sosa

“…These representations are then fed into a Convolutional Neural Network for classification. This network effectively learns to discriminate between different turbulence regimes based on the spatio-temporal features extracted from a real-world experiment captured in video slices.…”
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Modelling on Car-Sharing Serial Prediction Based on Machine Learning and Deep Learning by Nihad Brahimi, Huaping Zhang, Lin Dai, Jianzi Zhang

“…To achieve that, various machine learning models, namely vector autoregression (VAR), support vector regression (SVR), eXtreme gradient boosting (XGBoost), k-nearest neighbors (kNN), and deep learning models specifically long short-time memory (LSTM), gated recurrent unit (GRU), convolutional neural network (CNN), CNN-LSTM, and multilayer perceptron (MLP), were performed on different kinds of features. …”
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Study on the Strategy of Playing Doudizhu Game Based on Multirole Modeling by Shuqin Li, Saisai Li, Hengyang Cao, Kun Meng, Meng Ding

“…Role modeling learns different roles and behaviors by using a convolutional neural network. …”
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Hybrid Deep Learning Models for Sentiment Analysis by Cach N. Dang, María N. Moreno-García, Fernando De la Prieta

“…Hybrid deep sentiment analysis learning models that combine long short-term memory (LSTM) networks, convolutional neural networks (CNN), and support vector machines (SVM) are built and tested on eight textual tweets and review datasets of different domains. …”
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A hybrid bio-inspired augmented with hyper-parameter deep learning model for brain tumor classification by Morolake Oladayo Lawrence

“…The CNN model is adjusted for different convolutional layers and fully connected layers to identify patterns and features in brain tumor pictures using an enhanced salp swarm algorithm (SSA) with kernel extreme learning machine (KELM). …”
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