Super-resolution reconstruction of mine image based on generative adversarial network by Fan ZHANG, Ying LIU, Hui SONG, Jiarong ZHANG, Haixing CHENG

“…Based on SRGAN, this method improves the network structure and loss function. First, two 5×5 convolutional layers are used in the low-level feature extraction layer and reconstruction layer of the generator, and non-linearity is added after each convolutional layer of the low-level feature extraction layer, and the high-level feature extraction layer adopts the residual structure, and the sub-pixel convolutional layer is cascaded to achieve super-resolution reconstruction of different multiples. …”
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The Effect of Input Length on Prediction Accuracy in Short-Term Multi-Step Electricity Load Forecasting: A CNN-LSTM Approach by Seyda Ozdemir, Yakup Demir, Ozal Yildirim

“…The model is tested on 12 different configurations with symmetrically increasing input lengths, including weather data. …”
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Deep learning HRNet FCN for blood vessel identification in laparoscopic pancreatic surgery by Jile Shi, Ruohan Cui, Zhihong Wang, Qi Yan, Lu Ping, Hu Zhou, Junyi Gao, Chihua Fang, Xianlin Han, Surong Hua, Wenming Wu

“…By combining datasets from LDP and Whipple procedures, the model showed strong generalization across different surgical contexts and achieved real-time processing speeds of 11 frames per second during surgery process. …”
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A Novel Dual-Stream Attention-Based Hybrid Network for Solar Power Forecasting by Rafiq Asghar, Michele Quercio, Lorenzo Sabino, Assia Mahrouch, Francesco Riganti Fulginei

“…The proposed model’s performance is thoroughly assessed by a series of experiments that include various window sizes, four seasons, and different weather conditions. Subsequently, the predictive accuracy of the developed model is compared with three single and five hybrid deep learning models. …”
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Development of a Mobile-Based Application for Classifying Caladium Plants Using the CNN Algorithm by Rudy Chandra, Tegar Arifin Prasetyo, Heni Ernita Lumbangaol, Veny Siahaan, Johan Immanuel Sianipar

“…However, difficulties in recognizing the type of Caladium often occur because of the similarities in shape, pattern, and color of the leaves between the different kinds of Caladium. To overcome this problem, research will use machine learning with the Convolutional Neural Network (CNN) algorithm to build a mobile application that can accurately classify four types of Caladiums. …”
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A Review on Image Enhancement and Restoration Techniques for Underwater Optical Imaging Applications by N. Deluxni, Pradeep Sudhakaran, Kitmo, Mouhamadou Falilou Ndiaye

“…Various UIE techniques are studied for different data sets, and applications. However, the selection of suitable method for particular applications among available techniques is still a challenging task. …”
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Short Term Photovoltaic Power Combination Prediction Method Based on Similar Day Selection and Data Reconstruction by Qingbin CHEN, Genghuang YANG, Liqing GENG, Juan SU, Jingsheng SUN

“…Through practical examples, it has been verified that under different weather conditions, the overall prediction error of the model is the smallest, which can effectively improve the prediction accuracy.…”
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Advances to IoT security using a GRU-CNN deep learning model trained on SUCMO algorithm by Amit Sagu, Nasib Singh Gill, Preeti Gulia, Noha Alduaiji, Piyush Kumar Shukla, Mohd Asif Shah

“…The proposed model is evaluated through experiments on two different datasets i.e., UNSW-NB15 and BoT-IoT, and results demonstrates that proposed work outperforms the traditional work as well as state of the art works.…”
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Single Pixel Imaging Based on Multiple Prior Deep Unfolding Network by Quan Zou, Qiurong Yan, Qianling Dai, Ao Wang, Bo Yang, Yi Li, Jinwei Yan

“…To effectively fuse multiple prior information, we propose three different fusion strategies in the deep reconstruction sub-network. …”
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Facial expression recognition using visible and IR by early fusion of deep learning with attention mechanism by Muhammad Tahir Naseem, Chan-Su Lee, Tariq Shahzad, Muhammad Adnan Khan, Adnan M. Abu-Mahfouz, Khmaies Ouahada

“…The motivation of this research is to address the challenges of accurately recognizing emotions despite variations in expressions across emotions and similarities between different expressions. In this work, we propose an early fusion approach that combines features from visible and infrared modalities using publicly accessible VIRI and NVIE databases. …”
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MultiSenseNet: Multi-Modal Deep Learning for Machine Failure Risk Prediction by Mostafijur Rahman, Md Sabbir Hossain, Uland Rozario, Satyabrata Roy, M. F. Mridha, Nilanjan Dey

“…The model performed consistently across different hyperparameter settings, reaching peak accuracy of 94.7% on the classification dataset and 95.6% on the prediction dataset after 40 training epochs. …”
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Capsule Endoscopy Image Enhancement for Small Intestinal Villi Clarity by Shaojie Zhang, Yinghui Wang, Peixuan Liu, Yukai Wang, Liangyi Huang, Mingfeng Wang, Ibragim Atadjanov

“…Illumination gain factors are calculated from the low-frequency components, while gradient gain factors are derived from Laplacian convolutions on different regions. These factors enhance the high-frequency components, combined with the original image. …”
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Application of U-net models in estimating forest canopy closure based on multi-source remote sensing imagery by Lei Chen, TingTing Yang, ZhiQiang Wu, XinLong Li, YanZhen Lin, Yi Lian

“…These models are optimized by reordering the network output layers and enhancing feature fusion between convolutional and pooling operations. By experimenting with different combinations of multi-parameters with the improved U-Net architectures, we estimate CC and validate the results using airborne Light Detection and Ranging (LiDAR) CC data. …”
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Attention-Guided Multi-Task Learning for Prostate Cancer Pelvic Lymph Node Metastasis Prediction by ZHANG Zhiyuan, HU Jisu, ZHANG Yueyue, QIAN Xusheng, ZHOU Zhiyong, DAI Yakang

“…First, within the tumor segmentation network, a multi-branch anisotropic large kernel attention module is introduced, where a larger receptive field is obtained through different branches and anisotropic large convolutional kernels, effectively capturing both local and global tumor information. …”
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Classification of Lung Nodule Using Hybridized Deep Feature Technique by Malin Bruntha, Immanuel Alex Pandian, Siril Sam Abraham

“…Among many types of deep learning techniques, Convolutional Neural Networks (CNN) can be useful in image classification applications. …”
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Efficient neural network training method for unsteady flow field prediction based on data pool by Qiuchi Min, Tianyu Li, Guanxiong Li, Shuyuan Liu, Laiping Zhang, Xiaogang Deng

“…A detailed comparative study was conducted on different training methods within the architecture of convolutional neural networks. …”
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Application of Machine Learning for Bulbous Bow Optimization Design and Ship Resistance Prediction by Yujie Shen, Shuxia Ye, Yongwei Zhang, Liang Qi, Qian Jiang, Liwen Cai, Bo Jiang

“…Based on the ship resistance sample data obtained from computational fluid dynamics (CFD) simulation, this study uses a machine learning method to realize the fast prediction of ship resistance corresponding to different bulbous bows. To solve the problem of insufficient accuracy in the single surrogate model, this study proposes a CBR surrogate model that integrates convolutional neural networks with backpropagation and radial basis function models. …”
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Data Quality Monitoring for the Hadron Calorimeters Using Transfer Learning for Anomaly Detection by Mulugeta Weldezgina Asres, Christian Walter Omlin, Long Wang, David Yu, Pavel Parygin, Jay Dittmann, the CMS-HCAL Collaboration

“…Motivated by the need for improved model accuracy and robustness, particularly in scenarios with limited training data on systems with thousands of sensors, this research investigates the transferability of models trained on different sections of the Hadron Calorimeter of the Compact Muon Solenoid experiment at CERN. …”
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A Simple but Effective Way to Handle Rotating Machine Fault Diagnosis With Imbalanced-Class Data: Repetitive Learning Using an Advanced Domain Adaptation Model by Donghwi Yoo, Minseok Choi, Hyunseok Oh, Bongtae Han

“…By employing pseudo-labeling, weighted random sampling, and time-shifting, the proposed repetitive learning method generates pseudo-augmented source and target fault data. Deep convolutional domain adaptation networks are followed to extract features by minimizing different losses. …”
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A Spoofing Speech Detection Method Combining Multi-Scale Features and Cross-Layer Information by Hongyan Yuan, Linjuan Zhang, Baoning Niu, Xianrong Zheng

“…The method introduces a multi-scale feature adapter (MSFA), which enhances the model’s ability to perceive local features through residual convolutional blocks and squeeze-and-excitation (SE) mechanisms. …”
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