Systematic Research on the Application of Steel Slag Resources under the Background of Big Data by Le Kang, Hui Ling Du, Hao Zhang, Wan Li Ma

“…Secondly, the steel slag prediction model based on the convolution neural network (CNN) is established. The material data of steelmaking, the operation data of steelmaking process, and the data of steel slag composition are put into the model from the Hadoop platform, and the prediction of the slag composition is further realized. …”
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Improving Medical Image Quality Using a Super-Resolution Technique with Attention Mechanism by Dong Yun Lee, Jang Yeop Kim, Soo Young Cho

“…To address this challenge, this study proposes a convolutional neural network (CNN)-based super-resolution architecture, utilizing a melanoma dataset to enhance image resolution through deep learning techniques. …”
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Comprehensive dataset from high resolution UAV land cover mapping of diverse natural environments in Serbia by Bojana Ivošević, Nina Pajević, Sanja Brdar, Rana Waqar, Maryam Khan, João Valente

“…Our method compares the efficacy and accuracy of object-based image analysis (OBIA) combined with random forest and convolutional neural networks (CNN) for land cover classification. We produced detailed land cover maps for 27 varied landscapes across Serbia, identifying nine unique land cover classes and assessing human impact on natural habitats. …”
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Dual-Path Adaptive Channel Attention Network Based on Feature Constraints for Face Anti-Spoofing by Nana Li, Zhipeng Weng, Fangmei Liu, Zuhe Li, Wei Wang

“…Within this framework, we design a convolutional neural network (CNN) based on the Dual-path Adaptive Channel Attention (DACA) module, aiming to filter the features of the input facial images to extract key information. …”
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Multimodal Multiobject Tracking by Fusing Deep Appearance Features and Motion Information by Liwei Zhang, Jiahong Lai, Zenghui Zhang, Zhen Deng, Bingwei He, Yucheng He

“…After that, we use Convolutional Neural Network (CNN) to learn the deep appearance features of objects and employ Kalman Filter to obtain the motion information of objects. …”
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Rapid maize seed vigor classification using deep learning and hyperspectral imaging techniques by Papis Wongchaisuwat, Pongsan Chakranon, Achitpon Yinpin, Damrongvudhi Onwimol, Kris Wonggasem

“…The combination of wavelength selection and careful CNN architecture choice significantly contributed to the proposed model's exceptional performance. …”
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Social media network public opinion emotion classification method based on multi-feature fusion and multi-scale hybrid neural network by Yuan Yao, Xi Chen, Peng Zhang

“…Experimental comparisons on two multi-class microblog comment datasets demonstrate that the multi-feature fusion (WOOSD-CNN) word vector model achieves notable improvements in sentiment polarity accuracy and categorization effectiveness. …”
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Detection of cervical cell based on multi-scale spatial information by Gang Li, Xinyu Fan, Chuanyun Xu, Pengfei Lv, Ru Wang, Zihan Ruan, Zheng Zhou, Yang Zhang

“…Secondly, the Channel Attention Enhanced Module (CAE) is introduced to achieve channel-level weighted processing, dynamically optimizing each output feature using channel weights to focus on critical features. We use Sparse R-CNN as the baseline and integrate MSA and CAE into it. …”
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Rain removal method for single image of dual-branch joint network based on sparse transformer by Fangfang Qin, Zongpu Jia, Xiaoyan Pang, Shan Zhao

“…Additionally, since tokens with low relevance in the Transformer may influence image recovery, this study introduces a residual sparse Transformer branch (RSTB) to overcome the limitations of the Convolutional Neural Network’s (CNN’s) receptive field. Indeed, RSTB preserves the most valuable self-attention values for the aggregation of features, facilitating high-quality image reconstruction from a global perspective. …”
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A Lightweight Network with Domain Adaptation for Motor Imagery Recognition by Xinmin Ding, Zenghui Zhang, Kun Wang, Xiaolin Xiao, Minpeng Xu

“…This paper proposes an innovative method that combines a lightweight convolutional neural network (CNN) with domain adaptation. A lightweight feature extraction module is designed to extract key features from both the source and target domains, effectively reducing the model’s parameters and improving the real-time performance and computational efficiency. …”
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Comparison of deep transfer learning models for classification of cervical cancer from pap smear images by Harmanpreet Kaur, Reecha Sharma, Jagroop Kaur

“…In contrast, convolutional neural networks (CNN) models require large datasets to reduce overfitting and poor generalization. …”
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A Preliminary Study on 2D Convolutional Neural Network-Based Discontinuous Rail Position Classification for Detection on Rail Breaks Using Distributed Acoustic Sensing Data by Hye-Yeun Chun, Jungtai Kim, Dongkue Kim, Ilmu Byun, Kyeongjun Ko

“…In the third step, the spectrogram images are applied to the proposed 2D convolutional neural network (2D CNN) model and the network detects discontinuous rail positions along the track, which are geometrically distinct from continuous welded rails, such as rail breaks. …”
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FlowMFD: Characterisation and classification of tor traffic using MFD chromatographic features and spatial–temporal modelling by Liukun He, Liangmin Wang, Keyang Cheng, Yifan Xu

“…In addition, FlowMFD utilises a cascaded model with a two‐dimensional convolutional neural network (2D‐CNN) and a bidirectional gated recurrent unit to capture spatial‐temporal dependencies between MFDCF. …”
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MRM-BERT: a novel deep neural network predictor of multiple RNA modifications by fusing BERT representation and sequence features by Linshu Wang, Yuan Zhou

“…We developed MRM-BERT, a deep learning method that combined the pre-trained DNABERT deep sequence representation module and the convolutional neural network (CNN) exploiting four traditional sequence feature encodings to improve the prediction performance. …”
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A deep learning‐based framework to identify and characterise heterogeneous secure network traffic by Faiz Ul Islam, Guangjie Liu, Weiwei Liu, Qazi Mazhar ul Haq

“…The state‐of‐the‐art machine learning strategies (C4.5, random forest, and K‐nearest neighbour) are investigated for comparison. The proposed 1D‐CNN model achieved higher accuracy in classifying the heterogeneous secure network traffic. …”
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Hybrid Deep-Learning Framework Based on Gaussian Fusion of Multiple Spatiotemporal Networks for Walking Gait Phase Recognition by Tao Zhen, Jian-lei Kong, Lei Yan

“…With the data preprocessing, the framework constructs a spatial feature extractor with AutoEncoder and CNN modules and a multistream temporal feature extractor with three collateral modules combining RNN, LSTM, and GRU modules. …”
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Typhoon localization detection algorithm based on TGE-YOLO by Lan He, Ling Xiao, Peihao Yang, Sheng Li

“…The experimental results show that the proposed TGE-YOLO model outperforms Faster R-CNN, YOLOv5s, YOLOv9s, and YOLOv11n, with the typhoon identification mean average precision (mAP) reaching 87.8%, the mean square error (MSE) of typhoon center localization at 0.115, and the detection speed at 416.7 frames per second (FPS). …”
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A digital twin framework with MobileNetV2 for damage detection in slab structures by Huong Duong Nguyen, Huan Nguyen, Xiaohong Gao

“…The defection of the damaged slab under static loads is analyzed with two-dimensional discrete wavelet theory (DWT), whereas the diagonal wavelets are used to extract images data set used to train the convolutional neural network (CNN). MobileNetV2 uses transfer learning can reduce the number of trained parameters and hence perform fast convergence. …”
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Unsupervised Hyperspectral Denoising Based on Deep Image Prior and Least Favorable Distribution by Keivan Faghih Niresi, Chong-Yung Chi

“…Lately, it has been reported that the convolutional neural network (CNN), the core element used by deep image prior (DIP), is able to capture image statistical characteristics without the need of training, i.e., restore the clean image blindly. …”
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Comparison of Deep Learning Techniques in Detection of Sickle Cell Disease. by Mabirizi, Vicent, Kawuma, Simon, Kyarisiima, Addah, Bamutura, David, Atwiine, Barnabas, Nanjebe, Deborah, Oyesigye, Adolf Mukama

“…In our study, we have discovered that Inception V3 yielded the highest accuracy of 97.3% followed by VGG19 at 97.0%, VGG16 at 91%, ResNet50 at 82% and ReNet at 67%, and the CNN-scratch model achieved 81% accuracy. Results from our study will aid researchers and industry practitioners in making decisions on the best deep-learning model to use while detecting SCD.…”
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