A Hybrid Approach for Sports Activity Recognition Using Key Body Descriptors and Hybrid Deep Learning Classifier by Muhammad Tayyab, Sulaiman Abdullah Alateyah, Mohammed Alnusayri, Mohammed Alatiyyah, Dina Abdulaziz AlHammadi, Ahmad Jalal, Hui Liu

“…The system utilized a hybrid CNN (Convolutional Neural Network) + RNN (Recurrent Neural Network) classifier for event recognition, with Grey Wolf Optimization (GWO) for feature selection. …”
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Deep learning approach based on a patch residual for pediatric supracondylar subtle fracture detection by Qingming Ye, Zhilu Wang, Yi Lou, Yang Yang, Jue Hou, Zheng Liu, Weiguang Liu, Jiayu Li

“…The MPR framework combines a CNN for automatic feature extraction with a multiscale generative adversarial network (GAN) to model skeletal integrity using healthy samples. …”
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Deep learning-based prediction of HER2 status and trastuzumab treatment efficacy of gastric adenocarcinoma based on morphological features by Zhida Wu, Tao Wang, Junlin Lan, Jianchao Wang, Gang Chen, Tong Tong, Hejun Zhang

“…Results We developed a convolutional neural network (CNN) model using surgical specimens that achieved an area under the curve (AUC) value of 0.847 in predicting HER2 amplification, and achieved an AUC of 0.903 in predicting HER2 status specifically in patients with HER2 2 + expression. …”
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BIPE: A Bi-Layer Predictive Ensemble Framework for Forest Fire Susceptibility Mapping in Germany by Ling Hu, Volker Hochschild, Harald Neidhardt, Michael Schultz, Pegah Khosravani, Hadi Shokati

“…Our results confirm that BIPE outperforms traditional high-performance models like Support Vector Machine (SVM), Multilayer Perceptron (MLP), Extreme Gradient Boosting (XGBoost), Deep Neural Network (DNN), and Convolutional Neural Network (CNN), showcasing its practical effectiveness and reliability on the data of nonlinear, high-dimensional, and complex interactions. …”
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Optimizing Breast Cancer Mammogram Classification Through a Dual Approach: A Deep Learning Framework Combining ResNet50, SMOTE, and Fully Connected Layers for Balanced and Imbalanc... by Abdullah Fahad A. Alshamrani, Faisal Saleh Zuhair Alshomrani

“…The framework incorporates a blockwise Convolutional Neural Network (CNN), utilizing VGG16 preprocessing for input standardization and ResNet50 for feature extraction. …”
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Indonesia rupiah currency detection for visually impaired people using transfer learning VGG-19 by Raissa Alfatikarani, Laras Suciningtyas, Genta Garuda Bimasakti, Faqisna Putra Mardhatillah, Jessie R. Paragas, Hapsari Peni Agustin Tjahyaningtijas

“…Previous methods of currency detection using Convolutional Neural Network (CNN) techniques, including the VGG-19 architecture, have often encountered challenges, particularly the long training times required. …”
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BMNet: Enhancing Deepfake Detection Through BiLSTM and Multi-Head Self-Attention Mechanism by Demao Xiong, Zhan Wen, Cheng Zhang, Dehao Ren, Wenzao Li

“…When forgery techniques can generate highly realistic videos, traditional convolutional neural network (CNN)-based detection models often struggle to capture subtle forgery features and temporal dependencies. …”
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A Novel Audio Copy Move Forgery Detection Method With Classification of Graph-Based Representations by Beste Ustubioglu, Gul Tahaoglu, Arda Ustubioglu, Guzin Ulutas, Muhammed Kilic

“…Graph coloring algorithms are applied to convert the graph into a visual representation, which is then input into a specially designed Convolutional Neural Network (CNN) model for classification. The trained model was evaluated using five different datasets, demonstrating that this approach generally outperforms existing methods in terms of detection accuracy. …”
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Large-scale mapping of plastic-mulched land from Sentinel-2 using an index-feature-spatial-attention fused deep learning model by Lizhen Lu, Yunci Xu, Xinyu Huang, Hankui K. Zhang, Yuqi Du

“…In this paper, we demonstrated a large-scale PML mapping using Sentinel-2 data by combining the PML domain knowledge and the deep Convolutional Neural Network (CNN). We developed a dual-branch Index-Feature-Spatial-Attention fused Deep Learning Model (IFSA_DLM) for effectively acquiring and fusing multi-scale discriminative features and thus for accurately detecting PML. …”
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Normalized difference vegetation index prediction using reservoir computing and pretrained language models by John Olamofe, Ram Ray, Xishuang Dong, Lijun Qian

“…Using MODIS/Terra Vegetation Indices 16-Day L3 Global 250 m SIN Grid V061 dataset, we designed and implemented Reservoir Computing (RC) models and transformer-based models including pretrained language model, and compared the prediction performance of these models to traditional machine learning and deep learning methods such as Nonlinear Regression, Decision Tree, Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) network, and DLinear. …”
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Toward Semi-Autonomous Robotic Arm Manipulation Operator Intention Detection From Force Data by Abdullah S. Alharthi, Ozan Tokatli, Erwin Lopez, Guido Herrmann

“…We employ a hybrid Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) model to learn and forecast operator intentions based on the spatiotemporal data. …”
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A Synchronized Hybrid Brain-Computer Interface System for Simultaneous Detection and Classification of Fusion EEG Signals by Dalin Yang, Trung-Hau Nguyen, Wan-Young Chung

“…Furthermore, a four-layer convolutional neural network (CNN) is used as a classifier to distinguish different mental tasks. …”
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InSAR-YOLOv8 for wide-area landslide detection in InSAR measurements by Ruopu Ma, Haiyang Yu, Xuejie Liu, Xinru Yuan, Tingting Geng, Pengao Li

“…Compared with YOLOv8 and other advanced models (YOLOvX, Faster R-CNN, etc.), our model exhibits distinct advantages and possesses a wider range of potential applications in InSAR measurement for landslide detection.…”
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Multi-Scale Building Load Forecasting Without Relying on Weather Forecast Data: A Temporal Convolutional Network, Long Short-Term Memory Network, and Self-Attention Mechanism Appro... by Lanqian Yang, Jinmin Guo, Huili Tian, Min Liu, Chang Huang, Yang Cai

“…The experimental results show that on multiple time scales, the TCN–LSTM–self-attention prediction model proposed in this paper is more accurate than the LSTM, CNN-LSTM, and TCN-LSTM models. Especially in the task of predicting cooling, heating, and electrical loads on a 1-week scale, the model proposed in this paper achieves improvements of 16.58%, 6.77%, and 3.87%, respectively, in the RMSE indicator compared with the TCN-LSTM model.…”
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Automated Welder Safety Assurance: A YOLOv3-Based Approach for Real-Time Detection of Welding Helmet Availability by Mohammad Z. Shanti, Chan Yeob Yeun, Chung-Suk Cho, Ernesto Damiani, Tae-Yeon Kim

“…The system employs a Convolutional Neural Network (CNN) based on the YOLOv3 algorithm and is trained and validated using a diverse dataset that includes images with varying levels of blur, grayscale images, and drone-captured photos. …”
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DGFEG: Dynamic Gate Fusion and Edge Graph Perception Network for Remote Sensing Change Detection by Shengning Zhou, Genji Yuan, Zhen Hua, Jinjiang Li

“…Numerous networks combining CNN and transformer architectures have emerged, yet effectively balancing local detail and global context features remains a topic of ongoing discussion. …”
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Automated String Art Creation: Integrated Advanced Computational Techniques and Precision Art Designing by Spoorthi Singh, Navya T. Hegde, Mohammad Zuber, Yuvraj Singh Nain, Vishnu G. Nair

“…A convolutional neural network (CNN) was employed to process grayscale images, extracting and reconstructing features using pooling and deconvolution techniques, with the model achieving stable performance over multiple epochs. …”
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FFL-IDS: A Fog-Enabled Federated Learning-Based Intrusion Detection System to Counter Jamming and Spoofing Attacks for the Industrial Internet of Things by Tayyab Rehman, Noshina Tariq, Farrukh Aslam Khan, Shafqat Ur Rehman

“…This paper proposes a Fog-enabled Federated Learning-based Intrusion Detection System (FFL-IDS) utilizing Convolutional Neural Network (CNN) that mitigates these limitations. This framework allows multiple parties in IIoT networks to train deep learning models with data privacy preserved and low-latency detection ensured using fog computing. …”
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Breast mass classification based on supervised contrastive learning and multi‐view consistency penalty on mammography by Lilei Sun, Jie Wen, Junqian Wang, Zheng Zhang, Yong Zhao, Guiying Zhang, Yong Xu

“…In this paper, A novel classification algorithm based on Convolutional Neural Network (CNN) is proposed to improve the diagnostic performance for breast cancer on mammography. …”
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IncSAR: A Dual Fusion Incremental Learning Framework for SAR Target Recognition by George Karantaidis, Athanasios Pantsios, Ioannis Kompatsiaris, Symeon Papadopoulos

“…IncSAR combines the power of a Vision Transformer (ViT) and a custom-designed Convolutional Neural Network (CNN) in a dual-branch architecture, integrated via a late-fusion strategy. …”
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