Discriminative, generative artificial intelligence, and foundation models in retina imaging by Paisan Ruamviboonsuk, Niracha Arjkongharn, Nattaporn Vongsa, Pawin Pakaymaskul, Natsuda Kaothanthong

“…For discriminative tasks, conventional convolutional neural networks (CNNs) are still major AI techniques. …”
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Deep Learning Approach for Automatic Classification of Ocular and Cardiac Artifacts in MEG Data by Ahmad Hasasneh, Nikolas Kampel, Praveen Sripad, N. Jon Shah, Jürgen Dammers

“…We propose an artifact classification scheme based on a combined deep and convolutional neural network (DCNN) model, to automatically identify cardiac and ocular artifacts from neuromagnetic data, without the need for additional electrocardiogram (ECG) and electrooculogram (EOG) recordings. …”
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Multiscale Mask R-CNN–Based Lung Tumor Detection Using PET Imaging by Rui Zhang PhD, Chao Cheng PhD, Xuehua Zhao PhD, Xuechen Li PhD

“…In this article, we propose a multiscale Mask Region–Based Convolutional Neural Network (Mask R-CNN)–based method that uses PET imaging for the detection of lung tumor. …”
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Research on bearing fault diagnosis based on a multimodal method by Hao Chen, Shengjie Li, Xi Lu, Qiong Zhang, Jixining Zhu, Jiaxin Lu

“…In parallel, 13 key features are extracted from the original vibration data in the time-frequency domain. Convolutional neural networks are then employed for deep feature extraction. …”
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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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QoE-Driven Big Data Management in Pervasive Edge Computing Environment by Qianyu Meng, Kun Wang, Xiaoming He, Minyi Guo

“…Then, with respect to accuracy, we propose a Tensor-Fast Convolutional Neural Network (TF-CNN) algorithm based on deep learning, which is suitable for high-dimensional big data analysis in the pervasive edge computing environment. …”
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Automatic detection of floating instream large wood in videos using deep learning by J. Aarnink, J. Aarnink, T. Beucler, T. Beucler, M. Vuaridel, V. Ruiz-Villanueva, V. Ruiz-Villanueva

“…The approach uses a convolutional neural network to automatically detect wood. …”
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Enhancing Medical Diagnostics with Machine Learning: A Study on Ensemble Methods and Transfer Learning by Zhang Jiaming

“…The research focuses on leveraging Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Ensemble Methods, and Transfer Learning to enhance medical diagnostics. …”
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A Comprehensive Investigation of Fraud Detection Behavior in Federated Learning by Sun Rui

“…The comparison involves three machine learning models - Artificial Neural Networks (ANN), Random Forest (RF), and Convolutional Neural Networks (CNN) - to assess their efficacy in the FL context. …”
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Enhancing Rehabilitation Assessment with Artificial Intelligence: A Comprehensive Investigation of Posture Quality Prediction Using Machine Learning by Zhang Wenxi

“…AI techniques, including Support Vector Machines (SVM), decision trees, random forests, Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN), show great potential in improving the accuracy and personalization of rehabilitation assessment. …”
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Innovative Deep Learning Architecture for the Classification of Lung and Colon Cancer From Histopathology Images by Menatalla M. R. Said, Md. Sakib Bin Islam, Md. Shaheenur Islam Sumon, Semir Vranic, Rafif Mahmood Al Saady, Abdulrahman Alqahtani, Muhammad E. H. Chowdhury, Shona Pedersen

“…Subsequently, our novel model underwent comparison with five pretrained convolutional neural network (CNN) models: MobileNetV2-SelfMLP, Resnet18-SelfMLP, DenseNet201-SelfMLP, InceptionV3-SelfMLP, and MobileViTv2_200-SelfMLP, where each multilayer perceptron (MLP) was replaced with Self-MLP. …”
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Automatic Morpheme Segmentation for Russian: Can an Algorithm Re-place Experts? by Дмитрий Алексеевич Морозов, Тимур Александрович Гарипов, Ольга Николаевна Ляшевская, Светлана Олеговна Савчук, Борис Леонидович Иомдин, Анна Валерьевна Глазкова

“…Across both experiments, the algorithms that relied on ensembles of convolutional neural networks consistently demonstrated the highest performance. …”
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Fault Diagnosis of Subway Traction Motor Bearing Based on Information Fusion under Variable Working Conditions by Yanwei Xu, Weiwei Cai, Tancheng Xie

“…Then, the original signal is processed and extracted by the wavelet packet decomposition, and the normalized feature information is fused by the convolution neural network. Finally, the two-dimensional convolution neural network model is established to diagnose the bearing fault of the metro traction motor under different conditions. …”
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A prediction approach to COVID-19 time series with LSTM integrated attention mechanism and transfer learning by Bin Hu, Yaohui Han, Wenhui Zhang, Qingyang Zhang, Wen Gu, Jun Bi, Bi Chen, Lishun Xiao

“…Classical deep learning models including recurrent neural network (RNN), long and short-term memory (LSTM), gated recurrent unit (GRU) and temporal convolutional network (TCN) are initially trained, then RNN, LSTM and GRU are integrated with a new attention mechanism and transfer learning to improve the performance. …”
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A Lightweight CNN-Transformer Implemented via Structural Re-Parameterization and Hybrid Attention for Remote Sensing Image Super-Resolution by Jie Wang, Hongwei Li, Yifan Li, Zilong Qin

“…Remote sensing imagery contains rich information about geographical targets, and performing super-resolution (SR) reconstruction on such images requires greater feature representation capabilities. Convolutional neural network (CNN)-based methods excel at extracting intricate local features but fall short in terms of capturing global representations. …”
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Deep Ensemble Learning for Human Action Recognition in Still Images by Xiangchun Yu, Zhe Zhang, Lei Wu, Wei Pang, Hechang Chen, Zhezhou Yu, Bin Li

“…Firstly, we construct an end-to-end NCNN-based model by attaching the nonsequential convolutional neural network (NCNN) module to the top of the pretrained model. …”
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Application of Rotating Machinery Fault Diagnosis Based on Deep Learning by Wei Cui, Guoying Meng, Aiming Wang, Xinge Zhang, Jun Ding

“…After a brief review of early fault diagnosis methods, this paper focuses on the method models that are widely used in deep learning: deep belief networks (DBN), autoencoders (AE), convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), and transfer learning methods are summarized from the two aspects of principle and application in the field of fault diagnosis of rotating machinery. …”
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Assessment model of ozone pollution based on SHAP-IPSO-CNN and its application by Xiaolei Zhou, Xingyue Wang, Ruifeng Guo

“…To address this problem, a convolutional neural network (CNN) model combining the improved particle swarm optimization (IPSO) algorithm and SHAP analysis, called SHAP-IPSO-CNN, is developed in this study, aiming to reveal the key factors affecting ground-level ozone pollution and their interaction mechanisms. …”
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Ti3C2Tx Composite Aerogels Enable Pressure Sensors for Dialect Speech Recognition Assisted by Deep Learning by Yanan Xiao, He Li, Tianyi Gu, Xiaoteng Jia, Shixiang Sun, Yong Liu, Bin Wang, He Tian, Peng Sun, Fangmeng Liu, Geyu Lu

“…Over 6888 and 4158 pronunciation signals were collected by the pressure sensor and utilized for training the convolutional neural network model, allowing for accurate recognition of six dialects (96.2% accuracy) and seven words (96.6% accuracy).…”
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Innovative Framework for Historical Architectural Recognition in China: Integrating Swin Transformer and Global Channel–Spatial Attention Mechanism by Jiade Wu, Yang Ying, Yigao Tan, Zhuliang Liu

“…Through extensive experiments on a constructed historical building dataset, our model achieves an outstanding performance of over 97.8% in key metrics including accuracy, precision, recall, and F1 score (harmonic mean of the precision and recall), surpassing traditional CNN (convolutional neural network) architectures and contemporary deep learning models. …”
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