MaDis-Stereo: Enhanced Stereo Matching via Distilled Masked Image Modeling by Jihye Ahn, Hyesong Choi, Soomin Kim, Dongbo Min

“…Although Transformer-based stereo models have been studied recently, their performance still lags behind CNN-based stereo models due to the inherent data scarcity issue in the stereo matching task. …”
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An LJDRNN-based efficient energy intensity prediction in carbon fiber composite material manufacturing process by Rangaswamy Nikhil, Karthikeyan A G, Prabhu Loganathan, Tabrej Khan, Tamer A Sebaey

“…The proposed LJDRNN achieved an accuracy of 98.32%, outperforming the JRNN (92.10%), RNN (87%), ANN (78%), and CNN (86%), thus demonstrating its superiority in energy intensity prediction. …”
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RMHA-Net: Robust Optic Disc and Optic Cup Segmentation Based on Residual Multiscale Feature Extraction With Hybrid Attention Networks by Mohammad J. M. Zedan, Siti Raihanah Abdani, Jaesung Lee, Mohd Asyraf Zulkifley

“…This network’s encoder is designed based on advanced convolutional neural network (CNN) blocks that combine dilated convolution, which allows field-of-view expansion by capturing larger-scale features. …”
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Varying pixel resolution significantly improves deep learning-based carotid plaque histology segmentation by Yurim Lee, Rashid Al Mukaddim, Tenzin Ngawang, Shahriar Salamat, Carol C. Mitchell, Jenna Maybock, Stephanie M. Wilbrand, Robert J. Dempsey, Tomy Varghese

“…We were able to train Mask R-CNN using regions of interests with varied pixel resolution, with a $$7\%$$ increase in pixel accuracy versus training with patches. …”
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Land use and land cover classification for change detection studies using convolutional neural network by V. Pushpalatha, P.B. Mallikarjuna, H.N. Mahendra, S. Rama Subramoniam, S. Mallikarjunaswamy

“…Therefore, this paper proposed the Convolutional Neural Network (CNN)-based deep learning method for LULC classification. …”
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Advanced Detection of AI-Generated Images Through Vision Transformers by Darshan Lamichhane

“…The findings thus obtained demonstrate that the ViT model attains a high level of accuracy in differentiating between real and AI-generated images, outperforming traditional CNN-based approaches. Beyond performance evaluation, we also conducted an ablation study to examine the impact of various components of the ViT model, including the number of attention heads, patch size, the impact of data augmentation, and the depth of layers. …”
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MFCEN: A lightweight multi-scale feature cooperative enhancement network for single-image super-resolution by Jiange Liu, Yu Chen, Xin Dai, Li Cao, Qingwu Li

“…In the deep feature extraction part, a novel integrated multi-level feature module was introduced. Compared to existing CNN and transformer hybrid super-resolution networks, MFCEN significantly reduced the number of parameters while maintaining performance. …”
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GS-DTA: integrating graph and sequence models for predicting drug-target binding affinity by Junwei Luo, Ziguang Zhu, Zhenhan Xu, Chuanle Xiao, Jingjing Wei, Jiquan Shen

“…Meanwhile, for each protein, a framework combining CNN, Bi-LSTM, and Transformer is used to extract the contextual and structural information of the protein amino acid sequences, and this combination can help to understand a comprehensive and detailed features of the protein. …”
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CGV-Net: Tunnel Lining Crack Segmentation Method Based on Graph Convolution Guided Transformer by Kai Liu, Tao Ren, Zhangli Lan, Yang Yang, Rong Liu, Yuantong Xu

“…To address this issue, this study proposes CGV-Net (CNN, GNN, and ViT networks), a novel tunnel crack segmentation network model that integrates convolutional neural networks (CNNs), graph neural networks (GNNs), and Vision Transformers (ViTs). …”
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Mood Detection from Physical and Neurophysical Data Using Deep Learning Models by Zeynep Hilal Kilimci, Aykut Güven, Mitat Uysal, Selim Akyokus

“…For this purpose, Feedforward Neural Network (FFNN), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM) neural network are employed as deep learning methodologies. …”
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A multi-channel bioimpedance-based device for Vietnamese hand gesture recognition by Nhat-Minh Than, Son-Thuy Nguyen, Dang-Nguyen Huynh, Thao-Nguyen Tran, Nguyen-Khoa Le, Huu-Xuan Mai, Cao-Dang Le, Tan-Thi Pham, Quang-Linh Huynh, Trung-Hau Nguyen

“…To categorize hand gestures, Convolutional Neuron Network (CNN), XGBoost, and Random Forest were used. The model successfully recognized up to nine distinct gestures, achieving an average accuracy of 97.24% across ten subjects using a subject-dependent strategy, showcasing the efficacy of the bioimpedance-based system in hand gesture recognition. …”
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Deep Learning-Based Speech Emotion Recognition Using Multi-Level Fusion of Concurrent Features by Samuel, Kakuba, Alwin, Poulose, Dong, Seog Han, Senior Member, Ieee

“…Spatial and temporal features have been extracted sequentially in deep learning-based models using convolutional neural networks (CNN) followed by recurrent neural networks (RNN) which may not only be weak at the detection of the separate spatial-temporal feature representations but also the semantic tendencies in speech. …”
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Exploring Effects of Mental Stress with Data Augmentation and Classification Using fNIRS by M. N. Afzal Khan, Nada Zahour, Usman Tariq, Ghinwa Masri, Ismat F. Almadani, Hasan Al-Nashah

“…Linear discriminant analysis (LDA) showed a maximum accuracy of 60%, whereas non-augmented data classified by a convolutional neural network (CNN) provided the highest classification accuracy of 73%. …”
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Deep learning based decision-making and outcome prediction for adolescent idiopathic scoliosis patients with posterior surgery by Kai Chen, Xiao Zhai, Ziqiang Chen, Haojue Wang, Mingyuan Yang, Changwei Yang, Yushu Bai, Ming Li

“…Four deep learning models were designed, including Multi-Layer Perceptron model, Encoder-Decoder model, CNN-LSTM Attention model and Deep FM model. For the implementation of deep learning, 70% of the data was adopted for training and 30% for evaluation. …”
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A Framework for Early Detection of Acute Lymphoblastic Leukemia and Its Subtypes From Peripheral Blood Smear Images Using Deep Ensemble Learning Technique by Sajida Perveen, Abdullah Alourani, Muhammad Shahbaz, M. Usman Ashraf, Isma Hamid

“…Experimental results are obtained and comparative analysis among 7 well-known CNN Network architectures (AlexNet, VGGNet, Inception, ResNet-50, ResNet-18, Inception and DenseNet-121) is also performed that demonstrated that the proposed platform achieved comparatively high accuracy (99.95%), precision (99.92%), recall (99.92%), F1-Score (99.90%), sensitivity (99.92%) and specificity (99.97%). …”
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Use of Carry Chain Logic and Design System Extensions to Construct Significantly Faster and Larger Single-Stage N-Sorters and N-Filters by Robert B. Kent, Marios S. Pattichis

“…An example of the new, very large single-stage carry chain N-max filters is the 125-max <inline-formula> <tex-math notation="LaTeX">$5\times 5\times 5$ </tex-math></inline-formula> CNN video max pooling filter, which operates in only 2.075 nS. …”
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A Parallel Image Denoising Network Based on Nonparametric Attention and Multiscale Feature Fusion by Jing Mao, Lianming Sun, Jie Chen, Shunyuan Yu

“…It not only solved the problem of insufficient edge feature extraction but also solved the problem of the saturation of deep CNN performance. In this paper, a nonparametric attention mechanism is introduced in the two-branch feature extraction module, which enabled the network to pay attention to and learn the key information in the feature map, and improved the learning performance of the network. …”
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Progressive Self-Prompting Segment Anything Model for Salient Object Detection in Optical Remote Sensing Images by Xiaoning Zhang, Yi Yu, Daqun Li, Yuqing Wang

“…Most existing ORSI-SOD methods rely on pre-trained CNN- or Transformer-based backbones to extract features from ORSIs, followed by multi-level feature aggregation. …”
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The application of deep learning in early enamel demineralization detection by Ketai He, Rongxiu Zhang, Muchun Liang, Keyue Tian, Kaihui Luo, Ruoshi Chen, Jianpeng Ren, Jiajun Wang, Juan Li

“…A total of 624 high-quality digital images captured under standardized conditions were used to construct a deep learning model based on the Mask region-based convolutional neural network (Mask R-CNN). The model was trained to automate the detection of enamel demineralization. …”
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PortNet: Achieving lightweight architecture and high accuracy in lung cancer cell classification by Kaikai Zhao, Youjiao Si, Liangchao Sun, Xiangjiao Meng

“…Result: Our tests demonstrated that PortNet significantly reduces the total parameter count to 2,621,827, which is over a fifth smaller compared to some mainstream CNN models, marking a substantial advancement for deployment in portable devices. …”
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