Multimodal Fusion Mamba Network for Joint Land Cover Classification Using Hyperspectral and LiDAR Data by Haizhu Pan, Ruixiang Zhao, Haimiao Ge, Moqi Liu, Quanxiu Zhang

“…Recently, the emerging deep learning framework Mamba has shown superior performance over traditional architectures, including transformers and convolutional neural networks. However, its application to LCC faces challenges. …”
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RT-DETR-Smoke: A Real-Time Transformer for Forest Smoke Detection by Zhong Wang, Lanfang Lei, Tong Li, Xian Zu, Peibei Shi

“…First, we designed a high-efficiency hybrid encoder that combines convolutional and Transformer features, thus reducing computational cost while preserving crucial smoke details. …”
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A User-Friendly Machine Learning Pipeline for Automated Leaf Segmentation in by Michelle Lynn Yung, Kamila Murawska-Wlodarczyk, Alicja Babst-Kostecka, Raina Margaret Maier, Nirav Merchant, Aikseng Ooi

“…The pipeline integrates a fine-tuned Mask Region-based Convolutional Neural Network (Mask R-CNN) segmentation model trained on 176 plant images and achieves high performance despite the small training data set (Dice coefficient = 0.781). …”
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Advances in Remote Sensing and Deep Learning in Coastal Boundary Extraction for Erosion Monitoring by Marc-André Blais, Moulay A. Akhloufi

“…The presented algorithms range from basic convolutional networks to encoder–decoder architectures and attention mechanisms. …”
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Challenges in AI-driven multi-omics data analysis for Oncology: Addressing dimensionality, sparsity, transparency and ethical considerations by Maryem Ouhmouk, Shakuntala Baichoo, Mounia Abik

“…Non-generative approaches, such as feedforward neural networks (FFNs), graph convolutional networks (GCNs), and autoencoders, are designed to extract features and perform classification directly. …”
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A Deep Learning-Based Diagnostic Framework for Shaft Earthing Brush Faults in Large Turbine Generators by Katudi Oupa Mailula, Akshay Kumar Saha

“…The proposed framework combines advanced signal processing and convolutional neural networks (CNNs) to automatically recognize fault-related patterns in shaft grounding current and voltage signals. …”
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TSAS—YOLOv8: An Optimization Detection Model for Capturing Small Target Features and Processing Key Information by Yongbo Yuan, Linlin Cao

“…In object detection tasks, small targets are prone to losing critical information during feature extraction by traditional convolutional layers due to their tiny size and sparse features. …”
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Reducing lead requirements for wearable ECG: Chest lead reconstruction with 1D-CNN and Bi-LSTM by Kazuki Hebiguchi, Hiroyoshi Togo, Akimasa Hirata

“…Leveraging the PTB-XL ECG dataset, we preprocessed the signals to eliminate noise and trained a model integrating 1D convolutional layers with a Bi-directional Long Short-Term Memory (Bi-LSTM) architecture. …”
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Deep Learning with Dual-Channel Feature Fusion for Epileptic EEG Signal Classification by Bingbing Yu, Mingliang Zuo, Li Sui

“…Channel 2 employs a dual-branch convolutional neural network (CNN) to extract deeper and distinct features. …”
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A Deep-Learning Approach to Heart Sound Classification Based on Combined Time-Frequency Representations by Leonel Orozco-Reyes, Miguel A. Alonso-Arévalo, Eloísa García-Canseco, Roilhi F. Ibarra-Hernández, Roberto Conte-Galván

“…These images are used to train five convolutional neural networks (CNNs): AlexNet, VGG-16, ResNet50, a CNN specialized in STFT images, and our proposed CNN model. …”
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Revolutionizing Mental Health Sentiment Analysis With BERT-Fuse: A Hybrid Deep Learning Model by Md. Mithun Hossain, Sanjara, Md. Shakil Hossain, Sudipto Chaki, Md. Saifur Rahman, A. B. M. Shawkat Ali

“…Traditional methods are limited by the complexity of mental health-related texts, which contain specialized terminology and domain-specific nuances. …”
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Cytopathological quantification of NORs using artificial intelligence to oral cancer screening by Tatiana Wannmacher LEPPER, Luara Nascimento do AMARAL, Ana Laura Ferrares ESPINOSA, Igor Cavalcante GUEDES, Maikel Maciel RÖNNAU, Natália Batista DAROIT, Alex Nogueira HAAS, Fernanda VISIOLI, Manuel Menezes de OLIVEIRA NETO, Pantelis Varvaki RADOS

“…The present study aimed to define argyrophilic proteins of the nucleolar organizer region (AgNOR) cut-off risk points by oral exfoliative cytological smears comparing specialized humans with a convolutional neural network (CNN) system AgNOR Slide-Image Examiner. …”
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Deep Learning for Hyperspectral Image Classification: A Critical Evaluation via Mutation Testing by Zhifei Chen, Yang Hao, Qichao Liu, Yuyong Liu, Mingyang Zhu, Liang Xiao

“…Recently, there has been a surge in the adoption of deep learning (DL) techniques, especially convolutional neural networks (CNNs), to perform hyperspectral image (HSI) classification. …”
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Cost-Efficient Fall Risk Assessment With Attention Augmented Vision Machine Learning on Sit-to-Stand Test Videos by Chunhua Pan, Boting Qu, Rui Miao, Xin Wang

“…Furthermore, a novel Attention-augmented Spatial-Temporal Graph Convolutional Network (AST-GCN) is developed for reliably identifying the action in each frame, enabling accurate computation of key kinematic features for fall risk prediction. …”
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An enhanced method of CNNs by incorporating the clustering-guided block for concrete crack recognition by Hui Li, Chenyu Liu, Ning Zhang, Wei Shi

“…This paper introduces a novel Crack Segmentation method known as CG-CNNs, which combines a Clustering-guided (CG) block with a Convolutional Neural Network (CNN). The innovative CG block operates by categorizing extracted image features into K groups, merging these features, and then simultaneously feeding the augmented features and original image into the CNN for precise crack image segmentation. …”
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Automatic construction of risk transmission network about subway construction based on deep learning models by Yanxiang Liang, Na Xu, Hong Chang, Shan Qian, Yao Liu

“…Additionally, a domain-specific entity causal relation extraction model employing Convolutional Neural Networks (CNN) was also developed in thsi model. …”
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Research on Abnormal Ship Brightness Temperature Detection Based on Infrared Image Edge-Enhanced Segmentation Network by Xiaobin Hong, Guanqiao Chen, Yuanming Chen, Ruimou Cai

“…The Sobel operator is used to obtain edge feature maps, and the Convolutional Block Attention Module (CBAM) extracts key feature information. …”
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Revolutionizing sleep disorder diagnosis: A Multi-Task learning approach optimized with genetic and Q-Learning techniques by Soraya Khanmohmmadi, Toktam Khatibi, Golnaz Tajeddin, Elham Akhondzadeh, Amir Shojaee

“…The study proposes an innovative multi-task learning convolutional neural network with a partially shared structure that uses frequency-time images generated from EEG signals to address these limitations. …”
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Do more with less: Exploring semi-supervised learning for geological image classification by Hisham I. Mamode, Gary J. Hampson, Cédric M. John

“…When examining small, highly specialized datasets, without large amounts of unlabeled images, supervised transfer learning might be the best strategy to adopt. …”
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The Emerging Role of Artificial Intelligence in Dermatology: A Systematic Review of Its Clinical Applications by Ernesto Martínez-Vargas, Jeaustin Mora-Jiménez, Sebastian Arguedas-Chacón, Josephine Hernández-López, Esteban Zavaleta-Monestel

“…Additional tools such as convolutional neural networks and imaging systems like FotoFinder also showed promising results. …”
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