Meta-YOLOv8: multi-scale few-shot object detection for Chinese medicinal decoction pieces by Kai Hu, Chu-he Lin, Xing Jin, Hangjuan Lin

“…Compared to YOLOv8, our method improves the mean average precision ( $$mAP_{0.5}$$ m A P 0.5 ) by 2.2% to 20.4% in different settings, with only a marginal increase in the inference time of 6.6%. …”
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MESM: integrating multi-source data for high-accuracy protein-protein interactions prediction through multimodal language models by Feng Wang, Jinming Chu, Liyan Shen, Shan Chang

“…Finally, MESM uses Graph Convolutional Network (GCN) and SubgraphGCN to extract global and local features from the perspective of the overall graph and subgraphs. …”
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A few-shot diabetes foot ulcer image classification method based on deep ResNet and transfer learning by Cheng Wang, Zhen Yu, Zhou Long, Hui Zhao, Zhenwei Wang

“…Therefore, the methods include: (1) Data augmentation of the original DFU images by using geometric transformations and random noise; (2) Deep ResNet models selection based on different convolutional layers comparative experiments; (3) DFU classification model training with transfer learning by using the selected pre-trained ResNet model and fine tuning. …”
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Classification of Liver Fibrosis From Heterogeneous Ultrasound Image by Yunsang Joo, Hyun-Cheol Park, O-Joun Lee, Changhan Yoon, Moon Hyung Choi, Chang Choi

“…In other words, when the domain of data used for training is different from that of data applied for an actual diagnosis, it is unclear whether consistent performance can be provided by the domain bias. …”
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Enhancing Drought Forecast Accuracy Through Informer Model Optimization by Jieru Wei, Wensheng Tang, Pakorn Ditthakit, Jiandong Shang, Hengliang Guo, Bei Zhao, Gang Wu, Yang Guo

“…Simultaneously, the model exhibits equally optimal forecasting performance across different time scales. In order to validate the VMD-JAYA-Informer model, four meteorological stations in the Songliao River Basin were chosen at random. …”
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Development and validation of MRI-derived deep learning score for non-invasive prediction of PD-L1 expression and prognostic stratification in head and neck squamous cell carcinoma by Cong Ding, Yue Kang, Fan Bai, Genji Bai, Junfang Xian

“…Tumor regions were manually segmented, and features extracted from different MRI sequences were fused using a transformer-based model incorporating attention mechanisms. …”
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Estimating ocean currents from the joint reconstruction of absolute dynamic topography and sea surface temperature through deep learning algorithms by D. Ciani, C. Fanelli, B. Buongiorno Nardelli

“…To address these issues, we developed and tested different deep learning methodologies, specifically convolutional neural network (CNN) models that were originally proposed for single-image super resolution. …”
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Design of an Iterative Method for Time Series Forecasting Using Temporal Attention and Hybrid Deep Learning Architectures by Yuvaraja Boddu, A. Manimaran

“…Initially, it employs Temporal Attention Mechanisms to dynamically allocate attention across different timestamps and variables, significantly enhancing the model’s ability to capture critical temporal dependencies. …”
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Deep Residual Transfer Ensemble Model for mRNA Gene-Expression-Based Breast Cancer by Job Prasanth Kumar Chinta Kunta, Vijayalakshmi A. Lepakshi

“…Gene-expression analysis can be an alternative, where the different machine learning and artificial intelligence techniques can be developed to learn microarray RNA details for breast cancer detection. …”
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Cross-Domain Carotid Artery Segmentation Using Folding Fan ResNet and Quadratic Mapping Loss by Zhaozheng Chen, Zhiyin Liu, Bernard Chiu

“…However, the performance of these models declines substantially when applied to datasets with different properties from their training data, highlighting a need for more generalizable models. …”
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BCI‐control and monitoring system for smart home automation using wavelet classifiers by Amer Al‐Canaan, Hicham Chakib, Muhammad Uzair, Shuja‐uRehman Toor, Amer Al‐Khatib, Majid Sultan

“…Although the EEG data set contains thousands of samples and more than 15 different classes, we limit our study on 226 samples grouped into seven classes with 8‐second time duration per sample. …”
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Digital Twin-Supported Battery State Estimation Based on TCN-LSTM Neural Networks and Transfer Learning by Kai Zhao, Ying Liu, Yue Zhou, Wenlong Ming, Jianzhong Wu

“…The emergence of digital twin (DT) technology offers a novel opportunity for performance monitoring and management of lithium-ion batteries, enhancing collaborative capacity among different battery state estimation techniques and enabling optimal operation of battery storage units. …”
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CochleaSpecNet: An Attention-Based Dual Branch Hybrid CNN-GRU Network for Speech Emotion Recognition Using Cochleagram and Spectrogram by Atkia Anika Namey, Khadija Akter, Md. Azad Hossain, M. Ali Akber Dewan

“…SER system classifies the human emotional state based on speaker’s utterances in different catagories such as sad, happy, neutral, angry, surprise, calm and so on. …”
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Labor protests of employees of cultural organizations by Petr V. Bizyukov, Taisiya A. Vishnyakova

“…However, the relative indicator (average annual number of protests per 1 million people employed in the sector), which demonstrates the degree of social tension within the industry, paints a different picture: 3.82 protests per 1 million workers in culture, 9.92 protests in healthcare, and 6.79 protests in the cultural sector. …”
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The use of pretrained neural networks for solving the problem of reverse searching of X-ray images of prohibited items and substances by A. K. Volkov, L. V. Mironova, S. E. Potapova

“…A comparative analysis of image indexing and image search under different algorithms and feature number is presented. …”
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AI-Enabled Condition Monitoring Framework for Autonomous Pavement-Sweeping Robots by Sathian Pookkuttath, Aung Kyaw Zin, Akhil Jayadeep, M. A. Viraj J. Muthugala, Mohan Rajesh Elara

“…The training dataset is generated by extracting representative vibration and heterogeneous slope data using three types of interoceptive sensors mounted in different zones of the robot. These sensors complement each other to enable accurate class prediction. …”
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Machine Learning in the Management of Patients Undergoing Catheter Ablation for Atrial Fibrillation: Scoping Review by Aijing Luo, Wei Chen, Hongtao Zhu, Wenzhao Xie, Xi Chen, Zhenjiang Liu, Zirui Xin

“… BackgroundAlthough catheter ablation (CA) is currently the most effective clinical treatment for atrial fibrillation, its variable therapeutic effects among different patients present numerous problems. Machine learning (ML) shows promising potential in optimizing the management and clinical outcomes of patients undergoing atrial fibrillation CA (AFCA). …”
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MHOE-DETR: A Ship Detection Method for Small and Fuzzy Targets Based on Satellite Remote Sensing Image Data by Zhuhua Hu, Xiyu Fan, Yaochi Zhao, Wei Wu, Jie Liu

“…By decomposing the self-attention matrix and the spatial attenuation matrix, the spatial correlations of different directions and positions are captured, thus effectively alleviating the problem of catastrophic forgetting. …”
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Towards precision agriculture tea leaf disease detection using CNNs and image processing by Irfan Sadiq Rahat, Hritwik Ghosh, Suresh Dara, Shashi Kant

“…The innovative use of a convolutional layer with 64 7 × 7 filters, followed by batch normalization and Rel U activation, allows for the extraction and representation of intricate patterns from the input data. …”
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A Novel Dual-Modal Deep Learning Network for Soil Salinization Mapping in the Keriya Oasis Using GF-3 and Sentinel-2 Imagery by Ilyas Nurmemet, Yang Xiang, Aihepa Aihaiti, Yu Qin, Yilizhati Aili, Hengrui Tang, Ling Li

“…Effectively and timely mapping of different degrees of salinized soils is essential for sustainable land management and ecological restoration. …”
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