A derecho climatology (2004–2021) in the United States based on machine learning identification of bow echoes by J. Li, A. Geiss, Z. Feng, L. R. Leung, Y. Qian, W. Cui, W. Cui

“…The dataset consists of two subsets based on different gust speed data sources and is analyzed to document the climatology of derechos in the United States. …”
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Truth be told: a multimodal ensemble approach for enhanced fake news detection in textual and visual media by Rami Mohawesh, Islam Obaidat, Ahmed Abdallah AlQarni, Ali Abdulaziz Aljubailan, Moy’awiah A. Al-Shannaq, Haythem Bany Salameh, Ali Al-Yousef, Ahmad A. Saifan, Suboh M. Alkhushayni, Sumbal Maqsood

“…Similarly, uses the ResNet model, a deep convolutional neural network known for its efficacy in image feature extraction and recognition, to derive a feature vector from the image(s) in the new article. then combines these generated vectors using a weighted fusion strategy to obtain a unified feature representation capturing nuances from both textual and visual data. …”
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Enhancing personalized learning: AI-driven identification of learning styles and content modification strategies by Md. Kabin Hasan Kanchon, Mahir Sadman, Kaniz Fatema Nabila, Ramisa Tarannum, Riasat Khan

“…Furthermore, decision tree, random forest, support vector machine (SVM), logistic regression, XGBoost, blending ensemble, and convolutional neural network (CNN) algorithms with corresponding optimized hyperparameters and synthetic minority oversampling technique (SMOTE) have been applied for learning behavior classification. …”
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Estimation of Potato Growth Parameters Under Limited Field Data Availability by Integrating Few-Shot Learning and Multi-Task Learning by Sen Yang, Quan Feng, Faxu Guo, Wenwei Zhou

“…Independent spatiotemporal validation further confirmed the potential of MTL-MMOE in estimating LAI and AGB across different years and locations (R² = 0.37~0.52). …”
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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

“…With the advances in deep learning, including Convolutional Neural Networks (CNN), automated diagnosis technology using medical images has received considerable attention in medical science. …”
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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

“…We developed deep learning models based on the ResNet-101 convolutional neural network to analyze three MRI sequences (T1WI, T2WI, and contrast-enhanced T1WI). …”
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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

“…Unfortunately, the major existing methods use convolutional neural networks (CNN) to learn features from the RNA-specific 2D images. …”
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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

“…In this study, we propose a DT-supported battery state estimation method, in collaboration with the temporal convolutional network (TCN) and the long short-term memory (LSTM), to address the challenge of feature extraction. …”
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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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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

“…A simple-structured one-dimensional convolutional neural network (1D CNN) model is developed for fast and accurate prediction of the safe to unsafe classes for real-time application. …”
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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

“…In terms of model type, deep learning, represented by convolutional neural networks, was most frequently applied (14/23, 61%). …”
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