Deep transfer learning method for seasonal TROPOMI XCH₄ albedo correction by A. C. Bradley, A. C. Bradley, B. Dix, F. Mackenzie, J. P. Veefkind, J. P. Veefkind, J. A. de Gouw, J. A. de Gouw

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Prediction of Daily Entrance and Exit Passenger Flow of Rail Transit Stations by Deep Learning Method by Huaizhong Zhu, Xiaoguang Yang, Yizhe Wang

“…Furthermore, based on the history data of passenger flow of rail transit stations and relevant influence factors, it applies the deep learning method to choose the relatively optimal hidden layer node by means of the cut-and-try method, set up input data and labeled data, select the activation function and loss function, and use the Adam Gradient Descent Optimization Algorithm for iterative global convergence. …”
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A Self-Supervised Specific Emitter Identification Method Based on Contrastive Asymmetric Masked Learning by Dong Wang, Yonghui Huang, Tianshu Cui, Yan Zhu

“…However, current deep learning-based SEI methods heavily rely on large amounts of labeled data for supervised training, facing challenges in non-cooperative communication scenarios. …”
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Hybrid Optimized Feature Selection and Deep Learning Method for Emotion Recognition That Uses EEG Data by asmaa Bashar Hmaza, Rajaa K. Hasoun

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A deep learning-based method of acoustic holographic lens generation for transcranial focused ultrasound by Mengxu Bu, Wenting Gu, Boyi Li, Qiuchen Zhu, Xue Jiang, Dean Ta, Xin Liu

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Aprendiendo sobre el Método de los Elementos Finitos. // Learning on the Method of the Finite Elements. by L. L. Otero Pereiro

“…<br />According to the author's point of view the reality is that the abilities in the use of software can be acquired by most of the<br />professionals in the matter, but the theory on the method it is even not very understood by the most, even when they have<br />learned how to use one of this professional software. …”
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Learning More from Mixed Emotions: A Label Refinement Method for Emotion Recognition in Conversations by Jintao Wen, Geng Tu, Rui Li, Dazhi Jiang, Wenhua Zhu

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Fault Line Selection Method Based on Transfer Learning Depthwise Separable Convolutional Neural Network by Haixia Zhang, Wenao Cheng

“…Aiming at the problem of single-phase-to-ground fault line selection in resonant grounding system, a fault line selection method based on transfer learning depthwise separable convolutional neural network (DSCNN) is proposed. …”
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Towards saturation attack detection in SDN: a multi-edge representation learning-based method by Zhangli Ji, Yunhe Cui, Yinyan Guo, Guowei Shen, Yi Chen, Chun Guo

“…Although such threats are increasingly significant, network attack detection methods based on edge representation learning are still insufficiently studied. …”
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Experimental Study of an Approximate Method for Calculating Entropy-Optimal Distributions in Randomized Machine Learning Problems by Alexey Yu. Popkov, Yuri A. Dubnov, Ilya V. Sochenkov, Yuri S. Popkov

Subjects: “…randomized machine learning…”
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Definer: A computational method for accurate identification of RNA pseudouridine sites based on deep learning. by Bo Han, Sudan Bai, Yang Liu, Jiezhang Wu, Xin Feng, Ruihao Xin

“…In this study, we propose a deep learning-based computational method, Definer, to accurately identify RNA pseudouridine loci in three species, Homo sapiens, Saccharomyces cerevisiae and Mus musculus. …”
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Machine Learning Based Method for Insurance Fraud Detection on Class Imbalance Datasets With Missing Values by Ahmed A. Khalil, Zaiming Liu, Ahmed Fathalla, Ahmed Ali, Ahmad Salah

“…Prior research has employed machine learning methods to address this class imbalance dataset problem, but there is limited effort handling the class imbalance dataset present in insurance fraud datasets. …”
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Challenges and Benefits of Blended Learning in University EFL Reading Comprehension: A Mixed-Method Study by Dagnachew Tsegaye, Girma Gezahegn

“…The research focuses on exploring the experiences of first year university students engaged in BL-based reading comprehension. Using a mixed-method approach, data was collected from 36 participants enrolled in a noncredit reading skills course integrated with a BL platform over 16-weeks. …”
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New Method of Impact Localization on Plate-like Structures Using Deep Learning and Wavelet Transform by Asaad Migot, Ahmed Saaudi, Victor Giurgiutiu

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Prediction of Flexural Ultimate Capacity for Reinforced UHPC Beams Using Ensemble Learning and SHAP Method by Zhe Zhang, Xuemei Zhou, Ping Zhu, Zhaochao Li, Yichuan Wang

“…Finally, a Shapley additive explanations (SHAP) method is employed to interpret several EL models, thereby substantiating their reliability and determining the extent of influence exerted by each feature on the prediction results. …”
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On Training Spiking Neural Networks by Means of a Novel Quantum Inspired Machine Learning Method by Jean Michel Sellier, Alexandre Martini

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A Ship Underwater Radiated Noise Prediction Method Based on Semi-Supervised Ensemble Learning by Xin Huang, Rongwu Xu, Ruibiao Li

“…Therefore, this paper proposes an SSL method for URN prediction. First, an anti-perturbation regularization is constructed using unlabeled data to optimize the objective function of EL, which is then used in the Genetic algorithm to adaptively optimize base learner weights, to enhance pseudo-label quality. …”
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Multi-Stand Grouped Operations Method in Airport Bay Area Based on Deep Reinforcement Learning by Jie Ouyang, Changqing Zhu, Xiaowei Tang, Jian Zhang

“…To address the trade-off between safety levels and operational efficiency in the Bay Area, this study proposes a Multi-Stand Grouped Operations method based on deep reinforcement learning under the consideration of the safety domain. …”
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A Deep-Learning-Based Detection Method for Small Target Tomato Pests in Insect Traps by Song Wang, Daqing Chen, Jianxia Xiang, Cong Zhang

“…Finally, the SCYLLA-IoU (SIoU) loss function is introduced, and its components are redefined to incorporate direction information, which enhances the model’s learning ability and convergence performance. Experimental results show that our method can accurately detect three insects: whitefly (WF), macrolophus (MR), and nesidiocoris (NC) in the yellow sticky trap images of tomato crops. …”
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Intelligent Testing Method for Multi-Point Vibration Acquisition of Pile Foundation Based on Machine Learning by Ke Wang, Weikai Zhao, Juntao Wu, Shuang Ma

“…To address the limitations of the conventional low-strain reflected wave method for pile foundation testing, this study proposes an intelligent multi-point vibration acquisition testing model based on machine learning to evaluate the integrity of in-service, high-cap pile foundations. …”
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