Temporal and spatial self supervised learning methods for electrocardiograms by Wenping Chen, Huibin Wang, Lili Zhang, Min Zhang

“…Abstract The limited availability of labeled ECG data restricts the application of supervised deep learning methods in ECG detection. Although existing self-supervised learning approaches have been applied to ECG analysis, they are predominantly image-based, which limits their effectiveness. …”
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Two-Dimensional Human Pose Estimation with Deep Learning: A Review by Zheyu Zhang, Seong-Yoon Shin

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High-resolution Solar Image Registration Using Unsupervised Deep Learning by Hao Zhang, Bo Liang, Xiaoli Yan, Song Feng, Ding Yuan, Yunfang Cai, Wei Dai

“…In this study, we propose an unsupervised deep learning–based framework named hourGlass and haRdnEt (GRE) for accurate solar image registration. …”
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Modeling Traders’ Behavior with Deep Learning and Machine Learning Methods: Evidence from BIST 100 Index by Afan Hasan, Oya Kalıpsız, Selim Akyokuş

“…In this study, technical indicators were integrated into the methods of deep learning and machine learning, and the behavior of the traders was modeled in order to increase the accuracy of forecasting of the financial market direction. …”
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Optimized Fake News Classification: Leveraging Ensembles Learning and Parameter Tuning in Machine and Deep Learning Methods by Abubaker A. Alguttar, Osama A. Shaaban, Remzi Yildirim

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Variational Methods in Optical Quantum Machine Learning by Marco Simonetti, Damiano Perri, Osvaldo Gervasi

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A deep-learning-based consistency test approach for Earth system models on HPC systems by Yangyang Yu, Shaoqing Zhang, Haohuan Fu, Dexun Chen, Yang Gao, Xiaopei Lin, Zhao Liu, Xiaojing Lv

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Automatic Meniscus Segmentation Using YOLO-Based Deep Learning Models with Ensemble Methods in Knee MRI Images by Mehmet Ali Şimşek, Ahmet Sertbaş, Hadi Sasani, Yaşar Mahsut Dinçel

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Deep Learning in Power Systems: A Bibliometric Analysis and Future Trends by Seyed Mahdi Miraftabzadeh, Andrea Di Martino, Michela Longo, Dario Zaninelli

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Historical Perspectives in Volatility Forecasting Methods with Machine Learning by Zhiang Qiu, Clemens Kownatzki, Fabien Scalzo, Eun Sang Cha

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Detecting command injection attacks in web applications based on novel deep learning methods by Xinyu Wang, Jiqiang Zhai, Hailu Yang

“…To address these challenges, a novel detection model, the Convolutional Channel-BiLSTM Attention (CCBA) model, is proposed, leveraging deep learning techniques to enhance the identification of web command injection attacks. …”
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Physical Layer Spoof Detection and Authentication for IoT Devices Using Deep Learning Methods by Da Huang, Akram Al-Hourani

“…In this work, we propose an authentication method based on radio frequency fingerprinting (RFF) using deep learning. …”
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A Review of Intrusion Detection for Railway Perimeter Using Deep Learning-Based Methods by Jin Wang, Hongyang Zhai, Yang Yang, Niuqi Xu, Hao Li, Di Fu

“…This article reviews the background and importance of detecting railway perimeter intrusion, summarizes the limitations of traditional detection methods, and emphasizes the potential of improving detection accuracy and efficiency in image recognition with deep learning models. …”
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Comparative Evaluation of Traditional Methods and Deep Learning for Brain Glioma Imaging. Review Paper by Kiranmayee Janardhan, Vinay Martin D’Sa Prabhu, T. Christy Bobby

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From Backtracking To Deep Learning: A Survey On Methods For Solving Constraint Satisfaction Problems by Fatima AIT HATRIT, Kamal AMROUN

“…To address this, extensive research has been conducted, leading to the development of effective techniques and algorithms for different types of CSPs, ranging from exhaustive search methods, which explore the entire search space, to modern techniques that use deep learning to learn how to solve CSPs. …”
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UAV-Multispectral Based Maize Lodging Stress Assessment with Machine and Deep Learning Methods by Minghu Zhao, Dashuai Wang, Qing Yan, Zhuolin Li, Xiaoguang Liu

“…However, existing studies primarily use machine learning (ML) methods to qualitatively analyze maize lodging (lodging and non-lodging) or estimate the maize lodging percentage, while there is less research using deep learning (DL) to quantitatively estimate maize lodging parameters (type, severity, and direction). …”
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Assessing the Efficiency of Deep Learning Methods for Automated Vehicle Registration Recognition for University Entrance by Muhammad Syaqil Irsyad, Zarina Che Embi, Khairil Imran Bin Ghauth

“…The results show the system's potential to be easily adapted to different camera systems, angles, and lighting conditions by retraining the deep learning model. The paper also explores various deep learning methods, such as CenterNet, EfficientDet, and Faster R-CNN, and their effectiveness in automated vehicle registration detection. …”
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Quantifying uncertainty in foraminifera classification: How deep learning methods compare to human experts by Iver Martinsen, Steffen Aagaard Sørensen, Samuel Ortega, Fred Godtliebsen, Miguel Tejedor, Eirik Myrvoll-Nilsen

“…Uncertainty estimation in deep learning has gained significant attention and many methods have been developed. …”
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Research and application of deep learning object detection methods for forest fire smoke recognition by Luhao He, Yongzhang Zhou, Lei Liu, Yuqing Zhang, Jianhua Ma

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Construction of Knowledge Graph for Marine Diesel Engine Faults Based on Deep Learning Methods by Xiaohe Tian, Huibing Gan, Yanlin Liu

“…In this paper, we propose a BiLSTM-CRF-based knowledge graph construction method for ship diesel engine faults, aiming at integrating multi-source heterogeneous data through deep learning and knowledge graph technology, and mining the deep semantic associations among fault phenomena, causes, and solutions. …”
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