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121
The burden of unintentional drowning: global, regional and national estimates of mortality from the Global Burden of Disease 2017 Study
Published 2020“…Global age-standardised mortality rates decreased 57.4%, from 9.3 (8.5 to 10.0) in 1990 to 4.0 (3.8 to 4.1) per 100 000 per annum in 2017. …”
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122
Communication-efficient hierarchical federated learning for IoT heterogeneous systems with imbalanced data
Published 2022“…<p dir="ltr">Federated Learning (FL) is a distributed learning methodology that allows multiple nodes to cooperatively train a deep learning model, without the need to share their local data. …”
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123
Human Action Recognition: A Taxonomy-Based Survey, Updates, and Opportunities
Published 2023“…One of the most challenging issues for computer vision is the automatic and precise identification of human activities. A significant increase in feature learning-based representations for action recognition has emerged in recent years, due to the widespread use of deep learning-based features. …”
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Evaluating machine learning technologies for food computing from a data set perspective
Published 2023“…Food computing benefits from technologies based on modern machine learning techniques, including deep learning, deep convolutional neural networks, and transfer learning. …”
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A systematic review and meta-analysis on the impact of early vs. delayed pharmacological thromboprophylaxis in patients with traumatic brain injury
Published 2024“…Our findings indicated that early prophylaxis significantly reduced the incidence of VTE, deep vein thrombosis (DVT), pulmonary embolism (PE), and overall mortality when compared to late administration. …”
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126
A New Flow-Based Approach for Enhancing Botnet Detection Efficiency Using Convolutional Neural Networks and Long Short-Term Memory
Published 2025“…<p dir="ltr">Despite the growing research and development of botnet detection tools, an ever-increasing spread of botnets and their victims is being witnessed. …”
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Food fraud detection using explainable artificial intelligence
Published 2023“…<div><p>Recently, the global food supply chain has become increasingly complex, and its scalability has grown. …”
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TB-CXRNet: Tuberculosis and Drug-Resistant Tuberculosis Detection Technique Using Chest X-ray Images
Published 2024“…Moreover, due to the increase of drug-resistant tuberculosis, the disease becomes more challenging in recent years. …”
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Improving diagnosis and prognosis of lung cancer using vision transformers: a scoping review
Published 2023“…It provides key insights into how vision transformers complemented the performance of AI and deep learning methods for lung cancer. Furthermore, the review also identifies the datasets that contributed to advancing the field.…”
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133
Detection of Real-Time Malicious Intrusions and Attacks in IoT Empowered Cybersecurity Infrastructures
Published 2023“…To address these issues, we propose a deep learning-based novel method to detect cybersecurity vulnerabilities and breaches in cyber-physical systems. …”
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The global, regional, and national burden of stomach cancer in 195 countries, 1990–2017: a systematic analysis for the Global Burden of Disease study 2017
Published 2020“…We report on the incidence, mortality, and disability-adjusted life-years (DALYs) due to stomach cancer in 195 countries and territories from 21 regions between 1990 and 2017. …”
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137
Identifying Regional Trends in Avatar Customization
Published 2019“…We find that avatar customization correlates with increased social activity, and we are able to identify distinct visual trends among the U.S.…”
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138
Exploring Effects of Mental Stress with Data Augmentation and Classification Using fNIRS
Published 2025“…To improve our results and increase our dataset, we use data augmentation with a deep convolutional generative adversarial network (DCGAN). …”
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139
YOLO-SAIL: Attention-Enhanced YOLOv5 With Optimized Bi-FPN for Ship Target Detection in SAR Images
Published 2025“…It has recently become increasingly popular to apply deep learning algorithms to the identification of ships in SAR images. …”
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Hyperspectral-physiological based predictive model for transpiration in greenhouses under CO<sub>2</sub> enrichment
Published 2023“…Three machine learning models were investigated for transpiration modelling and prediction: deep neural networks (DNN), extreme gradient boosting (XGBoost), and support vector machine regression (SVR). …”