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Electrochemical Studies on the Corrosion Behavior of Common Metals in Eutectic Ionic Liquids
Published 2017Get full text
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Prevalence and attributable health burden of chronic respiratory diseases, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017
Published 2020“…Deaths due to chronic respiratory diseases numbered 3 914 196 (95% UI 3 790 578–4 044 819) in 2017, an increase of 18·0% since 1990, while total DALYs increased by 13·3%. …”
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Stressor pileup, family and couple relational well‐being, and parent stress during the COVID‐19 pandemic
Published 2023“…</p><h3>Background</h3><p dir="ltr">Public health measures helped contain COVID‐19 spread, but disrupted family life and increased parents' stress. Positive family relationships and beliefs about the impact of challenges can foster psychological resilience during adversity and may influence parents' stress.…”
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Decoding silent speech: a machine learning perspective on data, methods, and frameworks
Published 2025“…Examining state-of-the-art SSR frameworks, the paper covers important topics such signal processing, feature extraction, ML techniques for decoding and optimizing and assessing the performance of SSR models. We emphasize how deep learning (DL) and ML models have evolved to increase SSR resilience and accuracy. …”
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Intelligent scaling for 6G IoE services for resource provisioning
Published 2021“…IScaler is considered to be made for MEC in Deep Reinforcement Learning (DRL). The paper has considered several requirements for making service placement decisions. …”
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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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Evaluating machine learning technologies for food computing from a data set perspective
Published 2023“…We collected and reviewed more than 100 papers related to the usage of machine learning and deep learning for food computing tasks. We analyze their performance on publicly available state-of-art food data sets and their potential for usage in multimedia food-related applications for various needs (communication, leisure, tourism, blogging, reverse engineering, etc.). …”
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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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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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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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Allergen immunotherapy for atopic dermatitis: Systematic review and meta-analysis of benefits and harms
Published 2023“…Both routes of AIT increased adverse events (risk ratio [95% confidence interval] 1.61 [1.44-1.79]; 66% with SCIT vs 41% with placebo; 13% with SLIT vs 8% with placebo; high certainty). …”
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Artificial Intelligence–Driven Serious Games in Health Care: Scoping Review
Published 2022“…Summarizing the features of the current AI-driven serious games is very important to explore how they have been developed and used and their current state to plan on how to leverage them in the current and future health care needs.</p><h3>Objective</h3><p dir="ltr">This study aimed to explore the features of AI-driven serious games in health care as reported by previous research.…”
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