The future of critical care: AI-powered mortality prediction for acute variceal gastrointestinal bleeding and acute non-variceal gastrointestinal bleeding patients by Zhou Liu, Guijun Jiang, Liang Zhang, Palpasa Shrestha, Yugang Hu, Yi Zhu, Guang Li, Yuanguo Xiong, Liying Zhan

“…In case of ANGIB patients, gradient boosting model proven to be the optimal machine learning models, with the AUC of 0.985 ± 0.002, accuracy of 0.948 ± 0.009, precision of 0.949 ± 0.009, recall of 0.968 ± 0.009, and F1 score of 0.959 ± 0.007. …”
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VTGAN based proactive VM consolidation in cloud data centers using value and trend approaches by Aya I. Maiyza, Hanan A. Hassan, Walaa M. Sheta, Karim Banawan, Noha O. Korany

“…Additionally, incorporating VTGAN into the VM placement algorithm to disregard hosts predicted to become overloaded further improves performance. …”
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Impact of Public Space in Primary and Secondary Schools Based on Natural Visibility Ratio by Feng Liu, Hao Zhou, Jiangtao Xie, Yue Tang, Shuyu Liu

“…In recent years, the safety of primary and secondary schools has garnered considerable attention from policymakers and architects, necessitating rational design methods to develop effective strategies that optimize the campus environment. This study utilizes algorithmic simulations, spatial analysis, and statistical methods to examine the relationships between the layouts of buildings, public space morphology, and campus safety. …”
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Role of Artificial Intelligence and Personalized Medicine in Enhancing HIV Management and Treatment Outcomes by Ashok Kumar Sah, Rabab H. Elshaikh, Manar G. Shalabi, Anass M. Abbas, Pranav Kumar Prabhakar, Asaad M. A. Babker, Ranjay Kumar Choudhary, Vikash Gaur, Ajab Singh Choudhary, Shagun Agarwal

“…Despite these innovations, challenges such as data privacy, algorithmic bias, and the need for clinical validation remain. …”
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Artificial intelligence in hospital infection prevention: an integrative review by Rabie Adel El Arab, Zainab Almoosa, May Alkhunaizi, May Alkhunaizi, Fuad H. Abuadas, Joel Somerville, Joel Somerville

“…Advanced algorithms, including neural networks, decision trees, and random forests, significantly improved detection rates when integrated with EHRs, enabling real-time surveillance and timely interventions. …”
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Robust vector-weighted and matrix-weighted multi-view hard c-means clustering by Zhe Liu, Sarah Aljohani, Sijia Zhu, Tapan Senapati, Gözde Ulutagay, Salma Haque, Nabil Mlaiki

“…On this basis, we further propose a robust matrix-weighted multi-view hard c-means (MW-MVHCM) clustering, which assigns view-specific weights at the cluster level, allowing for more detailed intra-view contribution modeling. This matrix-weighted approach enables MW-MVHCM to dynamically capture the varying importance of each view across clusters, improving clustering performance. …”
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Convergence of nanotechnology and artificial intelligence in the fight against liver cancer: a comprehensive review by Manjusha Bhange, Darshan Telange

“…Simultaneously, AI contributes to improved diagnostic accuracy, predictive modeling, and the development of personalized treatment strategies. …”
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Comparison of Bioelectric Signals and Their Applications in Artificial Intelligence: A Review by Juarez-Castro Flavio Alfonso, Toledo-Rios Juan Salvador, Aceves-Fernández Marco Antonio, Tovar-Arriaga Saul

“…AI-driven approaches are shown to have the potential to enhance diagnostic precision and improve patient outcomes. However, further research is needed to optimize these models for diverse clinical environments and fully exploit the interaction between bioelectrical signals and AI technologies.…”
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Parsimonious and explainable machine learning for predicting mortality in patients post hip fracture surgery by Fouad Trad, Bassel Isber, Ryan Yammine, Khaled Hatoum, Dana Obeid, Mohammad Chahine, Rachid Haidar, Ghada El-Hajj Fuleihan, Ali Chehab

“…In this study, we developed machine learning (ML) algorithms to estimate 30-day mortality risk post-hip fracture surgery in the elderly using data from the National Surgical Quality Improvement Program (NSQIP 2012–2017, n = 62,492 patients). …”
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Machine learning analysis of molecular dynamics properties influencing drug solubility by Zeinab Sodaei, Saeid Ekrami, Seyed Majid Hashemianzadeh

“…Understanding solubility at the early stages of drug discovery is essential for minimizing resource consumption and enhancing the likelihood of clinical success via prioritizing compounds with optimal solubility. Molecular dynamics (MD) simulation is a powerful computational tool for modeling various physicochemical properties, particularly solubility. …”
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Words high-frequency drying processes simulation of wooden tangent towers in a vacuum chamber by A. N. Kachanov, D. A. Korenkov, A. A. Revkov, V. V. Maksimov, O. V. Vorkunov

“…This model is characterized by the possibility of using simple algorithms for analyzing differential equation systems based on the finite differe nce method and requiring less initial data on the drying material properties. …”
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Development of intelligent tools to predict neuroblastoma risk stratification and overall prognosis based on multiphase enhanced CT and clinical features by Wei Zhao, Yahui Han, Xiaokun Yu, Jianing Liu, Jiao Zhang, Juan Li

“…The 1-, 3-, and 5-year AUC values of the optimal prognostic model in the training cohort were 0.93 (95% CI: 0.927–0.942), 0.93 (95% CI: 0.929–0.946), and 0.96 (95% CI: 0.953–0.974), respectively. …”
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Deep learning radiomics based on MRI for differentiating tongue cancer T - staging by Zhaoyi Lu, Bowen Zhu, Hang Ling, Xi Chen

“…ResNet18 and ResNet50 algorithms were employed to build deep learning models (deep learning radiomics (DLR) resnet18 / DLRresnet50), compared with a radiomics model (Rad) based on 17 optimized features. …”
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Development of PYMUS+ Code for Quantitative Evaluation of Nuclear Material Accounting (NMA) System by Byung Hee Won, Hee-Sung Shin, Se Hwan Park, Seong-Kyu Ahn

“…It is also important to quantitatively evaluate the performance of NMA system including NRTA from the standpoints of Safeguards and Security by Design (SSBD) prior to construction of nuclear-material-handling facilities. Such evaluation improves safeguards effectiveness and efficiency. Modeling and Simulation (M&S) work is a good way to evaluate performance for various NMA systems and to determine the optimal one among different options. …”
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Machine Learning-Based Interpretable Screening for Osteoporosis in Tuberculosis Spondylitis Patients Using Blood Test Data: Development and External Validation of a Novel Web-Based... by Yasin P, Ding L, Mamat M, Guo W, Song X

“…The optimal model was further refined through threshold tuning to enhance performance metrics. …”
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Explainable Boosting Machines Identify Key Metabolomic Biomarkers in Rheumatoid Arthritis by Fatma Hilal Yagin, Cemil Colak, Abdulmohsen Algarni, Ali Algarni, Fahaid Al-Hashem, Luca Paolo Ardigò

“…EBM, LightGBM, and AdaBoost algorithms were applied to generate a discriminatory model between RA and controls. …”
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Performance evaluation of different building envelopes integrated with phase change materials in tropical climates by Rolains Golchimard Elenga, Li Zhu, Steivan Defilla

“…To achieve this goal, a multiobjective genetic algorithm is used in conjunction with EnergyPlus building energy models to determine the optimal balance between total building energy consumption, lifecycle cost, and CO2 emissions. …”
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Innovative approach for gauge-based QPE in arid climates: comparing neural networks and traditional methods by Bayan Banimfreg, Ernesto Damiani, Vesta Afzali Gorooh, Duncan Axisa, Luca Delle Monache, Youssef Wehbe

“…These improvements highlight the model’s ability to assimilate diverse climatic and topographical inputs for more accurate rainfall prediction, particularly in areas where conventional methods fall short due to sparse or irregular precipitation. …”
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Application and prospects of machine learning for rockfalls, landslides and debris flows by Jiazhu WANG, Yongbo TIE, Yongjian BAI, Yanchao GAO, Donghui WANG, Mingzhi ZHANG

“…Future research should prioritize enhancing data quality and quantity, optimizing model interpretability, improving model reliability and generalization, and establishing real-time monitoring and warning systems for automatic identification and rapid response to geological hazards. …”
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Radiomics analysis of thoracic vertebral bone marrow microenvironment changes before bone metastasis of breast cancer based on chest CT by Hao-Nan Zhu, Yi-Fan Guo, YingMin Lin, Zhi-Chao Sun, Xi Zhu, YuanZhe Li

“…Multiple machine learning algorithms were utilized to construct various radiomics models for predicting the risk of bone metastasis, and the model with optimal performance was integrated with clinical features to develop a nomogram. …”
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