Machine learning-based predictive model for enteral nutrition-associated diarrhea in ICU patients and its nursing applications by Xiaoying Liao, Chunhua Li, Qunyan Liu, Wang Xia, Zhenglin Liu, Jiamao Zhu, Wei Hu, Qionghua Hong

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System of Laser Monitoring of Water Pollution with Application of Relative Description of Signal Shape by V. A. Alekseev, S. I. Yuran, V. P. Usoltsev, D. N. Shulmin

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Exploring the role of repetitive negative thinking in the transdiagnostic context of depression and anxiety in children by Kuiliang Li, Lei Ren, Xiao Li, Chang Liu, Xuejiao Tan, Ming Ji, Xi Luo

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Use of machine learning in occupational risk communication for healthcare workers: protocol for scoping review by Gabriela Laudares Albuquerque de Oliveira, Clarice Alves Bonow, Itiberê de Oliveira Castellano Rodrigues, Amanda Xavier Geraldo

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Prediction of risk for acute kidney injury and its progression to mortality in obese patients admitted to ICU postoperatively by LI Qiang, LI Qiang, MU Guo, MU Guo, WANG Wenzhang

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Optimisation of the adaptive neuro-fuzzy inference system for adjusting low-cost sensors PM concentrations by Martina Casari, Piotr A. Kowalski, Laura Po

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Integration of multi-omics profiling reveals an epigenetic-based molecular classification of lung adenocarcinoma: implications for drug sensitivity and immunotherapy response predi... by Ning Wang, Yinan Li, Yaoyao Wang, Wenting Wang

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Spatial-temporal data analysis of digital twin [version 1; peer review: 1 approved, 2 approved with reservations] by Qian Ai, Lei Tang, Xing He, Bo Pan, Robert Qiu

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Time Frequency Analysis Based Fault Detection in PV Array Using Scaling Basis Chirplet Transform by S Ramana Kumar Joga, Chidurala SaiPrakash, Srikanth Velpula, Alivarani Mohapatra, Theophilus A. T. Kambo Jr.

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AI-powered IoT and UAV systems for real-time detection and prevention of illegal logging by Montaser N.A. Ramadan, Mohammed A.H. Ali, Shin Yee Khoo, Mohammad Alkhedher

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Exploring hypoxia-related genes in spinal cord injury: a pathway to new therapeutic targets by Shihuan Cheng, Le Li, Mengmeng Xu, Ningyi Ma, Yinhua Zheng

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Application of Quantitative Interpretability to Evaluate CNN-Based Models for Medical Image Classification by Nuan Cui, Yingjie Wu, Guojiang Xin, Jiaze Wu, Liqin Zhong, Hao Liang

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RGB-to-Infrared Translation Using Ensemble Learning Applied to Driving Scenarios by Leonardo Ravaglia, Roberto Longo, Kaili Wang, David Van Hamme, Julie Moeyersoms, Ben Stoffelen, Tom De Schepper

“…However, the lack of IR data in many datasets and simulation tools limits the development and validation of sensor fusion algorithms that exploit this complementarity. To address this, we propose an augmentation method that synthesizes realistic IR data from RGB images using gradient-boosting decision trees. …”
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Committee Machine Learning for Electrofacies-Guided Well Placement and Oil Recovery Optimization by Adewale Amosu, Dung Bui, Oluwapelumi Oke, Abdul-Muaizz Koray, Emmanuel Appiah Kubi, Najmudeen Sibaweihi, William Ampomah

“…In this study, we develop a workflow that mitigates the variability in results produced by different clustering algorithms using a committee machine. Using several unsupervised machine learning methods, including k-means, k-median, hierarchical clustering, spectral clustering, and the Gaussian mixture model, we predict electrofacies from wireline well log data and generate their 3D vertical and lateral distributions and inferred geological properties. …”
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Efficient Probabilistic Evaluation and Sensitivity Analysis of Load Supply Capability for Renewable-Energy-Based Power Systems by Jie Zhang, Kaixiang Fu, Weizhi Huang, Yilin Zhang, Qing Sun, Yuan Chi, Junjie Tang

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Automatic diagnosis of extraocular muscle palsy based on machine learning and diplopia images by Xiao-Lu Jin, Xue-Mei Li, Tie-Juan Liu, Ling-Yun Zhou

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SmartFSCV: An Artificial Intelligence Enabled Miniaturised FSCV Device Targeting Serotonin by Dean M. Corva, Egan H. Doeven, Brenna Parke, Scott D. Adams, Susannah J. Tye, Parastoo Hashemi, Michael Berk, Abbas Z. Kouzani

“…<italic>Results:</italic> Four experiments were conducted to validate SmartFSCV: static bench test, dynamic serotonin test and two artificial intelligence (AI) algorithm tests. <italic>Conclusions:</italic> These tests confirmed the ability of SmartFSCV to accurately sense and make informed decisions about the presence of serotonin using AI.…”
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An Enhanced Machine Learning Framework for Type 2 Diabetes Classification Using Imbalanced Data with Missing Values by Kumarmangal Roy, Muneer Ahmad, Kinza Waqar, Kirthanaah Priyaah, Jamel Nebhen, Sultan S Alshamrani, Muhammad Ahsan Raza, Ihsan Ali

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IoT BotScan: Ultra-Lightweight AI Defense Against Botnet Threats by Sapna Sadhwani, Urvi Kavan Modi, Raja Muthalagu, Pranav M. Pawar, Alavikunhu Panthakkan, Wathiq Mansoor

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A Machine Learning Approach for Predicting Maternal Health Risks in Lower-Middle-Income Countries Using Sparse Data and Vital Signs by Avnish Malde, Vishnunarayan Girishan Prabhu, Dishant Banga, Michael Hsieh, Chaithanya Renduchintala, Ronald Pirrallo

“…The ensemble model using stratified sampling achieved the highest accuracy (87.2%), outperforming CatBoost (84.7%), XGBoost (84.2%), random forest (81.3%) and decision trees (80.3%) without stratified sampling. …”
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