Predicting mortality in critically ill patients with hypertension using machine learning and deep learning models by Ziyang Zhang, Jiancheng Ye

“…The SHAP analysis revealed that these predictors had a substantial influence on model predictions, underscoring their importance in assessing mortality risk in this patient population.ConclusionDeep learning models, particularly the 1D CNN, demonstrated superior predictive accuracy compared to traditional ML models in predicting mortality among critically ill patients with hypertension. …”
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Towards predicting implant-induced fibrosis: A standardized network model of macrophage-fibroblast interactions by Matilde Marradi, Martijn van Griensven, Nick R.M. Beijer, Jan de Boer, Aurélie Carlier

“…In this study, we constructed a literature-based network of the FBR and developed it into a semi-quantitative predictive model to better understand its dynamics. …”
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A warning model for predicting patient admissions to the intensive care unit (ICU) following surgery by Li Li, Hongye He, Linjun Xiang, Yongxiang Wang

“…The logistic regression model developed using these 18 variables was identified as the most effective predictive model for postoperative ICU admission, achieving an ROC AUC of 0.925. …”
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Maxent modeling for predicting the potential distribution of Arbutus andrachne L. belonging to climate change in Turkey. by AYSE GUL SARIKAYA, Omer Kamil ORUCU

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A Near-Real-Time Model for Predicting Electricity Disruptions in Texas During Winter Storms by Jangjae Lee, Sangkeun Lee, Supriya Chinthavali, Stephanie Paal

Subjects: “…a near-real-time outage prediction model…”
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Predicting physiologically-relevant oxygen concentrations in precision-cut liver slices using mathematical modelling. by S J Chidlow, L E Randle, R A Kelly

“…The aim of this study was to predict whether it is possible to generate a physiologically relevant oxygen gradient of 35-65mmHg across a precision cut liver slice using mathematical modelling. …”
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Hybrid Machine Learning Models for Predicting the Impact of Light Wavelengths on Algal Growth in Freshwater Ecosystems by Himaranga Sumanasekara, Harshi Jayasingha, Gayan Amarasooriya, Narada Dayarathne, Bandita Mainali, Lalantha Senevirathna, Ashoka Gamage, Othmane Merah

“…The integration of empirical data with machine learning offers a robust framework for predictive modeling in algal research and industrial applications.…”
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Theoretical Model for Predicting Moisture Ratio during Drying of Spherical Particles in a Rotary Dryer by F. T. Ademiluyi, M. F. N. Abowei

“…A mathematical model was developed for predicting the drying kinetics of spherical particles in a rotary dryer. …”
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Development and validation of machine learning models for predicting low muscle mass in patients with obesity and diabetes by Jiaying Ge, Siqi Sun, Jiangping Zeng, Yujie Jing, Huihui Ma, Chunhua Qian, Ran Cui, Shen Qu, Hui Sheng

“…This study aimed to investigate the associations of these indices with LMM and to develop machine learning models for accurate and accessible LMM prediction. …”
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Development of an optimized deep learning model for predicting slope stability in nano silica stabilized soils by Ishwor Thapa, Sufyan Ghani, Prabhu Paramasivam, Mitiku Adare Tufa

“…This study suggests a hybrid classification model of deep learning, integration of convolutional neural networks (CNN), long short-term memory (LSTM), and recurrent neural networks (RNN), optimized by Optuna to predict the stability of NS stabilized infinite slope. …”
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A hybrid model combining environmental analysis and machine learning for predicting AI education quality by Xinyu Ren

“…The values of CCC, SROCC, PLCC and R2 indices related to the proposed model are equal to 0.9611, 0.9805, 0.9731, and 0.9803, respectively, which are all higher than the corresponding values in other models.…”
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A Parsimonious Yet Robust Regression Model for Predicting Limited Structural Responses of Remote Sensing by Alireza Entezami, Bahareh Behkamal, Carlo De Michele, Stefano Mariani

“…Besides the engineering challenge, small data is typically a demanding issue in machine learning applications related to the prediction of system evolutions. To address this challenge, this article proposes a parsimonious yet robust predictive model obtained as a combination of a regression artificial neural network and of a Bayesian hyperparameter optimization. …”
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Predicting COVID-19 vaccine uptake: Comparing the health belief model and theory of planned behavior by Salah S. Alshagrawi

“…The survey contained variables related to the HBM and TPB. The prediction level of the two models as well as a combined model were evaluated utilizing Structural Equation Modeling (SEM). …”
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Hybrid Metaheuristic Optimized Random Forest Models for Predicting Compressive Strength of Alkali Activated Concrete by Mana Alyami, Muhammad Faisal Javed, Irfan Ullah, Hisham Alabduljabbar, Furqan Ahmad

“…This study explores the application of hybrid machine learning models for predicting the compressive strength (CS) of alkali-activated concrete (AAC), a sustainable substitute for traditional Portland cement concrete. …”
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Artificial Intelligence-Driven Models for Predicting Chloride Diffusion in Concrete: A Comparative Regression Analysis by Yongjie Zhang, Yuhan Zhang

“…Utilizing experimental field findings, the application of artificial intelligence (AI) might create models to accurately predict the nonsteady state evident concrete’s chloride diffusion coefficient (Dc) over a prolonged duration. …”
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Performance of Various Artificial Intelligence Models for Predicting Temperature in an Industrial Building—A Case Study by Johan Roussel, Zoubeir Lafhaj, Pascal Yim, Thomas Danel, Laure Ducoulombier

“…This article presents a comparative analysis of the performance of various artificial intelligence models for predicting temperature in an industrial building. …”
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Critical evaluation of feature importance assessment in FFNN-based models for predicting Kamlet-Taft parameters by Yoshiyasu Takefuji

“…Mohan et al. developed a feed-forward neural network (FFNN) model to predict Kamlet-Taft parameters using quantum chemically derived features, achieving notable predictive accuracy. …”
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Machine learning models for predicting in-hospital mortality from acute pancreatitis in intensive care unit by Shuxing Wei, Hongmeng Dong, Weidong Yao, Ying Chen, Xiya Wang, Wenqing ji, Yongsheng Zhang, Shubin Guo

“…This study focuses on the development of advanced machine learning (ML) models to accurately predict in-hospital mortality among AP patients admitted to intensive care unit (ICU). …”
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Optimized ANN Model for Predicting Buckling Strength of Metallic Aerospace Panels Under Compressive Loading by Shahrukh Khan, Saiaf Bin Rayhan, Md Mazedur Rahman, Jakiya Sultana, Gyula Varga

“…The present research proposes an Artificial Neural Network (ANN) model to predict the critical buckling load of six different types of metallic aerospace grid-stiffened panels: isogrid type I, isogrid type II, bi-grid, X-grid, anisogrid, and waffle, all subjected to compressive loading. …”
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Predicting potential biomass production by geospatial modelling: The case study of citrus in a Mediterranean area by G.A. Catalano, P.R. D'Urso, C. Arcidiacono

“…The methodology combines Geographic Information System (GIS) tools, for data interpolation and map overlays, with Software for Assisted Habitat Modelling (SAHM) for local level simulations.The results of the different models showed accurate and spatially coherent predictions, with AUC values ranging from 0.85 to 0.90, and highest potentialities in the northern and eastern regions of the study area. …”
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