Development and Validation of a Machine Learning Model for Early Prediction of Sepsis Onset in Hospital Inpatients from All Departments by Pierre-Elliott Thiboud, Quentin François, Cécile Faure, Gilles Chaufferin, Barthélémy Arribe, Nicolas Ettahar

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Exploring explainable machine learning algorithms to model predictors of tobacco use among men in Sub Sahara Africa between 2018 and 2023 by Mequannent Sharew Melaku, Nebebe Demis Baykemagn, Lamrot Yohannes, Adem Tsegaw Zegeye

“…This study aimed to model predictors of tobacco use among men in Sub Sahara Africa between 2018 and 2023 using machine learning algorithms. Data from Demographic and Health Surveys covering 147,466 men were analyzed. …”
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Quantitative Prediction of Low-Permeability Sandstone Grain Size Based on Conventional Logging Data by Deep Neural Network-Based BP Algorithm by Hongjun Fan, Xiaoqing Zhao, Zongjun Wang, Zheqing Zhang, Ao Chang

“…Machine learning of median grain size from conventional logging data was systematically carried out through conventional logging sensitivity curve optimization, algorithm modeling, network parameter optimization, median grain size prediction, and validation, and the relative error in its quantitative prediction met application requirements. …”
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Development of a Secured IoT-Based Flood Monitoring and Forecasting System Using Genetic-Algorithm-Based Neuro-Fuzzy Network by Hero Rafael Castillo Arante, Edwin Sybingco, Maria Antonette Roque, Leonard Ambata, Alvin Chua, Alvin Neil Gutierrez

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Enhanced clustering techniques for lithofacies and electrofacies identification in post-salt carbonate and siliciclastic reservoirs: a case study from the campos basin, Brazil by Abel Carrasquilla, Herson Rocha

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Automation system for finding parking places by О. Napadii, N. Tsopa, Ye. Batrak

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Exploring the Impact of Artificial Intelligence on Healthcare Management: A Combined Systematic Review and Machine-Learning Approach by Vito Santamato, Caterina Tricase, Nicola Faccilongo, Massimo Iacoviello, Agostino Marengo

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A narrative review of the use of PROMs and machine learning to impact value-based clinical decision-making by Michal Pruski, Simone Willis, Kathleen Withers

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Predicting Live Weight for Female Rabbits of Meat Crosses From Body Measurements Using LightGBM, XGBoost and Support Vector Machine Algorithms by Hasan Önder, Cem Tirink, Taras Yakubets, Andriy Getya, Mykhalio Matvieiev, Ruslan Kononenko, Uğur Şen, Çağri Özgür Özkan, Tolga Tolun, Fahrettin Kaya

“…A highly efficient gradient‐boosting decision tree (LightGBM), eXtreme gradient‐boosting (XGBoost) and support vector machine (SVM) algorithms were evaluated and compared to the prediction of BW. …”
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Advanced KNN-based cost-efficient algorithm for precision localization and energy optimization in dynamic underwater sensor networks by Nadia Shamshad, Lei Wang, Kiran Saleem, Danish Sarwr, Salil Bharany, Ahmad Almogren, Jaeyoung Choi, Ateeq Ur Rehman, Ayman Altameem

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Enhancing patient rehabilitation outcomes: artificial intelligence-driven predictive modeling for home discharge in neurological and orthopedic conditions by Leonardo Buscarini, Paola Romano, Elena Sofia Cocco, Carlo Damiani, Sanaz Pournajaf, Marco Franceschini, Francesco Infarinato

“…Progress in both fields has enabled the generation of a vast amount of data used for making predictions and identifying interesting clusters and pathways. …”
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Prediction of compressive strength of fiber-reinforced concrete containing silica (SiO2) based on metaheuristic optimization algorithms and machine learning techniques by Hamed Shokrnia, Ashkan KhodabandehLou, Peyman Hamidi, Fedra Ashrafzadeh

“…The sensitivity assessment approves that the training and generalization abilities of the ELM and ANFIS models for the CS prediction of FRC are improved by their integration with the GWO algorithm. …”
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Using Supervised Machine Learning Algorithms to Predict Bovine Leukemia Virus Seropositivity in Florida Beef Cattle: A 10‐Year Retrospective Study by Ameer A. Megahed, Y. Reddy Bommineni, Michael Short, João H. J. Bittar

“…Objectives To compare six different supervised machine‐learning (SML) algorithms used to identify the most important risk factors for predicting BLV seropositivity in beef cattle in Florida. …”
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Predicting Minimum Temperatures of Plastic Greenhouse During Strawberry Growing in Changfeng, China: A Comparison of Machine Learning Algorithms and Multiple Linear Regression by Xuelin Wang, Qinqin Huang, Dong Wu, Jinhua Xie, Ming Cao, Jun Liu

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Prediction of the Gross Motor Function Measure-66 in Ambulant Children with Cerebral Palsy Based on Instrumental Gait Analysis Using Machine-Learning Algorithms by Stephanie Gross, Karoline Spiess, Stefanie Steven, Maja Zimmermann, Eckhard Schoenau, Ibrahim Duran

“…The IGA was performed with a Zebris FDM pressure plate. For the prediction of the GMFM-66, different statistical models were used (multiple linear regression and machine learning algorithms). …”
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Enhancing Attendance Management Through Face Recognition Technology: A Case Study at Rugarama School of Nursing and Midwifery. by Taremwa, Benjamin

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A Predictive Model of the Photosynthetic Rate of Chili Peppers Using Support Vector Regression and Environmental Multi-Factor Analysis by Bin Li, Bo Qiao, Qianyu Zhao, Dan Yang, Rongcheng Zhu, Zhexuan Wang, Yujie Yang

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Design and Implementation of Mobile Experiment Algorithm Designer Interface for Network Control System Laboratory by Jahan Zaib Bhatti, Guo-Ping Liu, Shafqat Ali, Nasim Ullah, Ivo Pergl, Lukas Prokop, Vojtech Blazek

Subjects: “…Control engineering education…”
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Prediction of Liquefaction-Induced Lateral Displacements Using Hybrid GBRT and EOA by Arash Ziaie, Bahareh Mehdizadeh, Farzad Safi Jahanshahi, Nazanin Ahmadi, Ali Reza Ghanizadeh

“…This research employs the Gradient Boosting Regression Tree (GBRT) approach, tuned through the Equilibrium Optimization Algorithm (EOA), to estimate LILD. A dataset containing 247 data points was used to build the predictive model. …”
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Machine learning algorithms for risk factor selection with application to 60-day sepsis morbidity risk for a geriatric hip fracture cohort by Zhe Xu, Ruguo Zhang, Qiuhan Chen, Guoxuan Peng, Shanpeng Luo, Chen Liu, Ling Zeng, Jin Deng

“…The purpose of this study was to screen for risk factors for 60-day sepsis morbidity after hip fracture and to establish a predictive model using various machine learning algorithms. …”
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