Predicting floods to protect property regimes: Situating flood modelling in the River Poddle Catchment, Dublin by Laure de Tymowski, Elliot Hurst

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Hybridization of Machine Learning Algorithms and an Empirical Regression Model for Predicting Debris-Flow-Endangered Areas by Xiang Wang, Mi Tian, Qiang Qin, Jingwei Liang

“…Recently, machine-learning models have been proved to be an effective tool in predicting debris-flow parameters. …”
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A Multimodal Deep Learning Model Integrating CNN and Transformer for Predicting Chemotherapy-Induced Cardiotoxicity by Ahmed Bouatmane, Abdelaziz Daaif, Abdelmajid Bousselham, Bouchra Bouihi, Omar Bouattane

“…To overcome data limitations, we employed Conditional Generative Adversarial Networks (cGANs) and Conditional Tabular Generative Adversarial Networks (CTGANs) to augment the dataset, improving its diversity and balance for better model training. We developed three architectures that integrate Convolutional Neural Networks (CNNs) for feature extraction from TDI images with Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Transformer models to capture temporal dependencies and enhance prediction accuracy. …”
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Construction and Validation of a Nomogram Model for Predicting Pulmonary Hypertension in Patients with Obstructive Sleep Apnea by Zhang R, Liu Z, Li R, Ai L, Li Y

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A comparative study of various statistical and machine learning models for predicting restaurant demand in Bangladesh by Md Shakhawath Hossain, Farjana Parvin

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Predicting Organic Acid Variation in White Wine Malolactic Fermentation Using a Logistic Model by Aikaterini Karampatea, Adriana Skendi, Maria Manoledaki, Elisavet Bouloumpasi

“…The experimental data obtained were fitted to the logistic model for each monitored parameter. The degree of fitting R2 was higher than 92.79%, indicating good predictive accuracy for substrate consumption (malic and citric acid), as well as product formation (lactic and acetic acid). …”
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The application of super-resolution ultrasound radiomics models in predicting the failure of conservative treatment for ectopic pregnancy by Mingyan Zhang, Junfa Sheng

“…This study aimed to develop and validate a predictive model that integrates radiomic features derived from super-resolution (SR) ultrasound images with clinical biomarkers to improve risk stratification. …”
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Predicting the subclinical carotid atherosclerosis in overweight and obese patients using a machine learning model by D. V. Gavrilov, T. Yu. Kuznetsova, M. A. Druzhilov, I. N. Korsakov, A. V. Gusev

“…Aim. To develop a model for predicting the subclinical carotid atherosclerosis (SCA) in order to refine cardiovascular risk (CVR) using machine learning methods in overweight and obese patients without hypertension, diabetes and/or cardiovascular disease (CVD).Material and methods. …”
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Development of a machine learning model for predicting renal damage in children with closed spinal dysraphism by Yu He, Wan-liang Guo, Ming-chang Zhang

“…This study aims to develop an effective machine learning model to predict renal damage in children with CSD. …”
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Development of an explainable machine learning model for predicting device-related pressure injuries in clinical settings by Yijie Qian, Hongying Pan, Jun Chen, Hongyang Hu, Mei Fang, Chen Huang, Yihong Xu, Yang Gao

“…Given the rapid advancements in computer technology, we aimed to develop an interpretable artificial intelligence (AI) model for predicting DRPI, utilizing SHAP (SHapley Additive exPlanations) to enhance the model’s transparency and provide insights into feature importance. …”
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Comprehensive evaluation of machine learning models for predicting the cognitive status of Alzheimer's disease subjects and susceptible by Lucien Gnegne Meteumba, Vaghawan Prasad Ojha, Shantia Yarahmadian

“…This requires strong predictive models to tease apart what we can do to detect and introduce early-prevention. …”
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PlantDeepMeth: A Deep Learning Model for Predicting DNA Methylation States in Plants by Zhongwei Guo, Wenyuan Fan, Chengcheng Cai, Kang Zhang, Xilin Hou, Ying Li, Feng Cheng

“…Here, we report PlantDeepMeth, a novel deep learning model that utilizes deep learning to predict DNA methylation states in plants. …”
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Analyzing and predicting global happiness index via integrated multilayer clustering and machine learning models by Boxu Yang, Xiang Xie

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Predicting Mortality in Low-Income Country ICUs: The Rwanda Mortality Probability Model (R-MPM). by Elisabeth D Riviello, Willy Kiviri, Robert A Fowler, Ariel Mueller, Victor Novack, Valerie M Banner-Goodspeed, Julia L Weinkauf, Daniel S Talmor, Theogene Twagirumugabe

“…<h4>Introduction</h4>Intensive Care Unit (ICU) risk prediction models are used to compare outcomes for quality improvement initiatives, benchmarking, and research. …”
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Deep Learning Model for Predicting Neurodevelopmental Outcome in Very Preterm Infants Using Cerebral Ultrasound by Tahani M. Ahmad, MD, ABR, Alessandro Guida, PhD, Sam Stewart, PhD, Noah Barrett, MSc, Michael J. Vincer, MD, Jehier K. Afifi, MD, MSc

“…Objective: To develop deep learning (DL) models applied to neonatal cranial ultrasound (CUS) and clinical variables to predict neurodevelopmental impairment (NDI) in very preterm infants (VPIs) at 3 years of corrected age. …”
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Benchmarking machine learning models in lesion-symptom mapping for predicting language outcomes in stroke survivors by Deepa Tilwani, Deepa Tilwani, Deepa Tilwani, Deepa Tilwani, Christian O'Reilly, Christian O'Reilly, Christian O'Reilly, Christian O'Reilly, Nicholas Riccardi, Valerie L. Shalin, Valerie L. Shalin, Dirk-Bart den Ouden, Julius Fridriksson, Svetlana V. Shinkareva, Svetlana V. Shinkareva, Amit P. Sheth, Amit P. Sheth, Amit P. Sheth, Rutvik H. Desai, Rutvik H. Desai

“…To test this promise, we benchmarked ML models on structural and functional MRI to predict aphasia severity (N = 238) and naming impairment (N = 191) for a cohort of chronic-stage stroke survivors. …”
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General models for predicting the liquid thermal conductivity of fatty acid esters based on smart methods by Chou-Yi Hsu, Ahmad Mohsin, Ramdevsinh Jhala, Nagaraj Patil, Debasish Shit, V.K. Bupesh Raja, Manoj Kumar Ojha, Abinash Mahapatro, Deepak Gupta, Fereydoon Ranjbar

“…A comparison with literature correlations showed that the novel models offer significant improvements in the thermal conductivity prediction. …”
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Predicting the impact of climate change on the distribution of rhododendron on the qinghai-xizang plateau using maxent model by Sen-Xin Chai, Li-Ping Ma, Zhong-Wu Ma, Yu-Tian Lei, Ya-Qiong Ye, Bo Wang, Yuan-Ming Xiao, Ying Yang, Guo-Ying Zhou

“…The results show that: [1] The area under curve (AUC) values of the five Rhododendron were all greater than 0.98, indicated that the model prediction effect was excellent; [2] Isothermality is the most important environmental factor affecting the distribution of Rhododendron (excluding Rhododendron przewalskii). …”
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Development and validation of a nomogram model for predicting low muscle mass in patients undergoing hemodialysis by Rongrong Tian, Liyang Chang, Ying Zhang, Hongmei Zhang

“…The nomogram demonstrated a high net benefit in the clinical decision curve for both sets.Conclusions The prediction model included age, sex, BMI, HGS, and GS, and it can successfully predict the presence of LSMI in patients undergoing HD. …”
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Prognostic models for predicting patient arrivals in emergency departments: an updated systematic review and research agenda by Luka Petravić, Kaja Gril Rogina, Tit Albreht, Andreja Kukec, Janez Žibert

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