Predicting the impact of climate change on the re-emergence of malaria cases in China using LSTMSeq2Seq deep learning model: a modelling and prediction analysis study by Eric Kamana, Jijun Zhao, Di Bai

“…The LSTMSeq2Seq model achieved an average prediction accuracy of 87.3%.Conclusions The LSTMSeq2Seq model significantly improved the prediction of malaria re-emergence based on the influence of climatic factors. …”
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Hospital Length-of-Stay Prediction Using Machine Learning Algorithms—A Literature Review by Guilherme Almeida, Fernanda Brito Correia, Ana Rosa Borges, Jorge Bernardino

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Prediction of the digestibility and digestible energy content of hay for horses using an enzymatic degradability method by D. Andueza, W. Martin-Rosset

“…The proposed models represent an alternative approach to the existing models for predicting the nutritive value and DE content of hay from their chemical composition.…”
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Bayesian compositional generalized linear mixed models for disease prediction using microbiome data by Li Zhang, Xinyan Zhang, Justin M. Leach, A. K. M. F. Rahman, Carrie R. Howell, Nengjun Yi

“…Abstract The primary goal of predictive modeling for compositional microbiome data is to better understand and predict disease susceptibility based on the relative abundance of microbial species. …”
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On the Prediction and Forecasting of PMs and Air Pollution: An Application of Deep Hybrid AI-Based Models by Youness El Mghouchi, Mihaela Tinca Udristioiu

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Mathematical procedure for prediction of dental anxiety in children with inherited bleeding disorders by Vujkov Sanja, Cveticanin Livija, Bajkin Branislav, Blagojević Duška, Milekić Bojana, Vučinić Predrag

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Comparative Analysis of Supervised Machine Learning Algorithms for COVID-19 Prediction by Rubina Shaheen, Beenish Akram, Amna Zafar, Talha Waheed

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Machine Learning for Dynamic Pressure Coefficient Prediction in Vertical Water Jets by Amin Salemnia, Seyedehmaryam Hosseini Boldaji, Vida Atashi, Manoochehr Fathi-Moghadam

“…This study emphasizes the importance of the Froude number in predicting jet behavior and shows the efficacy of advanced machine learning models in capturing complex fluid dynamics, providing valuable insights for optimizing engineering applications such as water jet cutting and cooling systems.…”
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Predicting Young’s Modulus of Aggregated Carbon Nanotube Reinforced Polymer by Roham Rafiee, Vahid Firouzbakht

“…Prediction of mechanical properties of carbon nanotube-based composite is one of the important issues which should be addressed reasonably. …”
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Prediction Models for Late-Onset Preeclampsia: A Study Based on Logistic Regression, Support Vector Machine, and Extreme Gradient Boosting Models by Yangyang Zhang, Xunke Gu, Nan Yang, Yuting Xue, Lijuan Ma, Yongqing Wang, Hua Zhang, Keke Jia

“…Logistic regression was used as the baseline model to assess the predictive performance of the support vector machine and extreme gradient boosting models for late-onset preeclampsia. …”
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The Use of Accounting Models, Statistical Models, and Securities Marketing Prices to Predict Corporate Failure and the Case for Continuing Fiduciary Obligations to Corporate Creditors by Razeen Sappideen

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RESEARCH ON MODELING METHOD OF EQUIVALENT TURNING FORCE MODEL (MT) by WU MingZhou, ZHANG GuangPeng, GOU JianFeng, ZHANG Peng

“…A turning force prediction model based on the equivalent model is established. …”
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Comparative analysis of machine learning techniques in metabolomic-based preterm birth prediction by Ying-Chieh Han, Jane Shearer, Chunlong Mu, Donna M. Slater, Suzanne C. Tough, Gavin E. Duggan

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Multistage Training of Fuzzy Cognitive Maps to Predict Preeclampsia and Fetal Growth Restriction by William Hoyos, Rodrigo Garcia, Jose Aguilar

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Investigating the Effect of Climatic Parameters Predicting the Mortality Rate Due to Cardiovascular and Respiratory Disease with Soft Computing Methods by Hamidreza Ghazvinian, Hojat Karami

“…It can be very important to accurately identify and predict with smart models in disease outbreaks and as a result in mortality statistics. …”
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Predicting macular hole surgery outcomes: Integrating preoperative OCT features with supervised machine learning statistical models by Ramesh Venkatesh, Priyanka Gandhi, Ayushi Choudhary, Gaurang Sehgal, Kanika Godani, Shubham Darade, Rupal Kathare, Prathiba Hande, Vishma Prabhu, Jay Chhablani

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A Fused Multi-Channel Prediction Model of Pressure Injury for Adult Hospitalized Patients—The “EADB” Model by Eba’a Dasan Barghouthi, Amani Yousef Owda, Majdi Owda, Mohammad Asia

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Heuristic modeling of the process of milling aviation parts with opposed end mills by synthesis of a predictive model by E. A. Shestakova, V. O. Ievlev, R. M. Yanbaev

“…The resulting model allows predicting the results of machining by the proposed method with an error of 10…15%.…”
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Two novel deep-learning models to predict spontaneous ureteral calculi passage: Model development and validation by Zhiying Xiao, Hui Bai, Yumeng Zhang

“…The aim of this study was to develop and evaluate two deep-learning (DL) models for predicting spontaneous ureteral stone passage (SSP). …”
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Stunting Prediction Modeling in Toddlers Using a Machine Learning Approach and Model Implementation for Mobile Application by Eko Abdul Goffar, Rosa Eliviani, Lili Ayu Wulandhari

“…The literature review section discusses the factors that influence stunting, and these factors are used as features to build a stunting prediction model. Then the features were used to build a model with three machine learning algorithms Extreme Gradient Boosting (XGBoost), Random Forest, and K-Nearest Neighbor (KNN) to build and evaluate models that predict stunting. …”
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