NDVI Prediction with RGB UAV Imagery Utilizing Advanced Machine Learning Regression Models by I. Aydin, U. G. Sefercik

“…In the literature, RGB camera-based NDVI prediction studies involving machine learning and deep learning algorithms have focused on the correlation of the results with the reference data (R²) or the model accuracy of the algorithms used. …”
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Feature importance analysis of solar flares and prediction research with ensemble machine learning models by Yun Yang, Yun Yang

“…In this study, these models were used to classify and predict flares with a magnitude ≥ C- and M-class, respectively. …”
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Assessment of models for the prediction of the Travelling Ionospheric Disturbance activity index in mid-latitude Europe by Ferreira Arthur Amaral, Borries Claudia, Borges Renato Alves

“…The feasibility of predicting LSTIDs in this region has been demonstrated using a linear regression model. …”
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Evaluation of Eight Decomposition-Hybrid Models for Short-Term Daily Reference Evapotranspiration Prediction by Yunfei Chen, Zuyu Liu, Ting Long, Xiuhua Liu, Yaowei Gao, Sibo Wang

“…However, the nonlinear and non-stationary characteristics of ET_o time series pose challenges for conventional prediction models. Given this, in this study we evaluate eight decomposition-hybrid models that integrate various decomposition techniques with a long short-term memory (LSTM) network to enhance short-term (5-day, 7-day, and 10-day) ET_o forecasting. …”
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Benchmarking ML in ADMET predictions: the practical impact of feature representations in ligand-based models by Gintautas Kamuntavičius, Tanya Paquet, Orestis Bastas, Dainius Šalkauskas, Alvaro Prat, Hisham Abdel Aty, Aurimas Pabrinkis, Povilas Norvaišas, Roy Tal

“…Abstract This study, focusing on predicting Absorption, Distribution, Metabolism, Excretion, and Toxicology (ADMET) properties, addresses the key challenges of ML models trained using ligand-based representations. …”
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Enhanced Fall Detection and Prediction Using Heterogeneous Hidden Markov Models in Indoor Environment by Oumaima Guendoul, Hamd Ait Abdelali, Youness Tabii, Rachid Oulad Haj Thami, Omar Bourja

“…The model achieved a 61% prediction rate with an accuracy of 81.51%. …”
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H∞ filtering of uncertain predictive models: Gain computation using LMI and performance evaluation by Eli G. Pale Ramon, Oscar G. Ibarra-Manzano, José A. Andrade-Lucio, Yuriy S. Shmaliy

“…The filter is designed for processes represented in discrete time using the forward Euler method, which allows for predictive modeling. Since the error covariance of a state estimator is a quadratic function of K, a new theorem is proved and a numerical algorithm is developed for computing K using linear matrix inequality (LMI). …”
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Risk prediction models for sarcopenia in elderly people: a systematic review and meta-analysis by Jie Yin, Yiyong Xu, Mian Cai, Xiwei Fang

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Systematic review of risk prediction models for arteriovenous fistula dysfunction in maintenance hemodialysis patients. by Shiyan Yao, Guannan Ma, Yongze Dong, Mengjiao Zhao, Luchen Chen, Wenhao Qi, Huajuan Shen

“…Although various models for predicting AVF risk have emerged, a comprehensive review of their advancements and challenges is currently lacking. …”
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Artificial intelligence models utilize lifestyle factors to predict dry eye related outcomes by Andrew D. Graham, Jiayun Wang, Tejasvi Kothapalli, Jennifer E. Ding, Helen Tasho, Alisa Molina, Vivien Tse, Sarah M. Chang, Stella X. Yu, Meng C. Lin

“…Abstract The purpose of this study is to examine and interpret machine learning models that predict dry eye (DE)-related clinical signs, subjective symptoms, and clinician diagnoses by heavily weighting lifestyle factors in the predictions. …”
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Machine learning models integrating intracranial artery calcification to predict outcomes of mechanical thrombectomy by Guangzong Li, Yuesen Zhang, Di Li, Manhong Zhao, Lin Yin

“…The Extra Trees model demonstrated the highest predictive accuracy. …”
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A systematic review of risk prediction models for post-stroke acute kidney injury by YANG Li, QIN Qin, LIU Handan, LI Huiming, WEI Xuemei, CUI Lijun

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Prediction models for patients with esophageal or gastric cancer: A systematic review and meta-analysis. by H G van den Boorn, E G Engelhardt, J van Kleef, M A G Sprangers, M G H van Oijen, A Abu-Hanna, A H Zwinderman, V M H Coupé, H W M van Laarhoven

“…<h4>Background</h4>Clinical prediction models are increasingly used to predict outcomes such as survival in cancer patients. …”
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Prediction models after hepatectomy for hepatocellular carcinoma-based ultrasonic radiomics: an observational study by Dong Jiang, Jialun Ren, Yi Qian, Yijun Gu, Ru Wang, Hua Yu, Hui Dong, Dongyu Chen, Yan Chen, Haozheng Jiang, Yiran Li

“…Abstract Background This study aims to develop and validate predictive models for postoperative complications and early recurrence in hepatocellular carcinoma (HCC) patients. …”
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Risk models to predict chronic kidney disease and its progression: a systematic review. by Justin B Echouffo-Tcheugui, Andre P Kengne

“…Although risk factors for occurrence and progression of CKD have been identified, their utility for CKD risk stratification through prediction models remains unclear. We critically assessed risk models to predict CKD and its progression, and evaluated their suitability for clinical use.…”
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Systematic Analysis and Critical Appraisal of Predictive Models for Lung Infection Risk in ICU Patients by Xuan Wu, Jing Kong, Zihan Sun, Ge Qiu, Zhengxiang Dai

“…Conclusion: Current predictive models for lung infection risk in ICU patients exhibit strong predictive performance. …”
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Reducing Judicial Inconsistency through AI: A Review of Legal Judgement Prediction Models by Wu Yifan

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Prediction Models with Multiple Linear Regression for Improving Acoustic Performance of Textile Industry Plants by Muammer YAMAN, Cüneyt KURTAY, Gülsu ULUKAVAK HARPUTLUGIL

“…The acoustic analyses of the scenario plants were performed in the ODEON Auditorium, and A-weighted sound pressure level (LA), noise reduction (NR), and reverberation time (RT) were determined. From the data, prediction equations were created with a multiple linear regression (MLR) model. …”
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Comparative analysis of supervised learning models for effluent quality prediction in wastewater treatment plants by Liu Bo-qi, Zhou Ding-jie, Zhao Yang, Shi Long-yu

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Prediction Models for Postoperative Delirium of Cardiovascular Surgery (PODOCVS): Protocol for a Systematic Review by Xuling Zhao, Yike Wang, Liju Li, Meijuan Lan, Xiaodi He

“…Two researchers (ZXL and WYK) will independently extract the data and assess the included studies’ model quality using the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS) checklist and the Predictive Model Bias Risk Assessment Tool (PROBAST). …”
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