Predicting the distribution of Sphagna (Bryophyta) in Türkiye: a perspective of present and future climate scenarios by Gökhan Abay, Serkan Gül

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An antibody developability triaging pipeline exploiting protein language models by James Sweet-Jones, Andrew C.R. Martin

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A comparative performance study on the development of hybrid extreme gradient boosting models for predicting rock layer subsidence in subsea gold mine by Weijun Liu, Zhixiang Liu, Meng Wang, Shuangxia Zhang

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Prospective Models of Financial Forecasting of budget Revenues by A. K. Karaev, O. V. Borisova

Subjects: “…predictive model…”
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Novel prognostic scoring models for hepatitis B virus-related acute-on-chronic liver failure: A comparison with classical models by Hongyan Guo, Fengling Fang, Lin Lin, Zhaopei Guo, Lu Lai, Yue Shi, Tianbin Chen, Ruimin Lai, Qishui Ou, Ya Fu

“…Logistic regression and nomogram models were used to construct three novel predictive models. …”
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Prediction of Diabetes in Middle-Aged Adults: A Machine Learning Approach by Gideon Addo, Bismark Amponsah Yeboah, Michael Obuobi, Raphael Doh-Nani, Seidu Mohammed, David Kojo Amakye

Subjects: “…diabetes prediction; diabetes symptoms; machine learning models; middle-aged adults; predictive performance metrics…”
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Development of regional mixed-effects height–diameter models for natural black pine stands by Ramazan Ozçelik, Onur Alkan

“…The findings suggest that the proposed model is highly suitable for forest inventory studies to predict tree heights in black pine stands.…”
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The current research status on building prediction models for rectal cancer based on imaging data using artificial intelligence by Zhang Tianshuai, Peng Zhiying, Jia Hang, Wen Rongbo, Zhou Leqi, Shen Fu, Yu Guanyu, Zhang Wei

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POVERTY MACRO SYSTEM DYNAMICS MODELING BASED ON SIMULTANEOUS EQUATIONS MODELS by Bagus Sumargo, Irman Firmansyah, Asep Anwar Nugraha, Mulyono Mulyono, Dania Siregar, Felia Aidah Nuriza

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Machine learning models for predicting interaction affinity energy between human serum proteins and hemodialysis membrane materials by Simin Nazari, Amira Abdelrasoul

“…Seven machine learning algorithms for predicting affinity energy are analyzed and compared, with random forest demonstrating superior predictive accuracy. …”
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Revised renal stratification and progression models for predicting long-term renal outcomes in immunoglobulin light chain amyloidosis by Muhammad Umaid Rauf, Steven Law, Marisa Santostefano, Philip N. Hawkins, Aviva Petrie, Francesco Cappelli, Federico Perfetto, Yousuf Razvi, Aldostefano Porcari, Sriram Ravichandran, Adam Ioannou, Joshua Bomsztyk, Alessia Argirò, Costanza Gaudio, Elisabetta Antonioli, Alessandro Barilaro, Marco Delsante, Vittorio Di Maso, Maria G. Chiappini, Olabisi Ogunbiyi, Oliver C. Cohen, Ana Martinez-Naharro, Carol Whelan, Helen J. Lachmann, Ashutosh D. Wechalekar, Federico Alberici, Marianna Fontana, Marco Allinovi, Julian D. Gillmore

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Predicting practical reduction potential of electrolyte solvents via computational hydrogen electrode and interpretable machine-learning models by Zonglin Yi, Yi Zhou, Hao Liu, Li Li, Yan Zhao, Jiayuan Li, Yixuan Mao, Fangyuan Su, Cheng-Meng Chen

“…Here, we provide a machine-learning-assisted workflow of thermodynamically quantified E red prediction for electrolyte solvents. A computational hydrogen electrode model based on density functional theory calculation is generalized for calculating the reaction free energy of electrochemical elementary steps. …”
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Elman and feedforward neural network based models for predicting mechanical properties of flow formed AA6082 tubes by Tarak Nath De, Bikramjit Podder, Nirmal Baran Hui, Chandan Mondal, Vimal Kumar Pathak

“…To address this, the present study proposes a parametric predictive model for H30 aluminium tubes produced via flow forming, enabling the estimation of the final mechanical properties without additional physical trials. …”
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Advanced generalized machine learning models for predicting hydrogen–brine interfacial tension in underground hydrogen storage systems by Ahmed Farid Ibrahim

“…DT performed slightly lower, with R2 values of 0.93, while LR struggled to model the non-linear behavior of IFT. A novel salt equivalency metric was introduced, transforming multiple salt variables into a single parameter and improving model generalization while maintaining high prediction accuracy (R2 = 0.98). …”
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Predicting visual acuity of treated ocular trauma based on pattern visual evoked potentials by machine learning models by Hongxia Hao, Jiemin Chen, Yifei Yan, Yifei Yan, Qi Zhang, Qi Zhang, Zhilu Zhou, Wentao Xia

“…PurposeTo develop effective machine learning models that analyze pattern visual evoked potentials (PVEPs) to predict the stabilized visual acuity (VA) of patients with treated ocular trauma.MethodsThis experiment included 260 patients (220 males, average age 42.54 years) with unilateral ocular trauma. …”
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Accreditation of new technologies for predicting intramuscular fat percentage: Combining Bayesian models and industry rules for transparent decisions. by Graham E Gardner, Clair L Alston-Knox

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A comparative analysis of variants of machine learning and time series models in predicting women’s participation in the labor force by Rasha Elstohy, Nevein Aneis, Eman Mounir Ali

“…This study proposes a hybrid machine-learning model that integrates principal component analysis (PCA) for feature extraction with various machine learning and time-series models to predict women’s employment in times of crisis. …”
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Predicting Treatment Outcomes in Patients with Low Back Pain Using Gene Signature-Based Machine Learning Models by Youzhi Lian, Yinyu Shi, Haibin Shang, Hongsheng Zhan

“…In this study, we aimed to develop gene signature-based machine learning models using transcriptomic data from peripheral immune cells to predict treatment outcomes in patients with LBP. …”
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Explainable models for predicting long-term outcomes in patients with spontaneous intracerebral haemorrhage: a retrospective cohort study by Feng Gao, Yu-jia Jin, Lusha Tong, Kai-cheng Yang, Li-Li Tang

“…Background and aim Recently, long-term outcomes in patients with spontaneous intracerebral haemorrhage (sICH) have gained increasing attention besides acute-phase characteristics. Predictive models for long-term outcomes are valuable for risk stratification and treatment strategies. …”
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Predicting Aboveground Carbon Storage in Different Types of Forests in South Subtropical Regions Using Machine Learning Models by Jiarun Liu, Zihang Yang, Lin Li, Xiaoxue Chu, Shiguang Wei, Juyu Lian

Subjects: “…aboveground carbon storage prediction…”
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