Development of an autoregressive neural network model for predicting accidents in the Khabarovsk Territory by E. V. Shokhirev, P. P. Volodkin, V. A. Lazarev, A. V. Konstantinov

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Leveraging transformer models to predict cognitive impairment: accuracy, efficiency, and interpretability by Kai Ma, Junzhi Zhang, Xinhang Huang, Mengyang Wang

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Assessment of Machine Learning Models for Predicting Aboveground Biomass in the Indian Subcontinent by S. Mamgain, B. Ghale, H. C. Karnatak, A. Roy

“…This study evaluates three machine learning models—Random Forest (RF), Gradient Tree Boosting (GTB), & Classification and Regression Trees (CART)—for predicting AGB across the subcontinent. …”
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Employing the Coupled EUHFORIA‐OpenGGCM Model to Predict CME Geoeffectiveness by Anwesha Maharana, W. Douglas Cramer, Evangelia Samara, Camilla Scolini, Joachim Raeder, Stefaan Poedts

“…We further employ the dynamic time warping (DTW) technique to assess the model performance in predicting Dst. The main highlight of this study is to use EUHFORIA simulated time series to predict the Dst and auroral indices 1–2 days in advance, as compared to using the observed solar wind data at L1, which only provides predictions 1–2 hr before the actual impact.…”
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Depth prediction of urban waterlogging based on BiTCN-GRU modeling. by Quan Wang, Mingjie Tang, Pei Shi

“…Compared to models such as GBDT, LSTM, and TCN-LSTM, the BiTCN-GRU model exhibits higher accuracy in predicting waterlogging depth. …”
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Local Differential Privacy Graph Data Modeling Method for Link Prediction by HANQilong, WUXiaoming

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A lightweight hybrid model for accurate ammonia prediction in pig houses by Jacqueline Musabimana, Qiuju Xie, Hong Zhou, Ping Zheng, Honggui Liu, Tiemin Ma, Jiming Liu

“…The model improves accuracy compared to other state-of-the-art and ability for NH3 prediction.…”
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Risk Factors for Digital Replantation Failure: A Nomogram Prediction Model by Guo T, Ma T, Gao R, Yu K, Bai J

“…Then, we constructed a nomogram prediction model with 0.7538 in AUC of the prediction model with good consistency in the correction curve and good clinical practicality by decision curve analysis.Conclusion: The level of D-dimer and CRP was found to be closely related to DN. …”
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TBM shield mud cake prediction model based on machine learning by Qi Zhang, Peng Xu, Jing Zhang, Zhao Yang, Yu Li, Xintong Kong, Xiao Yuan

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LOGISTIC AND PROBIT REGRESSION MODELING TO PREDICT THE OPPORTUNITIES OF DIABETES IN PROSPECTIVE ATHLETES by Danang Ariyanto, A'yunin Sofro, A’idah Nur Hanifah, Junaidi Budi Prihanto, Dimas Avian Maulana, Riska Wahyu Romadhonia

“…This study aimed to develop an early prediction model for diabetes in prospective athletic candidates using a bivariate logistic and probit regression approach while considering the influence of socio-demographic and anthropometric factors. …”
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Lifestyle factors and colorectal cancer prediction: A nomogram-based model by Wooin Seo, Se Young Jung, Yeonhoon Jang, Kiheon Lee

“…This study developed and validated an age-based CRC risk-prediction model incorporating lifestyle factors using the National Health Insurance Service (NHIS)-National Sample Cohort database. …”
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Collaborative multiview time series modeling for vehicle maintenance demand prediction by Fanghua Chen, Deguang Shang, Gang Zhou, Ke Ye, Fujie Ren, Guofang Wu

“…To address these challenges, we propose an innovative method for predicting vehicle all maintenance demands based on collaborative multiview time series modeling. …”
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Predicting Software Perfection Through Advanced Models to Uncover and Prevent Defects by Tariq Shahzad, Sunawar Khan, Tehseen Mazhar, Wasim Ahmad, Khmaies Ouahada, Habib Hamam

“…In this study, we evaluated and compared various machine learning models, including logistic regression (LR), random forest (RF), support vector machines (SVMs), convolutional neural networks (CNNs), and eXtreme Gradient Boosting (XGBoost), for software defect prediction using a combination of diverse datasets. …”
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Cellular automata model based power network attack prediction technology by Lijuan YE, Yiting WANG, Licheng ZHU

Subjects: “…cellular automata;power network;attack probability;network information;prediction model…”
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Development of Ensemble Tree Models for Generalized Blood Glucose Level Prediction by Aashima, Shashank Bhargav, Shruti Kaushik, Varun Dutt

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Taxi Demand Prediction Based on a Combination Forecasting Model in Hotspots by Zhizhen Liu, Hong Chen, Yan Li, Qi Zhang

“…In this study, we detected hotspots and proposed three methods to predict the taxi demand in hotspots. Next, we compared the predictive effect of the random forest model (RFM), ridge regression model (RRM), and combination forecasting model (CFM). …”
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BIMLP Model Based on Deep Learning for Predicting Electrical Load Demand by Somayeh Talebzadeh, Reza Radfar, Abbas Toloei Ashlaghi

“…To address this challenge, this research proposes a novel hybrid machine-learning approach for predicting electricity demand. In this research, first, different regression methods were investigated to solve the problem, the results showed that the multi-layer perceptron (MLP) regression model has the best performance in predicting electricity demand. …”
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Microplastic Deposit Predictions on Sandy Beaches by Geotechnologies and Machine Learning Models by Anderson Targino da Silva Ferreira, Regina Célia de Oliveira, Maria Carolina Hernandez Ribeiro, Pedro Silva de Freitas Sousa, Lucas de Paula Miranda, Saulo de Oliveira Folharini, Eduardo Siegle

“…Ultimately, incorporating geomorphological variables into predictive models enhances understanding of MPs deposition, providing a foundation for environmental policies focused on marine pollution mitigation and coastal ecosystem conservation while also contributing to achieve SDG 14.…”
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Digital model for predicting the risk of developing acute decompensated heart failure by N. B. Lebedeva, A. P. Egle, Yu. A. Argunova, O. L. Barbarash

“…Development and external validation of a risk prediction model for acute decompensated heart failure (ADHF) in patients with low left ventricular ejection fraction.Material and methods. …”
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SOIL MOISTURE PREDICTION MODEL IN PEATLAND USING RANDOM FOREST REGRESSOR by Helda Yunita Taihuttu, Imas Sukaesih Sitanggang, Lailan Syaufina

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