Failure prediction of T-peel adhesive joints by different cohesive laws and modelling approaches by Alessandro Pirondi

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Prediction on the Seasonal Behavior of Hydrogen Sulfide Using a Neural Network Model by Byungwhan Kim, Joogong Lee, Jungyoung Jang, Dongil Han, Ki-Hyun Kim

“…Models to predict seasonal hydrogen sulfide (H2S) concentrations were constructed using neural networks. …”
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Prediction Models for Gestational Diabetes Mellitus: Diagnostic Utility of Clinical and Biochemical Markers by Asha Dinakaran, Ramachandran Thiruvengadam, Abu R. Srinivasan, Manikandan, Sunil K. Nanda, Mary Daniel, Reeta Rajagambeeram

Subjects: “…gdm prediction models…”
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An Innovative Dynamic Model for Predicting Typhoon Track Deflections over Complex Terrain by Hung-Cheng Chen

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Intelligent Feature Selection Ensemble Model for Price Prediction in Real Estate Markets by Daniel Cristóbal Andrade-Girón, William Joel Marin-Rodriguez, Marcelo Gumercindo Zuñiga-Rojas

Subjects: “…ensemble model…”
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Prenatal depression level prediction using ensemble based deep learning model by Abinaya Gopalakrishnan, Xujuan Zhou, Revathi Venkataraman, Raj Gururajan, Ka Ching Chan, Guohun Zhu, Niall Higgins

“…This culminated in the development of a novel EBDL model to accurately predict stress levels. Results:: We subsequently applied the ensemble based deep learning model on a testing dataset and our method proved to be 93.87 percent accurate, proving its superiority over the standard supervised classification models. …”
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Explaining the Earnings Management Prediction Model Using the Hybrid of Machine Learning Methods by Hassan Hassani, Esfandiar Malekian Kallehbasti, Yahya Kamyabi

“…This result was confirmed in all prediction methods. However, the results did not show the superiority of the relief-based feature selection model over the principal component analysis-based feature selection model in predicting real earnings management. …”
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Prediction of Propellant Electrostatic Sensitivity Based on Small-Sample Machine Learning Models by Fei Wang, Kai Cui, Jinxiang Liu, Wenhai He, Qiuyu Zhang, Weihai Zhang, Tianshuai Wang

“…A dataset comprising 18 experimental formulations was employed to train and evaluate six machine learning models. Among them, the Random Forest (RF) model achieved the highest predictive accuracy (R² = 0.9681), demonstrating a strong generalization capability through leave-one-out cross-validation. …”
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A review of a priori regression models for warfarin maintenance dose prediction. by Ben Francis, Steven Lane, Munir Pirmohamed, Andrea Jorgensen

“…Of these, 641 patients were identified as having attained stable dosing and formed the dataset used for validation. Predicted maintenance doses from six criterion fulfilling regression models were then compared to individual patient stable warfarin dose. …”
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Performance of five dynamic models in predicting tuberculosis incidence in three prisons in Thailand. by Nithinan Mahawan, Thanapoom Rattananupong, Puchong Sri-Uam, Wiroj Jiamjarasrangsi

“…This study examined the ability of the following five dynamic models for predicting pulmonary tuberculosis (PTB) incidence in a prison setting: the Wells-Riley equation, two Rudnick & Milton-proposed models based on air changes per hour and liters per second per person, the Issarow et al. model, and the applied susceptible-exposed-infected-recovered (SEIR) tuberculosis (TB) transmission model. …”
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Outcome prediction model for patients with unresectable hepatocellular carcinoma treated with targeted therapy by Qiang Ruan, Xiaohui Wang, Qiang Li, Yu Tao, Yang Xun, Hua Yang, Han Zhao, Jinfeng Lu, Dazhi Yang, Yifan Chen, Li Wei

“…Abstract There is no well-established model to predict the outcomes of patients with unresectable hepatocellular carcinoma (u-HCC) receiving targeted therapy. …”
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Comparison of spatial prediction models from Machine Learning of cholangiocarcinoma incidence in Thailand by Oraya Sahat, Supot Kamsa-ard, Apiradee Lim, Siriporn Kamsa-ard, Matias Garcia-Constantino, Idongesit Ekerete

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Bitcoin Trend Prediction with Attention-Based Deep Learning Models and Technical Indicators by Ming-Che Lee

“…This study presents a comparative analysis of two advanced attention-based deep learning models—Attention-LSTM and Attention-GRU—for predicting Bitcoin price movements. …”
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Efficient Air Quality Prediction Models Based on Supervised Machine Learning Techniques by Oumoulylte Mariame, El Allaoui Ahmad, Farhaoui Yousef, Boughrous Ali Ait

“…Our findings suggest that machine learning models can reliably forecast air quality, helping manage pollution and protect public health, with Random Forest showing the best results among the models tested.…”
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Modeling and Simulation: A Study on Predicting the Outbreak of COVID-19 in Saudi Arabia by Ahmed Msmali, Mutum Zico, Idir Mechai, Abdullah Ahmadini

“…The novel coronavirus disease (COVID-19) has resulted in an ongoing pandemic affecting the health system and economy of more than 200 countries worldwide. Mathematical models are used to predict the biological and epidemiological tendencies of an epidemic and to develop methods for controlling it. …”
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Pedestrian trajectory prediction model based on self-supervised spatiotemporal graph network by Shiji Yang, Xuezhong Xiao

“…To improve the accuracy of pedestrian trajectory prediction, the graph - based pedestrian trajectory modeling method in the pedestrian trajectory prediction scenario is effective. …”
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Regression-Based Modeling for Energy Demand Prediction in a Prototype Retail Manipulator by Piotr Kroczek, Krzysztof Lis, Piotr Przystałka

“…The present study proposes two regression-based models for predicting the energy consumption of a four-axis prototype retail manipulator. …”
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Validation of user-friendly models predicting extracapsular extension in prostate cancer patients by Leandro Blas, Masaki Shiota, Shohei Nagakawa, Shigehiro Tsukahara, Takashi Matsumoto, Ken Lee, Keisuke Monji, Eiji Kashiwagi, Junichi Inokuchi, Masatoshi Eto

“…Objective: There are many models to predict extracapsular extension (ECE) in patients with prostate cancer. …”
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Predicting discharge coefficient of triangular side orifice using ANN and GEP models by Mohamed Kamel Elshaarawy, Abdelrahman Kamal Hamed

“…This study utilized machine learning models to predict the discharge coefficient for a sharp-crested triangular side orifice (TSO). …”
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Idiographic Lapse Prediction With State Space Modeling: Algorithm Development and Validation Study by Eric Pulick, John Curtin, Yonatan Mintz

“…Digital sensing and predictive modeling can augment scarce clinician resources to expand and personalize patient care. …”
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