Application of multimodal large language models for safety indicator calculation and contraindication prediction in laser vision correction by Joon Yul Choi, Doo Eun Kim, Sung Jin Kim, Hannuy Choi, Tae Keun Yoo

“…Abstract This study demonstrates the potential of multimodal large language models in calculating safety indicators and predicting contraindications for laser vision correction. …”
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Data-driven prediction of rate of penetration (ROP) in drilling operations using advanced machine learning models by Guoli Huang, Sarah Kanaan Hamzah, Pinank Patel, T. Ramachandran, Aman Shankhyan, A. Karthikeyan, Dhirendra Nath Thatoi, Deepak Gupta, S. AbdulAmeer, Mariem Alwan, Zahraa Saad Abdulali, Mahmood Jasem Jawad, Hiba Mushtaq, Mohammad Mahtab Alam, Hojjat Abbasi

“…Among the models tested, the Random Forest algorithm demonstrated outstanding performance, achieving an R2 of 0.955, a Mean Squared Error (MSE) of 0.119, and an Average Absolute Relative Error (AARE%) of 7.683, highlighting its reliability and robustness in predicting ROP. …”
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Predicting Social Media Popularity With Large Language Models: Transforming Metadata Into Semantic-Enriched and Contextualized Text by Tianjian Chen, Jiang Huang, Xuetong Wu, Changcheng Shao

“…Our principal innovation lies in substantially elevating the precision of social media popularity predictions by incorporating comprehensive semantic data descriptions into the modeling process. …”
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Using machine learning models based on cardiac magnetic resonance parameters to predict the prognostic in children with myocarditis by Dongliang Hu, Manman Cui, Xueke Zhang, Yuanyuan Wu, Yan Liu, Duchang Zhai, Wanliang Guo, Shenghong Ju, Guohua Fan, Wu Cai

“…Abstract Objective To develop machine learning (ML) models incorporating explanatory cardiac magnetic resonance (CMR) parameters for predicting the prognosis of myocarditis in pediatric patients. …”
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Mechanistic Models Predict Efficacy of CCR5‐Deficient Stem Cell Transplants in HIV Patient Populations by I Hosseini, F Mac Gabhann

“…Our model predicted that donor chimerism must exceed 75% to achieve 90% probability of cure across patient populations.…”
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A comparative analysis of LSTM, GRU, and Transformer models for construction cost prediction with multidimensional feature integration by Tang Shi, Kazuya Shide

“…The objective of this study is to identify the most effective deep learning model for accurately predicting construction costs by comparing the performance of LSTM, GRU, and Transformer models. …”
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Efficient Ensemble Learning-Based Models for Plastic Hinge Length Prediction of Reinforced Concrete Shear Walls by Naser Safaeian Hamzehkolaei, Mohammad Sadegh Barkhordari

“…This study aims to develop practical machine-learning (ML) models for PHL prediction of RCSWs. For this purpose, 721 data of nonplanar and rectangular RCSWs were utilized. …”
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Evaluation of Feature Selection and Regression Models to Predict Biomass of Sweet Basil by Using Drone and Satellite Imagery by Luana Centorame, Nicolò La Porta, Michela Papandrea, Adriano Mancini, Ester Foppa Pedretti

“…Eight regression models were trained and evaluated using leave-one-out cross-validation. …”
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3D ecological niche models outperform 2D in predicting coelacanth (Latimeria spp.) habitat by Emmaline Sheahan, Hannah Owens, Hannah Owens, Robert Guralnick, Gavin Naylor

“…We gauged each model’s success by how well it could predict L. menadoensis presences recorded from submersible observations.ResultsWhile the 2D model omitted 33% of occurrences at the most forgiving threshold, the 3D model successfully predicted all occurrences, regardless of threshold level.DiscussionIncorporating depth results in improved model accuracy when predicting coelacanth habitat, and projecting into 3 dimensions can give us insights as to where to target future sampling. …”
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Foretelling or foresight? Accounting-based bankruptcy prediction models and earnings quality in the case of Polish listed companies by Barbara Grabińska, Konrad Grabiński

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Comparison of dynamic mode decomposition with other data-driven models for lung cancer incidence rate prediction by L. Raymond Guo, Jifu Tan, M. Courtney Hughes

“…We also employed time series, a linear regression model, and DMD for comparison. All models were evaluated based on their ability to predict 2021 lung cancer incidence rates.ResultsThe time series model achieved the lowest root mean squared error, followed by random forest. …”
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SambaMixer: State of Health Prediction of Li-Ion Batteries Using Mamba State Space Models by Jose Ignacio Olalde-Verano, Sascha Kirch, Clara Perez-Molina, Sergio Martin

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RiNALMo: general-purpose RNA language models can generalize well on structure prediction tasks by Rafael Josip Penić, Tin Vlašić, Roland G. Huber, Yue Wan, Mile Šikić

“…Motivated by the successes of protein language models, we introduce RiboNucleic Acid Language Model (RiNALMo) to unveil the hidden code of RNA. …”
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Predicting aquaculture potential of an essential shrimp via species distribution models in China under climate change by Jie Wei, Yakun Wang, Kunhao Hong, Qiaoyan Zhou, Xinping Zhu, Caihong Liu, Lingyun Yu

“…Our models demonstrated high predictive accuracy, revealing that the distribution of suitable aquaculture areas for M. rosenbergii is primarily determined by extreme temperature variations during the warmest and coldest months. …”
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Development and validation of CNN-MLP models for predicting anti-VEGF therapy outcomes in diabetic macular edema by Xiangjie Leng, Ruijie Shi, Zhaorui Xu, Hai Zhang, Wenxuan Xu, Keyin Zhu, Xuejing Lu

“…No statistical difference was found between the actual and predicted values in all clinical indicators. This study demonstrated that the improved CNN-MLP regression models using multimodal data can accurately predict outcomes in BCVA, CST, CV, and CAT after anti-VEGF therapy in DME patients, which is valuable for ophthalmic clinical decisions and reduces the economic burden on patients.…”
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Comparison of Regression and Artificial Neural Network Models for Surface Roughness Prediction with the Cutting Parameters in CNC Turning by Muammer Nalbant, Hasan Gokkaya, İhsan Toktaş

“…Predictive neural network model showed better predictions than various regression models for surface roughness. …”
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Moisture Sorption Isotherms and Prediction Models of Carboxymethyl Chitosan Films from Different Sources with Various Plasticizers by Juthamas Tantala, Chitsiri Rachtanapun, Wirongrong Tongdeesoontorn, Kittisak Jantanasakulwong, Pornchai Rachtanapun

“…In conclusion, it can be stated that the GAB model was found to be better estimated for predicting the CMCH films than other models. …”
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Mortality prediction of heart transplantation using machine learning models: a systematic review and meta-analysis by Ida Mohammadi, Setayesh Farahani, Asal Karimi, Saina Jahanian, Shahryar Rajai Firouzabadi, Mohammadreza Alinejadfard, Alireza Fatemi, Bardia Hajikarimloo, Mohammadhosein Akhlaghpasand

“…IntroductionMachine learning (ML) models have been increasingly applied to predict post-heart transplantation (HT) mortality, aiming to improve decision-making and optimize outcomes. …”
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Integration of pre-trained protein language models with equivariant graph neural networks for peptide toxicity prediction by Shihu Jiao, Xiucai Ye, Tetsuya Sakurai, Quan Zou, Wu Han, Chao Zhan

“…By combining sequence embeddings from the ProtT5 language model and 3D structural data predicted by ESMFold, StrucToxNet can capture both sequential and spatial characteristics of peptides. …”
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Utilizing machine learning models for predicting outcomes in acute pancreatitis: development and validation in three retrospective cohorts by Kaier Gu, Yang Liu

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