Predicting Running Vertical Ground Reaction Forces Using Neural Network Models Based on an IMU Sensor by Shangxiao Li, Jiahui Pan, Dongmei Wang, Shufang Yuan, Jin Yang, Weiya Hao

“…Using sagittal-axis acceleration data may be an ideal model with good prediction accuracy and less input data. …”
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Predicting tilling and seeding operation times in grain production: A comparison of machine learning and mechanistic models by Luca Scheurer, Tobias Zimpel, Joerg Leukel

“…The aim of this study was to evaluate the prediction performance of ML models for these operation times by using readily available tractor and operations data rather than dynamic environmental data. …”
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Ensemble boosting-based soft-computing models for predicting the bond strength between steel and CFRP plate by Irwan Afriadi, Chanachai Thongchom, Divesh Ranjan Kumar, Suraparb Keawsawasvong, Warit Wipulanusat

“…For the machine learning boosting-based model approach, eight total input variables and one output variable were chosen to predict the maximum load (PU) of the bonding behavior between the CFRP and steel. …”
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Advanced hybrid machine learning models with explainable AI for predicting residual friction angle in clay soils by Mawuko Luke Yaw Ankah, Shalom Adjei-Yeboah, Yao Yevenyo Ziggah, Edmund Nana Asare

“…This study explores three advanced hybrid machine learning models: Gradient Boosting Neural Network (GrowNet), Reinforcement Learning Gradient Boosting Machine (RL-GBM), and a Stacking Ensemble to predict the residual friction angle of clay soils, addressing a critical gap in current predictive methodologies. …”
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Predicting Pharmacokinetics of Active Constituents in <i>Spatholobi caulis</i> by Using Physiologically Based Pharmacokinetic Models by Xiaoyan Liu, Ruihu Du, Tao Zhang, Yingzi Li, Ludi Li, Zheng Yang, Youbo Zhang, Qi Wang

“…These results confirm the successful establishment of PBPK models of these four constituents from SPC in this study, and these models were applicable to predict pharmacokinetics across various doses and extrapolate across species. …”
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Predicting Win-rates of Hearthstone Decks: Models and Features that Won AAIA’2018 Data Mining Challenge by Quang Hieu Vu, Dymitr Ruta, Andrzej Ruta, Ling Cen

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Predicting depression severity using machine learning models: Insights from mitochondrial peptides and clinical factors. by Toheeb Salahudeen, Maher Maalouf, Ibrahim Abe M Elfadel, Herbert F Jelinek

“…Notably, including mitochondrial peptides alongside clinical factors significantly enhances predictive capability, shedding light on the interplay between depression severity and mitochondrial oxidative stress pathways. …”
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Predicting Kinematic Viscosity and Cetane Number of Diesel- Biodiesel Blend Using Neural Network and Empirical Models by M. yari, Gh. Moradi, M. Abdolmaleki, Sh. Bashiri

“…Therefore, a reasonable approach is required for predicting the diesel-biodiesel blend properties. This study tries to estimate two substantial properties of blend, i.e. kinemattic viscosity (KV) and cetane number (CN), through neural network (NN) and empirical models which use pure properties of biodiesel (kinematic viscosity, boiling point, evaporation point, flash point, pour point, heat of combustion, cloud point, and specific gravity) as independent variables. …”
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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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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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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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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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Development of Machine Learning-Based Sub-Models for Predicting Net Protein Requirements in Lactating Dairy Cows by Mingyung Lee, Dong Hyeon Kim, Seongwon Seo, Luis O. Tedeschi

“…This study aimed to develop machine learning-based models to predict net protein requirements for maintenance (NPm) and lactation (NPl) using random forest regression (RFR) and support vector regression (SVR). …”
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Implementation of XGBoost Models for Predicting CO₂ Emission and Specific Tractor Fuel Consumption by Nebojša Balać, Zoran Mileusnić, Aleksandra Dragičević, Mihailo Milanović, Andrija Rajković, Rajko Miodragović, Olivera Ećim-Đurić

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Machine learning models for predicting severe acute kidney injury in patients with sepsis-induced myocardial injury by Te Mi, Xuelin Li, Qizhan Fang, Mingchen Feng

“…The machine learning models can be effective tools for predicting the risk of sAKI in patients with SIMI and the RF model performed best.…”
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Predicting chronic kidney disease progression using small pathology datasets and explainable machine learning models by Sandeep Reddy, Supriya Roy, Kay Weng Choy, Sourav Sharma, Karen M Dwyer, Chaitanya Manapragada, Zane Miller, Joy Cheon, Bahareh Nakisa

“…Methods: This study developed explainable machine learning models leveraging pathology data to accurately predict CKD trajectory, targeting improved prognostic capability even in early stages using limited datasets. …”
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Inspection Data-Driven Machine Learning Models for Predicting the Remaining Service Life of Deteriorating Bridge Decks by Gitae Roh, Changsu Shim, Hyunhye Song

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Comparison of the Performance of Different Thermodynamic Models for Liquid Phase in Predicting the Phase Behavior of Hydrogen Hydrates with THF

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Artificial Intelligence Models May Aid in Predicting Lymph Node Metastasis in Patients with T1 Colorectal Cancer by Ji Eun Baek, Hahn Yi, Seung Wook Hong, Subin Song, Ji Young Lee, Sung Wook Hwang, Sang Hyoung Park, Dong-Hoon Yang, Byong Duk Ye, Seung-Jae Myung, Suk-Kyun Yang, Namkug Kim, Jeong-Sik Byeon

“…We aimed to validate the usefulness of artificial intelligence (AI) models for predicting LNM in patients with T1 CRC. …”
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