Development and validation of a small-sample machine learning model to predict 5–year overall survival in patients with hepatocellular carcinoma by Tingting Jiang, Xingyu Liu, Wencan He, Hepei Li, Xiang Yan, Qian Yu, Shanjun Mao

“…Additionally, the optimal model was established after rigorous validation. …”
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Machine learning using random forest to model heavy metals removal efficiency using a zeolite-embedded sheet in water by N.D. Takarina, N. Matsue, E. Johan, A. Adiwibowo, M.F.N.K. Rahmawati, S.A. Pramudyawardhani, T. Wukirsari

“…The random forest model is very useful to provide information and determine the threshold of heavy metal contents, water potential of hydrogen and temperature to optimize the heavy metal removal efficiency using a zeolite-embedded sheet and reducing pollutants in the environment.…”
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A machine learning-driven semi-mechanistic model for estimating actual evapotranspiration: Integrating photosynthetic indicators with vapor pressure deficit by Yao Li, Xiongbiao Peng, Zhunqiao Liu, Xiaoliang Lu, Xiaobo Gu, Lianyu Yu, Jiatun Xu, Huanjie Cai

“…This study used on-site ground observation data with a 30-minute temporal resolution from a winter wheat field at the Yangling Station on the Guanzhong Plain, China, to evaluate the performance of machine learning-driven semi-mechanistic models driven by three machine learning methods (Ridge regression, Random Forest, and Support Vector Machine) in estimating ETc act. …”
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Development and Validation of an Interpretable Machine Learning Model for Prediction of the Risk of Clinically Ineffective Reperfusion in Patients Following Thrombectomy for Ischem... by Hu X, Qi D, Li S, Ye S, Chen Y, Cao W, Du M, Zheng T, Li P, Fang Y

“…The number of EVT attempts has emerged as a key determinant, underscoring the need for optimized procedural timing to improve outcomes.Keywords: machine learning, clinically ineffective reperfusion, predictive model, acute ischemic stroke, online predictive platform…”
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Development and validation of a machine learning-based risk prediction model for stroke-associated pneumonia in older adult hemorrhagic stroke by Yi Cao, Yi Cao, Haipeng Deng, Shaoyun Liu, Xi Zeng, Yangyang Gou, Weiting Zhang, Yixinyuan Li, Hua Yang, Min Peng

“…ObjectiveTo develop and validate a machine learning (ML)-based model for predicting stroke-associated pneumonia (SAP) risk in older adult hemorrhagic stroke patients.MethodsA retrospective collection of older adult hemorrhagic stroke patients from three tertiary hospitals in Guiyang, Guizhou Province (January 2019–December 2022) formed the modeling cohort, randomly split into training and internal validation sets (7:3 ratio). …”
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Towards Automated Quality Control in Industrial Systems: Developing Markov Decision Process Model for Optimized Decision-Making by Katerina Mitkovska-Trendova, Robert Minovski, Verica Bakeva, Simeon Trendov, Dimitar Bogatinov

“…Different MDP models and methods are explored to enhance adaptability and iterative learning, allowing for optimal policy refinement over time. …”
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Nutritional management adherence via an ePRO platform in patients with cancer: a machine learning model studyResearch in context by Si-Wei Xie, Jia-Xin Huang, Hui-Min Qu, Zhi-Gang Feng, Xin-Yi Wang, Zhen-Guang Du, Ming-Hui Zhang, Shu-Qing Wei, Jun Li, Li-Li Hong, Li-Li Wang, Jing-Hui Bai, Kai-Feng Wang, Xue-Bang Zhang, Xian Shen, Xiao-Dong Chen, Le Tian, Xi Zhang, Min Yang, Ning Li, Meng Tang, Chen-Xin Song, Bao-Hua Zou, Sheng-Ling Qin, Rong Qin, Ming-Hua Cong

“…The proportion of actual/prescribed intake <60% was set as low adherence. Explainable machine learning models were used to identify predictive features, with SHapley Additive exPlanation (SHAP) analysis ranking variable importance. …”
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Hardware Trojan Detection in Open-Source Hardware Designs Using Machine Learning by Victor Takashi Hayashi, Wilson Vicente Ruggiero

“…The use of LLMs with prompt optimization achieved a recall of 99%, minimizing false negatives. …”
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Phenological piecewise modelling is more conducive than whole-season modelling to winter wheat yield estimation based on remote sensing data by Xin Huang, Wenquan Zhu, Cenliang Zhao, Zhiying Xie, Hui Zhang

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Enhancing Office Comfort with Personal Comfort Systems: A Data-Driven Machine Learning Approach by Paulina Wegertseder-Martinez, Silvia E. Restrepo-Medina, Roberto Aedo-García, Raul Soto-Concha

“…This study evaluated the use of machine learning models generated by H2O AutoML to predict the use of three PCSs in four office buildings with effective occupancy. …”
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Optimizing ensemble learning for satellite-based multi-hazard monitoring and susceptibility assessment of landslides, land subsidence, floods, and wildfires by Seyed Vahid Razavi-Termeh, Abolghasem Sadeghi-Niaraki, Farman Ali, Biswajeet Pradhan, Soo-Mi Choi

“…Past studies have relied mainly on traditional machine learning models, but these models do not perform well for complex spatial patterns. …”
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Edge-Optimized Deep Learning Architectures for Classification of Agricultural Insects with Mobile Deployment by Muhammad Hannan Akhtar, Ibrahim Eksheir, Tamer Shanableh

“…The deployment of machine learning models on mobile platforms has ushered in a new era of innovation across diverse sectors, including agriculture, where such applications hold immense promise for empowering farmers with cutting-edge technologies. …”
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Distribution Ratio Prediction of Major Components in 30%TBP/kerosene-HNO3 System Based on Machine Learning by YU Ting1, ZHANG Yinyin2, ZHANG Ruizhi3, JIN Wenlei2, LUO Yingting2, ZHU Shengfeng3, HE Hui1, YE Guoan1, GONG Helin4

“…In this paper, machine learning is combined with distribution ratio prediction, which is defined as the distribution ratio of ionic liquids in different phases, which can reflect the extraction rate of ions, and plays an important role in Purex computer simulation, so the distribution ratio prediction model can help researchers to choose the optimal experimental conditions, optimize the process, and reduce the experimental cost and time. …”

Comparative Analysis of Neural Network Models for Predicting Battery Pack Safety in Frontal Collisions by Jun Wang, Ouyang Chen, Zhenfei Zhan, Zhiwei Zhao, Huanhuan Bao

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A Scalable, Lightweight AI-Driven Security Framework for IoT Ecosystems: Optimization and Game Theory Approaches by Krishna Chaitanya Chaganti

“…Optimization techniques improve the detection accuracy from 94.2% to 94.78%, reduce the response time by 14.98%, and optimize the energy consumption by 12.01%. …”
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Research on Deformation Prediction Surrogate Model of Thin-walled Parts for Digital Twin Modeling by YUE Caixu, ZHANG Jiahao, XIA Wei, JIA Ruhong, GUO Yandong, LIU Xianli

“…A lightweight model for machining deformation prediction of thin-walled parts driven by digital twin is constructed by using Bayesian optimized random forest surrogate model. …”
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Interpretable Machine Learning Framework for Non-Destructive Concrete Strength Prediction with Physics-Consistent Feature Analysis by Teerapun Saeheaw

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Developing a molecular diagnostic model for heatstroke-induced coagulopathy: a proteomics and metabolomics approach by Qingbo Zeng, Qingwei Lin, Longping He, Lincui Zhong, Ye Zhou, Xingping Deng, Nianqing Zhang, Qing Song, Qing Song, Jingchun Song, Jingchun Song

“…Building on these findings, an optimal machine learning diagnostic model was developed to boost the accuracy of HSIC diagnosis, integrating LDHA, NGAL, prothrombin, and GBE as key biomarkers.…”
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A Predictive Models for Advertisement Campaign Budget Allocation by Iqra kousar

“…These models use machine learning to analyze past performance, predict trends, and optimize resource distribution across channels, improving campaign outcomes and return on investment (ROI). …”
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A Predictive Models for Advertisement Campaign Budget Allocation by Iqra kousar

“…These models use machine learning to analyze past performance, predict trends, and optimize resource distribution across channels, improving campaign outcomes and return on investment (ROI). …”
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