Preliminary exploration and application research on the model of gathering distillate according to the quality based on Fourier transform near infrared spectroscopy by LIAO Li, ZHANG Guiyu, ZOU Yongfang, ZHU Xuemei, PENG Houbo, ZHANG Wei, LI Yan

“…Multiplicative scatter correction (MSC), competitive adaptive reweighting algorithms sampling (CARS) and support vector regression (SVR) were better methods to construct the regression prediction model, with coefficient of determination R² and root mean square error (RMSE) mean values of 0.8951 and 0.03, respectively. …”
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Evaluation and Application of Machine Learning Techniques for Quality Improvement in Metal Product Manufacturing by Katarzyna Antosz, Lucia Knapčíková, Jozef Husár

“…A variety of classification algorithms, including neural networks (NNs), bagged trees (BT), and support vector machines (SVMs), were employed to efficiently analyse and predict defects. …”
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Relationships Between Oat Phenotypes and UAV Multispectral Imagery Under Different Water Deficit Conditions by Structural Equation Modelling by Yayang Feng, Guoshuai Wang, Jun Wang, Hexiang Zheng, Xiangyang Miao, Xiulu Sun, Peng Li, Yan Li, Yanhui Jia

“…However, the prediction accuracy for moisture conditions using spectral data alone is low. …”
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Prevention of Cardiometabolic Syndrome in Children and Adolescents Using Machine Learning and Noninvasive Factors: The CASPIAN-V Study by Hamid Reza Marateb, Mahsa Mansourian, Amirhossein Koochekian, Mehdi Shirzadi, Shadi Zamani, Marjan Mansourian, Miquel Angel Mañanas, Roya Kelishadi

“…We applied the XGBoost algorithm to analyze key noninvasive variables, including self-rated health, sunlight exposure, screen time, consanguinity, healthy and unhealthy dietary habits, discretionary salt and sugar consumption, birthweight, and birth order, father and mother education, oral hygiene behavior, and family history of dyslipidemia, obesity, hypertension, and diabetes using five-fold cross-validation. …”
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An improved model accuracy for forecasting risk measures: application of ensemble methods by Katleho Makatjane, Kesaobaka Mmelesi

“…Using different ensemble algorithms to conglomerate the TBATS and the GEV distribution, we found that the stacking ensemble algorithm outperforms other ensembles hence the forecasting accuracy of risk measures is improved with the stacking ensemble algorithm. …”
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A new method for surge arrester placement in high‐voltage substations considering environmental effects by Faridoddin Safaei, Mohsen Niasati

“…Simulations are based on experimental results, and the number of calculations significantly decreases using the proposed algorithm. Simulations of the sample network and implementation of the proposed algorithm with MATLAB and EMTP‐RV prove the efficiency of the proposed method for optimum SA placement.…”
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Intelligent monitoring and control of farmland based on edge-cloud collaboration and digital twin for digital energy management: investment benefit analysis by Zheng Liu

“…In terms of intelligent control, deep reinforcement learning is used to optimize irrigation decision-making, and adaptive algorithms are combined to improve the flexibility of agricultural equipment scheduling. …”
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Work Place Safety: Machine Learning Techniques for Assessing Workplace Incident Severity by Munayr Hasan Khalleefah Hassan, Wagdi M. S. Khalifa

“…Occupational injury severity prediction is paramount across all industries, necessitating the adoption of advanced methodologies to enhance predictive analysis. …”
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Machine learning-based hybrid risk estimation system (ERES) in cardiac surgery: Supplementary insights from the ASA score analysis. by Ayşe Banu Birlik, Hakan Tozan, Kevser Banu Köse

“…A retrospective analysis of 543 patients was performed using six machine learning algorithms applied to preoperative clinical data to assess predictive accuracy and clinical outcomes. …”
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Hybrid LSTM–Seq2Seq Models: Improved Patient Interaction for Healthcare Chatbots by saba abdalmnam, Suha Mohammed Hadi, Mustafa Musa

“…The capability of the model for sequence prediction is attributed to its robustness. Other methods of evaluation employing measures such as the Nash–Sutcliffe efficiency coefficient, correlation coefficient, and normalized root mean square error demonstrate that the model is superior to other machine learning algorithms utilizing linear regression and GP regression. …”
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Habitat analysis of iron deposition in the basal ganglia for diagnosing cognitive impairment in chronic kidney disease: evidence from a case-control study by Hao Wang, Yu Qi, Xu Liu, Li-Jun Song, Wen-Bo Yang, Ming-An Li, Xiao-Yan Bai, Mao-Sheng Xu, Hao-Nan Zhu, Si-Qing Cai, Yi Wang, Zheng-Han Yang, Yuan-Zhe Li, Zhen-Chang Wang, Yi-Fan Guo

“…Habitat-based models for diagnosing cognitive impairment were constructed using the random forest algorithm. Logistic regression was employed to build the clinical model and the combined model. …”
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Static stress triggering of Morocco M 6.9 earthquake on 9 September 2023 by WANG Runyan, WAN Yongkui, GUAN Zhaoxuan, HUANG Shaohua, GU Peiyuan, SONG Zeyao, ZHOU Mingyue

“…Methods  Using the "central solution" algorithm, two possible nodal planes of the earthquake were determined, and the local stress field was projected onto these two planes. …”
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Surface Soil Organic Carbon Estimation Based on Habitat Patches in Southwest China by Jieyun Xiao, Wei Zhou, Ting Wang, Yao Peng, Zhan Shi, Saibo Li, Yang Li, Tianxiang Yue

“…This study proposed a new method using the partitioning around medoids clustering algorithm to partition the study area into distinct habitat patch types. …”
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Metabolic profiles in laryngeal cancer defined two distinct molecular subtypes with divergent prognoses by Dan Zheng, Dan Zheng, Xuan Pu, Xuan Pu, XuHui Deng, XuHui Deng, Cui Liu, Cui Liu, SiJun Li, SiJun Li

“…Furthermore, we explored the potentials of several key tumor markers for both diagnosis and prognosis prediction.…”
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Identification of subcellular localization of Arabidopsis AGO2 by ZHAO Li, XIA Wenqiang, CAI Xinzhong

“…Analysis using Predict Protein software showed that AtAGO1, AtAGO2, AtAGO3, AtAGO4 and AtAGO9 contained nuclear localization signals (NLS), although differing in sequence, demonstrating that they might be localized in cell nucleus. …”
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Associations between age, red cell distribution width and 180-day and 1-year mortality in giant cell arteritis patients: mediation analyses and machine learning in a cohort study by Si Chen, Rui Nie, Xiaoran Shen, Yan Wang, Haixia Luan, Xiaoli Zeng, Yanhua Chen, Hui Yuan

“…Logistic and Cox regression analyses, Kaplan–Meier (KM) survival analysis, restricted cubic spline (RCS) analysis, and mediation effect analysis were employed to investigate the association between age, RDW levels, and 180-day and 1-year mortality in GCA patients hospitalized or admitted to the ICU. Predictive models were constructed using machine learning algorithms, and SHapley Additive exPlanations (SHAP) analysis was applied to evaluate the contributions of age and RDW levels to mortality in this patient population. …”
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Exploring the role of neutrophil extracellular traps in neuroblastoma: identification of molecular subtypes and prognostic implications by Can Qi, Can Qi, Ziwei Zhao, Lin Chen, Le Wang, Yun Zhou, Guochen Duan, Guochen Duan

“…Univariate Cox analysis and the LASSO algorithm were used to identify biomarkers for prognosis. …”
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Wearable Artificial Intelligence for Sleep Disorders: Scoping Review by Sarah Aziz, Amal A M Ali, Hania Aslam, Alaa A Abd-alrazaq, Rawan AlSaad, Mohannad Alajlani, Reham Ahmad, Laila Khalil, Arfan Ahmed, Javaid Sheikh

“…The primary selection criterion was the inclusion of studies that utilized AI algorithms to detect or predict various sleep disorders using data from wearable devices. …”
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Interpretable multiparametric MRI radiomics-based machine learning model for preoperative differentiation between benign and malignant prostate masses: a diagnostic, multicenter st... by Wenjun Zhou, Wenjun Zhou, Zhangcheng Liu, Zhangcheng Liu, Jindong Zhang, Shuai Su, Yu Luo, Lincen Jiang, Kun Han, Guohua Huang, Jue Wang, Jianhua Lan, Delin Wang

“…SHAP can disclose the underlying prediction process of the ML model, which may promote its clinical applications.…”
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Multi-omics analysis constructs a novel neuroendocrine prostate cancer classifier and classification system by Junxiao Shen, Luyuan Lu, Zujie Chen, Wei Guo, Shuwen Wang, Ziqiao Liu, Xuke Gong, Yiming Qi, Ruyi Jin, Cheng Zhang

“…The random forest (RF) algorithm proved to be the most effective classifier for NEPC, leading to the establishment of the NEP100 model, which demonstrated robust validation across various datasets. …”
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