Mathematical Modeling of Multiple Quality Characteristics of a Laser Microdrilling Process Used in Al7075/SiCp Metal Matrix Composite Using Genetic Programming by Mohammed Yunus, Mohammad S. Alsoufi

“…This paper presents a derived mathematical model based on evolutionary computation methods using multivariate regression fitting for the prediction of multiple characteristics (circularity, taper, spatter, and HAZ) of neodymium: yttrium aluminum garnet laser drilling of aluminum matrix/silicon carbide particulate (Al/SiCp) MMCs using genetic programming. …”
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Ai-powered digital twin in the industrial IoT by Željko Bolbotinović, Saša D. Milić, Žarko Janda, Dragan Vukmirović

“…The paper presents the digital twin (DT) concept in a vertical Industrial Internet of Things (IIoT) framework powered by machine learning (ML) models for time series forecasting. …”
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Estimation of Tomato Quality During Storage by Means of Image Analysis, Instrumental Analytical Methods, and Statistical Approaches by Paris Christodoulou, Eftichia Kritsi, Georgia Ladika, Panagiota Tsafou, Kostantinos Tsiantas, Thalia Tsiaka, Panagiotis Zoumpoulakis, Dionisis Cavouras, Vassilia J. Sinanoglou

“…In this sense, tomato samples were effectively classified by ATR-FTIR spectral bands, linked to carotenoids, phenolics, and polysaccharides. Machine learning (ML) models, including Random Forest and Gradient Boosting, were trained on image-derived features and accurately predicted shelf life and quality traits, achieving R² values exceeding 0.9. …”
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Multilayer perceptron deep learning radiomics model based on Gd-BOPTA MRI to identify vessels encapsulating tumor clusters in hepatocellular carcinoma: a multi-center study by Mengting Gu, Wenjie Zou, Huilin Chen, Ruilin He, Xingyu Zhao, Ningyang Jia, Wanmin Liu, Peijun Wang

“…Then a radiomics score (rad-score) was generated, which combined significant clinicoradiological predictors to constituted the fusion model through multivariate logistic regression analysis. …”
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Adaptive biases-incorporated latent factorization of tensors for predicting missing data in water quality monitoring networks by Xuke Wu, Xuke Wu, Lan Wang, Lan Wang, Miao Ge, Miao Ge, Jing Jiang, Jing Jiang, Yu Cai, Bing Yang, Bing Yang

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Design, Development and Implementation of iALERTS (Informatics Analytics for Long-term Evaluation and Repercussions Tracking of SARS-CoV-2 Infection): A Research Protocol by Krishna Mohan Surapaneni, Manmohan Singhal, Ashish Joshi

“…Statistical analysis will be conducted using multivariable regression models to identify predictors of PASC and to evaluate the association between SARS-CoV-2 infection characteristics and long-term outcomes. …”
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Multi-port network based modeling and selection of capacitor for desired voltage regulation of a standalone six-phase short-shunt induction generator for application in remote area... by Saikat Ghosh, S.N. Mahato

“…The theory of multi-port network analysis has been applied for modelling of the SPIG, thus, the complex mathematical derivation to obtain the model equations is avoided. …”
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Construction of a novel radioresistance-related signature for prediction of prognosis, immune microenvironment and anti-tumour drug sensitivity in non-small cell lung cancer by Yanliang Chen, Chan Zhou, Xiaoqiao Zhang, Min Chen, Meifang Wang, Lisha Zhang, Yanhui Chen, Litao Huang, Junjun Sun, Dandan Wang, Yong Chen

“…The least absolute shrinkage and selection operator (LASSO) regression and random survival forest (RSF) were used to screen for prognostically relevant RRRGs. Multivariate Cox regression was used to construct a risk score model. …”
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Application of Big Data Technology to Assessments of Female Ovarian Reserve Dysfunction by Xia Ji'An, Ma YunFei, Wu YiYun, Zhao YouLin, Ni HaoRang, Liu XinYan

“…Hadoop and Spark frameworks were used to build a big data platform, and the MLlib parallel machine learning library was used to implement three multivariate classification models—multilayer perceptron, one-vs-rest, and random forest classifiers—to classify and analyse the ovarian reserve function dataset and evaluate the platform’s performance. …”
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Clinical characteristics and predictive models of HER2-low breast cancer patients who only received adjuvant chemotherapy: a real-world retrospective multicenter study by Qinchuan Wang, Yuying Wang, Zikang Chen, Yuqing Chao, Lin Chen, Wenjing Li, Xia Yang, Zhinong Jiang, Xiaoyan Li, Bo Gao, Wenxian Hu, Jichun Zhou, Linbo Wang, Haitao Li, Jun Zhang, Qiong Ding, Xudong Lv, Hao Zhang, Heming Zheng

“…HER2-status was defined according to ASCO/CAP guidelines. Multivariable Cox models and machine learning models were applied in the analysis of overall survival. …”
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Blood trace elements in association with esophageal squamous cell carcinoma risk, aggressiveness and prognosis in a high incidence region of China by Shuyi Qiu, Bingmeng Xie, Jiahui Liao, Jianan Luo, Xi Liu, Lihua He, Yiteng Huang, Lin Peng

“…Contrary to Se, the elements Pb, Cr and Cu were positively associated with ESCC risk. By Bayesian Kernel Machine Regression models, the mixtures of the eight trace elements were found to be significantly associated with ESCC risk and metastasis, with Cr, Mn, Cu, Zn, and Pb having a PIP of 1.000 for occurrence risk and Mn being the main contributor for metastatic risk (PIP = .6570). …”
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The value of a combined model based on ultra-radiomics and multi-modal ultrasound in the benign-malignant differentiation of C-TIRADS 4A thyroid nodules: a prospective multicenter... by Shuai Cui, Qifan Liu, Hailong Wang, Husha Li, Wei Li, Chenlong Li, Leilei Bi, Yang Mu, Wenjing Guo, Jundong Yao, Zhoulong Zhang

“…Based on 17 ultrasound radiomics features, a radiomics model was constructed using the RF machine learning algorithm. …”
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A study on predicting the risk of coronary artery disease in OSAHS patients based on a four-variable screening tool potential predictive model and its correlation with the severity... by Yanli Yao, Yu Li, Yulan Chen, Xuan Qiu, Gulimire Aimaiti, Ayiguzaili Maimaitimin

“…Propensity score matching (PSM) was used to balance covariables between groups, and 293 cases were included per group in a 1:1 ratio. Univariable and multivariable logistic regression analyses were employed to evaluate parameters independently associated with CAD and construct a nomogram model.Receiver operating characteristic (ROC) curve analysis, Hosmer-Lemeshow test, calibration curve and decision curve (DCA) analyses were employed to assess its predictive value in CAD. …”
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Multimodal nomogram integrating deep learning radiomics and hemodynamic parameters for early prediction of post-craniotomy intracranial hypertension by Zunfeng Fu, Jing Wang, Wenyi Shen, Yanqing Wu, Jiajun Zhang, Yan Liu, Chongqiang Wang, Yanlin Shen, Ye Zhu, Weifu Zhang, Chunju Lv, Lin Peng

“…Clinical-ultrasound variables were incorporated into the model through univariate and multivariate logistic regression. …”
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Novel Approach for Robust Control of Axial Piston Pump by Tsonyo Slavov, Alexander Mitov, Jordan Kralev

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Cardiometabolic risk factors in predicting obstructive coronary artery disease in patients with non-ST-segment elevation acute coronary syndrome by B. I. Geltser, M. M. Tsivanyuk, K. I. Shakhgeldyan, E. D. Emtseva, A. A. Vishnevskiy

“…In addition, for the development of predictive models, we used multivariate LR (MLR), support vector machine (SVM) and random forest (RF). …”
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Acute Psychological Stress Detection Using Explainable Artificial Intelligence for Automated Insulin Delivery by Mahmoud M. Abdel-Latif, Mudassir M. Rashid, Mohammad Reza Askari, Andrew Shahidehpour, Mohammad Ahmadasas, Minsun Park, Lisa Sharp, Lauretta Quinn, Ali Cinar

“…The extreme gradient boosting model is developed for classification of APS and non-stress (NS) with weighted training, achieving an overall accuracy of 99.93%. …”
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Visceral adiposity index as a predictor of metabolic dysfunction-associated steatotic liver disease: a cross-sectional study by Tuo Zhou, Xiang Ding, Linjie Chen, Qianxiong Huang, Linfang He

“…Methods This study employed data from the 2017-2018 National Health and Nutrition Examination Survey (NHANES). Weighted multivariable regression models, subgroup analyses, and machine learning algorithms were used to evaluate associations and predictive performance. …”
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Mediating role of systemic inflammation in the association between volatile organic compounds exposure and periodontitis: NHANES 2011–2014 by Zhida Dai, Zhixiang Zhang, Qiaobin Hu, Xinyuan Yu, Yixi Cao, Yian Xia, Yingyin Fu, Yuxuan Tan, Chunxia Jing, Chunlei Zhang

“…Concentrations of urinary metabolites of VOCs (mVOCs) were measured using electrospray tandem mass spectrometry to evaluate internal VOCs exposure. Multivariable logistic regression, restricted cubic spline regression (RCS), Bayesian kernel machine regression (BKMR) and Quantile g-computation (QGC) models were performed to investigate the impacts of VOCs exposure on periodontitis. …”
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Clinical characteristics of adrenal crisis in 371 adult patients with glucocorticoid-induced adrenal insufficiency by Ying Qiu, Ying Qiu, Ying Luo, Xinqian Geng, Yujian Li, Yujian Li, Yunhua Feng, Ying Yang

“…Among the prediction models constructed by machine learning algorithms, logistic regression model had the best prediction effect.ConclusionThis study investigated the clinical characteristics of AC in GIAI patients. …”
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