Survival Analysis by Penalized Regression and Matrix Factorization by Yeuntyng Lai, Morihiro Hayashida, Tatsuya Akutsu

“…We tried L1- (lasso), L2- (ridge), and L1-L2 combined (elastic net) penalized regression for diffuse large B-cell lymphoma (DLBCL) patients' microarray data and found that L1-L2 combined method predicts survival best with the smallest logrank P value. …”
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Systematic Framework to Predict Early-Stage Liver Carcinoma Using Hybrid of Feature Selection Techniques and Regression Techniques by Marium Mehmood, Nasser Alshammari, Saad Awadh Alanazi, Fahad Ahmad

“…Regression algorithms include Linear Regression, Ridge Regression, LASSO Regression, Support Vector Regression, Decision Tree Regression, Multilayer Perceptron Regression, and Random Forest Regression. …”
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Evolution trends and driving factors of industrial water use: A case study of Ordos City, China by Hong Lv, Yiqing He, Yuan Liu, Xinjian Guan, Wenxiu Shang, Zheng Xiaokang

“…A high-dimensional control factor identification method was developed, integrating ridge regression, panel Tobit, LMDI, and PSO-SVM techniques. …”
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Characteristics of Precipitation, Streamflow, and Sediment Transport of the Hangman Creek in the Pacific Northwest, USA: Implication for Agricultural Conservation Practice Implemen... by Yongping Yuan, Sean Kanyuk

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Machine learning identifies the association between second primary malignancies and postoperative radiotherapy in young-onset breast cancer patients. by Yulin Lai, Peiyuan Huang

“…<h4>Methods</h4>Machine learning components, including ridge regression, XGBoost, k-nearest neighbor, light gradient boosting machine, logistic regression, support vector machine, neural network, and random forest, were used to construct a predictive model and identify the risk factors for SPMs with data from the Surveillance, Epidemiology and End Results. …”
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Carriage of Neisseria Species in Communities with Different Rates of Meningococcal Disease by Nicole Le Saux, Fraser Ashton, Maksudar Rahman, Alan Ryan, Edward Ellis, Susan Tamblyn, Joan Morris, Al Borczyk, Caroll Mallory, Donald Mikel, Stephen Thompson, Lyall Black, Barbara Lacey, Chandar Anand

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Determination of 5-fluorouracil anticancer drug solubility in supercritical CO 2 using semi-empirical and machine learning models by Gholamhossein Sodeifian, Ratna Surya Alwi, Reza Derakhsheshpour, Nedasadat Saadati Ardestani

“…Three models with different approaches were applied to correlate and model the experimental data set: (i) seven density-based models, (ii) PR equations of state (vdW2 mixing rule), and (iii) machine learning-based models, namely non-linear regressions, Random Forest, Gradient Boosting, Decision Tree, and Kernel Ridge. All tested models successfully correlate and model the solubility data within an acceptable accuracy. …”
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Variables That Best Differentiate In-Patient Acute Stroke from Stroke-Mimics with Acute Neurological Deficits by P. Natteru, M. R. Mohebbi, P. George, D. Wisco, J. Gebel, C. R. Newey

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Zirconia Implants in Esthetic Areas: 4-Year Follow-Up Evaluation Study by Andrea Enrico Borgonovo, Rachele Censi, Virna Vavassori, Oscar Arnaboldi, Carlo Maiorana, Dino Re

“…Materials and Method. 13 patients were selected and 20 one-piece zirconia implants were used for the rehabilitation of single tooth or partially edentulous ridge in the esthetic jaw areas. Six months after surgery and then once a year, a clinical-radiographic evaluation was performed in order to estimate peri-implant tissue health and marginal bone loss. …”
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Information Entropy-Based Hybrid Models Improve the Accuracy of Reference Evapotranspiration Forecast by Anzhen Qin, Zhilong Fan, Liuzeng Zhang

“…In this study, four popularly used single models were selected to forecast ET0 values, including support vector regression, Bayesian linear regression, ridge regression, and lasso regression models, respectively. …”
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Variability of nutrient transport and associated upper ocean primary production induced by Kuroshio meanders in the Enshu-nada Sea, Japan by Namazue Gaku, Uchiyama Yusuke, Zhang Xu, Masunaga Eiji

“…The upper ocean nitrate flux budget analysis showed that the subsurface nitrate was transported upward as a vertical diffusive flux around the north (shoreward) of the Kuroshio path and as a mean vertical advective flux in the Kuroshio downstream region around the Izu-Ogasawara Ridge. In contrast, high-frequency eddy vertical advective fluxes caused downward transport, but to a degree about 20% smaller than the other fluxes. …”
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Early Iron Age Pottery Technology of the Kazakh Uplands (Saryarqa) by Valeriy G. Loman

“…In addition, 17 vessels from barrows with ‘moustaches’ (or stone ridges) previously dated to the Early Iron Age have also been investigated. …”
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Histological demonstration of gonad development in the banana shrimp, Fenneropenaeus merguiensis by Jirakanit Chimnual, Jirawat Saetan, Wilaiwan Chotigeat

“…Hematoxylin and Eosin staining and in situ hybridization with the VASA probe revealed that the PGCs migrated retrogradely along the midgut, colonizing the area between the hepatopancreas and heart, a region that becomes the genital ridge in the postlarval stage. External sexual organs appeared at approximately 4 months of age. …”
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Regularised Model Identification Improves Accuracy of Multisensor Systems for Noninvasive Continuous Glucose Monitoring in Diabetes Management by Mattia Zanon, Giovanni Sparacino, Andrea Facchinetti, Mark S. Talary, Andreas Caduff, Claudio Cobelli

“…In this work, by exploiting a dataset of 45 experimental sessions acquired in diabetic subjects, we show that regularisation-based techniques for the identification of the model, such as the least absolute shrinkage and selection operator (better known as LASSO), Ridge regression, and Elastic-Net regression, improve the accuracy of glucose estimates with respect to techniques, such as partial least squares regression, previously used in the literature. …”
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Parametric Estimation of Directional Wave Spectra from Moored FPSO Motion Data Using Optimized Artificial Neural Networks by Do-Soo Kwon, Sung-Jae Kim, Chungkuk Jin, MooHyun Kim

“…In addition, comparisons against other machine learning (ML) methods—such as Support Vector Machines, Random Forest, Gradient Boosting, and Ridge Regression—demonstrate the present ANN model’s superior ability to capture intricate nonlinear interdependencies between vessel motions and environmental conditions.…”
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Yeti claws: Cheliped sexual dimorphism and symmetry in deep-sea yeti crabs (Kiwaidae). by Christopher Nicolai Roterman, Molly McArthur, Cecilia Laverty Baralle, Leigh Marsh, Jon T Copley

“…A total of 135 specimens from the East Scotia Ridge were examined, revealing mean asymmetry indices close to zero with respect to propodus length and height, albeit with a significantly larger number of marginally left-dominant individuals with respect to propodus length, possibly indicative of some task specialisation between claws, or a vestigial ancestral trait. …”
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Classification of Stages 1,2,3 and Preplus, Plus disease of ROP using MultiCNN_LSTM classifier by Ranjana Agrawal, Sucheta Kulkarni, Madan Deshpande, Anita Gaikwad, Rahee Walambe, Ketan V. Kotecha

“…The fundus images were classified as without stage (Normal)/with Stage (ROP) by segmenting the ridge. Stages 1–3 were classified using machine Learning (ML) models. • This study aims to improve accuracy of Stages 1–3 classification and identify Pre-plus/ Plus disease using MultiCNN_LSTM networks. …”
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Soft Tissue Stability around Single Implants Inserted to Replace Maxillary Lateral Incisors: A 3D Evaluation by F. G. Mangano, F. Luongo, G. Picciocchi, C. Mortellaro, K. B. Park, C. Mangano

“…Twenty patients (8 males, 12 females) were selected, 10 with a failing/nonrestorable lateral incisor (test group: immediate placement in postextraction socket) and 10 with a missing lateral incisor (control group: conventional placement in healed ridge). Each patient received one immediately loaded implant (Anyridge®, Megagen, Gyeongbuk, South Korea). …”
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Examining different approaches for short-term load demand forecasting in microgrid management: a case study of a university in Nigeria by Barnabas Iliya Gwaivangmin

“…This study assessed three forecasting methodologies—Ridge Regression (RG), Autoregressive Integrated Moving Average (ARIMA), and Support Vector Machine (SVM) regression—for predicting electricity load demand in a microgrid at the University of Jos in Nigeria. …”
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Demographic disparities, service efficiency, safety, and user satisfaction in public bus transit system: A survey-based case study in the city of Charlotte, NC by Sanaz Sadat Hosseini, Babak Rahimi Ardabili, Mona Azarbayjani, Hamed Tabkhi

“…Statistical analyses, including GLM, POLR, CLM, and Multinomial Ridge Regression, identified significant factors influencing transit use and perceptions. …”
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