Uncovering employee insights: integrative analysis using structural topic modeling and support vector machines by Kai Ding, Ruihong Li, Zeyu Li, Shangui Hu

“…This study proposes a novel approach by integrating Structural Topic Modeling (STM) analysis with Support Vector Machine (SVM) techniques to scrutinize the robustness of STM findings, particularly concerning the relative significance of extracted topics. …”
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A Regularized Vector Autoregressive Hidden Semi-Markov model, with Application to Multivariate Financial Data by Zekun Xu, Ye Liu

“…However, HMM's implicit assumption that the state duration follows a geometric distribution is too strong to hold in practice. In this work, we propose a regularized vector autoregressive hidden semi-Markov model to analyze multivariate financial time series. …”
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A weather-driven mathematical model of Culex population abundance and the impact of vector control interventions by Suman Bhowmick, Patrick Irwin, Kristina Lopez, Megan Lindsay Fritz, Rebecca Lee Smith

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Research on wind temperature prediction of tunneling working site based on PSO−SVR by Yanhe LI, Zhijun WAN, Zhenzi YU, Hong GOU, Wanli ZHAO, Jiale ZHOU, Peng SHI, Zheng ZHEN, Yuan ZHANG

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Mathematical Modeling and Analysis of Different Vector Controlled CSI Fed 3-Phase Induction Motor Drive by Arul Prasanna Mark, Rajasekaran Vairamani, Gerald Christopher Raj Irudayaraj

“…The objective of this work is to compare the dynamic performances of the vector control methods for CSI fed IM drives. …”
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Main vectors in the pedagogical training of residents and postgraduate students of a medical university by N. P. Vanchakova, E. A. Vatskel, V. A. Denishenko

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Predicting the Event Types in the Human Brain: A Modeling Study Based on Embedding Vectors and Large-Scale Situation Type Datasets in Mandarin Chinese by Xiaorui Ma, Hongchao Liu

“…This work constructs datasets of considerable semantic complexity, comprising a substantial volume of verbs along with their feature vectors and situation type labels, which can be used for evaluating large language models in the future.…”
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Temporal Analysis of Climate Change Impact on the Spread and Prevalence of Vector-Borne Diseases in Campania (2018–2023) by Federica Gargano, Roberta Brunetti, Marialuisa Buonanno, Claudio De Martinis, Lorena Cardillo, Pellegrino Fenizia, Antonietta Anatriello, Giuseppe Rofrano, Luigi Jacopo D’Auria, Giovanna Fusco, Loredana Baldi, Esterina De Carlo, Maria Ottaiano

“…Vector-borne infections (Arbovirosis) represent a significant threat to public health worldwide. …”
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Prediction of the anti-carbonation performance of concrete based on random forest – least squares support vector machine model by Sivaraja M., Swaminathen A. N., Kuttimarks M. S., Rajprasad J., Sakthivel M., Rex J.

“…In this study, a novel hybrid model combining random forest (RF) regression with a least squares support vector machine (LSSVM) is proposed to enhance the accuracy of ACP predictions. …”
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Reciprocal Power-fed System for MW-level PMSG Based on Vector Control by NAN Yonghui, LUO Renjun, PENG Bo, CAO Yang

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Safety Status Prediction Model of Transmission Tower Based on Improved Coati Optimization-Based Support Vector Machine by Xinxi Gong, Yaozhong Zhu, Yanhai Wang, Enyang Li, Yuhao Zhang, Zilong Zhang

“…Subsequently, we employ the improved coati optimization algorithm (ICOA) to refine the penalty parameters and kernel function of the support vector machine (SVM), thereby developing the safety state prediction model for the transmission tower. …”
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KPMapNet: Keypoint Representation Learning for Online Vectorized High-Definition Map Construction by Bicheng Jin, Wenyu Hao, Wenzhao Qiu, Shanmin Pang

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Control strategies in the spatial population dynamics of Aedes aegypti vector using sterile mosquitoes and insecticides by Carmen Alicia Ramírez Bernate, Héctor Jairo Martínez Romero, Diana Marcela Erazo Borja

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SVF: Support Vector Federation by Mirko Polato, Roberto Esposito, Lorenzo Sciandra

“…Simple random perturbation works remarkably well in practice, and indeed we provide a bound on the approximation error of the learnt model which goes to zero as the number of input features grows. …”
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Predicting Freeway Work Zone Delays and Costs with a Hybrid Machine-Learning Model by Bo Du, Steven Chien, Joyoung Lee, Lazar Spasovic

“…A hybrid machine-learning model, integrating an artificial neural network (ANN) and a support vector machine (SVM) model, is developed to predict spatiotemporal delays, subject to road geometry, number of lane closures, and work zone duration in different periods of a day and in the days of a week. …”
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Predicting the time to get back to work using statistical models and machine learning approaches by George Bouliotis, M. Underwood, R. Froud

“…Methods The Inspiring Families programme aims to support members of families with complex issues to return to work. We explored predictors of time to return to work with proportional hazards (Semi-Parametric Cox in Stata) and (Flexible Parametric Parmar-Royston in Stata) against the Survival penalised regression with Elastic Net penalty (scikit-survival), (conditional) Survival Forest algorithm (pySurvival), and (kernel) Survival Support Vector Machine (pySurvival). …”
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Membrane Filtration of Nanoscale Biomaterials: Model System and Membrane Performance Evaluation for AAV2 Viral Vector Clarification and Recovery by Mara Leach, Kearstin Edmonds, Emily Ingram, Rebecca Dutch, Ranil Wickramasinghe, Malgorzata Chwatko, Dibakar Bhattacharyya

“…This study examined the role of nanoscale biomaterials in optimizing viral vector clarification through a model system mimicking real AAV2 crude harvest material. …”
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Modeling of the Power Station Boiler Combustion Efficiency Considering Multiple Work Condition with Feature Selection by TANG Zhenhao, WU Xiaoyan, CAO Shengxian

“…It is difficult for power station boiler efficiency to measure precisely A datadriven modeling method is proposed to establish the boiler combustion efficiency model, according to the machine learning theories A classification and regression trees (CART) algorithm provides correlated variables which have significant relation with the boiler combustion efficiency by data analysis Then, a KNearest Neighbor (KNN) classifies the samples to distinguish the data from different work conditions Based on the classified data, a least square support vector machine (LSSVM) optimized by differential evolution (DE) algorithm is proposed to establish a datadriven model (DDMMF) The parameters of LSSVM are optimized dynamically by DE to improve the model accuracy Finally, the prediction model is corrected dynamically for further improvement of the prediction accuracy The experimental results based on actual production data illustrate that the proposed approach can predict the boiler combustion efficiency accurately, which meets the requirements of boiler control and optimization…”
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Automatic system of mutually invariant vector control of mixer technology state variables by Rudolf A. Neydorf, Mohammed Neamah Mohsen

“…The materials and the research results presented show that for an effective synthesis of the vector control laws of nonlinear multiply connected objects, the method of reference mathematical models can be applied along with the quasi-optimization speed performance of these laws.…”
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Comparative analysis of machine learning models for wind speed forecasting: Support vector machines, fine tree, and linear regression approaches by Yousef Altork

“…Wind speed is an important parameter of wind energy conversion, and its forecast is significant for optimal power generation and maintaining the stability of the electricity supply. In this work, three predictive models, namely Fine Tree, Support Vector Machine (SVM), and Linear Regression, are assessed using meteorological data from the National Wind Technology Center (NWTC) in Boulder, Colorado, for the period 2019–2023. …”
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