Employee loyalty evaluation using machine learning in technology-based small and medium-sized enterprises by Yong Shi, Yuan Wang, Hongkun zuo

“…Through several machine learning models and algorithms to predict employee loyalty, the feasibility of machine learning to predict employee loyalty is proved, and the evaluation of talent in TSMEs is supported by decision analysis. …”
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Embedding With Preservation of Semantics of the Original Data by M. E. Vatkin, D. A. Vorobey, M. V. Yakovlev, M. G. Krivova

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Interperiod coherent integration of the received signal with a variable repetition period of the probing signal by S. R. Heister, P. G. Semashko

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Assessing Similarity Between Datasets Using Vector Representations by А. А. Usatoff, A. M. Nedzved, Guo Jiran

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Using Machine Learning for Recognition of Alzheimer’s Disease Based on Transcription Information by U. A. Vishniakou, Chu Yue Yu

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An Advanced Hybrid Forecasting System for Wind Speed Point Forecasting and Interval Forecasting by Haipeng Zhang, Hua Luo

“…Therefore, in this paper, we developed a prediction system integrating an advanced data preprocessing strategy, a novel optimization model, and multiple prediction algorithms. …”
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Privacy-Preserving Machine Learning (PPML) Inference for Clinically Actionable Models by Baris Balaban, Seyma Selcan Magara, Caglar Yilgor, Altug Yucekul, Ibrahim Obeid, Javier Pizones, Frank Kleinstueck, Francisco Javier Sanchez Perez-Grueso, Ferran Pellise, Ahmet Alanay, Erkay Savas, Cetin Bagci, Osman Ugur Sezerman

“…Machine learning (ML) refers to algorithms (often models) that are learned directly from data, germane to past experience. …”
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Revealing key regulatory factors in lung adenocarcinoma: the role of epigenetic regulation of autophagy-related genes from transcriptomics, scRNA-seq, and machine learning by Xianchang Zeng, Lingyun Wei, Lu Lv, Di Wu, Yingying Shen, Xinliang Lu, Xianghui Kong, Zhijian Cai, Jianli Wang, Jianli Wang

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Exploring the role of ELOVLs family in lung adenocarcinoma based on bioinformatic analysis and experimental validation by Zihan Wang, Wenjing Cui, Long Liang, Jingge Qu, Yuqiang Pei, Danyang Li, Ying Luo, Yue Zhang, Yifan Qiu, Yongchang Sun

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Integrated DDPG-PSO energy management systems for enhanced battery cycling and efficient grid utilization by Oladimeji Ibrahim, Mohd Junaidi Abdul Aziz, Razman Ayop, Wen Yao Low, Nor Zaihar Yahaya, Ahmed Tijjani Dahiru, Temitope Ibrahim Amosa, Shehu Lukman Ayinla

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Heterogeneity in willingness to share personal health information: a nationwide cluster analysis of 20,000 adults in Japan by Miho Sassa, Akifumi Eguchi, Keiko Maruyama-Sakurai, Takanori Fujita, Yumi Kawamura, Takayuki Kawashima, Yuta Tanoue, Daisuke Yoneoka, Hiroaki Miyata, Takanori Yamashita, Naoki Nakashima, Shuhei Nomura

“…Clustering analysis using Uniform Manifold Approximation and Projection (UMAP) and Ordering Points to Identify the Clustering Structure (OPTICS) algorithms was performed to identify distinct patterns in sharing preferences. …”
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Machine learning classification of consumption habits of creatine supplements in gym goers by Patrícia C. Magalhães, Samuel Encarnação, Andre C. Schneider, Pedro Forte, José Teixeira, Antonio Miguel Monteiro, Tiago M. Barbosa, Ana M. Pereira

“…The aim is to identify usage patterns and the main factors that influence creatine supplementation, providing a basis for future educational interventions and recommendations for safe and effective use. …”
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Screening and Validation of Potential Biomarkers of Immune Cells in Childhood Asthma Patients via Mendelian Randomization and Machine Learning by Zhang Y, Hai Y, Song B, Xu J, Cao L, Yasen R, Xu W, Zhang J, Hu J

“…LASSO logistic regression and SVM algorithms were used to identify key genes associated with childhood asthma. …”
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Study on Simplified Model and Numerical Solution of High-Speed Angular Contact Ball Bearing by Guang Zeng, Chunjiang Zhao, Xiaokai Yu, Biao Sun, Zhigang Xiao, Qiang Bian, Bingyang Liu

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Design and structure of overlapping regions in PCA via deep learning by Yan Zheng, Xi-Chen Cui, Fei Guo, Ming-Liang Dou, Ze-Xiong Xie, Ying-Jin Yuan

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NeuralConstraints: integrating a neural generative model with constraint-based composition by Juan S. Vassallo, Örjan Sandred, Julien Vincenot

“…‘NeuralConstraints’ combines the predictive generative abilities of neural networks trained on symbolic musical data with an advanced backtracking constraint algorithm. …”
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Development of an interpretable model for foot soft tissue stiffness based on gait plantar pressure analysis by Xiaotian Bai, Xiao Hou, Dazhi Lv, Dazhi Lv, Jialin Wei, Jialin Wei, Yiling Song, Zhengyan Tang, Hongfeng Huo, Hongfeng Huo, Jingmin Liu

“…A backpropagation neural network, optimized by integrating particle swarm optimization and genetic algorithm, was constructed to predict foot soft tissue stiffness using plantar pressure data collected during walking. …”
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Estimating Trends in Cardiovascular Disease Risk for the EXPOSE (Explaining Population Trends in Cardiovascular Risk: A Comparative Analysis of Health Transitions in South Africa a... by Shaun Scholes, Jennifer S Mindell, Mari Toomse-Smith, Annibale Cois, Kafui Adjaye-Gbewonyo

“…Laboratory- and non–laboratory-based World Health Organization (WHO) and Globorisk algorithms were used to calculate the predicted 10-year total (fatal and nonfatal) CVD risk. …”
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Fluctuation Classification and Feature Factor Extraction to Forecast Very Short-Term Photovoltaic Output Powers by Mao Yang, Xiaoxuan Shen, Dawei Huang, Xin Su

“…Then, parallel factor algorithm is used to reduce prediction model redundancy, which can reduce high-dimensional numerical weather prediction feature matrix to extract relevant features. …”
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RATIONALE FOR PREVENTION OF CARDIO-CEREBRAL COMPLICATIONS IN THE METABOLIC SYNDROME BASED ON MATHEMATICAL FORECASTING TECHNIQUES by S. V. Chernavskii, A. N. Fursov, N. P. Potekhin, V. N. Yakovlev

“…The diagnostic and treatment algorithms developed based on the stratification of cardiovascular risk index, allow us to give sound recommendations for targeted prevention of cardio-cerebral complications.…”
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