A Pilot Study on Thermal Comfort in Young Adults: Context-Aware Classification Using Machine Learning and Multimodal Sensors by Bibars Amangeldy, Timur Imankulov, Nurdaulet Tasmurzayev, Serik Aibagarov, Nurtugan Azatbekuly, Gulmira Dikhanbayeva, Aksultan Mukhanbet

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Analyzing customer churn behavior using datamining approach: hybrid support vector machine and logistic regression in retail chain by Mohammad Barzegar, Aliakbar Hasani

“…Finally, the proposed model has been implemented as a case study in the chain store industry. …”
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Estimating comprehensive growth index for drip-irrigated spring maize in junggar basin via satellite imagery and machine learning by Mingjie Ma, Jinghua Zhao, Tingrui Yang, Feng Liu, Yingying Yuan, Shijiao Ma, Zikang Chang

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Identification of age-specific risk factors for hyperuricemia: a machine learning-driven stratified analysis in health examination cohorts by ChuXia Tan, Yuan Liu, Lijun Li, Ying Li, Pingting Yang, Yinglong Duan, Xingxing Wang, Huiyi Zhang, Jingying Wang, Honglian Zhang

“…Then, logistic regression (LR), random forest (RF) and eXtreme Gradient Boosting (XGBoost) models were constructed using Python 3.8.2, and rank the feature importance of the optimal model. …”
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Data-driven machine learning approaches for simultaneous prediction of peak particle velocity and frequency induced by rock blasting in mining by Yewuhalashet Fissha, Prashanth Ragam, Hajime Ikeda, N. Kushal Kumar, Tsuyoshi Adachi, P.S. Paul, Youhei Kawamura

“…In the context of the mining and civil industry, the application of this study offers significant potential for enhancing safety protocols and optimizing operational efficiency. By employing machine learning models, this research aims to accurately predict and assess ground vibrations with frequency resulting from rock blasting.…”
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Identification and validation of glucocorticoid receptor and programmed cell death-related genes in spinal cord injury using machine learning by Feng Lu, Yingying Liu, Zhen Chen, Shuning Chen, Weidong Liang, Fuzhou Hua, Maolin Zhong, Lifeng Wang

“…A total of 113 diagnostic models were developed through 12 machine learning algorithms, with the optimal model, “Lasso + Stepglm[both],” featuring six genes: Abca1, Cdh1, Glipr1, Glt8d2, Il10ra, and Pde5a. …”
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Developing an Urban Landscape Fumigation Service Robot: A Machine-Learned, Gen-AI-Based Design Trade Study by Prithvi Krishna Chittoor, Bhanu Priya Dandumahanti, Prabakaran Veerajagadheswar, S. M. Bhagya P. Samarakoon, M. A. Viraj J. Muthugala, Mohan Rajesh Elara

“…This study proposes a machine-learned multimodal and feedback-based variational autoencoder (MMF-VAE) model that incorporates a readily available spraying robot dataset and includes design considerations from various research efforts to ensure real-time deployability. …”
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Integrating machine learning and reliability analysis: A novel approach to predicting heavy metal removal efficiency using biochar by Mohammad Sadegh Barkhordari, Chongchong Qi

“…This research introduces an advanced machine learning (ML) framework, utilizing deep forest (DF) algorithms, to predict and optimize the efficiency HM removal through biochar applications. …”
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Identification and verification of mitochondria-related genes biomarkers associated with immune infiltration for COPD using WGCNA and machine learning algorithms by Meijuan Peng, Chen Jiang, Ziyu Dai, Bin Xie, Qiong Chen, Jianing Lin

“…We utilized the limma package and Weighted Gene Co-expression Network Analysis (WGCNA) to analyze datasets from the Gene Expression Omnibus (GEO) database (GSE57148), identifying 12 key differentially expressed mitochondrial genes (MitoDEGs). Using 12 distinct machine learning algorithms (comprising 143 predictive models), we identified the optimal diagnostic model, which includes five pivotal MitoDEGs: ERN1, FASTK, HIGD1B, NDUFA7 and NDUFB7. …”
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A machine learning approach of exploring nexus between geopolitical risk and Indian economic uncertainty: a comparative review by Bharat Kumar Meher, Abhishek Anand, Virgil Popescu, Ramona Birau, Iuliana Carmen Bărbăcioru

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Machine Learning Aided Tapered Four-Port MIMO Antenna for V2X Communications With Enhanced Gain and Isolation by Nagesh Kallollu Narayanaswamy, Yazeed Alzahrani, Krishna Kanth Varma Penmatsa, Ashish Pandey, Ajay Kumar Dwivedi, Vivek Singh, Manoj Tolani

“…By leveraging machine learning, the final design was achieved more efficiently, significantly reducing the simulation time and enabling more precise parameter tuning for optimal performance. …”
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Machine Learning-Driven Radiomics Analysis for Distinguishing Mucinous and Non-Mucinous Pancreatic Cystic Lesions: A Multicentric Study by Neus Torra-Ferrer, Maria Montserrat Duh, Queralt Grau-Ortega, Daniel Cañadas-Gómez, Juan Moreno-Vedia, Meritxell Riera-Marín, Melanie Aliaga-Lavrijsen, Mateu Serra-Prat, Javier García López, Miguel Ángel González-Ballester, Maria Teresa Fernández-Planas, Júlia Rodríguez-Comas

“…This study aims to improve PCL evaluation by developing and validating a radiomics-based software tool leveraging machine learning (ML) for lesion classification. The model categorizes PCLs into mucinous and non-mucinous types using a custom dataset of 261 CT examinations, with 156 images for training and 105 for external validation. …”
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Validated visual features of Multi-Perspective imagery with Explainable Machine learning for detecting rural vacant courtyards in North China by Zehao Qiao, Maojun Wang, Xuexia Zhang, Juanjuan Zhao, Xiaojie Zhang, Tao Liu, Guangzhong Cao

“…We constructed a systematic set of visual features and employed an interpretable machine learning (XGBoost model) to detect courtyard utilisation status. …”
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Identifying and Validating Prognostic Hyper-Inflammatory and Hypo-Inflammatory COVID-19 Clinical Phenotypes Using Machine Learning Methods by Ji X, Guo Y, Tang L, Gao C

“…These phenotypes can be accurately recognized using machine learning models, with the AdaBoost model being optimal for predicting in-hospital mortality. …”
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A machine learning based radiomics approach for predicting No. 14v station lymph node metastasis in gastric cancer by Tingting Ma, Tingting Ma, Tingting Ma, Tingting Ma, Tingting Ma, Mengran Zhao, Mengran Zhao, Mengran Zhao, Mengran Zhao, Mengran Zhao, Xiangli Li, Xiangchao Song, Xiangchao Song, Xiangchao Song, Xiangchao Song, Lingwei Wang, Lingwei Wang, Lingwei Wang, Lingwei Wang, Zhaoxiang Ye, Zhaoxiang Ye, Zhaoxiang Ye, Zhaoxiang Ye

“…A total of 1,316 radiomics feature were extracted from portal venous phase images of CECT. Seven machine learning (ML) algorithms including naïve Bayes (NB), k-nearest neighbor (KNN), decision tree (DT), logistic regression (LR), random forest (RF), eXtreme gradient boosting (XGBoost) and support vector machine (SVM) were trained for development of optimal radiomics signature. …”
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Machine learning-based prediction of carotid intima–media thickness progression: a three-year prospective cohort study by An Zhou, Kui Chen, Kui Chen, Yonghui Wei, Qu Ye, Qu Ye, Yuanming Xiao, Rong Shi, Jiangang Wang, Wei-Dong Li

“…Model performance was assessed through discrimination (AUC, sensitivity, specificity) and calibration metrics, with Platt scaling applied to optimize probability estimates. …”
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Prediction of BTEX concentrations in the air of Southern East Azerbaijan province, Iran using ensemble machine learning and feature analysis by Mansour Baziar, Negar Jafari, Ali Oghazyan, Amir Mohammadi, Ali Abdolahnejad, Ali Behnami

“…To further optimize the stacking ensemble, CatBoost, a high-performing model not included in LazyRegressor, was incorporated. …”
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Identification of Risk Group for Root Caries and Analysis of Associated Factors in Older Adults Using Unsupervised Machine Learning Clustering by Jiang L, Huang S, Reissmann DR, Schmalz G, Li J

“…The identified factors, revealed through unsupervised machine learning, can facilitate personalized prevention and management strategies for root caries in older adults.Keywords: older adults, oral health, risk analysis, machine learning…”
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HERCULE: High-Efficiency Resource Coordination Using Kubernetes and Machine Learning in Edge Computing for Improved QoS and QoE by Garrik Brel Jagho Mdemaya, Miguel Landry Foko Sindjoung, Milliam Maxime Zekeng Ndadji, Mthulisi Velempini

“…In this work, we propose a smart Kubernetes scheduling solution that embeds a machine learning model into the Kube-scheduler for more effective application deployment. …”
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Machine learning based clinical decision tool to predict acute kidney injury and survival in therapeutic hypothermia treated neonates by Elif Keles, Syed Yaseen Ali, Pia Wintermark, Pieter Annaert, Floris Groenendaal, Suzan Şahin, Mehmet Yekta Öncel, Didem Armangil, Esin Koc, Malcolm R. Battin, Alistair J. Gunn, Adam Frymoyer, Valerie Chock, Djalila Mekahli, John van den Anker, Anne Smits, Karel Allegaert, Ulas Bagci

“…Our study aimed to utilize machine learning (ML) methods to predict the outcome of TH-treated NE neonates developing AKI and death during TH. …”
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