Prediction of biological evolution following blood product transfusion during liver transplantation: the contribution of machine learning to decision-making by Jacques Creteur, Thibault Martinez, Valerio Lucidi, Turgay Tuna, Florian Blanchard, Olivier Duranteau, Benjamin Popoff, Axel Abels, Eric Savier, Patrizia Loi, Desislava Germanova, Anne Demulder

“…This study aimed to develop machine learning models to predict the biological effects of blood product transfusions, assisting clinicians in selecting optimal therapeutic combinations.Methods Using data from two cohorts over 20 years from two academic hospitals, 10 supervised machine learning models were trained and validated on four biomarkers: fibrinogen, haemoglobin, prothrombin time and activated partial thromboplastin time ratio. …”
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Hybrid feature selection for real-time road surface classification on low-end hardware: A machine learning approach by Cong Ngo Van, Duc-Nghia Tran, Ton That Long, Nguyen Gia Minh Thao, Duc-Tan Tran

“…One of the challenges in this field is using optimal datasets and classification models that meet real-time applications on low-end hardware devices. …”
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Virtual machine scheduling and migration management across multi-cloud data centers: blockchain-based versus centralized frameworks by Mohammad A. Altahat, Tariq Daradkeh, Anjali Agarwal

“…Scheduling is a vital technique used to manage Virtual Machines (VMs), enabling placement and migration between hosts located in the same or different data centers. …”
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Investigating the Nonlinear Relationship Between the Built Environment and Urban Vitality Based on Multi-Source Data and Interpretable Machine Learning by Wenhao Liu, Zhen Yang, Chen Gui, Gen Li, Hongyi Xu

“…In this study, we investigate the potential non-linear interactions between the built environment and urban vitality by employing an interpretable spatial machine learning framework that integrates the XGBoost model with the SHapley Additive exPlanations (SHAP) algorithm. …”
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Tannery shaving dust-based charcoal blended adsorbent for efficient heavy metal remediation: An experimental and machine learning approach by Md. Sabbir Hosen, Md. Sahariar Sahen, Hasan Ahmed, Md. Selim Reza, Pranta Bhowmik, Farzana Mim, Md. Badrul Islam, Md. Azizul Haque Khan Naim, Mohammad Majibur Rahman, Md. Mostafizur Rahman

“…Notably, these datasets were then enhanced with the machine learning model, specifically the Random Forest (RF), aimed at predicting the removal of HMs. …”
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Rapid diagnosis of bacterial vaginosis using machine-learning-assisted surface-enhanced Raman spectroscopy of human vaginal fluids by Xin-Ru Wen, Jia-Wei Tang, Jie Chen, Hui-Min Chen, Muhammad Usman, Quan Yuan, Yu-Rong Tang, Yu-Dong Zhang, Hui-Jin Chen, Liang Wang

“…Preliminary SERS spectral analysis revealed notable disparities in characteristic peak features. Multiple ML models were constructed and optimized, with the convolutional neural network (CNN) model achieving the highest prediction accuracy at 99%. …”
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Advanced machine learning and experimental studies of polypropylene based polyesters tribological composite systems for sustainable recycling automation and digitalization by Abrar Hussain, Jakob Kübarsepp, Fjodor Sergejev, Dmitri Goljandin, Irina Hussainova, Vitali Podgursky, Kristo Karjust, Himanshu S. Maurya, Ramin Rahmani, Maris Sinka, Diāna Bajāre, Anatolijs Borodiņecs

“…In this research work, the experimental and Python based Archard deep learning wear rate models are introduced regarding recycling automation and composite tribological systems optimization. …”
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Comparison between logistic regression and machine learning algorithms on prediction of noise-induced hearing loss and investigation of SNP loci by Jie Lu, Xinhao Lu, Yixiao Wang, Hengdong Zhang, Lei Han, Baoli Zhu, Boshen Wang

“…Compared to conventional LR, the evaluated ML models Generalized Regression Neural Network (GRNN), Probabilistic Neural Network (PNN), Genetic Algorithm-Random Forests (GA-RF) demonstrate superior performance and were considered to be the optimal models for processing large-scale SNP loci dataset. …”
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Design of mTCN framework for disaster prediction a fusion of massive machine type communications and temporal convolutional networks by M. Umadevi, J. Arun Kumar, S. Vishnu Priyan, C. Vivek

“…Lightweight edge-based TCNs enable localized anomaly detection, while federated learning ensures privacy-preserving collaborative model training across edge devices. Blockchain integration secures model updates and provides traceability. …”
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A Comparative Analysis of Buckling Pressure Prediction in Composite Cylindrical Shells Under External Loads Using Machine Learning by Hyung Gi Lee, Jung Min Sohn

“…The study highlights the critical role of machine learning in predicting buckling pressure, which is essential for ensuring structural integrity and optimizing performance in marine engineering and other applications involving composite materials.…”
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Digital mapping of soil organic carbon in the hilly and mountainous landscape of Indian Himalayan region employing machine-learning techniques by Justin George Kalambukattu, Suresh Kumar, Bappa Das, Trisha Roy

“…A feature ranking and variable selection protocol was used for the selection of optimal set of covariates prior to model development. …”
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Inverse design of high-strength medium-Mn steel using a machine learning-aided genetic algorithm approach by Jin-Young Lee, Seung-Hyun Kim, Hyun-Bin Jeong, KeunWon Lee, KiSub Cho, Young-Kook Lee

“…To develop medium-Mn steels with an ultimate tensile strength (UTS) exceeding 2 GPa and excellent ductility, we created a highly accurate UTS prediction machine learning (ML) model using a boosted decision tree model and 1520 dataset of tensile properties of medium-Mn steels with micro-alloying elements. …”
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End-Region Losses in High-Power Electrical Machines: Impact of Material Thickness on Eddy Current Losses in Clamping Structures by Walid Mohand Oussaid, Abdelmounaïm Tounzi, Raphaël Romary, Abdelkader Benabou, Walid Boughanmi, Daniel Laloy

“…The study’s results offer valuable guidance for optimizing clamping structure designs in high-power electrical machines by selecting materials and thicknesses that minimize losses while maintaining mechanical integrity.…”
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Estimation of Daylily Leaf Area Index by Synergy Multispectral and Radar Remote-Sensing Data Based on Machine-Learning Algorithm by Minhuan Hu, Jingshu Wang, Peng Yang, Ping Li, Peng He, Rutian Bi

“…A high-precision LAI estimation model for daylilies was constructed by optimizing feature combinations. …”
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Decision-making method for residual support force of hydraulic supports during pressurized moving under fragmented roof conditions in ultra-thin coal seams by ZHANG Chuanwei, ZHANG Gangqiang, LU Zhengxiong, LI Linyue, HE Zhengwei, GONG Lingxiao, HUANG Junfeng

“…The IDBO algorithm was further employed to optimize the hyperparameters of the DHKELM model, forming the IDBO-DHKELM model. …”
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Machine Learning-Based Shelf Life Estimator for Dates Using a Multichannel Gas Sensor: Enhancing Food Security by Asrar U. Haque, Mohammad Akeef Al Haque, Abdulrahman Alabduladheem, Abubakr Al Mulla, Nasser Almulhim, Ramasamy Srinivasagan

“…In this study, we present a novel IoT-based shelf life estimation system that integrates multichannel gas sensors and a lightweight machine learning model deployed on an edge device. …”
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Assessing Hwa-byung Vulnerability Using the Hwa-byung Personality Scale: a comparative study of machine learning approaches by Chan-Young Kwon, Boram Lee, Sung-Hee Kim, Seok Chan Jeong, Jong-Woo Kim

“…Objectives: To develop and compare machine learning models to classify individuals vulnerable to Hwa-byung (HB) using an existing HB personality scale and to evaluate the efficacy of these models in predicting HB vulnerability.Methods: We analyzed data from 500 Korean adults (aged 19-44) using HB personality and symptom scales. …”
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Estimation of Moderate-Resolution Snow Depth in Xinjiang With Enhanced-Resolution Passive Microwave and Reanalysis Data by Machine Learning Methods by Yongchang Yan, Yan Qin, Yongqiang Liu, Yubao Qiu, Yang Liu

“…Therefore, this study constructs and optimizes SD retrieval models using four machine learning algorithms, including extreme gradient boosting (XGBoost), light gradient-boosting machine (LightGBM), categorical boosting (CatBoost), and random forest (RF) combing enhanced-resolution passive microwave data. …”
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ADMET evaluation in drug discovery: 21. Application and industrial validation of machine learning algorithms for Caco-2 permeability prediction by Dong Wang, Jieyu Jin, Guqin Shi, Jingxiao Bao, Zheng Wang, Shimeng Li, Peichen Pan, Dan Li, Yu Kang, Tingjun Hou

“…The transferability of machine learning models trained on publicly available data to internal pharmaceutical industry datasets was also investigated. …”
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Prediction of new-onset migraine using clinical-genotypic data from the HUNT Study: a machine learning analysis by Fahim Faisal, Antonios Danelakis, Marte-Helene Bjørk, Bendik Winsvold, Manjit Matharu, Parashkev Nachev, Knut Hagen, International Headache Genetics Consortium, Erling Tronvik, Anker Stubberud

“…Several standard machine learning architectures were constructed, trained, optimized and scored with area under the receiver operating characteristics curve (AUC). …”
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