Machine Learning Sensors for Diagnosis of COVID-19 Disease Using Routine Blood Values for Internet of Things Application by Andrei Velichko, Mehmet Tahir Huyut, Maksim Belyaev, Yuriy Izotov, Dmitry Korzun

“…Healthcare digitalization requires effective applications of human sensors, when various parameters of the human body are instantly monitored in everyday life due to the Internet of Things (IoT). …”
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Machine learning models for predicting interaction affinity energy between human serum proteins and hemodialysis membrane materials by Simin Nazari, Amira Abdelrasoul

“…The application of machine learning in predicting affinity energy holds significant promise for researchers and professionals in hemodialysis. …”
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Synergistic bioinformatics and sophisticated machine learning unveil ferroptosis-driven regulatory pathways and immunotherapy potential in breast carcinoma by Lei Xia, Zhen Ye, Man Zheng, Zhaofeng Tan

“…Conclusions The integration of bioinformatics and machine learning in this study underscores a strong correlation between FRG expression patterns and BRCA prognosis, affirming their potential as precise biomarkers for personalized immunotherapy.…”
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An effectiveness of machine learning models for estimate the financial cost of assistive services to disability care in the Kingdom of Saudi Arabia by Obaid Algahtani, Mohammed M. A. Almazah, Farouq Alshormani

“…Next, an ensemble of machine learning (ML) models comprises three classifiers such as hybrid kernel extreme learning machine (HKELM), extreme gradient boosting (XGBoost), and support vector regression (SVR) for predicting the financial cost. …”
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PERFORMANCE PREDICTION OF ROADHEADERS USING SUPPORT VECTOR MACHINE (SVM), FIREFLY ALGORITHM (FA) AND BAT ALGORITHM (BA) by Arash Ebrahimabadi, Alireza Afradi

“…The primary objective of this study is to develop models that can predict the Instantaneous Cutting Rate (ICR), which is defined as the production rate during the actual cutting period (measured in tons or cubic meters per cutting hour), based on the properties of the rock formations being excavated as well as machine parameters. In this research, the Instantaneous Cutting Rate of roadheaders at the Tabas coal mine was analyzed by examining the characteristics of both the rock and the machinery involved. …”
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Typhoon and Storm Surge Hazard Analysis Along the Coast of Zhejiang Province in China Using TCRM and Machine Learning by Yong Fang, Xiangyu Li, Yanhua Sun, Ailian Li, Yunxia Guo

“…Firstly, the input database of the TCRM has been updated and subsequently used to generate a 1000-year synthetic typhoon event catalog for the Northwest Pacific region. Secondly, four machine learning models—Long Short-Term Memory (LSTM), Back Propagation (BP), Support Vector Regression (SVR), and Random Forest (RF)—were developed to forecast storm surge component at the four sites, with sensitivity analysis conducted on the input parameters. …”
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Precision soil sampling strategy for the delineation of management zones in olive cultivation using unsupervised machine learning methods by John-Nick Bougiouklis, Pantelis E. Barouchas, Panagiotis Petropoulos, Dimitrios E. Tsesmelis, Nicholas Moustakas

“…PCA is a method that can help in the selection of critical parameters for the delineation of management zones. …”
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An interpretable stacking machine-learning model to predict the hot torsion flow characteristics of a micro-alloyed steel by Hojjat Emami, Mehdi Shaban Ghazani

“…The current work investigates the hot torsion behavior of Ti-Nb micro-alloyed steel at temperatures from 850 to 1100∘C and strain rates from 0.01 to 1 s−1. However, existing machine learning (ML) models often lack the robustness and generalizability needed to predict flow stress across diverse processing parameters accurately. …”
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Implementing a novel TOPSIS-sine cosine algorithm-based hybrid optimization in machining medium-hardened steel by Ramanuj Kumar, Mohammad Rafighi, Oğur İynen, M. Prem Kumar Reddy, Shrikant Zade, Mustafa Özdemir, Anish Pandey, Ramanpreet Singh

“…Abstract Machining medium-hardened steel is particularly challenging because of its high strength and wear resistance, which generate excessive cutting temperatures. …”
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A hybrid machine learning model with attention mechanism and multidimensional multivariate feature coding for essential gene prediction by Wu Yan, Fu Yu, Li Tan, Li Mengshan, Xie Xiaojun, Zhou Weihong, Sheng Sheng, Wang Jun, Wu Fu-an

“…Therefore, the identification of essential genes is significant. Machine learning has become the mainstream approach for essential gene prediction. …”
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Machining-Induced Burr Suppression in Edge Trimming of Carbon Fibre-Reinforced Polymer (CFRP) Composites by Tool Tilting by Tamás Sándor Tima, Norbert Geier

“…Several challenges arise during edge trimming of carbon fibre-reinforced polymer (CFRP) composites, such as the formation of machining-induced burrs and delamination. In a recent development, appropriate-quality geometric features in CFRPs can be machined using special cutting tools and optimised machining parameters. …”
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Ecological Suitability Assessment Methods of Waste Pile-Up along Railway Routes Based on Machine Learning Algorithms by Cuicui Ji, Zaoyang Huang, Xiangjun Pei, Bin Sun, Lichuan Chen, Dan Liang, Yanfei Kang

“…To develop ESWP maps, we employed Landsat 8, digital elevation model (DEM), soil database, land use, and meteorological data. We tested 3 machine learning methods—random forest (RF), deep neural network (DNN), and extreme gradient boosting (XGBoost)—using 7 key indicators as input parameters. …”
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Exploring the link between the ZJU index and sarcopenia in adults aged 20–59 using NHANES and machine learning by Huan Chen, Ning Du, Hong Xiao, Zhao Wang

“…To improve risk stratification and identify key predictors, machine learning techniques—including Random Forest, SHAP, and the Boruta algorithm—were applied. …”
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A Distributed Machine Learning-Based Scheme for Real-Time Highway Traffic Flow Prediction in Internet of Vehicles by Hani Alnami, Imad Mahgoub, Hamzah Al-Najada, Easa Alalwany

“…We train and validate a local Random Forest Regression (RFR) model for each vehicle’s cluster (highway-segment) using six different hyper parameters. Due to the variance of traffic flow patterns between segments, we build a global Distributed Machine Learning Random Forest (DMLRF) regression model to improve the system performance for abnormal traffic flows. …”
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Advanced machine learning techniques for predicting compressive strength and ultrasonic pulse velocity of concrete incorporating industrial by-products by Ehsan Mohsennia, Alireza Javid, Vahab Toufigh

“…This study introduces a novel, dual-stage machine learning framework that uniquely integrates both physical mix parameters and detailed chemical compositions of cement and multiple industrial by-products (IBPs)—including fly ash, GGBFS, silica fume, and metakaolin—into a unified predictive model. …”
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Evaluation of the predictors of tooth loss using artificial intelligence-based machine learning approach: A retrospective study by Amit Rajabhau Pawar, Sankari Malaiappan, Pradeep Kumar Yadalam, P R Ganesh

“…In this study, we examined the potential of machine learning to predict tooth loss based on diverse parameters, including age, systemic diseases (such as diabetes and hypertension), grades of tooth mobility, oral hygiene habits, and more. …”
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Design optimization and generating characteristics of a linear arc PM vernier machine for wave energy conversion system by Urooj Jadoon, Faisal Khan, Salar Ahmad Khalil, Hatim Alwadie, Adoalateef Alzhrani

“…This research introduces novel linear arc machine topologies designed specifically for wave energy conversion systems to address critical challenges such as substantial cogging force, low power output, and size constraints. …”
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Quality Evaluation Method for Base Baijiu Based on Support Vector Machine Optimized by Genetic and Bootstrap Aggregating Algorithm by PANG Tingting, ZHANG Guiyu, LIU Kecai, LI Xiaoping, TUO Xianguo, PENG Yingjie, ZENG Xianglin

“…Finally, GA was used to optimize the parameters (C, γ, and N) of the Bagging-SVM classifier to construct a GA-Bagging-SVM model. …”
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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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All-Cause Mortality Prediction in Subjects with Diabetes Mellitus Using a Machine Learning Model and Shapley Values by Oana Mirea, Mostafa Ghelich Oghli, Oana Neagoe, Mihaela Berceanu, Eugen Țieranu, Liviu Moraru, Victor Raicea, Ionuț Donoiu

“…Identifying risk factors for mortality in these patients is crucial, as early recognition can facilitate prompt therapeutic intervention. Machine learning (ML) models have proved to be valuable tools in different scenarios of healthcare decision making. …”
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