CLU, FOS, and CXCL8 as diagnostic biomarkers for heart failure progression post-acute myocardial infarction: an integrated RNA-Seq and multi-machine learning study by Jingjing Wei, Peng Yu, Peng Yu, Yucai Hu, Lijie Qiao, Lijie Qiao, Bin Li, Bin Li, Haitao Li, Shiyang Xie, Zhengwei Dong, Aolong Wang, Yilin Zhang, Xinlu Wang, Yongxia Wang, Mingjun Zhu

“…Subsequently, candidate biomarkers were identified using machine learning and the MCODE plugin, with ROC used to describe the accuracy. …”
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Cardiometabolic index predicts cardiovascular events in aging population: a machine learning-based risk prediction framework from a large-scale longitudinal study by Yuanxi Luo, Yuanxi Luo, Zhiyang Yin, Xin Li, Xin Li, Chong Sheng, Ping Zhang, Dongjin Wang, Dongjin Wang, Yunxing Xue

“…Sex-stratified analyses suggested differential predictive patterns between gender subgroups. Given CMI’s robust and consistent predictive capability for stroke outcomes, we developed a machine learning-derived nomogram incorporating five key predictors: age, CMI, hypertension status, high-sensitivity C-reactive protein (hsCRP) and renal function (measured as serum creatinine). …”
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Beyond Linearity: Uncovering the Complex Spatiotemporal Drivers of New-Type Urbanization and Eco-Environmental Resilience Coupling in China’s Chengdu–Chongqing Economic Circle with... by Caoxin Chen, Shiyi Wang, Meixi Liu, Ke Huang, Qiuyi Guo, Wei Xie, Jiangjun Wan

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Intelligent prediction of thyroid cancer in China based on GBD data and hospital electronic medical records: disease burden analysis combined with multiple machine learning models by Lina Yang, Shixia Zhang, Xinguo Wang, Jianjun Yang, Mengya Chen

“…This study aims to conduct an in-depth analysis of the disease burden pattern and future trends of thyroid cancer in China, and constructed an intelligent prediction model in combination with hospital electronic medical record data. …”
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A Comparative Study of Machine Learning Techniques for Predicting Mechanical Properties of Fused Deposition Modelling (FDM)-Based 3D-Printed Wood/PLA Biocomposite by Prashant Anerao, Atul Kulkarni, Yashwant Munde, Namrate Kharate

“…Four distinct machine learning algorithms have been selected for predictive modeling: Linear Regression, Support Vector Machine (SVM), eXtreme Gradient Boosting (XGBoost), and Adaptive Boosting (AdaBoost). …”
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Research on feature extraction and intelligent diagnosis method of reciprocating compressor bearing clearance fault by Feng Yan

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Recognition of Acoustic Signals of Loaded Synchronous Motor Using FFT, MSAF-5 and LSVM by Adam GLOWACZ

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Ukrainian Folk Ornaments in Modern Knitting by Liudmyla Melnyk, Olena Kyzymchuk, Liudmyla Zubkova

“…However, the use of a knitting machine allows creating a pattern during the item production. …”
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Decoding event-related potentials: single-dose energy dietary supplement acts on earlier brain processes than we thought by Karina J. Maciejewska

“…IntroductionThis paper describes an experimental work using machine learning (ML) as a “decoding for interpretation” to understand the brain’s physiology better.MethodsMultivariate pattern analysis (MVPA) was used to decode the patterns of event-related potentials (ERPs, brain responses to stimuli) in a visual oddball task. …”
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Empirical studies on the impact of filter‐based ranking feature selection on security vulnerability prediction by Xiang Chen, Zhidan Yuan, Zhanqi Cui, Dun Zhang, Xiaolin Ju

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A systematic review of multi-mode analytics for enhanced plant stress evaluation by Abdolrahim Zandi, Seyedali Hosseinirad, Hossein Kashani Zadeh, Kouhyar Tavakolian, Byoung-Kwan Cho, Fartash Vasefi, Moon S. Kim, Pantea Tavakolian, Pantea Tavakolian

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Real-time mobile broadband quality of service prediction using AI-driven customer-centric approach by Ayokunle A. Akinlabi, Folasade M. Dahunsi, Jide J. Popoola, Lawrence B. Okegbemi

“…The highlighted gap can be addressed by machine learning (ML), as it has been effectively used in the past to support the analysis and knowledge discovery of communication systems’ traffic data through identification of intricate and hidden patterns. …”
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Advances in Neural Network assisted Tool Pressure Prediction by Göltl Florian, Harst Felix, Birkert Arndt, Stache Nicolaj C.

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Detecting Transit Deserts Through a Blend of Machine Learning (ML) Approaches, Including Decision Trees (DTs), Logistic Regression (LR), and Random Forest (RF) in Lucknow by Alok Tiwari

“…This study advances ML-driven ITS analytics, offering a novel approach for classifying transit accessibility patterns at a granular level, thereby aiding policy interventions for improved urban mobility.…”
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A comparative study of machine learning algorithms for fall detection in technology-based healthcare system: Analyzing SVM, KNN, decision tree, random forest, LSTM, and CNN by Afuan Lasmedi, Isnanto R. Rizal

“…The superiority of CNN and LSTM in detecting more complex fall patterns aligns with previous studies emphasizing the capabilities of deep learning models in sensor data classification. …”
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Optimizing Power Forecasting Models with Customized Features for Academic and Industrial Buildings by David Cabezuelo, Izar Lopez-Ramirez, June Urkizu, Ander Goikoetxea

“…This study investigates the impact of data collection frequency and model selection on the predictive accuracy of power consumption in two distinct building types: an Academic one with 15-min interval data and an Industrial one with hourly data. Various machine learning models, including Support Vector Machine (SVM) with Radial and Sigmoid kernels, Random Forest (RF), and Deep Neural Networks (DNNs), across different data splits and feature sets, were considered. …”
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Investigating the contributory factors influencing speeding behavior among long-haul truck drivers traveling across India: Insights from binary logit and machine learning technique... by Balamurugan Shandhana Rashmi, Sankaran Marisamynathan

“…This study aims to develop a prediction model for speeding behavior and to identify the contributory factors and their influential patterns underlying speeding behavior among LHTDs in India. …”
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Wear Characteristics and Optimization Measures of Disc Cutters During Large-Diameter Slurry Tunnel Boring Machine Advancing in Soil-Rock Composite Strata: A Case Study by Yingran Fang, Xinggao Li, Yinggui Cao, Hongzhi Liu, Yidong Guo

“…The large-diameter slurry tunnel boring machine (TBM) is widely used in the construction of tunnels across rivers and seas. …”
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The Detection of Past and Future Land Use and Land Cover Change in Ugam Chatkal National Park, Uzbekistan, Using CA-Markov and Random Forest Machine Learning Algorithms by Bokhir Alikhanov, Bakhtiyor Pulatov, Luqmon Samiev

“…Utili-zing advanced CA-Markov and Random Forest machine learning algorithms, it meticulously analyzes historical data to understand past trends and projects future LULC changes. …”
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Analysis of vegetation dynamics from 2001 to 2020 in China's Ganzhou rare earth mining area using time series remote sensing and SHAP-enhanced machine learning by Lei Ming, Yuandong Wang, Guangxu Liu, Lihong Meng, Xiaojie Chen

“…This study addresses this gap by employing time series remote sensing and SHAP-enhanced machine learning to analyze vegetation dynamics in China's Ganzhou rare earth mining area from 2001 to 2020. …”
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