Enhanced water saturation estimation in hydrocarbon reservoirs using machine learning by Ali Akbari, Ali Ranjbar, Yousef Kazemzadeh, Dmitriy A. Martyushev

“…In this study, a comprehensive dataset consisting of 30,660 independent data points was utilized to develop machine learning (ML) models for Sw prediction. Nine well log parameters—Depth (DEPT), High-Temperature Neutron Porosity, True Resistivity, Computed Gamma Ray, Spectral Gamma Ray, Hole Caliper, Compressional Sonic Travel Time, Bulk Density, and Temperature—were used as input features to train and test five ML algorithms: Linear Regression, Support Vector Machine (SVM), Random Forest, Least Squares Boosting, and Bayesian methods. …”
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Calculation of the Respiratory Modulation of the Photoplethysmogram (DPOP) Incorporating a Correction for Low Perfusion by Paul S. Addison, Rui Wang, Scott J. McGonigle, Alberto A. Uribe, Sergio D. Bergese

“…It has been proposed as a noninvasive surrogate for pulse pressure variation (PPV) used in the prediction of the response to volume expansion in hypovolemic patients. …”
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Mir-494-3p enhances aggressive phenotype of non-small cell lung cancer cells by regulating SET/I2PP2A by Yuwen Du, Taisuke Kajino, Yukako Shimada, Takashi Takahashi, Ayumu Taguchi

“…Integration of RNA sequencing analysis of NSCLC cells with miR-494-3p inhibition and a bioinformatic search of miRNA target prediction algorithms resulted in identification of SET/I2PP2A as a direct target of miR-494-3p. …”
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Myoelectric Control in Rehabilitative and Assistive Soft Exoskeletons: A Comprehensive Review of Trends, Challenges, and Integration with Soft Robotic Devices by Alejandro Toro-Ossaba, Juan C. Tejada, Daniel Sanin-Villa

“…Key contributions include critically evaluating machine learning-based motion prediction, model-free adaptive control methods, and real-time validation strategies to enhance rehabilitation outcomes. …”
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Combination of Transformed-means Clustering and Neural Networks for Short-Term Solar Radiation Forecasting by M. Ghayekhloo, M. B. Menhaj

“…The proposed Transformed-Means clustering method is based on inverse data transformation and K-means algorithm that presents moreaccurate clustering results when compared to the K-Means algorithm; its improved version and also otherpopular clustering algorithms. …”
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A systematic review on artificial intelligence approaches for smart health devices by Lerina Aversano, Martina Iammarino, Ilaria Mancino, Debora Montano

“…In this context, machine learning (ML) and deep learning (DL) are emerging as successful technologies because they are suitable for application to advanced analysis and prediction of healthcare scenarios. Therefore, the objective of this work is to contribute to the current state of the literature by identifying challenges, best practices, and future opportunities in the field of smart health. …”
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Identification of the immune infiltration and biomarkers in ulcerative colitis based on liquid–liquid phase separation-related genes by Zhixing Hong, Shilin Fang, Haihang Nie, Jingkai Zhou, Yuntian Hong, Lan Liu, Qiu Zhao

“…We identified the hub LLPS-RGs (DE-LLPS-RGs) (HSPB3, SLC16A1, TRIM22, SRI, PLEKHG6, GBP1, PADI2) by machine learning algorithms. Hub genes were screened that displayed high prediction accuracy of UC patients. …”
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Assessment of seasonal variations in the air quality index (2019–2022) in Al-Jahra city, Kuwait by Alsowaidan S., Al-Hurban A., Alsaber A.

“…This study is designed to (1) evaluate overall and seasonal changes in pollutants, (2) investigate the correlation for PM10 and PM2.5 and other pollutants during each season, and (3) examine the best model for prediction of air pollutant concentration of PM10 and PM2.5. …”
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Deep Learning-Driven Geospatial Modeling of Elderly Care Accessibility: Disparities Across the Urban-Rural Continuum in Central China by Yi Yu, Tian Dong

“…At the 15-min walking threshold, building space ratio and transportation infrastructure coverage positively influenced walking convenience, while sky view ratio showed a negative correlation. (3) A random forest-based location prediction framework identified multiple service gaps in existing facilities. …”
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Improved stereo matching network based on dense multi-scale feature guided cost aggregation by ZHANG Bo, ZHANG Meiling, LI Xue, ZHU Lei

“…To further improve the disparity prediction accuracy of stereo matching algorithm in the ill-posed regions such as repeating textures, no texture, and edge, an improved dense multi-scale feature guided aggregation network (DGNet) based on PSMNet was proposed. …”
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Discovery, design, and engineering of enzymes based on molecular retrobiosynthesis by Ancheng Chen, Xiangda Peng, Tao Shen, Liangzhen Zheng, Dong Wu, Sheng Wang

“…Breakthroughs in AI‐based structural prediction methods are expected to significantly improve the accuracy of enzyme discovery. …”
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Color image reversible data hiding based on adaptive capacity allocation and pixel value similarity distance sorting by Ningxiong Mao, Chunping Ouyang, Yaping Wan, Lingfeng Qu, Yuan Yuan

“…Abstract Most existing reversible data hiding (RDH) algorithms for color images adopt a fixed-ratio capacity allocation approach, often resulting in allocation errors. …”
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Integrated bioinformatics, machine learning, and molecular docking reveal crosstalk genes and potential drugs between periodontitis and systemic lupus erythematosus by Junjie Wang, Qingao Deng, Lu Qi

“…We employed multi-omics methodologies, encompassing differential expression analysis, weighted gene co-expression network analysis (WGCNA), functional enrichment (GO/KEGG), LASSO regression, diagnostic model construction, protein-protein interaction (PPI) networks, immune infiltration profiling, computational drug prediction, molecular docking, and disease subtyping, to analyze PD and SLE expression datasets from the Gene Expression Omnibus (GEO) database (GSE10334, GSE16134, GSE50772, and GSE81622). …”
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Radiomic Feature Extraction Based on Computed Tomography for Stroke Patients by Alamgir Hossain, Jolly Sultana, Shahed Hossain, Hafizur Rahman

“…To improve diagnosis, classification, and prediction for the best possible patient outcomes, this study investigates how radiomics might improve computed tomography (CT)-based stroke evaluation. …”
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Multi-Feature Long Short-Term Memory Facial Recognition for Real-Time Automated Drowsiness Observation of Automobile Drivers with Raspberry Pi 4 by Michael Julius R. Moredo, James Dion S. Celino, Joseph Bryan G. Ibarra

“…Challenges in the drowsiness included diminished accuracy with MAR alone and delayed prediction during transitions from non-drowsy to drowsy status. …”
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State of Charge Estimation in Li-Ion Batteries Using a Parallel LSTM-Based Approach: The Impact of Modeling Based on Operating States by Osman Ozer, Hayri Arabaci

“…To address this, deep learning algorithms such as long short-term memory (LSTM) have been focused on for SOC prediction. …”
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Revolutionizing Marine Stability: A Review of Cutting-Edge Gyro Stabilizer Designs and Techniques for Vessels by Wan Rahiman, Mohamad Hazwan Mohd Ghazali, Iskandar Al-Thani Mahmood

“…However, key challenges persist, including accurate wave moment prediction during the design phase and gyroscopic drift, which can impact long-term performance and efficiency. …”
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Development of a Wireless Sensor Network and IoT-based Smart Irrigation System by Juliana Ngozi Ndunagu, Kingsley Eghonghon Ukhurebor, Moses Akaaza, Robert Birundu Onyancha

“…The evaluation, when compared to K-nearest neighbours using K = 6 and K = 1, shows the prediction accuracy to be 97% and 98%. The results indicate the system is highly efficient and reliable in performing irrigation and managing water resources and can be adopted in rural areas to boost agricultural productivity.…”
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Identification of biomarkers for the diagnosis in colorectal polyps and metabolic dysfunction-associated steatohepatitis (MASH) by bioinformatics analysis and machine learning by Ying Geng, Yifang Li, Ge Liu, Jian Jiao

“…This study investigated the biomarkers of MASH and colorectal polyps, and the prediction of targeted drugs using an integrated bioinformatics analysis method. …”
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Cooling Load Forecasting Method for Central Air Conditioning Systems in Manufacturing Plants Based on iTransformer-BiLSTM by Xiaofeng Huang, Xuan Zhou, Junwei Yan, Xiaofei Huang

“…Meanwhile, by integrating the SHAP sensitivity analysis method, the contributions of input variables to the cooling load prediction results are systematically evaluated, thereby enhancing the interpretability of the model. …”
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