Innovación en sueño by Laura Vigil, Toni Zapata, Andrea Grau, Marta Bonet, Montserrat Montaña, María Piñar

“…In addition, techniques such as cluster analysis are used to identify symptomatic patterns and phenotypes, which improves understanding of OSA pathophysiology and optimizes CPAP treatment.However, implementation of AI in hospitals faces technological, ethical, and legal barriers. …”
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Rotating Machinery Structural Faults Feature Enhancement and Diagnosis Based on Multi-Sensor Information Fusion by Baozhu Jia, Guanlong Liang, Zhende Huang, Xuewei Song, Zhiqiang Liao

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Predicting High-Cost Healthcare Utilization Using Machine Learning: A Multi-Service Risk Stratification Analysis in EU-Based Private Group Health Insurance by Eslam Abdelhakim Seyam

“…The research applied three machine learning algorithms, namely logistic regression using elastic net regularization, the random forest, and support vector machines. …”
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Development of a Drought Monitoring System for Winter Wheat in the Huang-Huai-Hai Region, China, Utilizing a Machine Learning–Physical Process Hybrid Model by Qianchuan Mi, Zhiguo Huo, Meixuan Li, Lei Zhang, Rui Kong, Fengyin Zhang, Yi Wang, Yuxin Huo

“…Finally, we utilized this monitoring system to examine the spatiotemporal variations in drought patterns in the HHH region over the past two decades. …”
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Investigating Stress and Coping Behaviors in African Green Monkeys (<i>Chlorocebus aethiops sabaeus</i>) Through Machine Learning and Multivariate Generalized Linear Mixed Models by Brittany Roman, Christa Gallagher, Amy Beierschmitt, Sarah Hooper

“…The statistical methodology utilized machine learning and multivariate generalized linear mixed models to find associations between behaviors and fluctuations of cortisol, lysozyme, and β-endorphin concentrations. …”
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Computational Estimation of Football Player Wages by Yaldo L., Shamir L.

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Risk of autism spectrum disorder at 18 months of age is associated with prenatal level of polychlorinated biphenyls exposure in a Japanese birth cohort by Hirokazu Doi, Akira Furui, Rena Ueda, Koji Shimatani, Midori Yamamoto, Akifumi Eguchi, Naoya Sagara, Kenichi Sakurai, Chisato Mori, Toshio Tsuji

“…There was no reliable relationship between PCB PCs and problematic behaviors at 5 years of age. Furthermore, machine learning-based analysis showed the possibility that, when the information of the pattern of infants’ spontaneous bodily motion, a potential marker of ASD risk, was used as the predictors together, prenatal PCB exposure levels predict ASD risk at 18 months of age. …”
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Development of a Next-Generation Cooling Channel Technology with High Cooling Efficiency by Roughing Cooling Channels Using a Combination of Laser Machining and Embossing Technique... by Chil-Chyuan Kuo, Geng-Feng Lin, Armaan Farooqui, Song-Hua Huang, Shih-Feng Tseng

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LiveDrive AI: A Pilot Study of a Machine Learning-Powered Diagnostic System for Real-Time, Non-Invasive Detection of Mild Cognitive Impairment by Firas Al-Hindawi, Peter Serhan, Yonas E. Geda, Francis Tsow, Teresa Wu, Erica Forzani

“…Using the LiveDrive AI system, equipped with multimodal sensing (MMS) technology and a driving performance assessment strategy, the proposed work analyzes the predictive capacity of driving patterns in indicating cognitive decline. Machine learning models, trained on an expert-annotated in-house dataset, were employed to detect MCI status from driving performance. …”
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Spatial Gap-Filling of Himawari-8 Hourly AOD Products Using Machine Learning with Model-Based AOD and Meteorological Data: A Focus on the Korean Peninsula by Youjeong Youn, Seoyeon Kim, Seung Hee Kim, Yangwon Lee

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Diagnosis of Pain Deception Using Minnesota Multiphasic Personality Inventory-2 Based on XGBoost Machine Learning Algorithm: A Single-Blinded Randomized Controlled Trial by Hyewon Chung, Kihwan Nam, Subin Lee, Ami Woo, Joongbaek Kim, Eunhye Park, Hosik Moon

“…The main goal of this study was to evaluate the diagnostic value of pain deception using machine learning (ML) analysis with the Minnesota Multiphasic Personality Inventory-2 (MMPI-2) scales, considering accuracy, precision, recall, and f1-score as diagnostic parameters. …”
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A Hybrid Machine Learning Approach: Analyzing Energy Potential and Designing Solar Fault Detection for an AIoT-Based Solar–Hydrogen System in a University Setting by Salaki Reynaldo Joshua, An Na Yeon, Sanguk Park, Kihyeon Kwon

“…Known for its ability to detect intricate time series patterns, the Transformer model exhibited solid predictive performance, with the MAE and MAE2 results reflecting consistent average errors, while the MSE pointed to areas with larger deviations requiring improvement. …”
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Evaluating Ecological Vulnerability and Its Driving Mechanisms in the Dongting Lake Region from a Multi-Method Integrated Perspective: Based on Geodetector and Explainable Machine... by Fuchao Li, Tian Nan, Huang Zhang, Kun Luo, Kui Xiang, Yi Peng

“…The EVI values were classified into five levels using the Natural Breaks (Jenks) method, and spatial autocorrelation analysis was applied to reveal spatial differentiation patterns. The Geodetector model was used to analyze the driving mechanisms of natural and socioeconomic factors on EVI, identifying key influencing variables. …”
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TFDGiniXML: A Novel Explainable Machine Learning Framework for Early Detection of Cardiac Abnormalities Based on Nonlinear Time-Frequency Distribution Gini Index Features by Mohamed Aashiq, Shaiful Jahari Hashim, Fakhrul Zaman Rokhani, Marsyita Hanafi, Ahmed Faeq Hussein

“…Eight machine learning classifiers, including SVM, Random Forest, XGBoost, Gaussian Na&#x00EF;ve Bayes, KNN, LinearBoost, CatBoost, and Logistic Regression were tested. …”
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Enhancing neuromolecular imaging classification in low-data regimes with generative machine learning: A case study in HDAC PET/MR imaging of alcohol use disorder by Tyler N. Meyer, Olga Andreeva, Roger D. Weiss, Wei Ding, Iris Shen, Changning Wang, Ping Chen, Tewodros Mulugeta Dagnew

“…Key hemispheric and subregional cingulate HDAC patterns were also identified as potential biomarkers. …”
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Research on Machine Learning-Based Extraction and Classification of Crop Planting Information in Arid Irrigated Areas Using Sentinel-1 and Sentinel-2 Time-Series Data by Lixiran Yu, Hongfei Tao, Qiao Li, Hong Xie, Yan Xu, Aihemaiti Mahemujiang, Youwei Jiang

“…Additionally, we integrated the vertical–vertical and vertical–horizontal polarization data obtained from synthetic aperture radar (SAR) satellite systems. Machine learning algorithms, including the random forest algorithm (RF), Classification and Regression Trees (CART), and Support Vector Machines (SVM), were employed for planting structure classification. …”
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Machine learning identification of a novel vasculogenic mimicry-related signature and FOXM1’s role in promoting vasculogenic mimicry in clear cell renal cell carcinoma by Chao Xu, Sujing Zhang, Jingwei Lv, Yilong Cao, Yao Chen, Hao Sun, Shengtao Dai, Bowei Zhang, Meng Zhu, Yuepeng Liu, Junfei Gu

“…Results: We examined VRG mutation and expression patterns in ccRCC at the gene level, identifying two distinct molecular clusters. …”
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Revealing the effects of environmental and spatio-temporal variables on changes in Japanese sardine (Sardinops melanostictus) high abundance fishing grounds based on interpretable... by Yongchuang Shi, Lei Yan, Shengmao Zhang, Fenghua Tang, Shenglong Yang, Wei Fan, Haibin Han, Yang Dai

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Development and Feasibility Study of HOPE Model for Prediction of Depression Among Older Adults Using Wi-Fi-based Motion Sensor Data: Machine Learning Study by Shayan Nejadshamsi, Vania Karami, Negar Ghourchian, Narges Armanfard, Howard Bergman, Roland Grad, Machelle Wilchesky, Vladimir Khanassov, Isabelle Vedel, Samira Abbasgholizadeh Rahimi

“…Furthermore, the importance of sleep patterns identified in our explainability analysis aligns with findings from previous research, emphasizing the need for more in-depth studies on the role of sleep in mental health, as suggested in the explainable machine learning study.…”
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Integrating CEUS Imaging Features and LI-RADS Classification for Postoperative Early Recurrence Prediction in Solitary Hepatocellular Carcinoma: A Machine Learning-Based Prognostic... by Liang L, Pang J, Zhang B, Que Q, Gao R, Wu Y, Peng J, Zhang W, Bai X, Wen R, He Y, Yang H

“…Keywords: hepatocellular carcinoma, CEUS, LI-RADS, early recurrence, machine learning, prognostic modeling…”
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