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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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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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 principal component analysis (PCA) with a Bayesian mixed model analysis reveals several significant patterns in specific behaviors and physiological responses, highlighting the need for further research to deepen our understanding of how behaviors correlate with animal welfare. …”
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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

“…The Taguchi L27 design of the experiments is utilized, and the key process parameters under consideration are infill pattern, layer thickness, raster angle, nozzle temperature, and infill density. …”
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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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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

“…<i>Conclusions</i>: Using MMPI-2 test results, ML can diagnose pain deception better than the conventional logistic regression analysis method by considering different scales and patterns together.…”
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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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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

“…These interpretable features provide clear insights into normal and abnormal ECG patterns. The proposed method was trained and validated using the MIT-BIH Arrhythmia and Fantasia-Normal databases. …”
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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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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

“…In view of problems such as insufficient optical images caused by cloudy weather in arid regions and the unclear spatiotemporal evolution patterns of the planting structures in irrigation areas over the years, in this study, we took the Santun River Irrigation Area, a typical arid region in Xinjiang, China, as an example. …”
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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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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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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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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

“…We aimed to identify PBMCs-related critical genes as diagnostic biomarkers for HFpAMI and analyze the immune infiltration patterns.MethodsDifferential expression genes (DEGs) from PBMCs microarray data of AMI with or without HF were identified. …”
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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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Analisis Sentimen Aplikasi Playstore Sirekap 2024 Pasca Pilpres Dengan Perbandingan Metode Support Vector Machine (SVM), Naïve Bayes Classifier Dan Random Forest. by Dede Ardian TARIGAN

“…The technical stages used in this research include data scraping, data pre-processing, pattern labeling, feature extraction/weighting, data splitting, and the sentiment analysis classification process. …”
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