How do neighborhood environments impact adolescent health: a comprehensive study from subjective and objective perspectives using machine learning method by Jie Sheng, Zhenhai Xiang, Pengfei Ban

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Data-driven analysis in the selective oligomerization of long-chain linear alpha olefin on zeolite catalysts: A machine learning-based parameter study by Sung Woo Lee, Marcel Jonathan Hidajat, Seung Hyeok Cha, Gwang-Nam Yun, Dong Won Hwang

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Author Correction: Predictive model for abdominal liposuction volume in patients with obesity using machine learning in a longitudinal multi-center study in Korea by Hyunji Sang, Jaeyu Park, Soeun Kim, Myeongcheol Lee, Hojae Lee, Sun-Ho Lee, Dong Keon Yon, Sang Youl Rhee

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Exploring determinant factors influencing muscle quality and sarcopenia in Bilbao's older adult population through machine learning: A comprehensive analysis approach. by Naiara Virto, Danielle Marie Dequin, Xabier Río, Amaia Méndez-Zorrilla, Begoña García-Zapirain

“…Three machine learning classification algorithms classified sarcopenia and MQI in each dataset, and the performance of classification models was compared.…”
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Drying kinetic for moisture content prediction of peels Tahiti lemon (Citrus latifolia): Approach by machine learning and optimization - genetic algorithms and nonlinear programming by Maressa O. Camilo, Romero F. Carvalho, Ariany B.S. Costa, Esly F.C. Junior, Andréa O.S. Costa, Robson C. Sousa

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Development and Validation of a Machine Learning Method Using Vocal Biomarkers for Identifying Frailty in Community-Dwelling Older Adults: Cross-Sectional Study by Taehwan Kim, Jung-Yeon Choi, Myung Jin Ko, Kwang-il Kim

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Machine Learning Enabled Prediction of Biologically Relevant Gene Expression Using CT‐Based Radiomic Features in Non‐Small Cell Lung Cancer by Shrey S. Sukhadia, Christoph Sadée, Olivier Gevaert, Shivashankar H. Nagaraj

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A Data-Driven Approach to Improve Cocoa Crop Establishment in Colombia: Insights and Agricultural Practice Recommendations from an Ensemble Machine Learning Model by Leonardo Talero-Sarmiento, Sebastian Roa-Prada, Luz Caicedo-Chacon, Oscar Gavanzo-Cardenas

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Hippocampal Functional Radiomic Features for Identification of the Cognitively Impaired Patients from Low-Back-Related Pain: A Prospective Machine Learning Study by Yang Z, Liang X, Ji Y, Zeng W, Wang Y, Zhang Y, Zhou F

“…After feature selection, machine learning models were trained. Finally, we further analyzed the relationship between the hippocampal functional radiomic features and clinical measures, to explore the clinical significance of these features.Results: The combined radiomic features model logistic regression algorithm superior performance in distinguishing cognitively impaired patients from LBLP (AUC = 0.970, accuracy = 92.3%, sensitivity = 92.3%, specificity = 92.3%) compared to the other models. …”
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Palmitoylation-related gene ZDHHC22 as a potential diagnostic and immunomodulatory target in Alzheimer’s disease: insights from machine learning analyses and WGCNA by Sanying Mao, Xiyao Zhao, Lei Wang, Yilong Man, Kaiyuan Li

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Classification and detection of Covid-19 based on X-Ray and CT images using deep learning and machine learning techniques: A bibliometric analysis by Youness Chawki, Khalid Elasnaoui, Mohamed Ouhda

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Study on SHP2 Conformational Transition and Structural Characterization of Its High-Potency Allosteric Inhibitors by Molecular Dynamics Simulations Combined with Machine Learning by Baerlike Wujieti, Mingtian Hao, Erxia Liu, Luqi Zhou, Huanchao Wang, Yu Zhang, Wei Cui, Bozhen Chen

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Ab-initio trained machine learning potential for MAX compound Ti2AlC: construction, validation, and study of non linear elasticity by Aishwaryo Ghosh, Amitava Moitra, Tanusri Saha-Dasgupta

Subjects: “…machine learning…”
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Integrating traditional machine learning with qPCR validation to identify solid drug targets in pancreatic cancer: a 5-gene signature study by Xiaoyan Wang, Pengcheng Yu, Wei Jia, Bingbing Wan, Zhougui Ling, Yangyang Tang

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Development and validation of an interpretable machine learning model for predicting left atrial thrombus or spontaneous echo contrast in non-valvular atrial fibrillation patients by Chaoqun Huang, Shangzhi Shu, Miaomiao Zhou, Zhenming Sun, Shuyan Li

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Graphene metamaterial solar absorber using Al-TiN-Fe for efficient solar thermal energy conversion and optimization using machine learning by Khaled Aliqab, Bo Bo Han, Om Prakash Kumar, Meshari Alsharari, Ammar Armghan, Shobhit K. Patel

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Building a near-infrared (NIR) soil spectral dataset and predictive machine learning models using a handheld NIR spectrophotometerZenodo by Colleen Partida, Jose Lucas Safanelli, Sadia Mannan Mitu, Mohammad Omar Faruk Murad, Yufeng Ge, Richard Ferguson, Keith Shepherd, Jonathan Sanderman

“…All scanning was performed on dried and sieved (<2 mm) soil samples. Machine learning predictive models were developed for soil organic carbon (SOC), pH, bulk density (BD), carbonate (CaCO3), exchangeable potassium (Ex. …”
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Prediction of microvascular obstruction from angio-based microvascular resistance and available clinical data in percutaneous coronary intervention: an explainable machine learning model by Zhe Zhang, Yang Dai, Peng Xue, Xue Bao, Xinbo Bai, Shiyang Qiao, Yuan Gao, Xuemei Guo, Yanan Xue, Qing Dai, Biao Xu, Lina Kang

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Prediction and unsupervised clustering of fertility intention among migrant workers based on machine learning: a cross-sectional survey from Henan, China by Xinghong Guo, Mingze Ma, Yiyang Chen, Zhaoyang Fang, Jingru Liu, Shuming Yan, Yifei Feng, Xinya Cheng, Jian Wu, Beizhu Ye

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What We Didn’t Know a Recipe Could Be: Political Commentary, Machine Learning Models, and the Fluidity of Form in Nineteenth-Century Newspaper Recipes by Avery Blankenship

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