Spectral Fingerprinting of Tencha Processing: Optimising the Detection of Total Free Amino Acid Content in Processing Lines by Hyperspectral Analysis by Qinghai He, Yihang Guo, Xiaoli Li, Yong He, Zhi Lin, Hui Zeng

“…This study employs VNIR-HSI combined with machine learning algorithms to develop a model for visualizing the total free amino acid content in Tencha samples that have undergone different processing steps on the production line. …”
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The Use of Artificial Intelligence in Medical Diagnostics: Opportunities, Prospects and Risks by Nataliia Sheliemina

“…However, the AI use in medicine carries certain risks related to ethics and data privacy, shortcomings in the quality of data for training algorithms, and importance of protecting against cyberthreats. …”
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Resampling-driven machine learning models for enhanced high streamflow forecasting by Nureehan Salaeh, Sirimon Pinthong, Warit Wipulanusat, Uruya Weesakul, Jakkarin Weekaew, Quoc Bao Pham, Pakorn Ditthakit

“…These results present a promising framework for high streamflow prediction that can be adapted and applied to other river basins.…”
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Machine learning-based Diagnostic model for determining the etiology of pleural effusion using Age, ADA and LDH by Qing-Yu Chen, Shu-Min Yin, Ming-Ming Shao, Feng-Shuang Yi, Huan-Zhong Shi

“…Feature importance and average prediction of age, Adenosine (ADA) and Lactate dehydrogenase (LDH) was analyzed. …”
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Developing a semi-automated technique of surface water quality analysis using GEE and machine learning: A case study for Sundarbans by Sheikh Fahim Faysal Sowrav, Sujit Kumar Debsarma, Mohan Kumar Das, Khan Mohammad Ibtehal, Mahfujur Rahman, Noshin Tabassum Hridita, Atika Afia Broty, Muhammad Sajid Anam Hoque

“…Key water quality parameters—Sea Surface Temperature (SST), Total Suspended Solids (TSS), Turbidity, Salinity, and pH—were predicted through ML algorithms and interpolated using the Empirical Bayesian Kriging (EBK) model in ArcGIS Pro. …”
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Comparative evaluation of machine learning models for enhancing diagnostic accuracy of otitis media with effusion in children with adenoid hypertrophy by Xiaote Zhang, Qiaoyi Xie, Ganggang Wu

“…Given the urgent need for improved diagnostic methods and extensive characterization of risk factors for OME in AH children, developing diagnostic models represents an efficient strategy to enhance clinical identification accuracy in practice.ObjectiveThis study aims to develop and validate an optimal machine learning (ML)-based prediction model for OME in AH children by comparing multiple algorithmic approaches, integrating clinical indicators with acoustic measurements into a widely applicable diagnostic tool.MethodsA retrospective analysis was conducted on 847 pediatric patients with AH. …”
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Estimation of soil organic carbon content and dynamics in Mediterranean climate regions considering long-term monthly climatic conditions by Lei Shen, Wei Zhang, Duanqiang Zhai, Shuo Han, Shuang Tian

“…Results showed that the introduction of long-term average monthly climate data significantly improved the prediction accuracy of the models. The R2 value of the RF model was enhanced by 0.22, whilst the R2 of the LightGBM model improved by 0.20–0.21. …”
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Deciphering the role of miR-71 in Echinococcus multilocularis early development in vitro. by Matías Gastón Pérez, Markus Spiliotis, Natalia Rego, Natalia Macchiaroli, Laura Kamenetzky, Nancy Holroyd, Marcela Alejandra Cucher, Klaus Brehm, Mara Cecilia Rosenzvit

“…Using genomic information and bioinformatic algorithms for miRNA binding prediction, we found a high number of potential miR-71 targets in E. multilocularis. …”
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Automatic Segmentation of Abdominal Aortic Aneurysm From Computed Tomography Angiography Using a Patch-Based Dilated UNet Model by Merjulah Roby, Juan C. Restrepo, Haehwan Park, Satish C. Muluk, Mark K. Eskandari, Seungik Baek, Ender A. Finol

“…Hence, there is a growing need for automated segmentation algorithms, particularly when these influence treatment planning. …”
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Development of an immune-related gene signature applying Ridge method for improving immunotherapy responses and clinical outcomes in lung adenocarcinoma by Zhen Chen, Yongjun Zhang

“…In addition, the IMMPS exhibited better prediction performance in comparison to 154 published gene signatures, suggesting that the IMMPS was an independent prognostic risk factor for evaluating the overall survival of LUAD patients. …”
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Analysis of prognostic factors and nomogram construction for postoperative survival of triple-negative breast cancer by Chenxi Wang, Xiangqian Zhao, Dawei Wang, Jinyun Wu, Jizhen Lin, Weiwei Huang, Yangkun Shen, Qi Chen

“…This study utilized the SEER database to investigate clinicopathologic characteristics and prognostic factors in TNBC patients.MethodsMachine learning algorithms specifically Gradient Boosting Machines (XGBoost) and Random Forest classifiers were applied to develop survival prediction models and identify key prognostic markers.ResultsResults indicated significant predictors of survival, including tumor size, lymph node involvement, and distant metastases. …”
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Use of machine learning in osteoarthritis research: a systematic literature review by Francis Berenbaum, Jérémie Sellam, David Klatzmann, Atul J Butte, Karine Louati, Encarnita Mariotti-Ferrandiz, Marie Binvignat, Valentina Pedoia

“…Twelve articles were related to diagnosis, 7 to prediction, 4 to phenotyping, 12 to severity and 11 to progression. …”
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CLINICAL AND LABORATORY ASPECTS OF DETECTING SPECIFIC IgE ANTIBODIES TO COW’S MILK AND ITS COMPONENTS by N. A. Alkhutova, N. A. Kovyazina, O. L. Zhizhina

“…So far, however, we have no generally approved laboratory algorithms for diagnostics and monitoring of treatment efficiency in the cow milk allergy and its compomemts.We have performed a laboratory study of 187 children at the age of 3 months to 10 years. …”
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Oxidative stress gene expression in ulcerative colitis: implications for colon cancer biomarker discovery by Ting Yan, Ting Su, Miaomiao Zhu, Qiyuan Qing, Binjie Huang, Jun Liu, Tenghui Ma

“…The model may be beneficial in prognostic prediction and guiding treatment decisions.…”
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Toward causal artificial intelligence approach for PM2.5 interpretation: A discovery of structural causal models by Mallika Kliangkhlao, Apaporn Tipsavak, Thanathip Limna, Racha Dejchanchaiwong, Perapong Tekasakul, Kirttayoth Yeranee, Thanyabun Phutson, Bukhoree Sahoh

“…Understanding the causal mechanisms underlying PM2.5 generation is critical for developing effective prevention strategies, necessitating an approach that goes beyond prediction and seeks deeper causal explanations to support decision-making. …”
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Data-Driven and Mechanistic Soil Modeling for Precision Fertilization Management in Cotton by Miltiadis Iatrou, Panagiotis Tziachris, Fotis Bilias, Panagiotis Kekelis, Christos Pavlakis, Aphrodite Theofilidou, Ioannis Papadopoulos, Georgios Strouthopoulos, Georgios Giannopoulos, Dimitrios Arampatzis, Evangelos Vergos, Christos Karydas, Dimitris Beslemes, Vassilis Aschonitis

“…By comparing the Mean Absolute Error (MAE) between predicted and observed cotton yield values across three ML algorithms, i.e., Random Forest (RF), XGBoost, and LightGBM, the RF model achieved the lowest error (422.6 kg/ha), outperforming XGBoost (446 kg/ha) and LightGBM (449 kg/ha). …”
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Machine vision and learning for evaluating different rancidity grades of Prunus mandshurica (Maxim.) Koehne by Yashun Wang, Huirong Chen, Jianting Gong, Yang Cui, Huiqin Zou, Yonghong Yan

“…Discrimination and prediction models based on color features combined with multiple machine learning algorithms were established using 10-fold cross-validation and external test set validation. …”
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Time series forecasting of Valley fever infection in Maricopa County, AZ using LSTMResearch in context by Xueting Jin, Fangwu Wei, Srinivasa Srivatsav Kandala, Tejas Umesh, Kayleigh Steele, John N. Galgiani, Manfred D. Laubichler

“…Two models with different lengths of forecasting periods, 10 days and 30 days, are identified with good prediction. Interpretation: LSTM algorithms, combined with traditional statistical methods, could help with the forecasting of CM cases. …”
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Transforming machine learning model knowledge into material insights for multi-principal-element superalloy phase design by Qiuling Tao, Xintong Yang, Longke Bao, Yuexin Zhou, Tao Yang, Yilu Zhao, Rongpei Shi, Zhifu Yao, Xingjun Liu

“…First, we construct two classification models using ML algorithms to predict the presence or absence of the L12 phase and other phases, respectively. …”
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Current Status of Application of Spaceborne GNSS-R Raw Intermediate-Frequency Signal Measurements: Comprehensive Review by Qiulan Wang, Jinwei Bu, Yutong Wang, Donglan Huang, Hui Yang, Xiaoqing Zuo

“…These research results have important application potential in fields such as environmental monitoring, climate change research, and weather prediction, and are expected to provide new technological means for global geophysical parameter retrieval.…”
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