MINIMIZATION OF RISK OF THE ERRONEOUS DECISION IN THE ASSESSMENT OF THE IMPORTANCE OF STATISTICAL RELATIONS OF TECHNICAL AND ECONOMIC INDICATORS OF THE OBJECTS OF ELECTRIC POWER SY... by E. M. Farhadzadeh, A. Z. Muradaliyev, Yu. Z. Farzaliyev, T. K. Rafiyeva, S. A. Abdullayeva

“…No less relevant is the problem of deciding on the statistical relationship between technical and economic indicators in conditions of equality of consequences of erroneous decisions, i.e. it is necessary to take into account both a Type I error and a Type II error. To overcome the mentioned difficulties a new method for estimating the critical values of correlation coefficients has been developed. …”
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Di George syndrome: early diagnosis in pediatric practice by A. A. Pavlikov, I. M. Melnikova, T. I. Pakhomova, N. P. Myakin

“…The symptoms of 22q11.2DS can vary in both severity and type. Some features may be identified at birth, such as cleft palate or congenital heart defect, while others are diagnosed in older children. …”
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Prediction of birthweight with early and mid-pregnancy antenatal markers utilising machine learning and explainable artificial intelligence by Manohar Pavanya, Krishnaraj Chadaga, Vennila J, Akhila Vasudeva, Bhamini Krishna Rao, Srikanth Prabhu, Shashikala K Bhat

“…Abstract Low birthweight (LBW) is a significant health challenge worldwide, as these neonates experience both short- and long-term disabilities. Factors affecting maternal and fetal health during early to mid-pregnancy can greatly influence fetal development. …”
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Trajectory of breastfeeding among Chinese women and risk prediction models based on machine learning: a cohort study by Yi Liu, Jie Xiang, Ping Yan, Yuanqiong Liu, Peng Chen, Yujia Song, Jianhua Ren

“…Methods This study conducted a three-wave prospective cohort analysis to examine maternal breastfeeding trajectories within the first six months postpartum and to develop risk prediction models for each period using advanced machine learning algorithms. Conducted at a leading Maternal and Children's hospital in China from October 2021 to June 2022, data were gathered via self-administered surveys and electronic health records. …”
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Sensitive Multispectral Variable Screening Method and Yield Prediction Models for Sugarcane Based on Gray Relational Analysis and Correlation Analysis by Shimin Zhang, Huojuan Qin, Xiuhua Li, Muqing Zhang, Wei Yao, Xuegang Lyu, Hongtao Jiang

“…Subsequently, three supervised learning algorithms—Gradient Boosting Decision Tree (GBDT), Random Forest (RF), and Support Vector Machine (SVM)—were employed to develop both single-stage and multi-stage yield prediction models. …”
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Optimizing spatial normalization of multisubject inner ear MRI: comparison of different geometry-preserving co-registration approaches by Johannes Gerb, Valerie Kirsch, Emilie Kierig, Thomas Brandt, Marianne Dieterich, Rainer Boegle

“…Abstract Spatial normalization of multisubject inner ear imaging data is challenging, due to both substantial intraindividual differences and the small size of the organ compared to other intracranial structures. …”
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Retrieving the Leaf Area Index of Dense and Highly Clumped Moso Bamboo Canopies from Sentinel-2 MSI Data by Weiliang Fan, Jun Wu, Guang Zheng, Qian Zhang, Xiaojun Xu, Huaqiang Du, Mengxiang Zheng, Kexin Zhang, Feng Zhang

“…In order to explore these two seemingly contradictory results, LAIs of 21 Moso bamboo canopies retrieved by the GOST2 model (incorporating leaf clumping), the 4SAIL model and the SNAP tool (both without leaf clumping), respectively, were validated against ground-based LAI estimations, including the direct allometric method and indirect digital hemispherical photograph (DHP) methods. …”
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Elucidating the dynamic tumor microenvironment through deep transcriptomic analysis and therapeutic implication of MRE11 expression patterns in hepatocellular carcinoma by Ruiqiu Chen, Chaohui Xiao, Zizheng Wang, Guineng Zeng, Shaoming Song, Gong Zhang, Lin Zhu, Penghui Yang, Rong Liu

“…Publicly available single-cell RNA sequencing (scRNA-seq) data and spatial transcriptomics were utilized to explore MRE11’s dynamic mechanisms in the tumor microenvironment (TME) of both primary and post-immunotherapy cases. We also screened for differentially expressed genes and constructed a robust HCC prognosis model using 101 machine-learning algorithms. …”
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A Novel Framework for Improving Soil Organic Carbon Mapping Accuracy by Mining Temporal Features of Time-Series Sentinel-1 Data by Zhibo Cui, Bifeng Hu, Songchao Chen, Nan Wang, Defang Luo, Jie Peng

“…The findings revealed the following: (1) The correlation between time-series S-1 data and SOC exhibited both interannual and monthly variations, with the optimal monitoring period from July to October. …”
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EcoTaskSched: a hybrid machine learning approach for energy-efficient task scheduling in IoT-based fog-cloud environments by Asfandyar Khan, Faizan Ullah, Dilawar Shah, Muhammad Haris Khan, Shujaat Ali, Muhammad Tahir

“…We will also assess its applicability by integrating other ML models, which could provide enhanced insights for optimizing scheduling algorithms across diverse fog-cloud settings.…”
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DIGITAL TOOLS FOR MATCHING QUALIFICATIONS TO THE LEVELS OF THE NATIONAL QUALIFICATIONS FRAMEWORK by Volodymyr Kovtunets, Sergiy Londar, Serhii Melnyk, Oles Kovtunets

“…Corresponding API is developed by PHP programming language. Both Excel and API are accessible for users at the website of the Institute of Educational Analytics in Kyiv. …”
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Combined Use of Spectral and Structural Features for Improved Early Detection of Pine Shoot Beetle Attacks in Yunnan Pines by Yujie Liu, Youqing Luo, Run Yu, Lili Ren, Qi Jiang, Shaoshun He, Xinqiang Chen, Guangzhao Yang

“…Individual tree segmentation algorithms were utilized to extract both spectral and structural variables of damaged tree crowns. …”
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Discovery of novel diagnostic biomarkers of hepatocellular carcinoma associated with immune infiltration by Qiang Liu, Hua Zhang, Heng Xiao, Ao Ren, Ying Cai, Rui Liao, Huarong Yu, Zhongjun Wu, Zuotian Huang

“…Machine learning approaches and big data analyses are viable strategies for identifying HCC diagnostic markers.Materials and methods In this study, we downloaded mRNA expression profiles of HCC from the GEO database and used random forest and machine learning algorithms, such as least absolute shrinkage and selection operator, to screen for reliable diagnostic genes. …”
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Diagnostic Accuracy of Deep Learning Models in Predicting Glioma Molecular Markers: A Systematic Review and Meta-Analysis by Somayeh Farahani, Marjaneh Hejazi, Sahar Moradizeyveh, Antonio Di Ieva, Emad Fatemizadeh, Sidong Liu

“…<b>Methods:</b> Following PRISMA guidelines, we systematically searched PubMed, Scopus, Ovid, and Web of Science until 27 February 2024 for studies employing DL algorithms to predict gliomas’ molecular markers from MRI sequences. …”
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Intelligent Prediction Platform for Sepsis Risk Based on Real-Time Dynamic Temporal Features: Design Study by Mingwei Zhang, Ming Zhong, Yunzhang Cheng, Tianyi Zhang

“…Three linear parameters (mean, SD, and endpoint value) were calculated to construct the prediction model using multiple ML algorithms. The interpretability module uses the TreeSHAP (Tree-Based Shapley Additive Explanations) method to enhance model transparency through both individual prediction and global explanations. …”
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“Voice is the New Blood”: a discourse analysis of voice AI health-tech start-up websites by Alden Blatter, Hortense Gallois, Emily Evangelista, Yael Bensoussan, Bridge2AI-Voice Consortium, Jean-Christophe Bélisle-Pipon, Yael E. Bensoussan, Olivier Elemento, Jean-Christophe Bélisle-Pipon, David Dorr, Satrajit Ghosh, Alistair Johnson, Phillip Payne, Maria E. Powell, Anaïs Rameau, Vardit Ravitsky, Alexandros Sigaras

“…Grounded in constitutive discourse analysis, which asserts that discourse both reflects and shapes realities, the study identifies patterns in how these companies describe their identities, technologies, and datasets.ResultsThe analysis shows start-ups consistently highlight the efficacy, reliability, and safety of their technologies, positioning them as transformative healthcare solutions. …”
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Radiomic Analysis and Liquid Biopsy in Preoperative CT of NSCLC: An Explorative Experience by Maria Paola Belfiore, Mario Sansone, Giovanni Ciani, Vittorio Patanè, Carlotta Genco, Roberta Grassi, Giovanni Savarese, Marco Montella, Riccardo Monti, Salvatore Cappabianca, Alfonso Reginelli

“…Methods This prospective study enrolled 60 NSCLC patients who underwent both computed tomography (CT)‐guided biopsy and liquid biopsy. …”
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Testicular appendix (hydatid of Morgagni) torsion in adult urological management: evaluation of treatment outcomes by I. S. Shormanov, D. N. Shedrov, D. Y. Garova, A. I. Ryzhkov

“…Accordingly, there are no clear algorithms for the management of such patients.Objective. …”
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An economic value of the glycated hemoglobin test in diabetes mellitus type 2 diagnosis by Marina V. Shestakova, Alexey S. Kolbin, Oleg I. Karpov, Gagik R. Galstyan, Alexander Y. Mayorov, Maria A. Arepeva, Aleksey A. Kurylev, Maxim A. Proskurin

“…METHODS: Retrospective analysis of aggregated epidemiological Russian NATION study data in 810 patients with prediabetes and DM2T with both analysis performed, with sensitivity and specificity of each is detected. …”
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An interpretable machine learning model for predicting early liver metastasis after pancreatic cancer surgery by Hao Zhu, Yiyan Zhou, Danyang Shen, Kejia Wu, Xiaojie Gan, Xiaofeng Xue, Weigang Zhang, Xiaohua Yang, Junyi Qiu, Ding Sun

“…Method This retrospective study included 407 pancreatic cancer patients who underwent surgery at the First Affiliated Hospital of Soochow University between January 2015 and December 2023, aiming to develop and validate a predictive model. Seven ML algorithms were employed to predict the risk of liver metastasis within one year after surgery. …”
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