NEW ORGANIZATION PROCESS OF FEATURE SELECTION BY FILTER WITH CORRELATION-BASED FEATURES SELECTION METHOD by Olga Solovei

“… The subject of the article is feature selection techniques that are used on data preprocessing step before building machine learning models. …”
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Anomaly distribution acquisition method for probabilistic damage tolerance assessment of hole features by Guo Li, Huimin Zhou, Junbo Liu, Shuyang Xia, Shuiting Ding

“…The default anomaly distribution of hole features has been established and published in airworthiness advisory circular 33.70-2 based on historical anomaly data collected from cracked or ruptured parts recorded in laboratory analysis reports of the special industries before 2005. …”
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Impact wear behavior of austenitic steel bucket teeth based on machine learning by Zhihui Cai, Jianfeng Yan, Yandong Qiao, Rongjie Li, Yanchun Shi, Junping Zhang, Zhixiong Zhang

“…A particle swarm optimization support vector machine was employed to accurately predict wear depth. The mean values of the determination coefficient (R2) before and after feature selection were 0.96294 and 0.98074, respectively, further validating the accuracy of the feature selection process and providing a ranking of importance for identifying key factors to enhance wear resistance.…”
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Non‐Invasive Tumor‐Naïve Minimal Residual Disease Detection of Liver Cancer by Incorporating Circulating Tumor DNA Features and Alpha‐Fetoprotein: A Prospective Study by Qingqi Ren, Shiyong Li, Guolin Zhong, Yunfei Li, Dao‐Ling Huang, Liangliang Zhang, Yumin Feng, Guanghui Long, Mao Mao

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PCSK9 is upregulated and correlated with more severe disease condition but fails to predict treatment outcomes in psoriasis patients by Xuwen Yin, Lei Shi, Ni Zhang, Heng Li, Jianwen Long, Xinjian Yu

“…Baseline characteristics and treatment response after 12-week treatment were collected. Their serum samples before treatment initiation were collected and sent to PCSK9 detection by enzyme-linked immunosorbent assay. …”
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Ensemble learning approach for prediction of early complications after radiotherapy for head and neck cancer using CT and MRI radiomic features by Benyamin Khajetash, Seied Rabi Mahdavi, Alireza Nikoofar, Lee Johnson, Meysam Tavakoli

“…Combined $$T_1$$ weighted image-based models RT-BN, RT-LSVM-BN and RT-NN-LSVM-BN also show good performance having AUC values 0.97, 0.92, and 0.90, respectively. These results show that radiomic features from MR images obtained before radiotherapy can be used in addition to other metrics as personalized and unique biomarkers for prediction of early-onset xerostomia. …”
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Integrating multi‐omics features enables non‐invasive early diagnosis and treatment response prediction of diffuse large B‐cell lymphoma by Weilong Zhang, Bangquan Ye, Yang Song, Ping Yang, Wenzhe Si, Hairong Jing, Fan Yang, Dan Yuan, Zhihong Wu, Jiahao Lyu, Kang Peng, Xu Zhang, Lingli Wang, Yan Li, Yan Liu, Chaoling Wu, Xiaoyu Hao, Yuqi Zhang, Wenxin Qi, Jing Wang, Fei Dong, Zijian Zhao, Hongmei Jing, Yanzhao Li

“…Detection sensitivity achieved 91% at 99% specificity in early‐stage patients, while the AUC values of the individual omics model were 0.942, 0.968, 0.989, 0.935, 0.921, 0.781 and 0.917, respectively, with lower sensitivity and specificity. …”
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Correlation between EpCAM expression and cervical lymph node metastasis in papillary thyroid carcinoma: a study integrating ultrasonographic features by Xia Zhang, Jie Li, Ming Gao, Yan Zhang

“…Methods Participants with suspected thyroid cancer underwent conventional and contrast-enhanced ultrasonography (CEUS) before surgery. Age, sex, and nodule features in ultrasound were recorded. …”
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5-hydroxymethylcytosine sequencing of plasma cell-free DNA identifies epigenomic features in prostate cancer patients receiving androgen deprivation therapies by Qianxia Li, Chiang-Ching Huang, Shane Huang, Yijun Tian, Jinyong Huang, Amirreza Bitaraf, Xiaowei Dong, Marja T. Nevalainen, Manishkumar Patel, Jodie Wong, Jingsong Zhang, Brandon J. Manley, Jong Y. Park, Manish Kohli, Elizabeth M. Gore, Deepak Kilari, Liang Wang

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Examination of the Psychometric Properties of the Borderline Personality Inventory in an Adolescent Sample by Yasemin Kahya, Koret Munguldar, Melis Gün

“…Additionally, the relationship between the borderline symptoms evaluated using BPI and the other psychopathology symptoms supports that borderline personality features in adolescents are not a passing developmental phenomenon and are related to emotional and behavioral problems. …”
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Selective Separation of Scandium in Acidic Water Using Carboxyl Functionalized Covalent Phosphonitrile Polymers by Yu BAI, Ayinuer WUSHUER, Lei OUYANG, Lijin HUANG, Qin SHUAI

“…Both materials have the ability to selectively adsorb Sc(III), with Kd values of 5.1×102mL/g and 4.0×103mL/g for Sc(III), respectively. …”
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Empowering Diagnostics: An Ensemble Machine Learning Model for Early Liver Disease Detection by Abdulrahman Ahmed Jasim, Hajer Alwindawi, Layth Rafea Hazim

“…We then perform correlation-based feature reduction before training a stacking classifier that combines Random Forest, XGBoost, and ExtraTrees base learners with an ExtraTrees meta‑learner. …”
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An Evaluation of the Possibility of Using Buckwheat Hulls as an Addition to Bread by Joanna Maria Klepacka, Marta Czarnowska-Kujawska

“…The test material consisted of a control bread (without the addition of husk), bread with 10 and 20% husk (mixed with flour at the stage of dough preparation), and bread with a surface sprinkled with buckwheat husk (25 g) before baking. The semi-consumer evaluation involved 33 pre-trained persons who determined the degree of acceptance (desirability) of the selected bread’s sensory characteristics (colour, texture, smell and taste) using the nine-point hedonic scale. …”
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The data dimensionality reduction and bad data detection in the process of smart grid reconstruction through machine learning. by Bo Yu, Zheng Wang, Shangke Liu, Xiaomin Liu, Ruixin Gou

“…When the number of iTree n is determined to be 100, and the corresponding number of samples w is determined to be 10, the algorithm has the best detection effect. …”
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Investigating the Effects of Spectroscopic Method in Estimating Soluble Solid Content Values and Firmness of Cherries from an Environmental Point of View: Prediction of Environment... by Shirzad Naim, Shahgholi Gholamhossein, Ardabili Sina, Szymanek Mariusz

“…Next, by combining the feature selection method (relief) and the spectrometry method (vis-NIR), the effective wavelengths were extracted to estimate the soluble solid content (SSC) values and firmness of the cherry product. …”
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Chemical Composition and Sensory Profile of Sauerkraut from Different Cabbage Hybrids by Elena V. Yanchenko, Galina S. Volkova, Elena V. Kuksova, Ivan I. Virchenko, Aleksey V. Yanchenko, Elena M. Serba, Maria I. Ivanova

“…The present research objective was to test several cabbage hybrids for natural fermentation, microbiological parameters, and native sugar content after four months of storage. The study featured twelve new-generation white cabbage hybrids of Russian selection and sauerkraut foods. …”
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Prediction of KRAS gene mutations in colorectal cancer using a CT-based radiomic model by Wenjing Wang, Qingbiao Zhang, Shimei Fan, Yuyin Wang, Xingyan Le, Min Ai, Chunqi Du, Junbang Feng, Chuanming Li

“…The Delong test was employed to assess the differences between the various models.ResultsAfter feature selection, the top 8 features with the highest mutual information scores were extracted to construct a prediction model. …”
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A donor heart scoring model to predict transplant outcomes by E. A. Tenchurina, M. G. Minina

“…In binomial logistic regression, non-selection of heart donor was used as a dependent variable, while donor characteristics were used as factor features. …”
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Identification of exosome-related genes in NSCLC via integrated bioinformatics and machine learning analysis by Zhenjie Sun, Tianyu Du, Guosheng Yang, Yinghuan Sun, Xuyang Xiao

“…However, further experimental verification is required to assess its practical value for NSCLC and other lung cancer subtypes before clinical application.…”
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Ensemble Machine Learning for the Prediction and Understanding of the Refractive Index in Chalcogenide Glasses by Miruna-Ioana Belciu, Alin Velea

“…This study employs various machine learning models to reliably predict the refractive index at 20 °C using a small dataset of 541 samples extracted from the SciGlass database. The input for the algorithms consists of a selected set of physico-chemical features computed for the chemical composition of each entry. …”
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