A comparative template-switching cDNA approach for HTS-based multiplex detection of three viruses and one viroid commonly found in apple trees by Francisco Mosquera-Yuqui, Daniel Ramos-Lopez, Xiaojun Hu, Yu Yang, Joshua L. Mendoza, Emmanuel Asare, Joshua Habiger, Oscar P. Hurtado-Gonzales, Andres S. Espindola

“…These treatments and random hexamers also generated the highest genome coverages, which were typically similar in both HTS workflows. …”
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PCA and PSO based optimized support vector machine for efficient intrusion detection in internet of things by Mutkule Prasad Raghunath, Shyam Deshmukh, Poonam Chaudhari, Sunil L. Bangare, Kishori Kasat, Mohan Awasthy, Batyrkhan Omarov, Rajesh R. Waghulde

“…The PSO-based SVM method is shown superior performance compared to random forest and linear regression methods in terms of precision, recall, and specificity.…”
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Comparison of time-to-event machine learning models in predicting biliary complication and mortality rate in liver transplant patients by Aref Andishgar, Sina Bazmi, Kamran B. Lankarani, Seyed Alireza Taghavi, Mohammad Hadi Imanieh, Gholamreza Sivandzadeh, Samira Saeian, Nazanin Dadashpour, Alireza Shamsaeefar, Mahdi Ravankhah, Hamed Nikoupour Deylami, Reza Tabrizi, Mohammad Hossein Imanieh

“…Model development employed 5-fold cross-validation, random oversampling, and hyperparameter tuning. Random oversampling addressed data imbalance. …”
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Rapid Detection of Hybrid Maize Parental Lines Using Stacking Ensemble Machine Learning by M. Aqil, M. Azrai, M. J. Mejaya, N. A. Subekti, F. Tabri, N. N. Andayani, Rahma Wati, S. Panikkai, S. Suwardi, Z. Bunyamin, E. Roy, M. Muslimin, M. Yasin, E. Prakasa

“…The integration of the model with machine learnings (logistic regression, SVM, random forest, and KNNs) enables rapid recognition of off-type plants even though it is operated by personnel with limited skills of seed technology on ideotype recognition. …”
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Pooled prevalence and associated factors of traditional uvulectom among children in Africa: A systematic review and meta-analysis. by Solomon Demis Kebede, Kindu Agmas, Demewoz Kefale, Amare Kassaw, Tigabu Munye Aytenew

“…A weighted inverse-variance random-effects model was employed to estimate the pooled prevalence and associated predictors of TCU. …”
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LIPUS promotes osteogenic differentiation of rat BMSCs and osseointegration of dental implants by regulating ITGA11 and focal adhesion pathway by Chao Liang, Yuqing Zhang, Yuwei Yan, Wei Geng, Jun Li, Xiu Liu

“…Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were used to identify enriched functional terms and signalling pathways for differentially expressed genes from LIPUS-treated rat BMSC RNAseq data obtained from the GEO database. The random forest method was used to identify key risk genes according to the mean decrease Gini (MDG) coefficient. …”
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Diabetic ketoacidosis treatment outcomes and its associated factors among adult patients with diabetes mellitus admitted to public hospitals in Nekemte Town, Ethiopia: a cross-sect... by Daniel Mitiku Yigazu, Matiyos Lema, Firomsa Bekele, Dawit Tesfaye Daka, Dagim Samuel, Nigatu Addisu

“…Independent predictors of DKA recovery were comorbidities [AOR: 3.45, 95% CI: 1.33, 9.72], admission Glasgow Coma Scale (GCS) score (<8) [AOR: 2.74, 95% CI: 1.02, 7.34], random blood glucose (RBS) (≥ 500) [AOR: 3.07 (95% CI: 1.12, 8.39)], and urine ketones (≥ +3) [AOR: 3.24, 95% CI: 1.18, 8.88].Conclusion and recommendationMost of the treated patients with DKA were discharged with improvement. …”
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Problem of pain in the USA: evaluating the generalisability of high-impact chronic pain models over time using National Health Interview Survey (NHIS) data by Sean Mackey, Titilola Falasinnu, Md Belal Hossain, Mohammad Ehsanul Karim, Kenneth Arnold Weber

“…We used logistic regression with Least Absolute Shrinkage and Selection Operator (LASSO) and random forest techniques. We assessed model discrimination, calibration and overall performance using metrics such as area under the curve (AUC), calibration slope and Brier score.Results Scenario 1, validating the NHIS 2016 model against 2017 data, demonstrated excellent discrimination with an AUC of 0.89 (95% CI 0.88 to 0.90) for both LASSO and random forest models. …”
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Characteristics and Associated Factors of Insomnia Among the General Population in the Post-Pandemic Era of COVID-19 in Zhejiang, China: A Cross-Sectional Study by Da M, Mou S, Hou G, Shen Z

“…Six machine learning models were employed to develop a predictive model for insomnia, namely logistic regression, random forest, neural network, support vector machine, CatBoost, and gradient boosting decision tree.Results: The study obtained 2769 and 1161 valid responses in T1 and T2, respectively. …”
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Comprehensive approach to predictive analysis and anomaly detection for road crash fatalities by Chopparapu Gowthami, S. Kavitha

“…These factors include weather, road features, and geographic regions. A Random Forest Regression model is trained to estimate the number of deaths arising from traffic crashes after data preprocessing, which includes feature selection and encoding. …”
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Hubungan Status Gizi dengan Prestasi Belajar Siswa Sekolah Dasar Negeri 01 Guguk Malintang Kota Padangpanjang by Rosita Hayatus Sa’adah, Rahmatina B. Herman, Susila Sastri

“…The subject in this experimental were 120 students from grade 1-5 were taken with Proportional random sampling technique. Research data from the antropometric based BMI index and high index and learning achievement from report cards. …”
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Novel approach for noninvasive pelvic floor muscle strength measurement using extracorporeal surface perineal pressure measurement and machine learning modeling by Ui-jae Hwang, Sun-hee Ahn, Hyeon-ju Lee, Yurin Jeon, Myung Jae Jeon

“…The top-performing models for predicting bladder base displacement were the support vector machine [root mean square error (RMSE) = 0.139, R2 = 0.542], random forest (RMSE = 0.123, R2 = 0.367), and AdaBoost (RMSE = 0.123, R2 = 0.320) on the training set, and AdaBoost (RMSE = 0.173, R2 = 0.537), random forest (RMSE = 0.177, R2 = 0.512), and support vector machine (RMSE = 0.178, R2 = 0.508) on the test set. …”
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Assessing recovery time of ecosystems in China: insights into flash drought impacts on gross primary productivity by M. Lu, M. Lu, H. Sun, H. Sun, H. Sun, Y. Yang, J. Xue, H. Ling, H. Zhang, W. Zhang

“…In this study, we investigate the recovery time patterns in a terrestrial ecosystem in China based on GPP using a random forest regression model and the SHapley Additive exPlanations (SHAP) method. …”
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Model Klasifikasi Dengan Logistic Regression Dan Recursive Feature Elimination Pada Data Tidak Seimbang by Sutarman, Rimbun Siringoringo, Dedy Arisandi, Edi Kurniawan, Erna Budhiarti Nababan

“…These results are better than those of four other classification models, namely Naïve Bayes, Decision Tree, K-NN, and Random Forest. …”
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Association between Anemia and Risk of Parkinson Disease by Yao-Chin Wang, Abel Po-Hao Huang, Sheng-Po Yuan, Chu-Ya Huang, Chieh-Chen Wu, Tahmina Nasrin Poly, Suleman Atique, Woon-Man Kung

“…Heterogeneity among the studies was assessed using the Q and I2 statistic. We utilized the random-effect model to calculate the overall RR with 95% CI. …”
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Structural Alteration of Medial Temporal Lobe Subfield in the Amnestic Mild Cognitive Impairment Stage of Alzheimer’s Disease by Pan He, Hang Qu, Ming Cai, Weijie Liu, Xinyi Gu, Qiang Ma

“…In the comparisons of AD-aMCI-NC, AD-aMCI, and AD-NC, the hippocampus, amygdala, and entorhinal cortex showed differences in the gray matter densities (p<0.05); the differences of mammillary body densities were not significant in the random comparison between these three groups (p>0.05). …”
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Hubungan Merokok dengan Kejadian Hipertensi pada Laki-Laki Usia 35-65 Tahun di Kota Padang by Yashinta Octavian Gita Setyanda, Delmi Sulastri, Yuniar Lestari

“…Jumlah subjek sebanyak 92 orang yang diambil secara multi stage random sampling. Instrumen dalam penelitian ini ialah kuesioner untuk data responden dan karakteristik kebiasaan merokok, serta sphygmomanometer untuk mengukur tekanan darah. …”
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Desain Penilaian Risiko Privasi pada Aplikasi Seluler Melalui Model Machine Learning Berbasis Ensemble Learning dan Multiple Application Attributes by R. Ahmad Imanullah Zakariya, Kalamullah Ramli

“…Hasil percobaan menunjukkan bahwa penerapan ensemble learning dengan algoritma klasifikasi Decision Tree (DT), K-Nearest Neighbor (KNN), dan Random Forest (RF) memiliki performa model yang lebih baik dibandingkan dengan menggunakan algoritma klasifikasi tunggal, dengan accuracy sebesar 95.2%, nilai precision 93.2%, nilai F1-score 92.4%, dan True Negative Rate (TNR) sebesar 97.6%. …”
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Spatiotemporal variation and influencing factors of phosphorus in Asia’s longest river based on receptor model and machine learning by Gege Cai, Jiamei Zhang, Wanlu Li, Jiejun Zhang, Yun Liu, Shanshan Xi, Guolian Li, Haibin Li, Xing Chen, Fanhao Song, Fazhi Xie

“…Machine learning models (e.g., RidgeCV, Random Forest, XGBoost) were used to establish a connection between total phosphorus concentrations and explanatory variables defining influencing factors, aiming to predict total phosphorus concentrations in the Yangtze River. …”
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Gambaran Golongan Sekretor dan Nonsekretor yang Diperiksa Melalui Saliva Mahasiswa Pendidikan Dokter Fakultas Kedokteran Universitas Andalas by Alqadri ., Zelly Dia Rofinda, Rika Susanti

“…Cara pengambilan sampel adalah dengan <em>simple random sampling</em>. Data mengenai golongan sekretor dan nonsekretor didapatkan melalui pemeriksaan saliva dengan metode absorpsi inhibisi. …”
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