Application of deep learning and feature selection technique on external root resorption identification on CBCT images by Nor Hidayah Reduwan, Azwatee Abdul Aziz, Roziana Mohd Razi, Erma Rahayu Mohd Faizal Abdullah, Seyed Matin Mazloom Nezhad, Meghna Gohain, Norliza Ibrahim

“…The performance of four DLMs including Random Forest (RF) + Visual Geometry Group 16 (VGG), RF + EfficienNetB4 (EFNET), Support Vector Machine (SVM) + VGG, and SVM + EFNET) and four hybrid models (DLM + FST: (i) FS + RF + VGG, (ii) FS + RF + EFNET, (iii) FS + SVM + VGG and (iv) FS + SVM + EFNET) was compared. …”
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Comparing the effectiveness, safety and tolerability of interventions for depressive symptoms in people with multiple sclerosis: a systematic review and network meta-analysis proto... by Amalia Karahalios, Allan G Kermode, Yvonne C Learmonth, Julia Lyons, Stephanie Campese, Alexandra Metse, Claudia H Marck

“…We plan to provide summary measures including forest plots, a geometry of the network, surface under the cumulative ranking curve, and a league table, and perform subgroup analyses. …”
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Habitat radiomics based on CT images to predict survival and immune status in hepatocellular carcinoma, a multi-cohort validation study by Kun Chen, Chunxiao Sui, Ziyang Wang, Zifan Liu, Lisha Qi, Xiaofeng Li

“…The habitat radiomic model based on the segmented habitat 4 involving decision tree (DT) screening and random forest (RF) classifier was identified as the optimal model with an AUCmean of 0.806. …”
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Non-pharmacological interventions for the reduction and maintenance of blood pressure in people with prehypertension: a systematic review protocol by Paul Rutter, Andrew Clegg, Valerio Benedetto, Caroline Watkins, Nefyn Williams, Joseph Spencer, Lucy Hives, Emma P Bray, Cath Harris, Rachel F Georgiou, Nafisa Iqbal

“…Heterogeneity will be assessed through visual inspection of forest plots and the calculation of the χ2 and I2 statistics and causes of heterogeneity will be assessed where sufficient data are available. …”
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A Simple Machine Learning-Based Quantitative Structure–Activity Relationship Model for Predicting pIC₅₀ Inhibition Values of FLT3 Tyrosine Kinase by Jackson J. Alcázar, Ignacio Sánchez, Cristian Merino, Bruno Monasterio, Gaspar Sajuria, Diego Miranda, Felipe Díaz, Paola R. Campodónico

“…<b>Methods:</b> Using a dataset which was 14 times larger than those employed in prior studies (1350 compounds with 1269 molecular descriptors), we trained a random forest regressor, chosen due to its superior predictive performance and resistance to overfitting. …”
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Prevalence of non-vaccine high-risk HPV cervical infections in vaccinated women: a systematic review and meta-analysis by Glauciane Resende do Nascimento, Ana Carolina da Silva Santos, Nayara Nascimento Toledo Silva, Nathalia Sernizon Guilmarães, Angélica Alves Lima, Wendel Coura-Vital

“…Intervention effects were expressed as prevalence ratios (PR). Forest plots were used to visualize vaccination effects. …”
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RADAR-AD: assessment of multiple remote monitoring technologies for early detection of Alzheimer’s disease by Manuel Lentzen, Srinivasan Vairavan, Marijn Muurling, Vasilis Alepopoulos, Alankar Atreya, Merce Boada, Casper de Boer, Pauline Conde, Jelena Curcic, Giovanni Frisoni, Samantha Galluzzi, Martha Therese Gjestsen, Mara Gkioka, Margarita Grammatikopoulou, Lucrezia Hausner, Chris Hinds, Ioulietta Lazarou, Alexandre de Mendonça, Spiros Nikolopoulos, Dorota Religa, Gaetano Scebba, Pieter Jelle Visser, Gayle Wittenberg, Vaibhav A. Narayan, Neva Coello, Anna-Katharine Brem, Dag Aarsland, Holger Fröhlich, on behalf of RADAR-AD

“…Additionally, we employed four machine learning classifiers – Logistic Regression, Decision Tree, Random Forest, and XGBoost – using a nested cross-validation approach to assess the discriminatory capabilities of the RMTs. …”
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Diurnal and Daily Variations of PM2.5 and its Multiple-Wavelet Coherence with Meteorological Variables in Indonesia by Nani Cholianawati, Tiin Sinatra, Ginaldi Ari Nugroho, Didin Agustian Permadi, Asri Indrawati, Halimurrahman, Meta Kallista, Moch Syarif Romadhon, Ilma Fauziah Ma’ruf, Dipo Yudhatama, Tesalonika Angela Putri Madethen, Asif Awaludin

“…Meanwhile, the investigation on the extreme rise of PM2.5 in Pontianak due to peatland forest fires using HYSPLIT shows that emission from the surrounding area significantly raises the maximum half-hourly in Pontianak to 700 μg m−3.…”
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Identification and susceptibility assessment of landslide disasters in the red bed formation along the Nanjian-Jingdong Expressway by Yifan Cao, Zhifang Zhao, Mingchun Wen, Xin Zhao, Dingyi Zhou, Jingyi Qin, Liu Ouyang, Jingyao Cao

“…By analyzing nine evaluation indicators, this study assesses the susceptibility of landslide disasters in the research area by applying Random Forest (RF), Extreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), and Stacking Ensemble Strategies (Stacking). …”
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Sub-District Level Spatiotemporal Changes of Carbon Storage and Driving Factor Analysis: A Case Study in Beijing by Yirui Zhang, Shouhang Du, Linye Zhu, Tianzhuo Guo, Xuesong Zhao, Junting Guo

“…The results show the following: (1) From 2000 to 2020, the overall land use change in Beijing showed a trend of “Significant decrease in cropland area; Forest increase gradually; Shrub and grassland area increase first and then decrease; Decrease and then increase in water; Impervious expands in a large scale”. (2) From 2000 to 2020, the carbon storage in Beijing showed a “decrease-increase” fluctuation, with an overall decrease of 1.3 Tg. …”
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The Efficacy of Lubiprostone in Patients of Constipation: An Updated Systematic Review and Meta‐Analysis by Umar Akram, Obaid Ur Rehman, Eeshal Fatima, Zain Ali Nadeem, Omer Usman, Waqas Rasheed, Ramsha Ali, Khawaja Abdul Rehman, Abdulqadir J. Nashwan

“…A meta‐analysis was performed and findings were presented using forest plots. Results A total of 14 studies, comprising 4550 patients, were included in the review. …”
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Seed Protein Content Estimation with Bench-Top Hyperspectral Imaging and Attentive Convolutional Neural Network Models by Imran Said, Vasit Sagan, Kyle T. Peterson, Haireti Alifu, Abuduwanli Maiwulanjiang, Abby Stylianou, Omar Al Akkad, Supria Sarkar, Noor Al Shakarji

“…Convolutional neural networks (CNNs) with attention mechanisms were proposed along with traditional machine learning models based on feature engineering including Random Forest (RF) and Support Vector Machine (SVM) regression for comparative analysis. …”
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Comparative assessment of empirical and hybrid machine learning models for estimating daily reference evapotranspiration in sub-humid and semi-arid climates by Siham Acharki, Ali Raza, Dinesh Kumar Vishwakarma, Mina Amharref, Abdes Samed Bernoussi, Sudhir Kumar Singh, Nadhir Al-Ansari, Ahmed Z. Dewidar, Ahmed A. Al-Othman, Mohamed A. Mattar

“…The ML models examined include Random Forest (RF), M5 Pruned (M5P), eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM), with hybrid combinations of RF-M5P, RF-XGBoost, RF-LightGBM, and XGBoost-LightGBM. …”
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Sustainable foam glass property prediction using machine learning: A comprehensive comparison of predictive methods and techniques by Mohamed Abdellatief, Leong Sing Wong, Norashidah Md Din, Ali Najah Ahmed, Abba Musa Hassan, Zainah Ibrahim, G. Murali, Kim Hung Mo, Ahmed El-Shafie

“…In this context, the current study proposes a novel approach by developing a thoughtful system for assessing performance and intelligent design utilizing ML models such as Gradient Boosting (GB), Random Forest (RF), Gaussian Process Regression (GPR), and Linear Regression (LR) to predict porosity and compressive strength (CS) of FG. …”
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Exploring the significance of medical humanities in shaping internship performance: insights from curriculum categories by Chao Ting Chen, Anna Y.Q. Huang, Po-Hsun Hou, Ji-Yang Lin, His-Han Chen, Shiau-Shian Huang, Stephen J. H. Yang

“…Ten-fold cross-validation machine learning models (support vector machines, logistic regression, random forest) were performed to predict the internship grades. …”
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A novel method for detecting intracranial pressure changes by monitoring cerebral perfusion via electrical impedance tomography by Ming-xu Zhu, Jun-yao Li, Zhan-xiu Cai, Yu Wang, Wei-ce Wang, Yi-tong Guo, Guo-bin Gao, Qing-dong Guo, Xue-tao Shi, Wei-chen Li

“…Under both circumstances, ROC curve analysis showed that the comprehensive model of perfusion parameters based on the random forest algorithm had a sensitivity and specificity of more than 90% and an area under the curve (AUC) of more than 0.9 for detecting ICP increments of both 5 and 10 mmHg. …”
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Design a Robust DDoS Attack Detection and Mitigation Scheme in SDN-Edge-IoT by Leveraging Machine Learning by Habtamu Molla Belachew, Mulatu Yirga Beyene, Abinet Bizuayehu Desta, Behaylu Tadele Alemu, Salahadin Seid Musa, Alemu Jorgi Muhammed

“…We evaluated four popular classifiers (K-Nearest Neighbor (K-NN), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and FeedForward Neural Network (FFNN)) on benchmark datasets CICIDS2017 and Edge-IIoTset, conducting both binary and multi-class classifications. …”
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Validation of In-field Calibration for Low-Cost Sensors Measuring Ambient Particulate Matter in Kolkata, India by Siddharth Nobell, Arnab Majumdar, Shovon Mukherjee, Sukumar Chakraborty, Sanjoy Chatterjee, Soumitra Bose, Anindita Dutta, Sandhya Sethuraman, Daniel M. Westervelt, Shairik Sengupta, Rakhi Basu, V. Faye McNeill

“…Data from the co-locations were used to calibrate the LCS network using random forest regression and multiple linear regression approaches. …”
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Systematic underestimation of type-specific ecosystem process variability in the Community Land Model v5 over Europe by C. Poppe Terán, C. Poppe Terán, C. Poppe Terán, B. S. Naz, B. S. Naz, H. Vereecken, H. Vereecken, R. Baatz, R. A. Fisher, H.-J. Hendricks Franssen, H.-J. Hendricks Franssen

“…GPP was underestimated by CLM5<span class="inline-formula">_PFT</span>, especially in deciduous forests (bias of <span class="inline-formula">−43.76</span> %). …”
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Clinical validation and optimization of machine learning models for early prediction of sepsis by Xi Liu, Meiyi Li, Xu Liu, Yuting Luo, Dong Yang, Hui Ouyang, Jiaoling He, Jinyu Xia, Fei Xiao, Fei Xiao, Fei Xiao

“…We trained models in predicting sepsis by machine learning (ML) methods, including logistic regression, decision tree, random forest (RF), multi-layer perceptron, and light gradient boosting. …”
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