Machine learning for classifying chronic kidney disease and predicting creatinine levels using at-home measurements by Brady Metherall, Anna K. Berryman, Georgia S. Brennan

“…We employ artificial neural networks (ANNs) and random forests (RFs) on a dataset of 400 patients with 25 input features, which we divide into three feature sets. …”
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A Survey of Alien Dendroflora of Bosnia and Herzegovina by Neđad Bašić, Fatima Pustahija, Aldin Boškailo, Edina Muratović

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Predictive modelling of hexagonal boron nitride nanosheets yield through machine and deep learning: An ultrasonic exfoliation parametric evaluation by Jerrin Joy Varughese, Sreekanth M․S․

“…A suite of machine learning regression models including Adaptive Boosting (AdaBoost) Regressor, Random Forest (RF) Regressor, Linear Regressor (LR), and Classification and Regression Tree (CART) Regressor, was employed alongside a deep neural network (DNN) architecture optimized using various algorithms such as Adaptive Moment Estimation (Adam), Root Mean Square Propagation (RMS Prop), Stochastic Gradient Descent (SGD), and Limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS). …”
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Enhanced Bug Priority Prediction via Priority-Sensitive Long Short-Term Memory–Attention Mechanism by Geunseok Yang, Jinfeng Ji, Jaehee Kim

“…Compared to baseline models such as Naïve Bayes, Random Forest, Decision Tree, SVM, CNN, LSTM, and CNN-LSTM, the proposed model achieved a superior performance. …”
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Design and Development of Polymer-Based Optical Fiber Sensor for GAIT Analysis by Mamidipaka Hema, Jami Venkata Suman, Boddepalli Kiran Kumar, Adisu Haile

“…The techniques used for classification of the obtained signals are random forest (RF) and support vector machine (SVM). The accuracy, sensitivity, and specificity results are obtained using SVM classifier and RF classifier. …”
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Borax and Boric Acid Combination As Fire Retardant and Anti-termite Agents on Red Meranti Wood by Mahdi Santoso, Sutjipto A. Hadikusumo, Abdul Aziz

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Very Short-Term Blackout Prediction for Grid-Tied PV Systems Operating in Low Reliability Weak Electric Grids of Developing Countries by Benson H. Mbuya, Aleksandar Dimovski, Marco Merlo, Thomas Kivevele

“…A very short-term power outage prediction model framework based on a hybrid random forest (RF) algorithm was developed using open-source Python machine learning libraries and using a dataset generated from the pilot project’s experimental microgrid. …”
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Increasing Neural-Based Pedestrian Detectors’ Robustness to Adversarial Patch Attacks Using Anomaly Localization by Olga Ilina, Maxim Tereshonok, Vadim Ziyadinov

“…The proposed method consists of a Deep Convolutional Neural Network to reconstruct a benign image from the adversarial one; a Calculating Maximum Error block to highlight the mismatches between input and reconstructed images; a Localizing Anomalous Fragments block to extract the anomalous regions using the Isolation Forest algorithm from histograms of images’ fragments; and a Clustering and Processing block to group and evaluate the extracted anomalous regions. …”
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Machine Learning- and Feature Selection-Enabled Framework for Accurate Crop Yield Prediction by Sandeep Gupta, Angelina Geetha, K. Sakthidasan Sankaran, Abu Sarwar Zamani, Mahyudin Ritonga, Roop Raj, Samrat Ray, Hussien Sobahi Mohammed

“…Machine learning predictors, namely, particle swarm optimization-support vector machine (PSO-SVM), K-nearest neighbor, and random forest, are used for classification.…”
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Classification-augmented survival estimation (CASE): A novel method for individualized long-term survival prediction with application to liver transplantation. by Hamed Shourabizadeh, Dionne M Aleman, Louis-Martin Rousseau, Katina Zheng, Mamatha Bhat

“…Machine learning classification models, even those designed for survival predictions like random survival forest (RSF), also struggle to provide accurate long-term predictions due to class imbalance. …”
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Study on the temperature prediction model of residual coal in goaf based on ACO-KELM by ZHAI Xiaowei, WANG Chen, HAO Le, LI Xintian, HOU Qinyuan, MA Teng

“…Compared to the prediction models based on extreme learning machine (ELM) and random forest (RF) algorithms, the ACO-KELM model achieved an average absolute error of 0.0701 ℃ and a root mean square error (RMSE) of 0.0748 ℃ on the test set, reducing these errors by 65% and 195%, respectively, compared to the ELM-based model, and by 53% and 156%, respectively, compared to the RF-based model. …”
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What Influences Low-cost Sensor Data Calibration? - A Systematic Assessment of Algorithms, Duration, and Predictor Selection by Lu Liang, Jacob Daniels

“…This study comprehensively assessed ten widely used data techniques, namely AdaBoost, Bayesian ridge, gradient tree boosting, K-nearest neighbors, Lasso, multivariable linear regression, neural network, random forest, ridge regression, and support vector machine. …”
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What Is Affecting the Residents’ Subjective Perception toward Objective Environment Quality? by Jihong Zhang, Chaopeng Xie, Chuan Chen, Ninghan Xu, Rui Gao

“…In order to analyze the differences between groups, firstly, the important factors driving the differences were extracted by random forest. Secondly, the key individual characteristics were identified by the model based on conditional inference tree. …”
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Parameter Acquisition Study of Mining-Induced Surface Subsidence Probability Integral Method Based on RF-AGA-ENN Model by Jinman Zhang, Liangji Xu, Jiewei Li, Yueguan Yan, Ruirui Xu

“…To obtain more accurate PIM parameters in the absence of observational data, we propose a combined machine learning model (RF-AGA-ENN)—random forest (RF) extracts the best combination of features as the input layer of Elman neural network (ENN); ant colony algorithm (ACO) and genetic algorithm (GA) are combined (called AGA) for the weights and thresholds of ENN optimization. …”
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Detection of multidrug resistant Vibrio parahaemolyticus and anti-Vibrio Streptomyces sp. MUM 178J by Ke-Yan Loo, Loh Teng-Hern Tan, Jodi Woan-Fei Law, Priyia Pusparajah, Learn-Han Lee, Vengadesh Letchumanan

“…This study aimed to investigate the prevalence of MDR V. parahaemolyticus to provide insight into the current antibiotic resistance patterns of this pathogen and to determine potential anti-Vibrio properties of Streptomyces MUM 178J which was previously isolated from the soils of a mangrove forest in Sarawak, Malaysia. Colony morphology and toxR-assay indicated that all samples tested positive for V. parahaemolyticus. …”
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Variability of morphometrical characteristics of needles at a clonal plantation of plus trees of scots pine (Pinus sylvestris L.) by N. N. Besschetnova, V. P. Besschetnov

“…The formation of plus trees assortment for seed orchards is one of the most difficult problems of contemporary forest breeding. This problem is related to the risk of inbreeding depression of the seed progeny of plus trees, which do not have any defense mechanism against self-pollination. …”
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Global Ionospheric TEC Forecasting for Geomagnetic Storm Time Using a Deep Learning‐Based Multi‐Model Ensemble Method by Xiaodong Ren, Pengxin Yang, Dengkui Mei, Hang Liu, Guozhen Xu, Yue Dong

“…In this study, we developed a new deep learning‐based multi‐model ensemble method (DLMEM) to forecast geomagnetic storm‐time ionospheric TEC that combines the Random Forest (RF) model, the Extreme Gradient Boosting (XGBoost) algorithm, and the Gated Recurrent Unit (GRU) network with the attention mechanism. …”
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Soil Parameter Mapping and Ad Hoc Power Analysis to Increase Blocking Efficiency Prior to Establishing a Long-Term Field Experiment by Doug Collins, Chris Benedict, Andy Bary, Craig Cogger

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A Machine Learning Method to improve Supplier Delivery Appointments in Supply Chain Industries by Anitha Palakshappa, Sumana Maradithaya, Charunayana V

“…Prediction algorithms namely, Logistic Regression (LR), K-Nearest Neighbour (KNN), and Random Forest (RF) are used for forecasting. The appointment is assigned to a supplier based on the delivery date of a previous supplier order. …”
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An interpretable and stacking ensemble model for predicting heat and mass transfer of desiccant wheel by Mengyang Li, Liu Chen

“…The model uses an integration approach, Light Gradient Boosting Machine, Random Forest and Back Propagation Neural Network models are used as the first-level base models to learn the data, and the Linear Regression model as a meta-model integrates the output of the base model to obtain the final prediction results. …”
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