Integrated Bioinformatics Identifies FREM1 as a Diagnostic Gene Signature for Heart Failure by Chenyang Jiang, Weidong Jiang

“…Integrating three machine learning methods, the least absolute shrinkage and selection operator (LASSO) algorithm, random forest (RF) algorithm, and support vector machine recursive feature elimination (SVM-RFE) are used to determine candidate diagnostic gene signals. …”
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Soil Quality Assessment Strategies for Evaluating Soil Degradation in Northern Ethiopia by Gebreyesus Brhane Tesfahunegn

“…Eight LUSMS selected for soil sampling and analysis included (i) natural forest (LS1), (ii) plantation of protected area, (iii) grazed land, (iv) teff (Eragrostis tef)-faba bean (Vicia faba) rotation, (v) teff-wheat (Triticum vulgare)/barley (Hordeum vulgare) rotation, (vi) teff monocropping, (vii) maize (Zea mays) monocropping, and (viii) uncultivated marginal land (LS8). …”
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The Impacts of Blue Stain Degradation on the Industrial Processing Properties of logs by Osman Komut, Atakan Öztürk

“…This study aims to determine the effects of blue stain degradation on manufacturing process of forest industry managements processing log and the losses caused by this. …”
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Feature selection based on Mahalanobis distance for early Parkinson disease classification by Mustafa Noaman Kadhim, Dhiah Al-Shammary, Ahmed M. Mahdi, Ayman Ibaida

“…Similarly, on the ''Parkinson Dataset with Replicated Acoustic Features'', the feature set was reduced from 45 to 18 features, achieving accuracy improvements ranging from 1.38 % to 13.88 %, with the Random Forest (RF) classifier achieving the best accuracy of 95.83 %. …”
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Gastrochilus obovatifolius (Orchidaceae, Aeridinae), a new species from the Daba Mountains of Chongqing, China by Chi Xiong, Xiao-Ying Fu, Ke Tan, Ya Huang, Hong-Jing Zhang, Yi-Chen Wang, Yu-Bing Yang, Si-Rong Yi

“…The new species is a trunk epiphyte in evergreen broad-leaved forest.…”
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Identifying RBBP7 as a Promising Diagnostic Biomarker for BK Virus-Associated Nephropathy by Yicun Wang, Yuxuan Wang, Di Zhang, Hao Zhang, Wei Wang, Xiaopeng Hu

“…We collected gene expression profiles of 169 kidney biopsies taken from BKVN, rejection, and stable functioning allografts, based on single sample gene set enrichment analysis and random forest algorithm, and three hallmark activities associated with DNA damage and proliferation were found to be relatively specific in BKVN. …”
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Harnessing synergy of machine learning and nature-inspired optimization for enhanced compressive strength prediction in concrete by Abba Bashir, Esar Ahmad, Shashivendra Dulawat, Sani I. Abba

“…This study assesses nine machine learning models, integrating conventional AI algorithms, such as artificial neural network (ANN), support vector regression (SVR), and random forest (RF) with nature-inspired optimization techniques including chicken swarm optimization (CSO), moth flame optimization algorithm (MFO), and whale optimization algorithm (WOA). …”
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The Impact of Selected Regimens of Chronic HIV‐Antiretroviral Treatment on Glycemic Control Markers and Correlates: A Systematic Review and Meta‐Analysis Protocol by Mlindeli Gamede, Mbulelo Aubrey Sosibo, Nontobeko Gumede, Mluleki Luvuno

“…Additionally, heterogeneity tests will be conducted using both Χ2 and I2 tests, meta‐analysis will be conducted using the Review Manager version 5.4 software (RevMan), and data will be presented in forest plots. Grading of Recommendations Assessment, Development, and Evaluation approach (GRADE) will be used to assess the strength of evidence in eligible reports. …”
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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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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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Parametric Estimation of Directional Wave Spectra from Moored FPSO Motion Data Using Optimized Artificial Neural Networks by Do-Soo Kwon, Sung-Jae Kim, Chungkuk Jin, MooHyun Kim

“…In addition, comparisons against other machine learning (ML) methods—such as Support Vector Machines, Random Forest, Gradient Boosting, and Ridge Regression—demonstrate the present ANN model’s superior ability to capture intricate nonlinear interdependencies between vessel motions and environmental conditions.…”
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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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Age group classification based on optical measurement of brain pulsation using machine learning by Martti Ilvesmäki, Hany Ferdinando, Kai Noponen, Tapio Seppänen, Vesa Korhonen, Vesa Kiviniemi, Teemu Myllylä

“…ML experiments utilized support vector machines and random forest learners, along with maximum relevance minimum redundancy and principal component analysis for feature selection. …”
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XAI-Enhanced Machine Learning for Obesity Risk Classification: A Stacking Approach With LIME Explanations by Mohammad Azad, Md Faraz Kabir Khan, Sameh Abd El-Ghany

“…Our proposed model employs an ensemble approach, specifically a stacking algorithm, where the base estimators include the Light Gradient Boosting Machine (LGBM) classifier, the Logistic Regression (LR) classifier, and the Random Forest (RF) Classifier, and the Stochastic Gradient Descent (SGD) classifier is selected as the final estimator. …”
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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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