Bridging the Gap: Limitations of Machine Learning in Real-World Prediction of Heavy Metal Accumulation in Rice in Hunan Province by Qing-Qian Peng, Xia Zhou, Hang Zhou, Ye Liao, Zi-Yu Han, Lu Hu, Peng Zeng, Jiao-Feng Gu, Rong Zhang

“…This study systematically evaluated the predictive performance of Random Forest (RF), Gradient Boosting Decision Tree (GBDT), and Residual Neural Networks (ResNet), using a multi-source soil–rice dataset comprising 57,200 samples from Hunan Province. …”
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Enhancing Network Security: A Study on Classification Models for Intrusion Detection Systems by Abeer Abd Alhameed Mahmood, Azhar A. Hadi, Wasan Hashim Al-Masoody

“…The meta-ensemble learning model does better at sub-multiclass classification than decision trees, random forests, and extreme gradient boosting. …”
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Personalized diagnosis of radiation pneumonitis in breast cancer patients based on radiomics by Xiaobo Wen, Xiaobo Wen, Xiaobo Wen, Yutao Zhao, Wen Dong, Congbo Yang, Jinzhi Li, Li Sun, Yutao Xiu, Chang’e Gao, Ming Zhang

“…Eight classifiers [logistic regression (LR), support vector machine (SVM), K-nearest neighbor (KNN), random forest (RF), Extra Tree (ET), XGBoost, LightGBM, and multilayer perceptron (MLP)] were trained and evaluated using accuracy, area under the receiver operating characteristic curve (AUC) with 95% confidence intervals, sensitivity, and specificity.ResultsIn the independent test cohort, LR achieved the highest performance [accuracy 0.897, AUC 0.929 (95% CI, 0.838–1.000), sensitivity 0.786, and specificity 1.000]. …”
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Proposal for Using AI to Assess Clinical Data Integrity and Generate Metadata: Algorithm Development and Validation by Caroline Bönisch, Christian Schmidt, Dorothea Kesztyüs, Hans A Kestler, Tibor Kesztyüs

“…Logistic regression, k-nearest neighbors, a naive bayes classifier, a decision tree classifier, a random forest classifier, extreme gradient boosting (XGB), and support vector machines (SVM) were selected as machine learning algorithms. …”
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Deterministic and Stochastic Machine Learning Classification Models: A Comparative Study Applied to Companies’ Capital Structures by Joseph F. Hair, Luiz Paulo Fávero, Wilson Tarantin Junior, Alexandre Duarte

“…The results indicate that decision trees, random forest, and XGBoost excelled in the training phase but showed higher overfitting when evaluated in the test sample. …”
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Supervised Machine Learning for Real-Time Intrusion Attack Detection in Connected and Autonomous Vehicles: A Security Paradigm Shift by Ahmad Aloqaily, Emad E. Abdallah, Hiba AbuZaid, Alaa E. Abdallah, Malak Al-hassan

“…The selected algorithms encompassed Decision Trees, Random Forests, Naive Bayes, Logistic Regression, XG Boost, LightGBM, and Multi-layer Perceptrons. …”
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#GAMEADDICTED: A Machine Learning Framework for Digital Game Addiction Detection and Early Intervention by Esra Kahya Ozyirmidokuz, Bekir Asim Celik, Eduard Alexandru Stoica

“…After extensive preprocessing, we trained and evaluated six machine learning models: XGBoost, Support Vector Machines (SVM), Logistic Regression, K-Nearest Neighbors (KNN), Decision Trees, and Random Forests. XGBoost consistently outperformed all other models, achieving the highest accuracy of 93.06% on the binary-labeled dataset and 92.49% on the expanded dataset. …”
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Predicting suicidal behavior outcomes: an analysis of key factors and machine learning models by Mohammad Bazrafshan, Kourosh Sayehmiri

“…In the second phase, missing data were removed, and the dataset was standardized. Five binary classification algorithms were utilized, including Random Forest, Logistic Regression, and Decision Trees, with hyperparameters optimized using the area under the receiver operating characteristic curve (AUC) and F1 score metrics. …”
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Predicting Student Performance and Enhancing Learning Outcomes: A Data-Driven Approach Using Educational Data Mining Techniques by Athanasios Angeioplastis, John Aliprantis, Markos Konstantakis, Alkiviadis Tsimpiris

“…Five machine learning algorithms—k-nearest neighbors, random forest, logistic regression, decision trees, and neural networks—were applied to identify correlations between courses and predict grades. …”
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A hybrid ensemble framework with particle swarm optimization for network anomaly detection by Narinder Verma, Neerendra Kumar, Gourav Kumar, Kuljeet Singh

“…We evaluate multiple machine learning models, including Decision Trees (DT), Artificial Neural Networks (ANN), K-Nearest Neighbors (KNN), Random Forests (RF), and an ensemble of these models for both binary and multi-class classification tasks. …”
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Machine learning analysis of rivaroxaban solubility in mixed solvents for application in pharmaceutical crystallization by Mohammed Alqarni, Ali Alqarni

“…Using a dataset with over 250 data points and including solvents encoded with one-hot encoding, four models were compared: Gradient Boosting (GB), Light Gradient Boosting (LGB), Extra Trees (ET), and Random Forest (RF). The Jellyfish Optimizer (JO) algorithm was applied to tune hyperparameters, enhancing model performance. …”
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Calibrating multiplex serology for Helicobacter pylori by Emmanuelle A. Dankwa, Martyn Plummer, Daniel Chapman, Rima Jeske, Julia Butt, Michael Hill, Tim Waterboer, Iona Y. Millwood, Ling Yang, Christiana Kartsonaki

“…We discuss these challenges and propose a novel solution to optimize the translation of the continuous measurements from multiplex serology into probabilities of H. pylori infection, using classification algorithms (Bayesian additive regressive trees (BART), multidimensional monotone BART, logistic regression, random forest and elastic net). …”
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A Deep Learning Approach for Multiclass Attack Classification in IoT and IIoT Networks Using Convolutional Neural Networks by Ali Abdi Seyedkolaei, Fatemeh Mahmoudi, José García

“…Leveraging the DNN-EdgeIIoT dataset, which includes a wide range of attack types and traffic scenarios, we conduct comprehensive experiments to compare the CNN-based model against traditional machine learning techniques, including decision trees, random forests, support vector machines, and K-nearest neighbors. …”
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Ensemble machine learning for predicting academic performance in STEM education by Aklilu Mandefro Messele

“…We gathered secondary data from the University of Gondar, Addis Ababa University, and Bahir Dar University, employing techniques such as Random Forest, CatBoost, Extreme Gradient Boosting, Gradient Boosting, Decision Trees, Logistic Regression, and Support Vector Machines. …”
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Cheating Detection in Online Exams Using Deep Learning and Machine Learning by Bahaddin Erdem, Murat Karabatak

“…For regression and classification, deep neural network (DNN) from deep learning algorithms and support vector machine (SVM), decision trees (DTs), k-nearest neighbor (KNN), random forest (RF), logistic regression (LR), and extreme gradient boosting (XGBoost) algorithms from machine learning algorithms were used. …”
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Enhancing seizure detection with hybrid XGBoost and recurrent neural networks by Santushti Santosh Betgeri, Madhu Shukla, Dinesh Kumar, Surbhi B. Khan, Muhammad Attique Khan, Nora A. Alkhaldi

“…Signal processing techniques were applied to enhance data quality, and all models were trained on the same dataset for binary classification. Sixteen models were evaluated, including traditional classifiers such as Logistic Regression, K-Nearest Neighbors, Decision Trees, ensemble methods that include Random Forest, Gradient Boosting, and advanced techniques such as Extreme Gradient Boosting, Support Vector Machines, Gated Recurrent Units, and Long Short-Term Memory networks. …”
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Explainable machine learning models for mortality prediction in patients with sepsis in tertiary care hospital ICU in low- to middle-income countries by Saumya Diwan, Vinay Gandhi, Esha Baidya Kayal, Puneet Khanna, Amit Mehndiratta

“…Four machine learning algorithms, Random Forest, XGBoost, Extra Trees and Gradient Boosting classifiers were trained for the binary classification task (discharge from ICU and death in ICU) with the selected influential feature set. …”
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Machine-learning prediction for hospital length of stay using a French medico-administrative database by Franck Jaotombo, Vanessa Pauly, Guillaume Fond, Veronica Orleans, Pascal Auquier, Badih Ghattas, Laurent Boyer

“…Logistic regression (LR), classification and regression trees (CART), random forest (RF), gradient boosting (GB) and neural networks (NN) were applied to the collected data. …”
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Machine Learning Model for Early Detection of COVID-19 by Heart Rhythm Abnormalities by M. S. Mezhov, V. O. Kozitsin, Iu. D. Katser

“…For machine learning purposes, the potential of five algorithms was evaluated: IsolationForest, LGBMClassifier, RandomForestClassifier, ExtraTreesClassifier, SGDOneClassSVM. …”
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An annual cropland extent dataset for Africa at 30 m spatial resolution from 2000 to 2022 by Z. Lou, Z. Lou, D. Peng, D. Peng, Z. Shi, Z. Shi, H. Wang, K. Liu, Y. Zhang, X. Yan, X. Yan, Z. Chen, Z. Chen, S. Ye, S. Ye, L. Yu, J. Hu, J. Hu, J. Hu, Y. Lv, Y. Lv, Y. Lv, H. Peng, H. Peng, Y. Zhang, Y. Zhang, Y. Zhang, B. Zhang, B. Zhang

“…AFCD also<span id="page3778"/> avoided the misclassification of buildings, roads, and trees surrounding cropland common in existing products. …”
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