Comprehensive Evaluation of Bankruptcy Prediction in Taiwanese Firms Using Multiple Machine Learning Models by Hung V. Pham, Tuan Chu, Tuan M. Le, Hieu M. Tran, Huong T.K. Tran, Khanh N. Yen, Son V. T. Dao

“…After selecting the best features, these were used to train the three ML algorithms, and hyper-parameter optimization was implemented to boost model performance. …”
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An Empirical Evaluation of Supervised Learning Methods for Network Malware Identification Based on Feature Selection by C. Manzano, C. Meneses, P. Leger, H. Fukuda

“…The selected features were used to train the algorithms using binary and multiclass classification. …”
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VLFSE: Enhancing visual tracking through visual language fusion and state update evaluator by Fuchao Yang, Mingkai Jiang, Qiaohong Hao, Xiaolei Zhao, Qinghe Feng

“…To address these issues, we propose a visual tracking algorithm through visual language fusion and a state update evaluator (VLFSE). …”
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Predicting sensory evaluation of spinach freshness using machine learning model and digital images. by Kento Koyama, Marin Tanaka, Byeong-Hyo Cho, Yusaku Yoshikawa, Shige Koseki

“…Our findings indicate that a model using support vector machine classifiers and artificial neural networks has the potential to replace freshness evaluations currently performed by non-trained panels.…”
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Evaluation of Smart Building Integration into a Smart City by Applying Machine Learning Techniques by Mustafa Muthanna Najm Shahrabani, Rasa Apanaviciene

“…Six optimised machine learning algorithms (K-Nearest Neighbours (KNNs), Support Vector Regression (SVR), Random Forest, Adaptive Boosting (AdaBoost), Decision Tree (DT), and Extra Tree (ET)) were employed to train the model and perform feature engineering and permutation importance analysis. …”
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Evaluation of machine learning techniques for real-time prediction of implanted lower limb mechanics by Chase Maag, Clare K. Fitzpatrick, Paul J. Rullkoetter

“…Several predictive algorithms were explored, including linear regression (LRM), multilayer perceptron (MLP), bi-directional long short-term memory (biLSTM), convolutional neural network (CNN), and transformer-based approaches. …”
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Using Vector Databases for the Selection of Related Occupations: An Empirical Evaluation Using O*NET by Lino Gonzalez-Garcia, Miguel-Angel Sicilia, Elena García-Barriocanal

“…Vector databases offer an opportunity to find related occupations based on large pre-trained word and sentence embeddings and their associated retrieval algorithms for similarity search. …”
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Evaluation of Diagnostic Recommendations Embedded in Medication Alerts: Prospective Single-Arm Interventional Study by Yu-Chen Liu, Guan-Ling Lin, Jeremiah Scholl, Yi-Chun Hung, Yu-Jing Lin, Yu-Chuan Li, Hsuan-Chia Yang

“…The system provided diagnostic recommendations based on machine learning algorithms trained on data from the National Health Insurance Research Database. …”
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Post-hoc Evaluation of Sample Size in a Regional Digital Soil Mapping Project by Daniel D. Saurette, Richard J. Heck, Adam W. Gillespie, Aaron A. Berg, Asim Biswas

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A geometric approach for accelerating neural networks designed for classification problems by Mohsen Saffar, Ahmad Kalhor, Ali Habibnia

“…An illustrative example of classifying CIFAR-10 dataset is presented to explain the algorithm step-by-step. The proposed method achieves impressive pruning results on networks trained by CIFAR-10 and ImageNet datasets, with 87.5%, 77.6%, and 78.8% of VGG16, GoogLeNet, and DenseNet parameters pruned, respectively. …”
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Hysteretic curve characteristics in rectangular shear walls predicted by machine learning by Jungui Dong, Ce Chen

“…Results show IEG-ML high accuracy and efficiency, particularly with a backpropagation network optimized by the dung beetle algorithm (DBO), making it a robust tool for seismic evaluation.…”
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Predicting Live Weight for Female Rabbits of Meat Crosses From Body Measurements Using LightGBM, XGBoost and Support Vector Machine Algorithms by Hasan Önder, Cem Tirink, Taras Yakubets, Andriy Getya, Mykhalio Matvieiev, Ruslan Kononenko, Uğur Şen, Çağri Özgür Özkan, Tolga Tolun, Fahrettin Kaya

“…The results showed that LightGBM, XGBoost and SVM algorithms were well fit for the BW using the biometric measures with over 95% accuracy for both train and test sets. …”
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A novel deep learning algorithm for real-time prediction of clinical deterioration in the emergency department for a multimodal clinical decision support system by Arom Choi, Kwanhyung Lee, Heejung Hyun, Kwang Joon Kim, Byungeun Ahn, Kyung Hyun Lee, Sangchul Hahn, So Yeon Choi, Ji Hoon Kim

“…A retrospective study was conducted using data from a level 1 tertiary hospital. The algorithm’s predictive performance was evaluated based on in-hospital cardiac arrest, inotropic circulatory support, advanced airway, and intensive care unit admission. …”
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Multi-Class Skin Cancer Classification Using a Hybrid Dynamic Salp Swarm Algorithm and Weighted Extreme Learning Machines with Transfer Learning by Ramya Panneerselvam, Sathiyabhama Balasubramaniam

“…The DSSA, the best feature selection algorithm enhances the classification accuracy of the WELM. …”
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Establishment of predictive models for postoperative delirium in elderly patients after knee/hip surgery based on total bilirubin concentration: machine learning algorithms by Shuhui Hua, Chuan Li, Yuanlong Wang, YiZhi Liang, Shanling Xu, Jian Kong, Hongyan Gong, Rui Dong, Yanan Lin, Xu Lin, Yanlin Bi, Bin Wang

“…Subsequently, we employed ten machine learning algorithms to train and develop the predictive models: Logistic Regression (LR), Support Vector Machine (SVM), Gradient Boosting Model (GBM), Neural Network (NN), Random Forest (RF), Xgboost, K-Nearest Neighbors (KNN), AdaBoost, LightGBM, and CatBoost. …”
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Development and validation of a prediction model for coronary heart disease risk in depressed patients aged 20 years and older using machine learning algorithms by Yicheng Wang, Yicheng Wang, Yicheng Wang, Chuan-Yang Wu, Hui-Xian Fu, Jian-Cheng Zhang, Jian-Cheng Zhang, Jian-Cheng Zhang

“…Several evaluation metrics were employed to assess and compare the performance of eight different machine learning models, aiming to identify the most effective algorithm for predicting coronary heart disease risk in individuals with depression. …”
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CEEMDAN-MRAL Transformer Vibration Signal Fault Diagnosis Method Based on FBG by Hong Jiang, Zhichao Wang, Lina Cui, Yihan Zhao

“…The average accuracy of fault diagnosis of the transformer winding core reaches 97.9375%, and the time taken on the large-scale complex training set is only 1705 s, which has higher diagnostic accuracy and shorter training time than other models.…”
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Quantification of training‐induced alterations in body composition via automated machine learning analysis of MRI images in the thigh region: A pilot study in young females by Saied Ramedani, Ebru Kelesoglu, Norman Stutzig, Hendrik Von Tengg‐Kobligk, Keivan Daneshvar Ghorbani, Tobias Siebert

“…In this study we developed a novel automatic machine learning approach for volumetric segmentation and quantitative assessment of MRI volumes and investigated the efficacy of using a machine learning algorithm to assess muscle, subcutaneous adipose tissue (SAT), and bone volume of the thigh before and after a strength training. …”
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Advancing flood susceptibility prediction: A comparative assessment and scalability analysis of machine learning algorithms via artificial intelligence in high‐risk regions of Paki... by Mirza Waleed, Muhammad Sajjad

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The Role of Artificial Intelligence in Transforming Language Learning: Opportunities and Ethical Considerations by Adenike A. Akinsemolu, Helen Onyeaka

“…AI has numerous applications in language teaching and learning, including algorithms that personalize learning for individual learners, identify and structure lessons and learning activities based on a learner’s strengths, evaluate and provide constant feedback on a learner’s progress, and simulate interactive learning environments. …”
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