Data efficient prediction of excited-state properties using quantum neural networks by Manuel Hagelueken, Marco F Huber, Marco Roth

“…We show that, in many cases, the procedure is able to outperform various classical models (support vector machines, Gaussian processes, and NNs) that rely solely on classical features, by up to two orders of magnitude in the test mean squared error.…”
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Performance of Sentiment Classification on Tweets of Clothing Brands by Muhammad Shafiq Jalani, Hu Ng, Timothy Tzen Vun Yap, Vik Tor Goh

“…The word embeddings are fed into classification models namely Support Vector Machine (SVM), Naïve Bayes (NB), Random Forest (RF), Logistic Regression (LR) and Multilayer Perceptron (MLP) by comparing their accuracy performances.  …”
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Development of Machine Learning Prediction Models to Predict ICU Admission and the Length of Stay in ICU for COVID‑19 Patients Using a Clinical Dataset Including Chest Computed Tom... by Seyed Salman Zakariaee, Negar Naderi, Hadi Kazemi-Arpanahi

“…For predicting the ICU admission of COVID-19 patients, k-nearest neighbors (k-NN) yielded better performance than J48, support vector machine, multi-layer perceptron, Naïve Bayes, logistic regression, random forest (RF), and XGBoostbased ML models. …”
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Towards an intelligent integrated methodology for accurate determination of volume percentages in three-phase flow systems by Abdullah M. Iliyasu, Mohammad Sh. Daoud, Ahmed Sayed Salama, John William Grimaldo Guerrero, Kaoru Hirota

“…Finally, to determine the volume percentages, we employ a support vector regression (SVR) neural network, which is trained on a refined dataset with capability to handle complex relationships and high-dimensional data. …”
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A Multi-Input Multi-Output Considering Correlation and Hysteresis Prediction Method for Gravity Dam Displacement with Interpretative Functions by Bo Xu, Yuan Yao, Xuan Wang, Linsong Sun, Bin Ou, Yanming Zhang

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Comparison of 7 artificial intelligence models in predicting venous thromboembolism in COVID-19 patients by Indika Rajakaruna, Mohammad Hossein Amirhosseini, Mike Makris, Mike Laffan, Yang Li, Deepa J. Arachchillage

“…Results: Age, female sex, white ethnicity, comorbidities (diabetes, liver disease, autoimmune disease), and laboratory features (increased hemoglobin, white cell count, D-dimer, lactate dehydrogenase, ferritin), and presence of multiorgan failure were major factors associated with the development of thrombosis. Support vector classifier (SVC) model outperformed all other models, achieving an accuracy of 97%. …”
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Predicting Hit Songs Using Audio and Visual Features by Cheng-Yuan Lee, Yi-Ning Tu

“…These features were applied using machine learning algorithms, including random forest, support vector machines, decision trees, K-nearest neural networks, and logistic regression. …”
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Development and validation of an ensemble learning risk model for sepsis after abdominal surgery by Xin Shu, Yujie Li, Yiziting Zhu, Zhiyong Yang, Xiang Liu, Xiaoyan Hu, Chunyong Yang, Lei Zhao, Tao Zhu, Yuwen Chen, Bin Yi

“…After feature selection, the ensemble learning model constructed by integrating k-Nearest Neighbor (KNN) and Support Vector Machine (SVM) yielded the ROC AUC of 0.892 (0.841–0.944) and accuracy of 85.0% on the test data, and the ROC AUC of 0.782 (0.727–0.838) and accuracy of 68.1% on the validation data, which performed best. …”
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Frailty in older adults patients: a prospective observational cohort study on subtype identification by Zhikai Yang, Chen Ji, Ting Wang, Wei He, Yuhao Wan, Min Zeng, Di Guo, Lingling Cui, Hua Wang

“…Key variables for predictive modeling were identified through LASSO (least absolute shrinkage and selection operator) regression, SVM–RFE (support vector machine–recursive feature elimination), and random forest techniques. …”
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Stacking data analysis method for Langmuir multi-probe payload by Jin Wang, Jin Wang, Duan Zhang, Qinghe Zhang, Qinghe Zhang, Xinyao Xie, Fangye Zou, Qingfu Du, Qingfu Du, V. Manu, Yanjv Sun

“…The integrated characteristics of the stacking model make full use of the advantages of various models such as multilayer perceptron (MLP), support vector regression (SVR), K-nearest neighbors (KNN), and light gradient boosting machine (LightGBM). …”
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Missing values imputation using Fuzzy K-Top Matching Value by Azza Ali, Mervat Abu-Elkheir, Ahmed Atwan, Mohammed Elmogy

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Footwork recognition and trajectory tracking in track and field based on image processing by Jiaju Zhu, Zhong Zhang, Runnan Liu, Junyi Liu

“…To solve the problem that traditional footwork is inaccurate and not well accepted by people, this paper has used an image processing method based on support vector machine (SVM) algorithm to identify and track the footwork. …”
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A Comparative Evaluation of Texture Features for Semantic Segmentation of Breast Histopathological Images by R. Rashmi, Keerthana Prasad, Chethana Babu K. Udupa, V. Shwetha

“…Various textures such as Filter Banks, Gray Level Co-occurrence matrix and Local Binary Patterns are studied along with colour features for semantic segmentation of nuclei from histopathological images. Support Vector Machine and Multi Layer Perceptron algorithms are trained to perform pixelwise classification. …”
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Mapping the landscape of Artificial intelligence for serious games in Health: An enhanced meta review by Xiya Tao, Nicolás Sáenz-Lechón, Martina Eckert

“…The review also examines the growing trend of using multimodal data as input for machine learning models.Results show that a few well-known algorithms, such as Decision Trees (DT), Artificial Neural Networks (ANN), Fuzzy Logic (FL), Naïve Bayes (NB), and Support Vector Machines (SVM), are frequently used. …”
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Explainable Ensemble Learning Model for Residual Strength Forecasting of Defective Pipelines by Hongbo Liu, Xiangzhao Meng

“…The results indicate that, compared with Random Forest, LightGBM, Support Vector Machine, gradient boosting regression tree, and Multi-Layer Perceptron, the BO-XGBoost model exhibits the best prediction performance, with MAPE, R², and RMSE values of 5.5%, 0.971, and 1.263, respectively. …”
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Graph convolution network for fraud detection in bitcoin transactions by Ahmad Asiri, K. Somasundaram

“…We have run different algorithms for predicting illicit transactions like Logistic Regression, Long Short Term Memory, Support Vector Machine, Random Forest, and a variation of Graph Neural Networks, which is called Graph Convolution Network (GCN). …”
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Recent Trends and Advances in Utilizing Digital Image Processing for Crop Nitrogen Management by Bhashitha Konara, Manokararajah Krishnapillai, Lakshman Galagedara

“…Interest in integrating machine learning and deep learning algorithms with DIP has increased, with the frequently used algorithms—Random Forest, Support Vector Machine, Extreme Gradient Boost, and Convolutional Neural Networks—achieving higher prediction accuracy levels. …”
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USP21 is involved in the development of chronic hepatitis B by modulating the immune microenvironment by Pengyu Luo, Yuna Tang, Nan Chen, Pei Liu, Jing Wang, Yuchen Fan, Huihui Liu, Kai Wang

“…With three advanced machine learning algorithms, random forest, least absolute shrinkage and selection operator, and selected support vector machine recursive feature elimination, we identified the PCD signature genes associated with CHB from the candidate genes. …”
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Design and Application of an Energy Management System Based on Artificial Intelligence Technology by Hongye Lin, Xuanying Bai, Chun Li, Shenghan Xu, Haibin Xu, Zne-Jung Lee, Yun Lin, Qunshan Zhou, Jingxun Cai

“…Among the various types of regression algorithms, the mean-square error (<i>MSE</i>) of decision tree regression is 0.36, the <i>MSE</i> of support vector regression (SVR) is 0.09, the <i>MSE</i> of K-nearest neighbor (KNN) regression is 0.57, and the <i>MSE</i> of extreme gradient boosting (XGBoost) regression is 0.32. …”
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Sentiment Analysis Using Stacking Ensemble After the 2024 Indonesian Election Results by Andy Victor Pakpahan, Fahmi Reza Ferdiansyah, Robby Gustian, Muhammad Nur Faiz, Sukma Aji

“…Sentiment analysis is a text processing technique aimed at identifying opinions and emotions within a sentence. Machine learning is commonly applied in this area, with algorithms such as Naïve Bayes, Support Vector Machine (SVM), and Random Forest being frequently used. …”
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