The uneven deployment of algorithms as tools in government: evidence from the use of an expert system by Andrew B. Whitford, Anna M. Whitford

“…This empirical case and this theory of innovation provide broad evidence about the historical utilization of expert systems as algorithms in public sector applications.…”
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Identifying Precipitation Types From Surface Meteorological Variables With Machine Learning by Adrienn VARGA-BALOGH, Ádám LEELŐSSY, Róbert MÉSZÁROS

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Artificial intelligence in the service of entrepreneurial finance: knowledge structure and the foundational algorithmic paradigm by Robert Kudelić, Tamara Šmaguc, Sherry Robinson

“…The results demonstrate a high representation of artificial neural networks, deep neural networks, and support vector machines across almost all identified topic niches. …”
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Adaptive lift chiller units fault diagnosis model based on machine learning. by Yang Guo, Zengrui Tian, Hong Wang, Mengyao Chen, Pan Chu, Yingjie Sheng

“…In this paper, a fault diagnosis model of Chiller is designed by combining least squares support vector machine (LSSVM) optimized by hybrid improved northern goshawk optimization algorithm (HINGO) and improved IAdaBoost ensemble learning algorithm. …”
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Study on the influence of light environment on brain fatigue of support workers in fully mechanized excavation face by Shuicheng TIAN, Peng TU, Hongxia LI

“…The evaluation indexes of brain fatigue were extracted by single factor analysis of variance and paired sample T-test. Support vector machine, K-nearest neighbor algorithm and random forest algorithm were used to construct the brain fatigue recognition model of the support worker in fully mechanized excavation face, and the confusion matrix was established to comprehensively compare the recognition effect of each model, and the optimal recognition model was selected. …”
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Romanian Fake News Detection Using Machine Learning and Transformer-Based Approaches by Elisa Valentina Moisi, Bogdan Cornel Mihalca, Simina Maria Coman, Alexandrina Mirela Pater, Daniela Elena Popescu

“…The NEW dataset was build using a scrapping algorithm applied on the Veridica platform. Our approach uses the following machine learning models for detection: Naive Bayes (NB), Logistic Regression (LR), and Support Vector Machine (SVM). …”
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Diagnostics and Prognostics of Boilers in Power Plant Based on Data-Driven and Machine Learning by Achmad Widodo, Toni Prahasto, Mochamad Soleh, Herry Nugraha

“…The proposed method utilizes machine learning techniques through support vector machine (SVM) and random forest algorithm (RFA) for anomaly detection and similarity-based method of dynamic time warping (DTW) for RUL prediction. …”
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EVALUATION OF MACHINE LEARNING MODELS FOR BORON PREDICTION IN ANDISOL SOILS OF NARIÑO-COLOMBIA by David Álvarez Sánchez, Xilena López Estrella, Eduar Manso Ordoñez, Laura López Rivera, Jeison Rodriguez Valenzuela

“…A total of 1,067 soil samples collected in various fields of five municipalities in the southern subregion of the department were used, where the supervised learning models, Random Forest (RF), K-Nearest Neighbors (K-NN), Support Vector Machine (SVM) and Naive Bayes (NB) were evaluated. …”
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An integrated approach of feature selection and machine learning for early detection of breast cancer by Jing Zhu, Zhenhang Zhao, Bangzheng Yin, Canpeng Wu, Chan Yin, Rong Chen, Youde Ding

“…The efficacy of the proposed method was assessed using five machine learning models, K-Nearest Neighbor (KNN), Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), and Light Gradient Boosting Machine (LightGBM), applied to the Wisconsin Breast Cancer Diagnosis (WBCD) datasets. …”
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Importance Analysis of Vegetation Change Factors in East Africa Based on Machine Learning by Zhang Xiumei, Ma Bo, Zhang Yijie

“…The independent treatment variables were two climatic factors and five human activity factors affecting vegetation changes in East Africa. Six machine learning algorithms were used to establish NDVI prediction models: random forest (RF), BP neural networks (BP), support vector machines (SVM), genetic algorithm (GA), radial basis function (RBF), and convolutional neural networks (CNN). …”
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The Implementation of Machine Learning in Lithofacies Classification using Multi Well Logs Data by Sudarmaji Saroji*, Ekrar Winata, Putra Pratama Wahyu Hidayat, Suryo Prakoso, Firman Herdiansyah

“…The support vector machine (SVM) algorithm has been applied successfully to the Damar field, Indonesia. …”
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Application Analysis of Credit Scoring of Financial Institutions Based on Machine Learning Model by Yi Wu, Yuwen Pan

“…On this basis, WOE coding was carried out on the dataset, which was applied to random forest, support vector machine, and logistic regression models, and the performance was compared. …”
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Evaluating Feature Impact Prior to Phylogenetic Analysis Using Machine Learning Techniques by Osama A. Salman, Gábor Hosszú

“…Applying machine learning models for Arabic and Aramaic scripts, such as deep neural networks (DNNs), support vector machines (SVMs), and random forests (RFs), each model was used to compare the phylogenies. …”
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Evaluating regional sustainable energy potential through hierarchical clustering and machine learning by Selen Avcı Azkeskin, Zerrin Aladağ

“…To validate the clustering results, supervised classification methods—including K-Nearest Neighbors (KNN), Support Vector Machines (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost)—are utilized, alongside ensemble models based on RF and XGBoost. …”
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Machine Learning-Driven Acoustic Feature Classification and Pronunciation Assessment for Mandarin Learners by Gulnur Arkin, Tangnur Abdukelim, Hankiz Yilahun, Askar Hamdulla

“…A speech corpus containing samples from advanced, intermediate, and elementary learners (N = 50) and standard speakers (N = 10) was constructed, with a total of 5880 samples. Support Vector Machine (SVM) and ID3 decision tree algorithms were employed to classify vowel formant parameters (F1-F2) patterns. …”
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Artificial liver classifier: a new alternative to conventional machine learning models by Mahmood A. Jumaah, Yossra H. Ali, Tarik A. Rashid

“…The results demonstrate competitive performance, with ALC achieving up to 100% accuracy on the Iris dataset–surpassing logistic regression, multilayer perceptron, and support vector machine–and 99.12% accuracy on the Breast Cancer dataset, outperforming XGBoost and logistic regression. …”
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Comprehensive Machine Learning Model for Cervical Cancer Prediction and Risk Factor Identification by null Mahendra, Mila Desi Anasanti

“…We addressed 22.14% of missing values using the MICE iterative imputer and balanced the data through the synthetic minority oversampling technique (SMOTE). We applied five machine learning algorithms: random forest (RF), linear regression (LR), support vector machine (SVM), K-nearest neighbors (KNN), and extreme gradient boosting (XGBoost). …”
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Exploring machine learning classification for community based health insurance enrollment in Ethiopia by Seyifemickael Amare Yilema, Seyifemickael Amare Yilema, Yegnanew A. Shiferaw, Yikeber Abebaw Moyehodie, Setegn Muche Fenta, Denekew Bitew Belay, Denekew Bitew Belay, Haile Mekonnen Fenta, Haile Mekonnen Fenta, Teshager Zerihun Nigussie, Ding-Geng Chen, Ding-Geng Chen

“…The CBHI were predicted using seven machine learning models: linear discriminant analysis (LDA), support vector machine with radial basis function (SVM), k-nearest neighbors (KNN), classification and regression tree (CART), and random forest (RF). …”
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Innovative approaches for skin disease identification in machine learning: A comprehensive study by Kuldeep Vayadande, Amol A. Bhosle, Rajendra G. Pawar, Deepali J. Joshi, Preeti A. Bailke, Om Lohade

“…Investigate the effectiveness and performance of several algorithms, such as the flexible k-nearest neighbor, the sturdy support vector machine (SVM), and the complex convolutional neural networks (CNNs), advanced techniques for automated skin disease detection encompass deep learning methods such as recurrent neural networks (RNNs) for sequential data processing, generative adversarial networks (GANs) for generating synthetic data, and attention mechanisms for focusing on relevant image regions by means of a thorough examination of the most recent studies. …”
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Machine learning and molecular docking prediction of potential inhibitors against dengue virus by George Hanson, Joseph Adams, Daveson I. B. Kepgang, Luke S. Zondagh, Lewis Tem Bueh, Andy Asante, Soham A. Shirolkar, Maureen Kisaakye, Hem Bondarwad, Olaitan I. Awe

“…This study aims to identify novel potential inhibitors of the Dengue virus (DENV) using an integrative drug discovery approach encompassing machine learning and molecular docking techniques.MethodUtilizing a dataset of 21,250 bioactive compounds from PubChem (AID: 651640), alongside a total of 1,444 descriptors generated using PaDEL, we trained various models such as Support Vector Machine, Random Forest, k-nearest neighbors, Logistic Regression, and Gaussian Naïve Bayes. …”
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