Surveying the knowledge of pregnant women towards sport activities during pregnancy using data mining algorithms by Fatemeh Bagherı, Fatemeh Islamı, Fatemeh Mohammadı

“…In predicting the level of knowledge using decision tree in both prediction level (5 label and 3 label), C5.0 algorithm had the most accurate prediction. …”
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Comparative Analysis of Gradient Descent Learning Algorithms in Artificial Neural Networks for Forecasting Indonesian Rice Prices by Rica Ramadana, Agus Perdana Windarto, Dedi Suhendro

“…The commonly used algorithm for prediction in ANN is Backpropagation, which yields high accuracy but tends to be slow during the training process and is prone to local minima. …”
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Severity Classification of Freezing of Gait Using Machine-Learning Algorithms: A Hidden State Model Approach by Aditi Site, Elena Simona Lohan, Jari Nurmi

“…We also trained an ensemble algorithm to model the prediction of severity levels marked by HMM model. …”
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Forecasting Wind Farm Production in the Short, Medium, and Long Terms Using Various Machine Learning Algorithms by Gökhan Ekinci, Harun Kemal Ozturk

“…The results indicate that Min-Max Scaling improved short-term predictions with KNN, while XGBoost and Random Forest provided more stable and accurate forecasts in medium- and long-term predictions. …”
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Enhancing flood susceptibility mapping in Meghna River basin by introducing ensemble Naive Bayes with stacking algorithms by Abu Reza Md. Towfiqul Islam, Md. Uzzal Mia, Nourin Akter Nova, Rabin Chakrabortty, Md. Sanjid Islam Khan, Bonosri Ghose, Subodh Chandra Pal, A. B. M. Mainul Bari, Edris Alam, Md Kamrul Islam, Mohammed Ali Alshehri, Hazem Ghassan Abdo, Romulus Costache

“…This article intends to assess flood susceptibility mapping in Meghna River basin (MRB) and identified flood susceptible regions using three benchmark models including random forest (RF), support vector machine (SVM) and bagging with Naïve Bayes (NB) stacking ensemble algorithms (e.g. RF-NB; SVM-NB and Bagging-NB). The flood sample was partitioned into a training set (70%), and a validation set (30%), and the capability of prediction of flood-influencing variables was quantified by the multi-collinearity test. …”
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Prospects for predicting and preventing the heart failure deterioration: an analytical review by V. N. Larina, I. K. Skiba

“…An integrated approach using scales, algorithms and relevant therapy strategies can significantly improve treatment outcomes and quality of life in patients with HF.…”
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Applying Different Independent Component Analysis Algorithms and Support Vector Regression for IT Chain Store Sales Forecasting by Wensheng Dai, Jui-Yu Wu, Chi-Jie Lu

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Smart Tool-Related Faults Monitoring System Using Process Simulation-Based Machine Learning Algorithms by Arash Ebrahimi Araghizad, Faraz Tehranizadeh, Kemal Kilic, Erhan Budak

“…These findings have been supported by actual measurement data, with a notable accuracy rate of 93% in the predictions. Furthermore, the results indicate that process simulation-based machine learning algorithms will have a significant impact on the tools condition monitoring and the efficiency of manufacturing processes more generally. …”
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Assessing Volatility Behaviors of Cross-Currency Derivatives in India's Exchange Markets Using Machine Learning Algorithms by Aman Shreevastava, Bharat Kumar Meher, Virgil Popescu, Ramona Birau, Mritunjay Mahato

“…This paper studies the volatility of INR based cross country futures (USD, JPY and EUR) and performs forecasting using ML Algorithm and utilizes LSTM for prediction. The study proves to be a first of its kind study involving cross-country futures and is a beacon of hope for all future research on similar subjects. …”
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Identification and validation of NETs-related biomarkers in active tuberculosis through bioinformatics analysis and machine learning algorithms by Shengfang Xia, Qi An, Rui Lin, Yalan Tu, Zhu Chen, Dongmei Wang

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Forecasting Foreign Direct Investment Inflow to Egypt and Determinates: Using Machine Learning Algorithms and ARIMA Model by Mohamed F. Abd El-Aal, Ali Algarni, Aisha Fayomi, RAahayu Abdul Rahman, Khudir Alrashidi

“…This study aims to determine the primary determination of FDI inflow to Egypt using machine learning algorithms and the ARIMA model and get an accurate prediction of FDI inflow to Egypt during the current decade (2020–2030) and approved that the gradient boosting model is the most accurate algorithms. …”
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Construction of Empirical Models of Complex Oscillation Processes with Non-Multiple Frequencies Based on the Principles of Genetic Algorithms by Mykhailo Horbiychuk, Olha Bila, Nataliia Lazoriv

“…The resulting empirical model can be used to predict the water level depending on weather conditions.…”
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A Comparative Study of Machine Learning Algorithms for Intrusion Detection Systems using the NSL-KDD Dataset by Rulyansyah Permata Putra, Amarudin Amarudin

“…The research methodology includes the collection of the NSL-KDD dataset, followed by data transformation, cleaning, normalization, and partitioning into training and testing sets. Each algorithm was trained using tuned parameters, and performance was evaluated using metrics such as accuracy, precision, recall, F1-score, and an analysis of training and prediction time. …”
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Combined Forecasting Schemes Based on Random Forest Regression and Meta-heuristic Algorithms for Maximum Dry Density by Huaxuan Lu

“…All the created schemes, particularly RFGJ—a hybrid of the RFR and the GJO algorithm—exhibited exceptional performance in predicting MDD values, attaining the top R² of 0.9966 and the least RMSE of 13.688.…”
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The use of artificial neural network algorithms to enhance tourism economic efficiency under information and communication technology by Yun Tian, Xiaojie Tang

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Recent Advances in Early Earthquake Magnitude Estimation by Using Machine Learning Algorithms: A Systematic Review by Andrés Navarro-Rodríguez, Oscar Alberto Castro-Artola, Enrique Efrén García-Guerrero, Oscar Adrian Aguirre-Castro, Ulises Jesús Tamayo-Pérez, César Alberto López-Mercado, Everardo Inzunza-Gonzalez

“…The described methods and algorithms illustrate the strong performance of ML in earthquake magnitude estimation despite limited implementation in real-time systems. …”
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Simulation of direct mapped, k-way and fully associative cache on all pairs shortest paths algorithms by A. A. Prihozhy

“…The miss rate strongly depends on the executed algorithm. The all pairs shortest paths algorithms solve many practical problems, and it is important to know what algorithm and what cache type match best. …”
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Integrated Landslide Risk Assessment via a Landslide Susceptibility Model Based on Intelligent Optimization Algorithms by Xin Dai, Jianping Chen, Tianren Zhang, Chenli Xue

“…The landslide susceptibility predictions notably influence high-risk regions with concentrated populations.…”
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Safe AI for coral reefs: Benchmarking out-of-distribution detection algorithms for coral reef image surveys by Mathew Wyatt, Sharyn Hickey, Ben Radford, Manuel Gonzalez-Rivero, Nader Boutros, Nikolaus Callow, Nicole Ryan, Arjun Chennu, Mohammed Bennamoun, James Gilmour

“…Although deep learning has demonstrated significant advances in qualitative domains, deep learning algorithms remain poor at quantifying the uncertainty of their predictions. …”
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Governance for anti-racist AI in healthcare: integrating racism-related stress in psychiatric algorithms for Black Americans by Christopher T. Fields, Christopher T. Fields, Carmen Black, Carmen Black, Jannat K. Thind, Oluwole Jegede, Oluwole Jegede, Damla Aksen, Matthew Rosenblatt, Shervin Assari, Chyrell Bellamy, Elijah Anderson, Avram Holmes, Dustin Scheinost

“…Racial heteroscedasticity refers to the unequal variance in health outcomes and algorithmic predictions across racial groups, driven by differential exposure to racism-related stress. …”
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