Maternidade negra em Um defeito de cor: a representação literária como disrupção do nacionalismo by Fabiana Carneiro da Silva

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Credit risk prediction with corruption perception index: machine learning approaches by Cuong Nguyen Thanh, Tam Phan Huy, Tuyet Pham Hong, An Bui Nguyen Quoc

“…This study examines the impact of corruption on credit risk in Southeast Asian commercial banks by using machine learning models to predict non-performing loans (NPLs) based on the Corruption Perception Index (CPI). …”
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Analyzing and forecasting under-5 mortality trends in Bangladesh using machine learning techniques. by Shayla Naznin, Md Jamal Uddin, Ishmam Ahmad, Ahmad Kabir

“…<h4>Methods</h4>Data from the 1993-94 to 2017-18 Bangladesh Demographic and Health Survey (BDHS) was analyzed using advanced machine learning algorithms. Key metrics, including Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), R-squared, and Mean Absolute Percentage Error (MAPE), were employed to evaluate model performance. …”
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Estimation of Ultimate Bearing Capacity in Rock-Socketed Piles Using Optimized Machine Learning Approaches by Ali Hassan, Hamza Rashid

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Prediction of buckling damage of steel equal angle structural members using hybrid machine learning techniques by Nang Xuan Ho, Tien-Thinh Le, The-Hung Dinh, Van-Hai Nguyen

“…Abstract This article deals with prediction of buckling damage of steel equal angle structural members using a surrogate model combining machine learning and metaheuristic optimization technique. …”
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Perbandingan Metode Supervised Machine Learning untuk Prediksi Prevalensi Stunting di Provinsi Jawa Timur by M Syauqi Haris, Ahsanun Naseh Khudori, Wahyu Teja Kusuma

“…Selain itu, beberapa metode dalam supervised machine learning juga dibandingkan yaitu, linier regression, support vector regression, dan random forest regression.Metode support vector regression dalam penelitian ini memiliki nilai galat yang lebih rendah yaitu 0,91 untuk MAE dan 1,30 untuk MSE. …”
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Geospatial digital mapping of soil organic carbon using machine learning and geostatistical methods in different land uses by Yahya Parvizi, Shahrokh Fatehi

“…The SOC changes were simulated using multivariate analysis and machine learning methods including generalized linear model (GLM), linear additive model (LAM), cubist, random forest (RF), and support vector machine (SVM) models. …”
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Estimating the Pile Settlement Using a Machine Learning Technique Optimized by Henry's Gas Solubility Optimization and Particle Swarm Optimization by Saravana Kumar, Savarimuthu Robinson

“…Also, test phase results showed the better performance of SVR-HGSO with an MAE index of 0.278, which is 57.10% lower than the other one. …”
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Machine learning analysis of the relationships between traumatic childbirth experience with positive and negative fertility motivations in Iran in a community-based sample by Mahdieh Arian, Talat Khadivzadeh, Mahla Shafeei, Sedigheh Abdollahpour

“…Elastic network modeling predicts using RMSE, MAE and R-squared that religious beliefs, married duration, and women’s education have the greatest increasing effect on positive fertility motivation. …”
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Performance prediction and optimization of a high-efficiency tessellated diamond fractal MIMO antenna for terahertz 6G communication using machine learning approaches by Kamal Hossain Nahin, Jamal Hossain Nirob, Akil Ahmad Taki, Md Ashraful Haque, Narinderjit Sawaran SinghSingh, Liton Chandra Paul, Reem Ibrahim Alkanhel, Hanaa A. Abdallah, Abdelhamied A. Ateya, Ahmed A. Abd El-Latif

“…Leveraging a meta learner-based stacked generalization ensemble strategy, this study integrates classical machine learning techniques with an optimized multi-feature stacked ensemble to predict antenna properties with greater accuracy. …”
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Predicting Iran Cooperative Development Bank's Profit/Loss: Two-stage Collective Learning by Seyed Bagher Fattahi, Seyed Mozafar Mirbargkar, Ebrahim Chirani, Mohammadreza Vatanparast

“…MethodsIn the initial stage of machine learning, Support Vector Machines, and Decision Trees serve as the base models, while the second stage employs a weighted averaging approach. …”
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Using deep learning model integration to build a smart railway traffic safety monitoring system by Chin-Chieh Chang, Kai-Hsiang Huang, Tsz-Kin Lau, Chung-Fah Huang, Chun-Hsiung Wang

“…Therefore, this study aimed to build a smart railway traffic safety system using the integration of object detection, segmentation, machine learning, and notification system. First, the Mask R-CNN model was applied to automatically build the digital boundaries of railway, which achieved an average Interest of Union (IOU) of over 0.9. …”
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An optimized approach for predicting water quality features and a performance evaluation for mapping surface water potential zones based on Discriminant Analysis (DA), Geographical... by Abhijeet Das

“…Again, this research used a strong methodology by incorporating Machine learning (ML) algorithms, such as: Artificial Neural Network (ANN), Gaussian Process Regression (GPR), Support Vector Machine (SVM), and Linear Regression Model (LRM), were applied to forecast and confirm the quality of the water. …”
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Water Quality Variations in the Lower Yangtze River Based on GA-RF Model From GF-1, Landsat-8, and Sentinel-2 Images by Wentao Hu, Shuanggen Jin, Yuanyuan Zhang

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Minimum Description Length and Multi-Criteria Decision Analysis in Predictive Modeling by Petr Silhavy, Katerina Hlavackova-Schindler, Radek Silhavy

“…Various regression models and feed-forward neural networks were evaluated using criteria such as MAE, MAPE, RMSE, and Adjusted <inline-formula> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula>. …”
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Leveraging transformer models to predict cognitive impairment: accuracy, efficiency, and interpretability by Kai Ma, Junzhi Zhang, Xinhang Huang, Mengyang Wang

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Appraising the Pile Settlement Rates by Support Vector Regression Optimized Using the Novel Optimization Algorithms by Argyros Maris

“…In this regard, the present research has used a machine learning technique: support vector regression (SVR). …”
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Meta Learning Strategies for Comparative and Efficient Adaptation to Financial Datasets by Kubra Noor, Ubaida Fatima

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Modeling Multimedia Ad Exposure: The Role of Low-Level Audiovisual Features by Urban Burnik, Andrej Kosir, Gregor Strle

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ANN-based software cost estimation with input from COCOMO: CANN model by Chaudhry Hamza Rashid, Imran Shafi, Bilal Hassan Ahmed Khattak, Mejdl Safran, Sultan Alfarhood, Imran Ashraf

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