Prediction of HIV status based on socio-behavioural characteristics in East and Southern Africa. by Erol Orel, Rachel Esra, Janne Estill, Amaury Thiabaud, Stéphane Marchand-Maillet, Aziza Merzouki, Olivia Keiser

“…<h4>Methods</h4>We analysed the most recent Demographic and Health Survey from these 10 countries to predict individual's HIV status using four different algorithms (a penalized logistic regression, a generalized additive model, a support vector machine, and a gradient boosting trees). …”
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Multispectral Sensors and Machine Learning as Modern Tools for Nutrient Content Prediction in Soil by Rafael Felippe Ratke, Paulo Roberto Nunes Viana, Larissa Pereira Ribeiro Teodoro, Fábio Henrique Rojo Baio, Paulo Eduardo Teodoro, Dthenifer Cordeiro Santana, Carlos Eduardo da Silva Santos, Alan Mario Zuffo, Jorge González Aguilera

“…This soil analysis technique requires more refined correlation models for accurate prediction.…”
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Improving the Predictability of the Madden‐Julian Oscillation at Subseasonal Scales With Gaussian Process Models by Haoyuan Chen, Emil Constantinescu, Vishwas Rao, Cristiana Stan

“…In spite of the improvement in MJO predictions made by machine learning algorithms, such as neural networks, most of them cannot provide the uncertainty levels in the MJO forecasts directly. …”
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Systematic analysis of somatic mutations in phosphorylation signaling predicts novel cancer drivers by Jüri Reimand, Gary D Bader

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Comparative Analysis of Diabetes Prediction Models Using the Pima Indian Diabetes Database by Zhao Yize

“…The K-means model operates by grouping data points into separate clusters according to their characteristics, achieving an accuracy of 90.04% in diabetes prediction. In comparison, the random forest model, which builds multiple decision trees (DT) to do their predictions, demonstrates superior performance over several widely used algorithms such as K-Nearest Neighbours (KNN), Logistic Regression (LR), DT, Support Vector Machines (SVM), and Gradient Boosting (GB). …”
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Using machine learning to predict gamma shielding properties: a comparative study by T A Nahool, A M Abdelmonem, M S Ali, A M Yasser

“…This study employed machine learning (ML) algorithms to predict the linear attenuation coefficients (LACs) of materials in inorganic scintillation detectors, which are crucial for evaluating self-shielding properties. …”
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Application Research of Cross-Attention Mechanism for Traffic Prediction Based on Heterogeneous Data by Feng Zhihao

“…Through an analysis of these methods, the research demonstrates how applying advanced deep learning algorithms and cross-attention processes has significantly improved prediction robustness and accuracy. …”
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Predicting safety attitudes in aviation maintenance using machine learning: An exploratory study by Christos Emexidis, Anna V. Chatzi, Kyriakos I. Kourousis

“…The Random Forest machine learning algorithm was utilised to identify the relationships and to enable predictions. …”
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SVM-Based Spectrum Mobility Prediction Scheme in Mobile Cognitive Radio Networks by Yao Wang, Zhongzhao Zhang, Lin Ma, Jiamei Chen

“…Numerical results validate that SVM-SMP gains better short-time prediction accuracy rate and miss prediction rate performance than the two algorithms just depending on the location and speed information. …”
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Programming Languages Prediction from Stack Overflow Questions Using Deep Learning by Razowana Khan Mim, Tapu Biswas

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Interval price prediction of livestock product based on fuzzy mathematics and improved LSTM. by Weimin Ma, Lingling Peng, Hu Chen, Haisheng Yan

“…An empirical study was conducted on the weekly price data of pork, beef, and mutton in China from 2009 to 2023, incorporating discussions on different embedding dimensions, prediction step, fuzzy granulation window sizes, decomposition techniques, and prediction algorithms. …”
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Strength prominence index: a link prediction method in fuzzy social network by Sakshi Dev Pandey, Sovan Samanta, A. S. Ranadive, Leo Mrsic, Antonios Kalampakas, Tofigh Allahviranloo

“…In our experiments, we used three well-known estimators to evaluate the accuracy of link prediction algorithms: precision, area under the precision-recall curve, and area under the receiver operating characteristic curve. …”
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Predicting Subcontractor Performance Using Web-Based Evolutionary Fuzzy Neural Networks by Chien-Ho Ko

“…This study develops web-based Evolutionary Fuzzy Neural Networks (EFNNs) to predict subcontractor performance. EFNNs are a fusion of Genetic Algorithms (GAs), Fuzzy Logic (FL), and Neural Networks (NNs). …”
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Prediction of Ground Subsidence Risk in Urban Centers Using Underground Characteristics Information by Sungyeol Lee, Jaemo Kang, Jinyoung Kim

“…The random forest, XGBoost, and LightGBM machine learning algorithms were used to develop the prediction model, and the SMOTE sampling technique was employed to address data imbalance. …”
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AI-Driven Drought Monitoring: Advanced Machine Learning Techniques for Early Prediction by Vij Priya, Tiwari Ankita

“…Amid the escalating impacts of climate change, droughts are becoming increasingly frequent and severe, necessitating advanced monitoring and predictive strategies to mitigate their adverse effects on agriculture, water resources, and ecosystems. …”
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An optimized machine learning framework for predicting and interpreting corporate ESG greenwashing behavior. by Fanlong Zeng, Jintao Wang, Chaoyan Zeng

“…The IHPO algorithm was then employed to optimize the hyperparameters of the XGBoost model, forming an IHPO-XGBoost ensemble learning model for predicting corporate ESG greenwashing behavior. …”
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Efficient Air Quality Prediction Models Based on Supervised Machine Learning Techniques by Oumoulylte Mariame, El Allaoui Ahmad, Farhaoui Yousef, Boughrous Ali Ait

“…To tackle these issues, it's crucial to set up prediction systems allowing officials to act before high pollution levels occur. …”
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Research on Default Prediction for Credit Card Users Based on XGBoost-LSTM Model by Jing Gao, Wenjun Sun, Xin Sui

“…The resulting XGBoost-LSTM model showed good classification performance in default prediction. The results of this study can provide a reference for the application of deep learning algorithms in the field of finance.…”
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Optimizing Photovoltaic Power Prediction Using Computational Methods and Artificial Neural Networks by Cempaka Amalin Mahadzir, Ahmad Fateh Mohamad Nor, Siti Amely Jumaat, Noor Syahirah Ahmad Safawi

“… This paper focuses on utilizing an Artificial Neural Network (ANN) to predict photovoltaic (PV) panel output power. Since solar power output is fluctuating and depends on climatic, geographical and temporal factors, precise prediction requires the implementation of computational approaches. …”
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Prediction of Large Springback in the Forming of Long Profiles Implementing Reverse Stretch and Bending by Mohammad Reza Vaziri Sereshk, Hamed Mohamadi Bidhendi

“…Comparing the results of this algorithm for different sheet metal forming processes with experimental measurements demonstrates that this technique successfully predicts a wide range of springback with reasonable accuracy. …”
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