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AI Reveals EU Tourists Prioritize Environmental Over Social Sustainability in Travel Choices

Researchers in Portugal analyzed EU citizens' sustainability priorities in tourism, using AI to classify preferences and suggest eco-friendly, community-focused recommendations. The study highlights the undervaluation of social sustainability across demographics.

18 Nov 2024

Machine Learning Boosts Earthquake Prediction Accuracy in Los Angeles

Researchers have enhanced earthquake prediction accuracy in Los Angeles using advanced machine learning models, achieving 97.97% accuracy by comparing 16 algorithms.

22 Oct 2024

Machine Learning Model Enhances Drone Formation Stability and Accuracy

Researchers developed a machine learning model using CatBoost to predict disturbances in drone formations with an R² of 83.3%, significantly improving from the previous baseline of 54%. The SHAP method was applied for better model explainability, aiding UAV operators in minimizing disruptions.

2 Oct 2024

Machine Learning Identifies Seismic Precursors, Advancing Earthquake Forecasting Capabilities

Researchers identified abnormal low-magnitude seismicity as a precursor to large earthquakes, using machine learning to enhance earthquake forecasting and early warning systems.

11 Sep 2024

Boost Machine Learning Trust With HEX's Human-in-the-Loop Explainability

Researchers introduced HEX, a human-in-the-loop deep reinforcement learning method that improves trust and explanation quality in machine learning models used in high-stakes decision-making.

11 Sep 2024

Machine Learning Enhances Water Quality Monitoring

This study uses machine learning algorithms and satellite imagery to estimate dissolved oxygen levels in Baiyangdian Lake. The approach, particularly the Extra Tree Regression model, offers rapid, accurate water quality monitoring, outperforming traditional methods in urban water bodies.

30 Aug 2024

Machine Learning Models Predict Arsenic Contamination

A comparative study showed that random forest models outperformed traditional linear regression in predicting arsenic contamination in groundwater. The research highlighted the importance of hydro-chemical and geological factors in managing contamination risks, especially in Asia.

27 Aug 2024

Evaluating Tea Quality with Computer Vision

Researchers utilized computer vision and machine learning to develop an objective method for evaluating the color quality of needle-shaped green tea. The study showed that the DT-Adaboost model accurately assessed tea quality, offering a reliable and efficient alternative to traditional sensory analysis.

22 Aug 2024

ML Discovers 2D Materials with Unique Thermal Expansion

Researchers used machine learning and symbolic regression to identify 2D materials with diverse thermal expansion properties, including ZTE and extreme expansion cases. The study provided critical insights for designing materials with tailored thermal properties for advanced applications.

21 Aug 2024

ML Predicts CO2 Corrosion Rates in Oil Industry

Researchers applied machine learning to predict CO2 corrosion rates and severity in the oil and gas industry. The random forest model outperformed others, offering accurate predictions that could enhance material selection, maintenance, and corrosion management strategies.

21 Aug 2024

ML Enhances Predictions in Metal Additive Manufacturing

A comprehensive machine learning framework was developed to predict mechanical properties in metal additive manufacturing. By leveraging a vast dataset and advanced featurization techniques, the framework achieved high accuracy, offering a standardized platform for optimizing additive manufacturing processes.

19 Aug 2024

Quantum Deep Learning Boosts Rice Yield Forecasting

A hybrid quantum deep learning model was developed for rice yield forecasting, combining quantum computing with BiLSTM and XGBoost techniques. This model significantly improved prediction accuracy, supporting global agricultural planning and food security efforts.

18 Aug 2024

ML-based Biochar Optimization for Heavy Metal Remediation

This study explores machine learning models to predict biochar’s effectiveness in immobilizing heavy metals in soil-plant systems. The findings emphasize the importance of soil and biochar properties, offering insights to enhance remediation strategies in contaminated soils.

18 Aug 2024

AI Improves Air Pollution Prediction Accuracy

A recent review highlights the superiority of machine learning methods over traditional statistical models in predicting air pollution levels. ML techniques, particularly tree-based algorithms, offer enhanced accuracy in modeling pollutants like NO₂, UFPs, and black carbon, crucial for health impact assessments.

16 Aug 2024

Tree-Based ML Predicts 2D Material Impurity Energy

Researchers employed tree-based machine learning (ML) algorithms, including LightGBM, to predict the formation energy of impurities in 2D materials by integrating chemical and structural features, such as Jacobi–Legendre polynomials.

13 Aug 2024

Machine Learning Boosts Corrosion Predictions for Steel Structures

A study published in Scientific Reports demonstrates how machine learning (ML) algorithms, particularly random forests, can more accurately predict the corrosion rate of steel buried in soil. By considering multiple soil parameters, the research highlights the limitations of traditional models and offers a more robust approach to improving the durability and safety of soil-buried structures.

13 Aug 2024

Machine Learning Models Predict Obesity Risk

Researchers developed a machine learning technique to predict obesity risk by analyzing sociodemographic, lifestyle, and health factors. The study, which achieved 79% accuracy, identified significant predictors like age, sex, education, diet, and smoking habits, offering valuable insights for personalized obesity prevention.

6 Aug 2024

Advanced ML Model Predicts Potato Blight

Machine learning models predicted potato leaf blight with 98.3% accuracy using over 4000 weather records. Techniques like K-means clustering, PCA, and copula analysis identified key weather factors. Feature selection significantly enhanced model precision, aiding proactive disease management in agriculture.

1 Aug 2024

Predicting Concrete Strength with Machine Learning Models

Researchers developed a machine-learning model to predict concrete compressive strength using 228 samples and six algorithms. The XGBoost model delivered the highest accuracy, aligning predictions with conventional theory and demonstrating the potential of ML in concrete strength forecasting.

30 Jul 2024

Stochastic Models Outshine ML in Stock Index Prediction

In a comparative study, stochastic models, especially the CIR model, outperformed machine learning algorithms in predicting stock indices across various sectors. While machine learning showed flexibility, optimizing hyperparameters is crucial for enhancing its predictive performance, suggesting a hybrid approach for future forecasts.

30 Jul 2024