In the context of AI, a Decision Tree is a type of supervised learning algorithm that is mostly used in classification problems. It works for both categorical and continuous input and output variables. In this technique, we split the data into two or more homogeneous sets based on the most significant differentiator in input variables. Each internal node of the tree corresponds to an attribute, and each leaf node corresponds to a class label.
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.
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.
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.
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.
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.
Researchers combined hyperspectral imagery with machine learning models to detect early Fusarium wilt in strawberries. The ANN model achieved the highest accuracy, predicting stress indicators like stomatal conductance and photosynthesis before visual symptoms, enhancing early disease detection and management.
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.
Researchers applied multiple machine learning techniques to predict the unconfined compressive strength (UCS) of cohesive soil reconstituted with cement and lime. Gradient boosting and k-nearest neighbor models demonstrated the highest accuracy, revealing maximum dry density, consistency limits, and cement content as key factors influencing UCS, providing reliable predictions for engineering applications.
Researchers used machine learning (ML) to predict the compressive strength (CS) of graphene nanoplatelet (GrN)-reinforced cement composites. They employed CatBoost and other ML models on a dataset of 172 data points, highlighting GrN thickness as a critical predictor via SHAP analysis.
Published in Intelligent Systems with Applications, this study introduces SensorNet, a hybrid model combining deep learning (DL) with chemical sensor data to detect toxic additives in fruits like formaldehyde. SensorNet integrates convolutional layers for image analysis and sensor data preprocessing, achieving a high accuracy of 97.03% in distinguishing fresh from chemically treated fruits.
Researchers explored 13 machine learning models to predict the efficacy of titanium dioxide (TiO2) in degrading air pollutants. Models like XG Boost, decision tree, and lasso regression demonstrated high accuracy, with XG Boost notably excelling with low mean absolute error and root mean squared error.
A study in Applied Sciences utilized machine learning models to predict pedestrian compliance at crosswalks in Jordan, revealing significant influences of local infrastructure and traffic conditions. Among the models tested, the random forest (RF) model demonstrated the highest accuracy and precision, highlighting ML's potential to improve urban traffic management and pedestrian safety.
In a study published in Scientific Reports, researchers used machine learning to predict upper secondary education dropout with high accuracy. By analyzing comprehensive data from kindergarten to Grade 9, the study identified key factors influencing dropout, enabling early intervention strategies to support at-risk students.
Researchers in Digital Chemical Engineering applied six machine learning algorithms to predict the solubility of salicylic acid in 13 solvents, achieving high accuracy. The random forest (RF) algorithm outperformed others with the lowest total error, showcasing the efficacy of ML in pharmaceutical applications.
This study introduces an AI-driven approach to optimize tunnel boring machine (TBM) performance in soft ground conditions by predicting jack speed and torque settings. By synchronizing operator decisions with machine data and utilizing machine learning models, the research demonstrates significant improvements in TBM operational efficiency, paving the way for enhanced tunneling projects.
Researchers in a recent Nature Communications paper introduced a novel autoencoding anomaly detection method utilizing deep decision trees (DT) deployed on field programmable gate arrays (FPGA) for real-time detection of rare phenomena at the Large Hadron Collider (LHC).
Researchers utilized machine learning algorithms to predict anemia prevalence among young girls in Ethiopia, analyzing data from the 2016 Ethiopian Demographic and Health Survey. The study identified socioeconomic and demographic predictors of anemia and highlighted the efficacy of advanced ML techniques, such as random forest and support vector machine, in forecasting anemia status.
Researchers evaluated 13 machine learning models to forecast compressive strength in preplaced aggregate concrete. Extreme gradient boosting (XGBoost) emerged as the most accurate, with sensitivity and SHAP analyses highlighting crucial factors like gravel and water-to-binder ratio.
This study introduced an innovative approach to address airborne particulate matter (PM) pollution in surface mines using Internet of Things (IoT) technology and machine learning (ML) techniques. The study highlighted PM 1.0 as the predominant pollutant and employed a real-time monitoring system to track PM concentrations in ball clay surface mine sites.
Researchers propose an AI-driven approach for predicting and managing water quality, crucial for environmental sustainability. Utilizing explainable AI models, they showcase the significance of transparent decision-making in classifying drinkable water, emphasizing the potential of their methodology for real-time monitoring and proactive risk mitigation in water management practices.
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