However, the impact of machine learning algorithms on the prediction performance of mine fault diagnosis models has not been fully evaluated. In this study, the windage alteration faults (WAFs) diagnosis models, which are based on K-nearest neighbor algorithm (KNN), multi-layer perceptron (MLP),...
Thus, radiation absorption estimations utilizing AI algorithms can ultimately replace traditional approaches and time-consuming large-scale energy estimates. The ANN methodology outperforms other machine learning techniques in predicting the objective function, which contains the amount of solar radiation ...
Machine learningThis paper presents a novel approach for combining machine learning (data driven) and geostatistical (model-based) algorithms to generate stochastic models of the reservoir. The traditional geostatistical approach to reservoir property modeling is through sequential simulation. However, the ...
In recent years, machine learning (ML) based atomic simulations are evolving rapidly, achieving huge progresses on both the methodology for PES evaluations to the algorithms for structure and reaction pathway sampling61,62. In particular, the latest neural network (NN) potential calculations can be ...
Simio supports training, testing, and embedding Deep Neural Network agents into Process Digital Twin models, along with bidirectional interaction with Machine Learning algorithms to enhance model intelligence, optimize results, and reduce execution run times. Simio also supports the import and direct use...
It has been well accepted that machine learning algorithms are able to provide a mechanistic understand- ing to biological systems in addition to improving prediction results by adjusting multiple parameters. In terms of protein–protein binding, for example, a feature selection and regression algorithm...
The learning rate hyperparameter in the optimization method controls the step size as moving toward the minimum. Many optimization algorithms have been developed to train the DNN, such as stochastic gradient descent (SGD) (Bottou, 2010), and adaptive moment estimation (Adam) (Kingma and Ba, ...
The objective of this work is to illustrate how to algorithmically integrate Machine-Learning Algorithms (MLA’s) with multistage/multicomponent fire spread models. In order to tangibly illustrate this process, this work develops a framework for a specific model problem combining: (I) a meshless dis...
Use data science tools and solutions to uncover patterns and build predictions by using data, algorithms, machine learning and AI techniques. Data and analytics consulting services Unlock the value of enterprise data with IBM Consulting, building an insight-driven organization that delivers business adva...
This research aimed to establish different machine learning (ML) algorithms which can simulate and predict the photocatalytic degradation of PFOA. The published results were used to estimate and predict the photocatalytic degradation of PFOA. Statistical criteria including the coefficient of determination (...