In joint research with the University of Tokyo (UTokyo), the National Institute of Advanced Industrial Science and Technology ...

Isolation Forest detects anomalies by isolating observations. It builds binary trees (called iTrees) by recursively ...

This review examines AI and ML's role in transforming thermoelectric materials design, focusing on defect engineering and ...

After uncovering a unifying algorithm that links more than 20 common machine-learning approaches, researchers organized them into a 'periodic table of machine learning' that can help scientists ...

This study examines the prediction accuracy of ensemble machine learning models by comparing local and global precision, recall, and accuracy for multiclass grading of engineering students. It also ...

To preform accurate forecasts on energy storage, a data-driven machine learning approach based on artificial neural networks (ANN) was optimized via a Bayesian optimization algorithm to predict the ...

Then, they used this database to train and validate a hybrid machine learning model combining the Extra Trees (ET) technique and the Moth-Flame Optimization (MFO) algorithm.

Machine learning, a data-driven computational method, leverages existing data and algorithms to predict yet-to-be-discovered properties of materials. Recently, our group and some researchers have ...

Machine learning has become an essential component in the design of intelligent systems across several disciplines. This widespread use of machine learning has led to the importance of evaluating how ...

Data engineers are responsible for the management of data infrastructure, the delivery of large-scale data processing and the preparation of datasets for analysis.

The team found that this transfer of learning between the physics model and machine learning algorithm not only improves accuracy, but also happens in just a few seconds, significantly cutting down ...