Artificial neural networks for photonic applications—from algorithms to implementation: tutorial
This tutorial–review on applications of artificial neural networks in photonics targets a broad
audience, ranging from optical research and engineering communities to computer science …
audience, ranging from optical research and engineering communities to computer science …
Survey of optimization algorithms in modern neural networks
The main goal of machine learning is the creation of self-learning algorithms in many areas
of human activity. It allows a replacement of a person with artificial intelligence in seeking to …
of human activity. It allows a replacement of a person with artificial intelligence in seeking to …
SMT 2.0: A Surrogate Modeling Toolbox with a focus on hierarchical and mixed variables Gaussian processes
Abstract The Surrogate Modeling Toolbox (SMT) is an open-source Python package that
offers a collection of surrogate modeling methods, sampling techniques, and a set of sample …
offers a collection of surrogate modeling methods, sampling techniques, and a set of sample …
Enhancing HVDC transmission line fault detection using disjoint bagging and bayesian optimization with artificial neural networks and scientometric insights
DC grid fault protection techniques have previously faced challenges such as fixed
thresholds, insensitivity to high-resistance faults, and dependency on specific threshold …
thresholds, insensitivity to high-resistance faults, and dependency on specific threshold …
Ensemble learning-based feature engineering to analyze maternal health during pregnancy and health risk prediction
Maternal health is an important aspect of women's health during pregnancy, childbirth, and
the postpartum period. Specifically, during pregnancy, different health factors like age, blood …
the postpartum period. Specifically, during pregnancy, different health factors like age, blood …
Multi-fidelity cost-aware Bayesian optimization
Bayesian optimization (BO) is increasingly employed in critical applications such as
materials design and drug discovery. An increasingly popular strategy in BO is to forgo the …
materials design and drug discovery. An increasingly popular strategy in BO is to forgo the …
A survey on multi-objective hyperparameter optimization algorithms for machine learning
Hyperparameter optimization (HPO) is a necessary step to ensure the best possible
performance of Machine Learning (ML) algorithms. Several methods have been developed …
performance of Machine Learning (ML) algorithms. Several methods have been developed …
[HTML][HTML] Hybrid deep learning model for efficient state of charge estimation of Li-ion batteries in electric vehicles
State of charge (SoC) estimation is critical for the safe and efficient operation of electric
vehicles (EVs). This work proposes a hybrid multi-layer deep neural network (HMDNN) …
vehicles (EVs). This work proposes a hybrid multi-layer deep neural network (HMDNN) …
Towards learning universal hyperparameter optimizers with transformers
Meta-learning hyperparameter optimization (HPO) algorithms from prior experiments is a
promising approach to improve optimization efficiency over objective functions from a similar …
promising approach to improve optimization efficiency over objective functions from a similar …
Fraud detection in banking data by machine learning techniques
As technology advanced and e-commerce services expanded, credit cards became one of
the most popular payment methods, resulting in an increase in the volume of banking …
the most popular payment methods, resulting in an increase in the volume of banking …