Artificial neural networks for photonic applications—from algorithms to implementation: tutorial

P Freire, E Manuylovich, JE Prilepsky… - Advances in Optics and …, 2023 - opg.optica.org
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 …

Survey of optimization algorithms in modern neural networks

R Abdulkadirov, P Lyakhov, N Nagornov - Mathematics, 2023 - mdpi.com
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 …

SMT 2.0: A Surrogate Modeling Toolbox with a focus on hierarchical and mixed variables Gaussian processes

P Saves, R Lafage, N Bartoli, Y Diouane… - … in Engineering Software, 2024 - Elsevier
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 …

Enhancing HVDC transmission line fault detection using disjoint bagging and bayesian optimization with artificial neural networks and scientometric insights

MZ Yousaf, AR Singh, S Khalid, M Bajaj, BH Kumar… - Scientific Reports, 2024 - nature.com
DC grid fault protection techniques have previously faced challenges such as fixed
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

A Raza, HUR Siddiqui, K Munir, M Almutairi, F Rustam… - Plos one, 2022 - journals.plos.org
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 …

Multi-fidelity cost-aware Bayesian optimization

ZZ Foumani, M Shishehbor, A Yousefpour… - Computer Methods in …, 2023 - Elsevier
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 …

A survey on multi-objective hyperparameter optimization algorithms for machine learning

A Morales-Hernández, I Van Nieuwenhuyse… - Artificial Intelligence …, 2023 - Springer
Hyperparameter optimization (HPO) is a necessary step to ensure the best possible
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

MH Zafar, M Mansoor, M Abou Houran, NM Khan… - Energy, 2023 - Elsevier
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) …

Towards learning universal hyperparameter optimizers with transformers

Y Chen, X Song, C Lee, Z Wang… - Advances in …, 2022 - proceedings.neurips.cc
Meta-learning hyperparameter optimization (HPO) algorithms from prior experiments is a
promising approach to improve optimization efficiency over objective functions from a similar …

Fraud detection in banking data by machine learning techniques

SK Hashemi, SL Mirtaheri, S Greco - IEEE Access, 2022 - ieeexplore.ieee.org
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 …