High-precision identification of power quality disturbances based on discrete orthogonal S-transforms and compressed neural network methods
Power quality disturbances (PQDs) occur as the use of non-linear load and renewable-
based micro-grids increases. This paper presents a new algorithm that consists of the …
based micro-grids increases. This paper presents a new algorithm that consists of the …
Characterizing the variability of footstep-induced structural vibrations for open-world person identification
Person identification is important in providing personalized services in smart buildings.
Many existing studies focus on closed-world person identification, which only identifies a …
Many existing studies focus on closed-world person identification, which only identifies a …
Locally alignment based manifold learning for simultaneous feature selection and extraction in classification problems
M Fattahi, MH Moattar, Y Forghani - Knowledge-Based Systems, 2023 - Elsevier
Dimensionality reduction is an important step in increasing the performance of machine
learning algorithms while decreasing the processing time. From feature reduction …
learning algorithms while decreasing the processing time. From feature reduction …
Feature extraction from satellite-derived hydroclimate data: Assessing impacts on various neural networks for multi-step ahead streamflow prediction
Enhancing the generalization capability of time-series models for streamflow prediction
using dimensionality reduction (DR) techniques remains a major challenge in water …
using dimensionality reduction (DR) techniques remains a major challenge in water …
[HTML][HTML] An AUC-maximizing classifier for skewed and partially labeled data with an application in clinical prediction modeling
Partially labeled and skewed datasets are common in many applications including
healthcare, due to the high costs and time constraints of data collection and annotation …
healthcare, due to the high costs and time constraints of data collection and annotation …
A data-driven approach for linear and nonlinear damage detection using variational mode decomposition and GARCH model
VR Gharehbaghi, H Kalbkhani… - Engineering with …, 2023 - Springer
In this article, an original data-driven approach is proposed to detect both linear and
nonlinear damage in structures using output-only responses. The method deploys …
nonlinear damage in structures using output-only responses. The method deploys …
Feature importance‐based interpretation of UMAP‐visualized polymer space
T Ehiro - Molecular Informatics, 2023 - Wiley Online Library
Dimensionality reduction (DR) techniques are used for various purposes such as
exploratory data analysis. A commonly employed linear DR technique is principal …
exploratory data analysis. A commonly employed linear DR technique is principal …
A novel auc maximization imbalanced learning approach for predicting composite outcomes in covid-19 hospitalized patients
The COVID-19 patient data for composite outcome prediction often comes with class
imbalance issues, ie, only a small group of patients develop severe composite events after …
imbalance issues, ie, only a small group of patients develop severe composite events after …
Quantum dimensionality reduction by linear discriminant analysis
K Yu, S Lin, GD Guo - Physica A: Statistical Mechanics and its Applications, 2023 - Elsevier
The dimensionality reduction is generally used as the data preprocessing stage of many
machine learning tasks and has become an essential branch of information processing. Due …
machine learning tasks and has become an essential branch of information processing. Due …
Algorithm for orthogonal matrix nearness and its application to feature representation
Various learning problems can be represented as certain canonical forms of orthogonal
matrix nearness problems under the unitarily invariant norm. Since varying unitarily invariant …
matrix nearness problems under the unitarily invariant norm. Since varying unitarily invariant …