Dimensionality reduction techniques in structural and earthquake engineering

Hajibabaee, Parisa; Pourkamali-Anaraki, Farhad; Hariri-Ardebili, Mohammad Amin

doi:10.1016/j.engstruct.2022.115485

Cited by 13 publications

(5 citation statements)

References 55 publications

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“…The main goal of data reduction is to increase the e ciency of the model by reducing the data that does not affect its effectiveness [27]. Data reduction is the process of ignoring the data that will not make any differences in the predicted result [28].…”

Section: Dimensionality Reductionmentioning

confidence: 99%

Multi-Category Prediction of Students’ Academic Performance Using Machine Learning: For Students Joining Higher Educational Institution in Ethiopia

Ahmed,

Tolera,

shebera

2023

Preprint

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Following the deployment of the Learning Management System (LMS) platform in higher educational institutions in Ethiopia, a massive amount of potentially helpful but as-yet untapped educational data has been generated. Despite the fact that the data is powerful enough to contribute to reducing student dropout rates through the application of modern educational data mining techniques such as machine learning, it has not been successfully employed to tackle student academic performance problems in higher education institutions(HEIs). As a result, a machine learning model was proposed based on data from three semesters of undergraduate students at Bule Hora University. To predict students' academic achievement, five machine learning methods (SVM, Random Forest, KNN, Gradient Boosting, and Decision Tree) were used. The Decision Tree model outperformed other models with a promising result of 97.3% on test accuracy and was selected as a proposed model. Moreover, our findings suggest that academic factors (entrance result, study time, attendance, and Internet access) and socio-demographic factors (age, gender, father job, mother job, family size, and address) had a greater impact on students' academic success. However, the academic performance of students was less affected by additional features like extra classes, another job, and guidance and counselling. Moreover, we are working on improving the accuracy of the proposed model. In this study, the student entrance result is the only variable used from the pre-university data. However, a CGPA result is not sufficient to qualify a student. Therefore, in the future, the exit exam results of the students will be incorporated.

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Section: Dimensionality Reductionmentioning

confidence: 99%

Multi-Category Prediction of Students’ Academic Performance Using Machine Learning: For Students Joining Higher Educational Institution in Ethiopia

Ahmed,

Tolera,

shebera

2023

Preprint

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“…Naïve Bayes Classifier, K-Nearest Neighbor, Support Vector Machine, and Random Forest are the most classifiers used in machine learning. This study uses the classifier referring to previous research [19] and adding LSTM classifier for handling multi-class classifier tasks to obtain optimal results through comparative analysis on Precision, Recall, and F1score metrics.…”

Section: E Classifiermentioning

confidence: 99%

“…3) Support Vector Machine: Support Vector Machine (SVM) works by constructing hyperplanes in a multidimensional space that separates the different cases of class labels. SVM has two important parameters, namely c and γ [19]. Parameter c adds a penalty for each misclassified data point.…”

Section: P R(c|tmentioning

confidence: 99%

Comparative Analysis using Various Performance Metrics in Imbalanced Data for Multi-class Text Classification

Riyanto¹,

Sitanggang²,

Djatna³

et al. 2023

IJACSA

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Precision, Recall, and F1-score are metrics that are often used to evaluate model performance. Precision and Recall are very important to consider when the data is balanced, but in the case of unbalanced data the F1-score is the most important metric. To find out the importance of these metrics, a comparative analysis is needed in order to determine which metric is appropriate for the data being analyzed. This study aims to perform a comparative analysis of various evaluation metrics on unbalanced data in multi-class text classification. This study uses an unbalanced multi-class text dataset including: association, negative, cause of disease, and treatment of disease. This study involves five classifiers as algorithm-level approach, namely: Multinomial Naive Bayes, K-Nearest Neigbors, Support Vector Machine, Random Forest, and Long Short-Term Memory. Meanwhile, data-level approach, this study involves under sampling, over sampling, and synthetic minority oversampling technique. Several evaluation metrics used to evaluate model performance include Precision, Recall, and F1-score. The results show that the most suitable evaluation metric for use on unbalanced data depends on the purpose of use and the desired priority, including the classifier that is suitable for handling multi-class assignments on unbalanced data. The results of this study can assist practitioners in selecting evaluation metrics that are in accordance with the goals and application needs of multi-class text classification.

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“…These procedures vary in computational requirements and their capacity to preserve data structure, crucial for accurate damage detection and localization. [6][7][8] The efficiency of wind turbines (WTs) as a renewable energy source depends on factors like blade angle alignment and turbine health. Modern WTs consist of key parts like a generator, nacelle, gearbox, rotor hub, blades, and shafts.…”

Section: Introductionmentioning

confidence: 99%

“…These procedures vary in computational requirements and their capacity to preserve data structure, crucial for accurate damage detection and localization. [ 6–8 ]…”

Section: Introductionmentioning

confidence: 99%

Dimensionality Reduction in Structural Health Monitoring: A Case Study on Damaged Wind Turbine Blades

Rezazadeh,

Polverino,

Perfetto

et al. 2024

Macromolecular Symposia

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In recent years, advances in structural health monitoring (SHM) of composite materials have been observed, driven by the adoption of intelligent diagnostic techniques. Vibration signals, which embed valuable structural health information, have been increasingly utilized in SHM. However, the high dimensionality of this data has necessitated significant computational resources and has made the differentiation between health states more complex. Highlighting the need for effective feature extraction and dimensionality reduction (DR), a study is conducted. In this research, advanced DR techniques, i.e., principal component analysis (PCA), locally linear embedding (LLE), t‐distributed stochastic neighbors embedding (t‐SNE), and uniform manifold approximation and projection (UMAP), are applied to an experimental dataset from a wind turbine blade under various health and environmental conditions (controlled‐environment vibration signals). The extracted features are then processed for the classification phase, and it is found that UMAP provides the best performance, albeit with a slightly increased computational demand. The findings from this research offer invaluable insights for researchers and engineers, assisting in the selection of the most appropriate DR method for SHM, and weighing up accuracy against computational time.

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Dimensionality reduction techniques in structural and earthquake engineering

Cited by 13 publications

References 55 publications

Multi-Category Prediction of Students’ Academic Performance Using Machine Learning: For Students Joining Higher Educational Institution in Ethiopia

Multi-Category Prediction of Students’ Academic Performance Using Machine Learning: For Students Joining Higher Educational Institution in Ethiopia

Comparative Analysis using Various Performance Metrics in Imbalanced Data for Multi-class Text Classification

Dimensionality Reduction in Structural Health Monitoring: A Case Study on Damaged Wind Turbine Blades

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