A Comparative Analysis on Prediction Performance of Regression Models during Machining of Composite Materials

Bhattacharya, Shibaprasad; Kalita, Kanak; Čep, Róbert; Chakraborty, Shankar

doi:10.3390/ma14216689

Cited by 27 publications

(12 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Overfitting/underfitting problems can cause unstable and inaccurate linear regression models. Therefore, the linear regression model includes a modified version of the loss function, referred to as “regularized or penalized linear regression” [ 34 , 35 ]. The regression model using the L2 regularization method is known as ridge regression.…”

Section: Analysis Processmentioning

confidence: 99%

See 1 more Smart Citation

Suitability Analysis of Machine Learning Algorithms for Crack Growth Prediction Based on Dynamic Response Data

Omar

Khan

Starr

2023

Sensors

View full text Add to dashboard Cite

Machine learning has the potential to enhance damage detection and prediction in materials science. Machine learning also has the ability to produce highly reliable and accurate representations, which can improve the detection and prediction of damage compared to the traditional knowledge-based approaches. These approaches can be used for a wide range of applications, including material design; predicting material properties; identifying hidden relationships; and classifying microstructures, defects, and damage. However, researchers must carefully consider the appropriateness of various machine learning algorithms, based on the available data, material being studied, and desired knowledge outcomes. In addition, the interpretability of certain machine learning models can be a limitation in materials science, as it may be difficult to understand the reasoning behind predictions. This paper aims to make novel contributions to the field of material engineering by analyzing the compatibility of dynamic response data from various material structures with prominent machine learning approaches. The purpose of this is to help researchers choose models that are both effective and understandable, while also enhancing their understanding of the model’s predictions. To achieve this, this paper analyzed the requirements and characteristics of commonly used machine learning algorithms for crack propagation in materials. This analysis assisted the authors in selecting machine learning algorithms (K nearest neighbor, Ridge, and Lasso regression) to evaluate the dynamic response of aluminum and ABS materials, using experimental data from previous studies to train the models. The results showed that natural frequency was the most significant predictor for ABS material, while temperature, natural frequency, and amplitude were the most important predictors for aluminum. Crack location along samples had no significant impact on either material. Future work could involve applying the discussed techniques to a wider range of materials under dynamic loading conditions.

show abstract

Section: Analysis Processmentioning

confidence: 99%

“…The L2 regularization aspect is represented by

. The ridge regression is the squared magnitude of the coefficient “penalty” added to the loss function [ 35 , 38 , 39 ] as:

λ ≠ 0 (Lambda not equal 0) …”

Section: Analysis Processmentioning

confidence: 99%

Suitability Analysis of Machine Learning Algorithms for Crack Growth Prediction Based on Dynamic Response Data

Omar

Khan

Starr

2023

Sensors

View full text Add to dashboard Cite

show abstract

“…Analisis Ridge dilakukan dengan dasar pada data asli atau komponen utama. Ortogonalitas data dan data prior yang memberikan perkiraan berat rata-rata sederhana dari perkiraan kemunculan kemungkinan dan rata-rata dari data prior (Bhattacharya et al, 2021). Persamaan Ridge ditunjukkan pada persamaan (3).…”

Section: 𝑓(𝑥) = 𝑤 𝑇 𝜑(𝑥) + 𝑏unclassified

Perbandingan Performa Regresi Menggunakan Poisson, Support Vector, dan Ridge pada Prediksi Hasil Penggergajian Sengon

Hartanta

2022

j. kunnskapsteknol. inform. dan kompʹût.

View full text Add to dashboard Cite

Kayu Sengon merupakan material yang sering digunakan untuk bahan perkakas dan konstruksi bangunan yang disediakan dengan cara penggergajian kayu. Prediksi hasil penggergajian Sengon dilakukan untuk mengetahui jumlah hasil yang bisa didapat yang biasanya dilakukan dengan cara menebak atau melalui perhitungan manual. Metode tersebut membutuhkan pengalaman, waktu lama, dan susah untuk dilakukan sehingga perlu solusi untuk membantu mempercepat proses prediksi dengan menerapkan data mining. Penelitian ini dilakukan untuk melakukan prediksi hasil penggergajian kayu Sengon yang dilakukan dengan langkah akuisisi data, dataset, model regresi (Poisson, Support Vector, dan Ridge), dan evaluasi. Hasil penelitian menunjukkan bahwa regresi Poisson merupakan model terbaik karena memiliki error terkecil dengan Mean Absolute Error (MAE) 0,514 dan Root Mean Square Error (RMSE) 0,672 sehingga model ini tepat diterapkan pada sistem penggergajian Sengon.

show abstract

“…Random Forests are based on decision trees [27][28][29]. The computational methodology is the best method to classify phishing in phishing attack mechanisms.…”

Section: Random Forestmentioning

confidence: 99%

An Effective and Secure Mechanism for Phishing Attacks Using a Machine Learning Approach

et al. 2022

View full text Add to dashboard Cite

Phishing is one of the biggest crimes in the world and involves the theft of the user’s sensitive data. Usually, phishing websites target individuals’ websites, organizations, sites for cloud storage, and government websites. Most users, while surfing the internet, are unaware of phishing attacks. Many existing phishing approaches have failed in providing a useful way to the issues facing e-mails attacks. Currently, hardware-based phishing approaches are used to face software attacks. Due to the rise in these kinds of problems, the proposed work focused on a three-stage phishing series attack for precisely detecting the problems in a content-based manner as a phishing attack mechanism. There were three input values—uniform resource locators and traffic and web content based on features of a phishing attack and non-attack of phishing website technique features. To implement the proposed phishing attack mechanism, a dataset is collected from recent phishing cases. It was found that real phishing cases give a higher accuracy on both zero-day phishing attacks and in phishing attack detection. Three different classifiers were used to determine classification accuracy in detecting phishing, resulting in a classification accuracy of 95.18%, 85.45%, and 78.89%, for NN, SVM, and RF, respectively. The results suggest that a machine learning approach is best for detecting phishing.

show abstract

A Comparative Analysis on Prediction Performance of Regression Models during Machining of Composite Materials

Cited by 27 publications

References 30 publications

Suitability Analysis of Machine Learning Algorithms for Crack Growth Prediction Based on Dynamic Response Data

Suitability Analysis of Machine Learning Algorithms for Crack Growth Prediction Based on Dynamic Response Data

Perbandingan Performa Regresi Menggunakan Poisson, Support Vector, dan Ridge pada Prediksi Hasil Penggergajian Sengon

An Effective and Secure Mechanism for Phishing Attacks Using a Machine Learning Approach

Contact Info

Product

Resources

About