Unified SVM algorithm based on LS-DC loss

Zhou, Shuisheng; Zhou, Wendi

doi:10.1007/s10994-021-05996-7

Cited by 13 publications

(5 citation statements)

References 30 publications

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“…K-Nearest Neighbors (KNN) is a supervised learning algorithm used to classify data based on the membership of its k nearest neighbors. By examining which class the k nearest neighbors belong to, with a predetermined value of k, the example data is placed in the class with the highest number of observations [6,17].…”

Section: Methodsmentioning

confidence: 99%

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An artificial intelligence model estimation for functionalized graphene quantum dot-based diode characteristics

Öter,

Ersöz,

Berktaş

et al. 2024

Phys. Scr.

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The use of artificial intelligence (AI) approaches, one of the most significant technological advances of the 21st century, to determine the diode parameters that can be obtained from Schottky diode characterization allows data to be collected, processed, analyzed, and results obtained faster than ever before, with high accuracy. It also supports the development of a selection and modeling tool for future studies and, most importantly, facilitates modeling implementation with faster and fewer experimental results. In this context, this study presents a machine learning (ML) model to predict diode parameters from current-voltage (I-V) measurements of polyethyleneimine-functionalized graphene quantum dots (GQDs)-based Schottky hybrid diode. This study used K-Nearest Neighbor, Random Forest, Multilayered Perceptron, and Support Vector Machine algorithms. In research, the lowest model error of each model was compared, and the performance of the models obtained was evaluated. In addition, out of 30 diodes on the fabricated structure, the diode with the best rectification ratio(RR) was identified within a few seconds using machine learning, verified to be the same as the diode selected by the researchers, and the optimal estimation of RR and ideality factor(n) was made from the diode parameters using the thermionic emission (TE)method. The experimental results were compared with machine learning models. Among these algorithms, the RF algorithm performed best with a root mean square error (RMSE)value of 4.1 E-05 and an R-squared value of(R2) 0.999998. The success of RF depends on the characteristics of the dataset used, its size, and data distribution. RF reduces overfitting by taking the average of multiple decision trees and is less sensitive to noise and outliers in the data. The results obtained will allow the development of a selection and modeling tool for future studies and, most importantly, facilitate modeling faster and with fewer experimental results, enabling the integration of AI into science.

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Section: Methodsmentioning

confidence: 99%

“…AI is a discipline that has been applied to various fields in recent years, with satisfactory results [6]. Unlike in the past, when it solved one problem at a time, AI is now a discipline capable of solving multiple problems simultaneously.…”

Section: Introductionmentioning

confidence: 99%

An artificial intelligence model estimation for functionalized graphene quantum dot-based diode characteristics

Öter,

Ersöz,

Berktaş

et al. 2024

Phys. Scr.

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“…Ye et al [24] proposed a -norm LSSVR by using the -norm regularization term and the absolute constraint. Lu et al [25] proposed a robust LSSVM by minimizing both the mean and variance of the modeling errors.…”

Section: P L Pmentioning

confidence: 99%

Robust Least Squares Projection Twin SVM and its Sparse Solution

Zhou,

Zhang,

Chen

et al. 2023

J. of Syst. Eng. Electron.

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Least squares projection twin support vector machine (LSPTSVM) has faster computing speed than classical least squares support vector machine (LSSVM). However, LSPTSVM is sensitive to outliers and its solution lacks sparsity. Therefore, it is difficult for LSPTSVM to process large-scale datasets with outliers. In this paper, we propose a robust LSPTSVM model (called R-LSPTSVM) by applying truncated least squares loss function. The robustness of R-LSPTSVM is proved from a weighted perspective. Furthermore, we obtain the sparse solution of R-LSPTSVM by using the pivoting Cholesky factorization method in primal space. Finally, the sparse R-LSPTSVM algorithm (SR-LSPTSVM) is proposed. Experimental results show that SR-LSPTSVM is insensitive to outliers and can deal with large-scale datasets fastly.

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“…Pandas generates data that is frequently used as input for SciPy's statistical analysis, SciPy's graphing routines, and Scikit-machine Learns learning algorithms. Any text editor can be used to run the Pandas program; however, Jupiter Notebook is preferred because it allows you to only run the code in a specific cell rather than the entire file [10].…”

Section: B) Pandasmentioning

confidence: 99%

Diabetes Prediction using Machine Learning

Faraz

2022

J. Appl. Sci. Educ.

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Machine Learning is a type of AI (also known as Artificial Intelligence) that makes the pc or computer act like individuals and learn more as they experience additional information from their client or user. So here in this report we got basic introduction about machine learning like actually what is it, what is its use, how it works and many more things. Thereafter, we discussed about the python the language, which is used for making pro-ject, important libraries such as pandas and numPy which is being is used for this partic-ular project and we have also discussed about Support Vector machine which has been used as classifier. We have also talked about the linear and non-linear svm that is used to check the accuracy of the predictive system. For the implementation we have started with importing the dependencies numpy smp and pandas pd and for the analysis we have taken a csv Pima Indian diabetes dataset after that we have trained the model with the help of support vector classifier. For the model evaluation we have checked the accuracy of the training data and test data. Numerous people suffer from diabetes mellitus, one of the most serious diseases. Age, obesity, inactivity, genetic diabetes, a poor diet, high blood pressure, and other factors can all contribute to diabetes mellitus. Diabetes in-creases a person's risk of developing various illnesses, including heart disease, renal dis-ease, stroke, vision problems, nerve damage, etc. So, here we will be building a system that can predict whether a person has diabetes or not with the help of Machine Learning.

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Unified SVM algorithm based on LS-DC loss

Cited by 13 publications

References 30 publications

An artificial intelligence model estimation for functionalized graphene quantum dot-based diode characteristics

An artificial intelligence model estimation for functionalized graphene quantum dot-based diode characteristics

Robust Least Squares Projection Twin SVM and its Sparse Solution

Diabetes Prediction using Machine Learning

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