Supervised Clustering Algorithm for University Student Learning Algebra

Yih, Jeng Ming

doi:10.4028/www.scientific.net/amr.542-543.1376

Cited by 3 publications

(2 citation statements)

References 11 publications

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“…In this instance, reducing the Mean Square Error (MSE) in between forecasts and the inputs equates to completing a linear interpolation; MSE thus reflects the algorithm's gradient descent. MSE is also known as L2 loss in the ML literature, while the L1 losses are indeed the Mean Absolute Error (MAE), which seems to be a viable option for regression issues [13]. Regression analysis is susceptible to misclassification because to its minimalism and incapacity to capture non -linear behavior.…”

Section: Methods Based On Statisticsmentioning

confidence: 99%

Supervised Learning Methods and Applications in Medical Research

Ming

Yuan

2022

JCNS

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Machine Learning (ML) and Artificial Intelligence (AI) methods are transforming many commercial and academic areas, including feature extraction, autonomous driving, computational linguistics, and voice recognition. These new technologies are now having a significant effect in radiography, forensics, and many other areas where the accessibility of automated systems may improve the precision and repeatability of essential job performance. In this systematic review, we begin by providing a short overview of the different methods that are currently being developed, with a particular emphasis on those utilized in biomedical studies.

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Section: Methods Based On Statisticsmentioning

confidence: 99%

Supervised Learning Methods and Applications in Medical Research

Ming

Yuan

2022

JCNS

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“…The methods to detect the local extreme value were developed by this paper. Focusing attention to these two faults, an improved new algorithm, "Fuzzy C-Means based on Particle Swarm Optimization with Mahalanobis distance (PSO-FCM-M)", is proposed [9,14].…”

Section: Organization Of the Textmentioning

confidence: 99%

The Particle Swarm Optimization Based on Mahalanobis Distance

Yih¹

2017

dtetr

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Abstract. An improved new fuzzy clustering algorithm is developed to obtain better quality of fuzzy clustering results. The objective function includes the regulating terms about the covariance matrices. The cluster centers and the covariance matrices are directly derived from the Lagrange's method. The fuzzy c-mean algorithm is different from the GK and GG algorithms. The singular problem and the selecting initial values problem are improved by the Eigenvalue method and the Ratio method. We proposed a proposition which pointed out that the initial memberships of fuzzy c-mean algorithm which was based on Mahalanobis distance Algorithm and the traditional FCM Algorithm can not be all equal. The other important issue is how to approach the global minimum value that can improve the cluster accuracy. The methods to detect the local extreme value were developed by this paper. Focusing attention to these two faults, an improved new algorithm, "Fuzzy C-Means based on Particle Swarm Optimization with Mahalanobis distance (PSO-FCM-M)", is proposed. We have two aims and goals of our research summary. One is to compare the classification accuracies of fuzzy clustering algorithms based on mahalanobis distances and euclidean distances. The other is to choose the initial membership to promote the classification accuracies.

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Face Recognition Attendance Management System Based on Data Analytics

Lekhaa¹,

Kumar²

2022

JCNS

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Face recognition systems are utilised in a variety of situations. In this digital age, every industry is affected. One of the most well-known Face recognition is one of the most widely used biometrics. It can be used for a variety of things. Among other things, security, authentication, and identification are all important. Considering its limited accuracy when compared to retina and thumbprint recognition, and because it is a popular method of identification, it is widely used. Recognition of people's faces for Attendance tracking systems can also be utilised in Educational Institutions, Organizations. Because the Current manual is outdated, It takes a long time to set up and maintain an attendance system. Fundamentally, this approach aims to establish a class attendance system Face recognition technology is used in this system. There's also the option of having a proxy attend. As a result, As a result, demand for this system is increasing. Database development, face detection, face recognition, and attendance updating are the four steps of this system.The photos of the kids in class are used to generate the database.

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Supervised Clustering Algorithm for University Student Learning Algebra

Cited by 3 publications

References 11 publications

Supervised Learning Methods and Applications in Medical Research

Supervised Learning Methods and Applications in Medical Research

The Particle Swarm Optimization Based on Mahalanobis Distance

Face Recognition Attendance Management System Based on Data Analytics

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