A Novel Supervised Learning Model for Figures Recognition by Using Artificial Neural Network

2018 ◽  
pp. 199-208
Author(s):  
Zeyad M. Alfawaer ◽  
Saleem Alzoubi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Supervised Learning ◽  
Learning Model ◽  
Artificial Neural

scholarly journals Prediction Data Processing Scheme using an Artificial Neural Network and Data Clustering for Big Data

2016 ◽  
Vol 6 (1) ◽  
pp. 330 ◽  
Author(s):  
Se-Hoon Jung ◽  
Jong-Chan Kim ◽  
Chun-Bo Sim
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Artificial Neural Network ◽  
Data Clustering ◽  
Principal Component ◽  
Component Analysis ◽  
Learning Model ◽  
Data Generation ◽  
Processing Scheme ◽  
Artificial Neural

Various types of derivative information have been increasing exponentially, based on mobile devices and social networking sites (SNSs), and the information technologies utilizing them have also been developing rapidly. Technologies to classify and analyze such information are as important as data generation. This study concentrates on data clustering through principal component analysis and K-means algorithms to analyze and classify user data efficiently. We propose a technique of changing the cluster choice before cluster processing in the existing K-means practice into a variable cluster choice through principal component analysis, and expanding the scope of data clustering. The technique also applies an artificial neural network learning model for user recommendation and prediction from the clustered data. The proposed processing model for predicted data generated results that improved the existing artificial neural network–based data clustering and learning model by approximately 9.25%.

scholarly journals A New Method for Human Posture Recognition Using Principal Component Analysis and Artificial Neural Network

2015 ◽  
Vol 7 (3) ◽  
pp. 11-19 ◽  
Author(s):  
M. Z. Uddin ◽  
M. A. Yousuf
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Artificial Neural Network ◽  
Supervised Learning ◽  
Principal Component ◽  
Component Analysis ◽  
Human Action ◽  
Human Posture ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The recognition of human posture from images is currently a very active area of research in computer vision. This paper presents a novel recognition method to determine a human posture is of walking or sitting using Principal Component Analysis (PCA) and Artificial Neural Network (ANN). In this paper, two types of learning are used to recognize the human posture. One is unsupervised and another is supervised learning. We have used PCA for unsupervised learning and ANN for supervised learning. To evaluate the performance of the proposed method, we have considered four types of human posture; walking, sitting, right leg up-down and left leg up-down. The experimental results on the human action of walking, sitting, right leg up-down and left leg up-down database show that our approach produces accurate recognition.

scholarly journals Deteksi Dini Status Keanggotaan Industri Kebugaran Menggunakan Pendekatan Supervised Learning

2020 ◽  
Vol 6 (2) ◽  
Author(s):  
Julio Narabel ◽  
Setia Budi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Supervised Learning ◽  
Nearest Neighbor ◽  
Confusion Matrix ◽  
Roc Curves ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Green Zone ◽  
Artificial Neural

In the fitness industry, the number of members is a major factor for the sustainability of its business. The ability of managers and trainers to detect members who represent traits to quit membership is critical. Four supervised learning classification methods like Support Vector Machine, Random Forest, K-Nearest Neighbor, and Artificial Neural Network were used to generate early detection using two variants of datasets that have different amounts of data. Classification results are separated into three different zones, which are Green Zone, Yellow Zone, and Red Zone. Artificial Neural Network methods using backpropagation training give 99.90% of accuracy on a dataset which has more amount of data. The evaluation has been done using the confusion matrix and AUC-ROC curves.

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