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Kalaivani, R.
- Face Feature Age Prediction through Optimized Wavelet Back Propagation Network
Abstract Views :188 |
PDF Views:1
Authors
Affiliations
1 Department of Computer Application, Ayya Nadar Janaki Ammal College, Sivakasi, IN
2 Computer Application Department, Ayya Nadar Janaki Ammal College, Sivakasi, IN
3 Department of Computer Science and Information Technology, Ayya Nadar Janaki Ammal College, Sivakasi, IN
1 Department of Computer Application, Ayya Nadar Janaki Ammal College, Sivakasi, IN
2 Computer Application Department, Ayya Nadar Janaki Ammal College, Sivakasi, IN
3 Department of Computer Science and Information Technology, Ayya Nadar Janaki Ammal College, Sivakasi, IN
Source
Artificial Intelligent Systems and Machine Learning, Vol 5, No 6 (2013), Pagination: 278-286Abstract
With the advancement in technology, one thing that concerns the world and especially in the developing countries is the tremendous increase in population. With such a rapid rate of increase, it is becoming difficult to recognize each and every person because we have to keep up photos either in digital or hard copy format of every person at different time periods of his/her life. Sometimes database has the required information of that particular person, but it’s of no use as it is now obsolete. Deciding age of a person from digital photography is an intriguing problem. Age changes cause many variations in visible of human faces. Many aspects affect the appearance of a person’s face during the process of growing older. The aging process will explain with many factors such as health, living style, living place and weather condition etc. Face is a non-intrusive recognition, without user co-ordination able to recognize the person. Age classification system is generally composed of feature extraction and classification. It is used to estimate the age of a person from his/her face features. For the aging feature extraction, face images interpreted as decomposition of optimized wavelet transform with 49 feature vectors using Daubechies wavelet and the classifier of supervised neural network to discriminate the ranges of ages. The work is to classify the age range into child (1-10), teenage (11-20) young (21-30), middle aged (31-50) and old (51 and above).Keywords
ASM, Wavelet, Age Classification, Neural Network.- Pattern Classification Using Optimized Machine Learning Techniques
Abstract Views :183 |
PDF Views:1
Authors
Affiliations
1 Department of Computer Science & Information Technology, Ayya Nadar Janaki Ammal College, Sivakasi 626 124, Tamilnadu, IN
2 Computer Science Department, The S.F.R. College for Women, Sivakasi 626123, Tamilnadu, IN
3 MCA Department, Ayya Nadar Janaki Ammal College, Sivakasi 626 124, Tamilnadu, IN
4 Department of Computer Application, ANJA College, Sivakasi 626 124, Tamilnadu, IN
1 Department of Computer Science & Information Technology, Ayya Nadar Janaki Ammal College, Sivakasi 626 124, Tamilnadu, IN
2 Computer Science Department, The S.F.R. College for Women, Sivakasi 626123, Tamilnadu, IN
3 MCA Department, Ayya Nadar Janaki Ammal College, Sivakasi 626 124, Tamilnadu, IN
4 Department of Computer Application, ANJA College, Sivakasi 626 124, Tamilnadu, IN
Source
Artificial Intelligent Systems and Machine Learning, Vol 5, No 6 (2013), Pagination: 287-296Abstract
Most of the real world problems in engineering, medicine, industry, science and business also involve data classification. Classification is a supervised machine learning technique used to predict group membership for data instances. Pattern classification problems belong to the category of supervised learning. Pattern Classification involves assigning a label to a given input data. Neural Networks are an effective tool in the field of pattern classification, using training and testing data to build a model. Training neural networks in classification problems, especially when biological data are is a very challenging task. The protein superfamily classification problem, which consists of determining the superfamily membership of a given unknown protein sequence, is very important for a biologist for many practical reasons, such as drug discovery, prediction of molecular function and medical diagnosis. The objective of this work is creating a classification model for classifying data using Multilayer feed forward network. It contain two phases. First, classifier model was build for iris plant classification. Second, classifier model was build for protein sequence classification to know the organism of protein and family of the given protein sequence.Keywords
Data Mining, Pattern Classification, Neural Network, Back Propagation, Iris Plant, Bioinformatics, Protein Sequence.- Performance Comparison of Multilayer Feed Forward and Radial Basis Feed Forward Neural Networks in River Stage Prediction
Abstract Views :156 |
PDF Views:1
Authors
R. Kalaivani
1,
P. Thangaraj
2
Affiliations
1 Institute of Road and Transport Technology, Erode, Tamilnadu, IN
2 Department of CSE, Bannari Amman Institute of Technology, Sathyamangalam, Tamilnadu, IN
1 Institute of Road and Transport Technology, Erode, Tamilnadu, IN
2 Department of CSE, Bannari Amman Institute of Technology, Sathyamangalam, Tamilnadu, IN