scholarly journals Unsupervised texture classification using vector quantization and deterministic relaxation neural network

1997 ◽  
Vol 6 (10) ◽  
pp. 1376-1387 ◽  
Author(s):  
P.P. Raghu ◽  
R. Poongodi ◽  
B. Yegnanarayana
Keyword(s):  
Neural Network ◽  
Vector Quantization ◽  
Texture Classification

scholarly journals Soft Computing Tool Approach for Texture Classification Using Discrete Cosine Transform

2011 ◽  
pp. 12-16
Author(s):  
Pankaj H. Chandankhede
Keyword(s):  
Neural Network ◽  
Soft Computing ◽  
Learning Algorithm ◽  
Texture Classification ◽  
Fuzzy Model ◽  
Performance Comparison ◽  
Cosine Transform ◽  
Neuro Fuzzy ◽  
Soft Computing Techniques ◽  
Computing Models

Texture can be considered as a repeating pattern of local variation of pixel intensities. Cosine Transform (DCT) coefficients of texture images. As DCT works on gray level images, the color scheme of each image is transformed into gray levels. For classifying the images using DCT, two popular soft computing techniques namely neurocomputing and neuro-fuzzy computing are used. A feedforward neural network is used to train the backpropagation learning algorithm and an evolving fuzzy neural network to classify the textures. The soft computing models were trained using 80% of the texture data and the remaining was used for testing and validation purposes. A performance comparison was made among the soft computing models for the texture classification problem. In texture classification the goal is to assign an unknown sample image to a set of known texture classes. It is observed that the proposed neuro-fuzzy model performed better than the neural network.

scholarly journals Diagnosa Penyakit Demam Berdarah Dengue (DBD) menggunakan Metode Learning Vector Quantization (LVQ)

2020 ◽  
Vol 4 (3) ◽  
pp. 56
Author(s):  
Firman Tawakal ◽  
Ahmedika Azkiya
Keyword(s):  
Neural Network ◽  
Typhoid Fever ◽  
Vector Quantization ◽  
Dengue Hemorrhagic Fever ◽  
Hemorrhagic Fever ◽  
Learning Vector Quantization ◽  
Mean Square ◽  
Northern Australia ◽  
Neural Network Learning ◽  
Network Learning

Dengue Hemorrhagic Fever is a disease that is carried and transmitted through the mosquito Aedes aegypti and Aedes albopictus which is commonly found in tropical and subtropical regions such as in Indonesia to Northern Australia. in 2013 there are 2.35 million reported cases, which is 37,687 case is heavy cases of DHF. DHF’s symthoms have a similarity with typhoid fever, it often occur wrong handling. Therefore we need a system that is able to diagnose the disease suffered by patients, so that they can recognize whether the patient has DHF or Typhoid. The system will be built using Neural Network Learning Vector Quantization (LVQ) based on the best training results. This research is to diagnose Dengue Hemorrhagic Fever using LVQ with input parameters are hemoglobin, leukocytes, platelets, and heritrocytes. Based on result, the best accuracy is 97,14% with Mean Square Error (MSE) is 0.028571 with 84 train data and 36 test data. Conclution from the research is LVQ method can diagnose DHF Keywords: Dengue Hemorrhagic Fever; Learning Vector Quantization; classification; Neural Network;

Emotion Recognition From Speech Using Perceptual Filter and Neural Network

2020 ◽  
pp. 78-91 ◽  
Author(s):  
Revathi A. ◽  
Sasikaladevi N.
Keyword(s):  
Neural Network ◽  
Emotion Recognition ◽  
Vector Quantization ◽  
Group Performance ◽  
Back Propagation ◽  
Critical Band ◽  
Emotion Classification ◽  
Back Propagation Algorithm ◽  
Propagation Algorithm ◽  
Speaker Independent

This chapter on multi speaker independent emotion recognition encompasses the use of perceptual features with filters spaced in Equivalent rectangular bandwidth (ERB) and BARK scale and vector quantization (VQ) classifier for classifying groups and artificial neural network with back propagation algorithm for emotion classification in a group. Performance can be improved by using the large amount of data in a pertinent emotion to adequately train the system. With the limited set of data, this proposed system has provided consistently better accuracy for the perceptual feature with critical band analysis done in ERB scale.

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