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Deepa, S. N.
- A Survey on Artificial Intelligence Approaches for Medical Image Classification
Abstract Views :1003 |
PDF Views:345
Authors
S. N. Deepa
1,
B. Aruna Devi
1
Affiliations
1 Department of EEE, Anna University of Technology, Coimbatore-641047, IN
1 Department of EEE, Anna University of Technology, Coimbatore-641047, IN
Source
Indian Journal of Science and Technology, Vol 4, No 11 (2011), Pagination: 1583-1595Abstract
In this paper, a survey has been made on the applications of intelligent computing techniques for diagnostic sciences in biomedical image classification. Several state-of-the-art Artificial Intelligence (AI) techniques for automation of biomedical image classification are investigated. This study gathers representative works that exhibit how AI is applied to the solution of very different problems related to different diagnostic science analysis. It also detects the methods of artificial intelligence that are used frequently together to solve the special problems of medicine. SVM neural network is used in almost all imaging modalities of medical image classification. Similarly fuzzy C means and improvements to it are important tool in segmentation of brain images. Various diagnostic studies like mammogram analysis, MRI brain analysis, bone and retinal analysis etc., using neural network approach result in use of back propagation network, probabilistic neural network, and extreme learning machine recurrently. Hybrid approach of GA and PSO are also commonly used for feature extraction and feature selection.Keywords
Medical Imaging, Artificial Intelligence (AI), Neural Networks (NN), Fuzzy Logic (FL), Genetic Algorithms (GA), Particle Swarm Optimization (PSO)Full Text
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- A Modified Particle Swarm Optimization Based Approach for Lower Order Model Formulation of Linear Time Invariant Systems
Abstract Views :205 |
PDF Views:2
Authors
S. N. Deepa
1,
G. Sugumaran
1
Affiliations
1 Department of Electrical and Electronics, Anna University, Coimbatore-641047, Tamil Nadu, IN
1 Department of Electrical and Electronics, Anna University, Coimbatore-641047, Tamil Nadu, IN
Source
Artificial Intelligent Systems and Machine Learning, Vol 2, No 2 (2010), Pagination: 33-42Abstract
This paper proposes a computationally simple approach for model formulation of linear time invariant systems. New version of the particle swarm optimization (PSO), namely, MPSO is used for the lower order model formulation of linear time invariant systems. In the modified PSO, the movement of a particle is governed by three behaviors, namely, inertia, cognitive, and social. The cognitive behavior helps the particle to remember its previous visited best position. This paper proposes to split the cognitive behavior into two sections .This modification helps the particle to search the target very effectively. In order to minimize the integral squared error of the lower order model, MPSO is proposed and results are shown in the form of unit impulse response curves and are compared with the response of the original higher order model and with the other model formulation methods. The proposed method is illustrated through numerical examples from literature.Keywords
Higher Order Model, Integral Squared Error, Lower Order Model, Modified Particle Swarm Optimization, Single Input Single Output.
- An Intelligent Hybrid Neural Network Model in Renewable Energy Systems
Abstract Views :161 |
PDF Views:0
Authors
Affiliations
1 Department of Electrical and Electronics Engineering, Anna University, Regional Office, Coimbatore, IN
1 Department of Electrical and Electronics Engineering, Anna University, Regional Office, Coimbatore, IN