Now showing items 41-51 of 51
| Abstract: | A spectral angle based feature extraction method, Spectral Clustering Independent Component Analysis (SC-ICA), is proposed in this work to improve the brain tissue classification from Magnetic Resonance Images (MRI). SC-ICA provides equal priority to global and local features; thereby it tries to resolve the inefficiency of conventional approaches in abnormal tissue extraction. First, input multispectral MRI is divided into different clusters by a spectral distance based clustering. Then, Independent Component Analysis (ICA) is applied on the clustered data, in conjunction with Support Vector Machines (SVM) for brain tissue analysis. Normal and abnormal datasets, consisting of real and synthetic T1-weighted, T2-weighted and proton density/fluid-attenuated inversion recovery images, were used to evaluate the performance of the new method. Comparative analysis with ICA based SVM and other conventional classifiers established the stability and efficiency of SC-ICA based classification, especially in reproduction of small abnormalities. Clinical abnormal case analysis demonstrated it through the highest Tanimoto Index/accuracy values, 0.75/98.8%, observed against ICA based SVM results, 0.17/96.1%, for reproduced lesions. Experimental results recommend the proposed method as a promising approach in clinical and pathological studies of brain diseases |
| Description: | Biomedical Signal Processing and Control 8 (2013) 667– 674 |
| URI: | http://dyuthi.cusat.ac.in/purl/4228 |
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| Spectral cluste ... ication from brain MRI.pdf | (1.561Mb) |
| Abstract: | Learning Disability (LD) is a neurological condition that affects a child’s brain and impairs his ability to carry out one or many specific tasks. LD affects about 15 % of children enrolled in schools. The prediction of LD is a vital and intricate job. The aim of this paper is to design an effective and powerful tool, using the two intelligent methods viz., Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System, for measuring the percentage of LD that affected in school-age children. In this study, we are proposing some soft computing methods in data preprocessing for improving the accuracy of the tool as well as the classifier. The data preprocessing is performed through Principal Component Analysis for attribute reduction and closest fit algorithm is used for imputing missing values. The main idea in developing the LD prediction tool is not only to predict the LD present in children but also to measure its percentage along with its class like low or minor or major. The system is implemented in Mathworks Software MatLab 7.10. The results obtained from this study have illustrated that the designed prediction system or tool is capable of measuring the LD effectively |
| Description: | Soft Comput (2014) 18:1093–1112 DOI 10.1007/s00500-013-1129-0 |
| URI: | http://dyuthi.cusat.ac.in/purl/4219 |
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| Learning disabi ... ol using ANN and ANFIS.pdf | (2.950Mb) |
| Abstract: | Retrieval of similar anatomical structures of brain MR images across patients would help the expert in diagnosis of diseases. In this paper, modified local binary pattern with ternary encoding called modified local ternary pattern (MOD-LTP) is introduced, which is more discriminant and less sensitive to noise in near-uniform regions, to locate slices belonging to the same level from the brain MR image database. The ternary encoding depends on a threshold, which is a user-specified one or calculated locally, based on the variance of the pixel intensities in each window. The variancebased local threshold makes the MOD-LTP more robust to noise and global illumination changes. The retrieval performance is shown to improve by taking region-based moment features of MODLTP and iteratively reweighting the moment features of MOD-LTP based on the user’s feedback. The average rank obtained using iterated and weighted moment features of MOD-LTP with a local variance-based threshold, is one to two times better than rotational invariant LBP (Unay, D., Ekin, A. and Jasinschi, R.S. (2010) Local structure-based region-of-interest retrieval in brain MR images. IEEE Trans. Inf. Technol. Biomed., 14, 897–903.) in retrieving the first 10 relevant images |
| Description: | The Computer Journal,bxu008 |
| URI: | http://dyuthi.cusat.ac.in/purl/4224 |
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| Content-Based I ... ith a Ternary Encoding.pdf | (2.206Mb) |
| Abstract: | Learning Disability (LD) is a classification including several disorders in which a child has difficulty in learning in a typical manner, usually caused by an unknown factor or factors. LD affects about 15% of children enrolled in schools. The prediction of learning disability is a complicated task since the identification of LD from diverse features or signs is a complicated problem. There is no cure for learning disabilities and they are life-long. The problems of children with specific learning disabilities have been a cause of concern to parents and teachers for some time. The aim of this paper is to develop a new algorithm for imputing missing values and to determine the significance of the missing value imputation method and dimensionality reduction method in the performance of fuzzy and neuro fuzzy classifiers with specific emphasis on prediction of learning disabilities in school age children. In the basic assessment method for prediction of LD, checklists are generally used and the data cases thus collected fully depends on the mood of children and may have also contain redundant as well as missing values. Therefore, in this study, we are proposing a new algorithm, viz. the correlation based new algorithm for imputing the missing values and Principal Component Analysis (PCA) for reducing the irrelevant attributes. After the study, it is found that, the preprocessing methods applied by us improves the quality of data and thereby increases the accuracy of the classifiers. The system is implemented in Math works Software Mat Lab 7.10. The results obtained from this study have illustrated that the developed missing value imputation method is very good contribution in prediction system and is capable of improving the performance of a classifier. |
| Description: | I.J. Intelligent Systems and Applications, 2013, 12, 34-52 |
| URI: | http://dyuthi.cusat.ac.in/purl/4209 |
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| Performance Imp ... in School-age Children.pdf | (779.6Kb) |
| Abstract: | A connected digit speech recognition is important in many applications such as automated banking system, catalogue-dialing, automatic data entry, automated banking system, etc. This paper presents an optimum speaker-independent connected digit recognizer forMalayalam language. The system employs Perceptual Linear Predictive (PLP) cepstral coefficient for speech parameterization and continuous density Hidden Markov Model (HMM) in the recognition process. Viterbi algorithm is used for decoding. The training data base has the utterance of 21 speakers from the age group of 20 to 40 years and the sound is recorded in the normal office environment where each speaker is asked to read 20 set of continuous digits. The system obtained an accuracy of 99.5 % with the unseen data. |
| Description: | Sadhana Vol. 38, Part 6, December 2013, pp. 1339–1346 |
| URI: | http://dyuthi.cusat.ac.in/purl/4227 |
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| Connected digit ... for Malayalam language.pdf | (242.7Kb) |
| Abstract: | For a set S of vertices and the vertex v in a connected graph G, max x2S d(x, v) is called the S-eccentricity of v in G. The set of vertices with minimum S-eccentricity is called the S-center of G. Any set A of vertices of G such that A is an S-center for some set S of vertices of G is called a center set. We identify the center sets of certain classes of graphs namely, Block graphs, Km,n, Kn −e, wheel graphs, odd cycles and symmetric even graphs and enumerate them for many of these graph classes. We also introduce the concept of center number which is defined as the number of distinct center sets of a graph and determine the center number of some graph classes |
| Description: | arXiv preprint arXiv:1312.3182 |
| URI: | http://dyuthi.cusat.ac.in/purl/4226 |
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| On The Center S ... of Some Graph Classes.pdf | (198.9Kb) |
| Abstract: | Given a graph G and a set X ⊆ V(G), the relative Wiener index of X in G is defined as WX (G) = {u,v}∈X 2 dG(u, v) . The graphs G (of even order) in which for every partition V(G) = V1 +V2 of the vertex set V(G) such that |V1| = |V2| we haveWV1 (G) = WV2 (G) are called equal opportunity graphs. In this note we prove that a graph G of even order is an equal opportunity graph if and only if it is a distance-balanced graph. The latter graphs are known by several characteristic properties, for instance, they are precisely the graphs G in which all vertices u ∈ V(G) have the same total distance DG(u) = v∈V(G) dG(u, v). Some related problems are posed along the way, and the so-called Wiener game is introduced. |
| Description: | Discrete Optimization 12 (2014) 150–154 |
| URI: | http://dyuthi.cusat.ac.in/purl/4220 |
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| Equal opportuni ... raphs, and Wiener game.pdf | (367.3Kb) |
| Abstract: | The focus of this article is to develop computationally efficient mathematical morphology operators on hypergraphs. To this aim we consider lattice structures on hypergraphs on which we build morphological operators. We develop a pair of dual adjunctions between the vertex set and the hyper edge set of a hypergraph H, by defining a vertex-hyperedge correspondence. This allows us to recover the classical notion of a dilation/erosion of a subset of vertices and to extend it to subhypergraphs of H. Afterward, we propose several new openings, closings, granulometries and alternate sequential filters acting (i) on the subsets of the vertex and hyperedge set of H and (ii) on the subhypergraphs of a hypergraph |
| Description: | arXiv preprint arXiv:1402.4258 |
| URI: | http://dyuthi.cusat.ac.in/purl/4225 |
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| Morphological filtering on hypergraphs.pdf | (203.1Kb) |
| Abstract: | Few major Research works are going in the field of Handwriting Word Recognition (HWR) of Indian languages. This paper surveys the major works of offline/online handwritten word recognition. Techniques involved in word recognition are also discussed. Major works carried out in Bangla, Urdu, Tamil and Hindi are mentioned in this paper. Advancement towards HWR in other Indian languages are also discussed. Application of offline HWR is also discussed |
| URI: | http://dyuthi.cusat.ac.in/purl/4223 |
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| HWR for Indian ... A Comprehensive Survey.pdf | (381.5Kb) |
| Abstract: | The focus of this paper is to develop computationally efficient mathematical morphology operators on hypergraphs. To this aim we consider lattice structures on hypergraphs on which we build morphological operators. We develop a pair of dual adjunctions between the vertex set and the hyperedge set of a hypergraph 𝐻, by defining a vertex-hyperedge correspondence. This allows us to recover the classical notion of a dilation/erosion of a subset of vertices and to extend it to subhypergraphs of 𝐻. This paper also studies the concept of morphological adjunction on hypergraphs for which both the input and the output are hypergraphs |
| Description: | ISRN Discrete Mathematics Volume 2014, Article ID 436419, 6 pages |
| URI: | http://dyuthi.cusat.ac.in/purl/4222 |
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| Mathematical Mo ... peredge Correspondence.pdf | (2.544Mb) |
| Abstract: | There are several centrality measures that have been introduced and studied for real world networks. They account for the different vertex characteristics that permit them to be ranked in order of importance in the network. Betweenness centrality is a measure of the influence of a vertex over the flow of information between every pair of vertices under the assumption that information primarily flows over the shortest path between them. In this paper we present betweenness centrality of some important classes of graphs. |
| Description: | arXiv preprint arXiv:1403.4701 |
| URI: | http://dyuthi.cusat.ac.in/purl/4221 |
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| Betweenness Cen ... Some Classes of Graphs.pdf | (197.9Kb) |
Now showing items 41-51 of 51
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