Now showing items 40-49 of 49
| Abstract: | The aim of this study is to show the importance of two classification techniques, viz. decision tree and clustering, in prediction of learning disabilities (LD) of school-age children. LDs affect about 10 percent of all children enrolled in schools. The problems of children with specific learning disabilities have been a cause of concern to parents and teachers for some time. Decision trees and clustering are powerful and popular tools used for classification and prediction in Data mining. Different rules extracted from the decision tree are used for prediction of learning disabilities. Clustering is the assignment of a set of observations into subsets, called clusters, which are useful in finding the different signs and symptoms (attributes) present in the LD affected child. In this paper, J48 algorithm is used for constructing the decision tree and K-means algorithm is used for creating the clusters. By applying these classification techniques, LD in any child can be identified |
| URI: | http://dyuthi.cusat.ac.in/purl/4192 |
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| Significance Of ... Learning Disabilities.pdf | (200.4Kb) |
| Abstract: | The distance DG(v) of a vertex v in an undirected graph G is the sum of the distances between v and all other vertices of G. The set of vertices in G with maximum (minimum) distance is the antimedian (median) set of a graph G. It is proved that for arbitrary graphs G and J and a positive integer r 2, there exists a connected graph H such that G is the antimedian and J the median subgraphs of H, respectively, and that dH(G, J) = r. When both G and J are connected, G and J can in addition be made convex subgraphs of H. |
| Description: | Networks vol 56(2),pp 90-94 |
| URI: | http://dyuthi.cusat.ac.in/purl/4203 |
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| Simultaneous Em ... d Antimedian Subgraphs.pdf | (146.2Kb) |
| 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: | Digit speech recognition is important in many applications such as automatic data entry, PIN entry, voice dialing telephone, automated banking system, etc. This paper presents speaker independent speech recognition system for Malayalam digits. The system employs Mel frequency cepstrum coefficient (MFCC) as feature for signal processing and Hidden Markov model (HMM) for recognition. The system is trained with 21 male and female voices in the age group of 20 to 40 years and there was 98.5% word recognition accuracy (94.8% sentence recognition accuracy) on a test set of continuous digit recognition task. |
| Description: | Nature & Biologically Inspired Computing, 2009. NaBIC 2009. World Congress on |
| URI: | http://dyuthi.cusat.ac.in/purl/4190 |
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| Speech Recognition of Malayalam Numbers.pdf | (243.0Kb) |
| Abstract: | A graph G is strongly distance-balanced if for every edge uv of G and every i 0 the number of vertices x with d.x; u/ D d.x; v/ 1 D i equals the number of vertices y with d.y; v/ D d.y; u/ 1 D i. It is proved that the strong product of graphs is strongly distance-balanced if and only if both factors are strongly distance-balanced. It is also proved that connected components of the direct product of two bipartite graphs are strongly distancebalanced if and only if both factors are strongly distance-balanced. Additionally, a new characterization of distance-balanced graphs and an algorithm of time complexity O.mn/ for their recognition, wheremis the number of edges and n the number of vertices of the graph in question, are given |
| Description: | European Journal of Combinatorics 30 (2009) 1048- 1053 |
| URI: | http://dyuthi.cusat.ac.in/purl/4198 |
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| Strongly distan ... phs and graph products.pdf | (373.0Kb) |
| Abstract: | In this paper, we propose a handwritten character recognition system for Malayalam language. The feature extraction phase consists of gradient and curvature calculation and dimensionality reduction using Principal Component Analysis. Directional information from the arc tangent of gradient is used as gradient feature. Strength of gradient in curvature direction is used as the curvature feature. The proposed system uses a combination of gradient and curvature feature in reduced dimension as the feature vector. For classification, discriminative power of Support Vector Machine (SVM) is evaluated. The results reveal that SVM with Radial Basis Function (RBF) kernel yield the best performance with 96.28% and 97.96% of accuracy in two different datasets. This is the highest accuracy ever reported on these datasets |
| Description: | I.J. Image, Graphics and Signal Processing, 2013, 4, 53-59 |
| URI: | http://dyuthi.cusat.ac.in/purl/4204 |
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| A System for Of ... rs in Malayalam Script.pdf | (535.2Kb) |
| Abstract: | Speckle noise formed as a result of the coherent nature of ultrasound imaging affects the lesion detectability. We have proposed a new weighted linear filtering approach using Local Binary Patterns (LBP) for reducing the speckle noise in ultrasound images. The new filter achieves good results in reducing the noise without affecting the image content. The performance of the proposed filter has been compared with some of the commonly used denoising filters. The proposed filter outperforms the existing filters in terms of quantitative analysis and in edge preservation. The experimental analysis is done using various ultrasound images |
| Description: | I.J. Information Technology and Computer Science, 2013, 06, 1-9 |
| URI: | http://dyuthi.cusat.ac.in/purl/4210 |
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| Ultrasound Imag ... ghted Linear Filtering.pdf | (978.9Kb) |
| Abstract: | This paper presents the application of wavelet processing in the domain of handwritten character recognition. To attain high recognition rate, robust feature extractors and powerful classifiers that are invariant to degree of variability of human writing are needed. The proposed scheme consists of two stages: a feature extraction stage, which is based on Haar wavelet transform and a classification stage that uses support vector machine classifier. Experimental results show that the proposed method is effective |
| Description: | Procedia Engineering 30 (2012) 598 – 605 |
| URI: | http://dyuthi.cusat.ac.in/purl/4195 |
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| Unconstrained H ... tor Machine Classifier.pdf | (442.7Kb) |
| Abstract: | In our study we use a kernel based classification technique, Support Vector Machine Regression for predicting the Melting Point of Drug – like compounds in terms of Topological Descriptors, Topological Charge Indices, Connectivity Indices and 2D Auto Correlations. The Machine Learning model was designed, trained and tested using a dataset of 100 compounds and it was found that an SVMReg model with RBF Kernel could predict the Melting Point with a mean absolute error 15.5854 and Root Mean Squared Error 19.7576 |
| Description: | PROCEEDINGS OF ICETECT 2011 |
| URI: | http://dyuthi.cusat.ac.in/purl/4235 |
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| Using Neural Ne ... Drug – like compounds.pdf | (1.156Mb) |
| Abstract: | Multispectral analysis is a promising approach in tissue classification and abnormality detection from Magnetic Resonance (MR) images. But instability in accuracy and reproducibility of the classification results from conventional techniques keeps it far from clinical applications. Recent studies proposed Independent Component Analysis (ICA) as an effective method for source signals separation from multispectral MR data. However, it often fails to extract the local features like small abnormalities, especially from dependent real data. A multisignal wavelet analysis prior to ICA is proposed in this work to resolve these issues. Best de-correlated detail coefficients are combined with input images to give better classification results. Performance improvement of the proposed method over conventional ICA is effectively demonstrated by segmentation and classification using k-means clustering. Experimental results from synthetic and real data strongly confirm the positive effect of the new method with an improved Tanimoto index/Sensitivity values, 0.884/93.605, for reproduced small white matter lesions |
| Description: | International conference on Communication and Signal Processing, April 3-5, 2013, India |
| URI: | http://dyuthi.cusat.ac.in/purl/4231 |
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| Wavelet based I ... ispectral Brain Tissue.pdf | (825.4Kb) |
Now showing items 40-49 of 49
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