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: | Axial brain slices containing similar anatomical structures are retrieved using features derived from the histogram of Local binary pattern (LBP). A rotation invariant description of texture in terms of texture patterns and their strength is obtained with the incorporation of local variance to the LBP, called Modified LBP (MOD-LBP). In this paper, we compare Histogram based Features of LBP (HF/LBP), against Histogram based Features of MOD-LBP (HF/MOD-LBP) in retrieving similar axial brain images. We show that replacing local histogram with a local distance transform based similarity metric further improves the performance of MOD-LBP based image retrieval |
Description: | 2012 IEEE International Conference on Computational Intelligence and Computing Research |
URI: | http://dyuthi.cusat.ac.in/purl/4205 |
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Level Identific ... ogram of a LBP variant.pdf | (2.083Mb) |
Abstract: | Development of Malayalam speech recognition system is in its infancy stage; although many works have been done in other Indian languages. In this paper we present the first work on speaker independent Malayalam isolated speech recognizer based on PLP (Perceptual Linear Predictive) Cepstral Coefficient and Hidden Markov Model (HMM). The performance of the developed system has been evaluated with different number of states of HMM (Hidden Markov Model). The system is trained with 21 male and female speakers in the age group ranging from 19 to 41 years. The system obtained an accuracy of 99.5% with the unseen data |
Description: | International Journal of Advanced Information Technology (IJAIT) Vol. 1, No.5, October 2011 |
URI: | http://dyuthi.cusat.ac.in/purl/4214 |
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Malayalam Isola ... P cepstral coefficient.pdf | (172.8Kb) |
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: | Following the Majority Strategy in graphs, other consensus strategies, namely Plurality Strategy, Hill Climbing and Steepest Ascent Hill Climbing strategies on graphs are discussed as methods for the computation of median sets of pro¯les. A review of algorithms for median computation on median graphs is discussed and their time complexities are compared. Implementation of the consensus strategies on median computation in arbitrary graphs is discussed |
Description: | Report/Econometric Institute, Erasmus University Rotterdam,EI 2007-34 |
URI: | http://dyuthi.cusat.ac.in/purl/4218 |
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Median computat ... g consensus strategies.pdf | (195.4Kb) |
Abstract: | The median of a profile = (u1, . . . , uk ) of vertices of a graph G is the set of vertices x that minimize the sum of distances from x to the vertices of . It is shown that for profiles with diameter the median set can be computed within an isometric subgraph of G that contains a vertex x of and the r -ball around x, where r > 2 − 1 − 2 /| |. The median index of a graph and r -joins of graphs are introduced and it is shown that r -joins preserve the property of having a large median index. Consensus strategies are also briefly discussed on a graph with bounded profiles. |
Description: | Discrete Applied Mathematics 156 (2008) 2882–2889 |
URI: | http://dyuthi.cusat.ac.in/purl/4216 |
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The median func ... with bounded profiles.pdf | (410.2Kb) |
Abstract: | A periphery transversal of a median graph G is introduced as a set of vertices that meets all the peripheral subgraphs of G. Using this concept, median graphs with geodetic number 2 are characterized in two ways. They are precisely the median graphs that contain a periphery transversal of order 2 as well as the median graphs for which there exists a profile such that the remoteness function is constant on G. Moreover, an algorithm is presented that decides in O(mlog n) time whether a given graph G with n vertices and m edges is a median graph with geodetic number 2. Several additional structural properties of the remoteness function on hypercubes and median graphs are obtained and some problems listed |
Description: | University of Ljubljana Institute of Mathematics, Physics and Mechanics Department of Mathematics Preprint series, Vol. 46 (2008), 1046 |
URI: | http://dyuthi.cusat.ac.in/purl/4237 |
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Median Graphs, ... nd Geodetic Number Two.pdf | (256.4Kb) |
Abstract: | A profile is a finite sequence of vertices of a graph. The set of all vertices of the graph which minimises the sum of the distances to the vertices of the profile is the median of the profile. Any subset of the vertex set such that it is the median of some profile is called a median set. The number of median sets of a graph is defined to be the median number of the graph. In this paper, we identify the median sets of various classes of graphs such as Kp − e, Kp,q forP > 2, and wheel graph and so forth. The median numbers of these graphs and hypercubes are found out, and an upper bound for the median number of even cycles is established.We also express the median number of a product graph in terms of the median number of their factors. |
Description: | International Scholarly Research Network ISRN Discrete Mathematics Volume 2012, Article ID 583671, 8 pages doi:10.5402/2012/583671 |
URI: | http://dyuthi.cusat.ac.in/purl/4212 |
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Median Sets and Median Number of a Graph.pdf | (1.907Mb) |
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: | Optical Character Recognition plays an important role in Digital Image Processing and Pattern Recognition. Even though ambient study had been performed on foreign languages like Chinese and Japanese, effort on Indian script is still immature. OCR in Malayalam language is more complex as it is enriched with largest number of characters among all Indian languages. The challenge of recognition of characters is even high in handwritten domain, due to the varying writing style of each individual. In this paper we propose a system for recognition of offline handwritten Malayalam vowels. The proposed method uses Chain code and Image Centroid for the purpose of extracting features and a two layer feed forward network with scaled conjugate gradient for classification |
Description: | Emerging Trends in Electrical and Computer Technology (ICETECT), 2011 International Conference on |
URI: | http://dyuthi.cusat.ac.in/purl/4196 |
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Offline Handwri ... n Chain Code Histogram.pdf | (1.324Mb) |
Abstract: | A feasible way of implementing a temporal database is by mapping temporal data model onto a conventional data model followed by a commercial database management system. Even though extensions were proposed to standard SQL for supporting temporal databases, such proposals have not yet come across standardization processes. This paper attempts to implement database operators such as aggregates and universal quantifier for temporal databases, implemented on top of relational database systems, using currently available SQL standards. |
URI: | http://dyuthi.cusat.ac.in/xmlui/purl/2006 |
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On Implementing Joins....pdf | (261.1Kb) |
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: | The set of vertices that maximize (minimize) the remoteness is the antimedian (median) set of the profile. It is proved that for an arbitrary graph G and S V (G) it can be decided in polynomial time whether S is the antimedian set of some profile. Graphs in which every antimedian set is connected are also considered. |
URI: | http://dyuthi.cusat.ac.in/purl/4217 |
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On the generali ... blemantimedian subsets.pdf | (142.4Kb) |
Abstract: | A profile on a graph G is any nonempty multiset whose elements are vertices from G. The corresponding remoteness function associates to each vertex x 2 V.G/ the sum of distances from x to the vertices in the profile. Starting from some nice and useful properties of the remoteness function in hypercubes, the remoteness function is studied in arbitrary median graphs with respect to their isometric embeddings in hypercubes. In particular, a relation between the vertices in a median graph G whose remoteness function is maximum (antimedian set of G) with the antimedian set of the host hypercube is found. While for odd profiles the antimedian set is an independent set that lies in the strict boundary of a median graph, there exist median graphs in which special even profiles yield a constant remoteness function. We characterize such median graphs in two ways: as the graphs whose periphery transversal number is 2, and as the graphs with the geodetic number equal to 2. Finally, we present an algorithm that, given a graph G on n vertices and m edges, decides in O.mlog n/ time whether G is a median graph with geodetic number 2 |
Description: | Discrete Applied Mathematics 157 (2009) 3679- 3688 |
URI: | http://dyuthi.cusat.ac.in/purl/4197 |
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On the remoteness function in median graphs.pdf | (609.5Kb) |
Abstract: | This work proposes a parallel genetic algorithm for compressing scanned document images. A fitness function is designed with Hausdorff distance which determines the terminating condition. The algorithm helps to locate the text lines. A greater compression ratio has achieved with lesser distortion |
Description: | 2010 International Conference on Electronics and Information Engineering (ICEIE 2010) vol 2,V2-483-V2-487 |
URI: | http://dyuthi.cusat.ac.in/purl/4215 |
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Parallel Geneti ... mpression Optimization.pdf | (793.8Kb) |
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: | The Majority Strategy for finding medians of a set of clients on a graph can be relaxed in the following way: if we are at v, then we move to a neighbor w if there are at least as many clients closer to w than to v (thus ignoring the clients at equal distance from v and w). The graphs on which this Plurality Strategy always finds the set of all medians are precisely those for which the set of medians induces always a connected subgraph |
Description: | Report/Econometric Institute, Erasmus University Rotterdam |
URI: | http://dyuthi.cusat.ac.in/purl/4208 |
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The Plurality Strategy on Graphs.pdf | (149.0Kb) |
Abstract: | This paper highlights the prediction of learning disabilities (LD) in school-age children using rough set theory (RST) with an emphasis on application of data mining. In rough sets, data analysis start from a data table called an information system, which contains data about objects of interest, characterized in terms of attributes. These attributes consist of the properties of learning disabilities. By finding the relationship between these attributes, the redundant attributes can be eliminated and core attributes determined. Also, rule mining is performed in rough sets using the algorithm LEM1. The prediction of LD is accurately done by using Rosetta, the rough set tool kit for analysis of data. The result obtained from this study is compared with the output of a similar study conducted by us using Support Vector Machine (SVM) with Sequential Minimal Optimisation (SMO) algorithm. It is found that, using the concepts of reduct and global covering, we can easily predict the learning disabilities in children |
Description: | International Journal of Computer and Electrical Engineering, Vol.3, No.1, February, 2011 1793-8163 |
URI: | http://dyuthi.cusat.ac.in/purl/4194 |
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Prediction of K ... ldren Using Rough Sets.pdf | (1.457Mb) |
Abstract: | This paper highlights the prediction of Learning Disabilities (LD) in school-age children using two classification methods, Support Vector Machine (SVM) and Decision Tree (DT), with an emphasis on applications of data mining. About 10% of children enrolled in school have a learning disability. Learning disability prediction in school age children is a very complicated task because it tends to be identified in elementary school where there is no one sign to be identified. By using any of the two classification methods, SVM and DT, we can easily and accurately predict LD in any child. Also, we can determine the merits and demerits of these two classifiers and the best one can be selected for the use in the relevant field. In this study, Sequential Minimal Optimization (SMO) algorithm is used in performing SVM and J48 algorithm is used in constructing decision trees. |
Description: | (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 2 (2) , 2011, 829-835 |
URI: | http://dyuthi.cusat.ac.in/purl/4202 |
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Prediction of L ... SVM and Decision Tree.pdf | (385.3Kb) |
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) |
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