DEVELOPMENT OF GRAMMAR IN HINDI AND MALAYALAM BASED ON CLASSICAL TEXTS

HINDI

Statistical Machine Learning Techniques for the Prediction of Learning Disabilities in School-Age Children

Learning Disability (LD) is a general term that describes specific kinds of learning problems. It is a neurological condition that affects a child's brain and impairs his ability to carry out one or many specific tasks. The learning disabled children are neither slow nor mentally retarded. This disorder can make it problematic for a child to learn as quickly or in the same way as some child who isn't affected by a learning disability. An affected child can have normal or above average intelligence. They may have difficulty paying attention, with reading or letter recognition, or with mathematics. It does not mean that children who have learning disabilities are less intelligent. In fact, many children who have learning disabilities are more intelligent than an average child. Learning disabilities vary from child to child. One child with LD may not have the same kind of learning problems as another child with LD. There is no cure for learning disabilities and they are life-long. However, children with LD can be high achievers and can be taught ways to get around the learning disability. In this research work, data mining using machine learning techniques are used to analyze the symptoms of LD, establish interrelationships between them and evaluate the relative importance of these symptoms. To increase the diagnostic accuracy of learning disability prediction, a knowledge based tool based on statistical machine learning or data mining techniques, with high accuracy,according to the knowledge obtained from the clinical information, is proposed. The basic idea of the developed knowledge based tool is to increase the accuracy of the learning disability assessment and reduce the time used for the same. Different statistical machine learning techniques in data mining are used in the study. Identifying the important parameters of LD prediction using the data mining techniques, identifying the hidden relationship between the symptoms of LD and estimating the relative significance of each symptoms of LD are also the parts of the objectives of this research work. The developed tool has many advantages compared to the traditional methods of using check lists in determination of learning disabilities. For improving the performance of various classifiers, we developed some preprocessing methods for the LD prediction system. A new system based on fuzzy and rough set models are also developed for LD prediction. Here also the importance of pre-processing is studied. A Graphical User Interface (GUI) is designed for developing an integrated knowledge based tool for prediction of LD as well as its degree. The designed tool stores the details of the children in the student database and retrieves their LD report as and when required. The present study undoubtedly proves the effectiveness of the tool developed based on various machine learning techniques. It also identifies the important parameters of LD and accurately predicts the learning disability in school age children. This thesis makes several major contributions in technical, general and social areas. The results are found very beneficial to the parents, teachers and the institutions. They are able to diagnose the child’s problem at an early stage and can go for the proper treatments/counseling at the correct time so as to avoid the academic and social losses.

Prediction of Key Symptoms of Learning Disabilities in School-Age Children Using Rough Sets

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

Prediction of Learning Disabilities in School Age Children using SVM and Decision Tree

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.

Performance Improvement of Fuzzy and Neuro Fuzzy Systems: Prediction of Learning Disabilities in School-age Children

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.

Brain Glutamate Dehydrogenase Changes In Streptozotocin Diabetic Rats As A Function Of Age

Kinetic parameters of brain glutamate dehydrogenase (GDH) were compared in the brain stem, cerebellum and cerebral cortex of three weeks and one year old streptozotocin (STZ) induced four day diabetic rats with respective controls. A single intrafemoral dose of STZ (60mg/Kg body weight) was administered to induce diabetes in both age groups. After four days the blood glucose levels showed a significant increase in the diabetic animals of both age groups compared with the respective controls. The increase in blood glucose was significant in one year old compared to the three weeks old diabetic rats. The Vmm of the enzyme was decreased in all the brain regions studied, of the three weeks old diabetic rats without any significant change in the Km. In the adult the Vmax of GDH was increased in cerebellum and brain stem but was unchanged in the cerebral cortex. The K. was unchanged in cerebellum and cerebral cortex but was increased in the brain stem. These results suggest there may be an important regulatory role of the glutamate pathway in brain neural network disturbances and neuronal degeneration in diabetes as a function of age.

Studies on gaussian effective potentials in some models

In classical field theory, the ordinary potential V is an energy density for that state in which the field assumes the value ¢. In quantum field theory, the effective potential is the expectation value of the energy density for which the expectation value of the field is ¢o. As a result, if V has several local minima, it is only the absolute minimum that corresponds to the true ground state of the theory. Perturbation theory remains to this day the main analytical tool in the study of Quantum Field Theory. However, since perturbation theory is unable to uncover the whole rich structure of Quantum Field Theory, it is desirable to have some method which, on one hand, must go beyond both perturbation theory and classical approximation in the points where these fail, and at that time, be sufficiently simple that analytical calculations could be performed in its framework During the last decade a nonperturbative variational method called Gaussian effective potential, has been discussed widely together with several applications. This concept was described as a means of formalizing our intuitive understanding of zero-point fluctuation effects in quantum mechanics in a way that carries over directly to field theory.

Author Identification in Malayalam using n-grams

Author identification is the problem of identifying the author of an anonymous text or text whose authorship is in doubt from a given set of authors. The works by different authors are strongly distinguished by quantifiable features of the text. This paper deals with the attempts made on identifying the most likely author of a text in Malayalam from a list of authors. Malayalam is a Dravidian language with agglutinative nature and not much successful tools have been developed to extract syntactic & semantic features of texts in this language. We have done a detailed study on the various stylometric features that can be used to form an authors profile and have found that the frequencies of word collocations can be used to clearly distinguish an author in a highly inflectious language such as Malayalam. In our work we try to extract the word level and character level features present in the text for characterizing the style of an author. Our first step was towards creating a profile for each of the candidate authors whose texts were available with us, first from word n-gram frequencies and then by using variable length character n-gram frequencies. Profiles of the set of authors under consideration thus formed, was then compared with the features extracted from anonymous text, to suggest the most likely author.

Community Participation in Ecotourism: An Inclusive Development Option for Kerala

The inferences obtained from the study are presented in coherent area-specific levels so as to understand the ecotourism and its sub-sector areas for the researchers and policy makers about the issues, importances and potentialities of the sector. An analysis of the tourism sector in Kerala has shown tremendous growth both in terms of tourist arrivals and in terms of revenue generation from direct and indirect sources. The foreign tourist visitors in Kerala in 2014 was 9,23,336 which shows 7.60 percent increase from the last year and the domestic tourist visitors were 1,16,95,411 which again shows 7.71 percent increase, is a clear evidence of its potential. In 2014 the industry contributed revenue of 24885.44 crores from direct and indirect sources giving rise to an increase of 12.11 percent from the last year. A dichotomy of tourists and ecotourists shows that tourists in the ecotourism destinations come to 42.6 percent of the total, shows the scope, significance and its potential. Correlation of zone-wise tourist arrivals based on the ecotourism destinations highlights the fact that with only 19 of the 64 destinations that come in the central zone are the most preferred centres (around 54 percent) for the domestic as well as foreign tourists. The north zone encompassing 6 districts with rich biodiversity shows that the tourists‟ arrival patterns exhibit less promising results. Though the north zone has 31 ecotourism destinations of the state receives only 6.19 percent of the foreign visitors. The ecotourism activities in the state are primarily managed by the Eco-Development Committees (EDCs) and the Vana Samrakshana Samithies (VSS) under the Forest Development Agency of Kerala. Social class-wise categorization of membership shows that 13142 families have membership in 190 EDCs with SC (28 percent), ST (33 percent) and other marginalised communities (39 percent). But this in the VSS shows that 400 VSS have 59085 members actively engaged in ecotourism activities and social category of the VSS makes clear that majority are from the other marginalized fringe households with 62 percent where as the participation of SC is 12 percent and ST is 26 percent. An evaluation of the socio-economic and demographic matrix of the community members involved in ecotourism activities brings out region specific differences. About 75.70 percent of the respondents are males and the rest are females. Majority of the respondents (about 60 percent) are in the age group of 20 to 40 years, followed by the age group of 40-50 (20 percent). The average age of respondents in the three zones is between 35 and 37 years. The majority of the respondents are married, a few are unmarried. Average family size is 4-5 members and differences are identified among zones. Average number of adults per household is 3 and child per household is 2. Majority have an education of 10th class and below i.e. about 60 percent of the sample have only basic school education like primary, secondary and high school (i.e. up to SSLC but not passed) level. About 18 percent are SSLC passed, 10 percent are undergraduates whereas 6 percent constitute respondents having qualification of graduation and above. Majority of the „graduates and above‟ are from south and central zone. Inter-zone differences in educational profile are also identified with lesser number of „graduates and above‟ are identified in the north zone compared to the other two zones. Investigating into the income and livelihood options of the respondents gives insight about the prominence of ecotourism as an employment and livelihood option for the community members, as more than 90 percent of the respondents have cited tourism sector as their main employment option. Most (49.30 percent) of respondents get 100 percent income from tourism related activities, followed by 37.30 percent of community members have income between 75-99 percent from tourism whereas the rest (13 percent) have less than 74 percent of their income from tourism and there exists difference between zones and percentage of income. Financial habit shows that about 49.7 percent hold active bank accounts, 61 percent have savings behaviour and 73.8 percent have indebtedness. Analysis about the ownership of house brings to light that 37 percent of respondents live in their own house followed by 25.7 percent in government funded/provided house and 21 percent in their parent‟s house and 3.5 percent in rented house. About 12 percent of the respondents have other kinds of accommodation facilities such as staff quarters, etc. But in the case of north zone majority i.e. 52 percent primarily depend on the government funded house indicating the effectiveness of government housing programme. Standard of living measured in SLI frameworks shows that majority of the respondents have medium SLI values (42.3 percent); the remaining 47.7 percent have low SLI and 10 percent have high SLI. The community members have been benefitted immensely from forest and its resources. Since the ecotourism destinations are located amidst the wildlife settings, majority of them depend on forest for their livelihood. The information on the tourist‟s demographic characteristics like age, sex, educational qualification and annual income show that the age category of domestic and foreign tourists falls below the age group of less than 35 years (about 65 percent), whereas only 16 percent of tourists are aged above 46 years. The age group below 25 years consists of more international tourists (31.3 percent) compared to the proportion of domestic tourists (12.5 percent). Male-female ratio shows that the males constitute 56 percent of the sample and females with 44 percent. The factors determining the impact of ecotourism programmes in the community was evaluated with the aid of a factor analysis with 12 selected statements. The worries and concerns of the community members about the impact of ecotourism on the environment are well understood from this analysis. It can be drawn that environment protection and the role of ecotourism in improving the income and livelihood options of the local communities is the most important factor concerning the community members.