Attribute reduction and missing value imputing with ANN: prediction of learning disabilities

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 10% of children enrolled in schools. There is no cure for learning disabilities and they are lifelong. The problems of children with specific learning disabilities have been a cause of concern to parents and teachers for some time. Just as there are many different types of LDs, there are a variety of tests that may be done to pinpoint the problem The information gained from an evaluation is crucial for finding out how the parents and the school authorities can provide the best possible learning environment for child. This paper proposes a new approach in artificial neural network (ANN) for identifying LD in children at early stages so as to solve the problems faced by them and to get the benefits to the students, their parents and school authorities. In this study, we propose a closest fit algorithm data preprocessing with ANN classification to handle missing attribute values. This algorithm imputes the missing values in the preprocessing stage. Ignoring of missing attribute values is a common trend in all classifying algorithms. But, in this paper, we use an algorithm in a systematic approach for classification, which gives a satisfactory result in the prediction of LD. It acts as a tool for predicting the LD accurately, and good information of the child is made available to the concerned

Modification of Natural Rubber using Thermoset Resins

This thesis presents the findings of a study on incorporating vanous thermoset resins into natural rubber for property improvement. Natural rubber is an important elastomer with the unique attribute of being a renewable agricultural product. The study was undertaken to investigate the extent to which the drawbacks of natural rubber, especially its poor thermal and oil resistance propel1ies could be nullified by blending with common thermoset resins. A thorough and comparative understanding of the perfonnance of different resins from this viewpoint will be beneficial for both natural IUbber processors and consumers. In this study the thennoset resins used were epoxy resin, phenolics, epoxidised phenolics and unsaturated polyester resin.The resins were incorporated into NR during compounding and their effects on the properties of NR were studied after vulcanization. Properties were studied for both gum and filled N R compounds. The important properties studied are cure characteristics, mechanical properties, ageing propel1ies, thermal propel1ies, crosslink density and extractability. Characterization studies were also conducted using FTIR, TGA and DSC.Improvement in mechanical properties was noticed in many cases. The results show that most resins lead to a reduction in the cure time of NR. The perfonnance of epoxy resin is most noticeable in this respect. Mechanical properties of the modified IUbber show maximum improvement in the case of epoxidised novolacs. Most resins are seen to improve the thermal and oil resistance propel1ies of NR. Epoxy novolacs show maximum effect in this respect also. However the presence of tillers is found to moderate the positive effects of the thermoset resins considerably.

Apptication of Biofloc Technology (BFI) in Ihe nunery rearing and farming of giant freshwater prawn, Macrobrachillm rosenbergii (de Man)

This thesis Entitled Application of Biofloc technology (BFT) In the Nursery Rearing and Farming of Giant Freshwater Prawn,Macrobrachium Rosenbergii(De Man). Aquaculture, rearing plants and animals under controlled conditions is growing with an annual growth rate of 8.3% in the period 1970-2008 (FAO, 2010). This trend of growth is essential for the supply of protein-rich food for ever increasing world population. But growth and development of aquaculture should be in sustainable manner, preferably without jeopardizing the aquatic environment.In the present study, the application of BFT in the nursery rearing and farming ofgiant freshwater prawn, M. rosenbergii, is attempted. The result of the study is organised into eight chapters. In the first chapter, the subject is adequately introduced. Various types of aquaculture practices followed, development and status of Indian aquaculture, present status of freshwater pravm culture, BF T and its use for the sustainable aquaculture systems, theory of BFT based aquaculture practices, hypothesis, objective and outline of the thesis are described. An extensive review of literature on studies carried out so far on biofloc based aquaculture are given in chapter 2. The third chapter deals with the application of BFT in the primary nursery phase of freshwater prawn. Several workers suggested the need for an intermediate nursery phase in the culture system of freshwater prawn for the successful production. Thirty day experiment was conducted to study the effect of BFT on the water quality, and animal welfare under the various stocking densities. The study concluded that stocking finfishes in biofloc-based monoculture system of freshwater prawns has the potential of increasing total yield. Prawns having a higher commercial value than finfishes besides ensuring economic sustainability. Results showed that prawn yield and survival was better in catla dominated tanks. Based on the results of the study, it is recommended to incorporate 25% rohu and 75% catla in the biofloc-based culture system of giant freshwater prawns. The results of the present study also recommend to stock relatively larger catla for biofloc-based culture system. Fish production was also higher in the 100% catla tank. When catla was added in higher percentages it should ensured that the hiding objects in the culture ponds shall be used in order to reduce the chance of cannibalism among prawns. rohu and catla equally have the ability to harvest the biofloc, catla consumes the planktonic contributes in the floc whereas rohu grazed on the bacterial consortium suspended in the water column. In Chapter 8, recommendations and future research perspectives in the field of biofloc based aquaculture is presented.

A Novel Decision Tree Algorithm for Numeric Datasets - C 4.5*Stat

Decision trees are very powerful tools for classification in data mining tasks that involves different types of attributes. When coming to handling numeric data sets, usually they are converted first to categorical types and then classified using information gain concepts. Information gain is a very popular and useful concept which tells you, whether any benefit occurs after splitting with a given attribute as far as information content is concerned. But this process is computationally intensive for large data sets. Also popular decision tree algorithms like ID3 cannot handle numeric data sets. This paper proposes statistical variance as an alternative to information gain as well as statistical mean to split attributes in completely numerical data sets. The new algorithm has been proved to be competent with respect to its information gain counterpart C4.5 and competent with many existing decision tree algorithms against the standard UCI benchmarking datasets using the ANOVA test in statistics. The specific advantages of this proposed new algorithm are that it avoids the computational overhead of information gain computation for large data sets with many attributes, as well as it avoids the conversion to categorical data from huge numeric data sets which also is a time consuming task. So as a summary, huge numeric datasets can be directly submitted to this algorithm without any attribute mappings or information gain computations. It also blends the two closely related fields statistics and data mining

Learning disability prediction tool using ANN and ANFIS

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

Development of Hierarchical Clustering Techniques for Gridded Data from Mixed Data Sequences

Knowledge discovery in databases is the non-trivial process of identifying valid, novel potentially useful and ultimately understandable patterns from data. The term Data mining refers to the process which does the exploratory analysis on the data and builds some model on the data. To infer patterns from data, data mining involves different approaches like association rule mining, classification techniques or clustering techniques. Among the many data mining techniques, clustering plays a major role, since it helps to group the related data for assessing properties and drawing conclusions. Most of the clustering algorithms act on a dataset with uniform format, since the similarity or dissimilarity between the data points is a significant factor in finding out the clusters. If a dataset consists of mixed attributes, i.e. a combination of numerical and categorical variables, a preferred approach is to convert different formats into a uniform format. The research study explores the various techniques to convert the mixed data sets to a numerical equivalent, so as to make it equipped for applying the statistical and similar algorithms. The results of clustering mixed category data after conversion to numeric data type have been demonstrated using a crime data set. The thesis also proposes an extension to the well known algorithm for handling mixed data types, to deal with data sets having only categorical data. The proposed conversion has been validated on a data set corresponding to breast cancer. Moreover, another issue with the clustering process is the visualization of output. Different geometric techniques like scatter plot, or projection plots are available, but none of the techniques display the result projecting the whole database but rather demonstrate attribute-pair wise analysis

Influence of Frequent Flyer Programme and Airline Service Quality on Re-buy Intentions of Airline Frequent Passengers

The growth potential of service sector, especially the aviation sector in the Indian economy is splendid. Therefore, it is crucial for the airline service providers to realize their customers, design offers and deliver the desired value to their customers. This study reveals the effect of airline passenger satisfactions particularly on re-buy intentions derived from the attributes-level performance dimensions of both service aspects and loyalty programme of an airline. The mediation effect of satisfaction and other selected antecedents on the re-buy intention of a passenger is hypothesized in this study. Critical areas affecting buying intentions such as core service quality and loyalty attribute-level performances, effect of frequent flyer programme and service quality satisfaction, passenger trust on airline, brand image and moderating effects of perceived value, frequent programme status and travel frequency of airline passengers are linked in a structural model to assess the strength of each facet in affecting re-buy intentions. Implications to the airlines were made based on the finding that re-buy intentions cannot be attributed solely to the impacts of frequent flyer programme, rather affected through the mediation effect of airline service quality satisfaction, which is very much valid for the higher FFP status category of frequent travelers. The effects of moderation caused by perceived value, FFP status and flying experience were also found to be significant in making re-buy intentions.