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.

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

Design and Development of data mining models for the predictions of manpower placement in the technical Domain

Data mining is one of the hottest research areas nowadays as it has got wide variety of applications in common man’s life to make the world a better place to live. It is all about finding interesting hidden patterns in a huge history data base. As an example, from a sales data base, one can find an interesting pattern like “people who buy magazines tend to buy news papers also” using data mining. Now in the sales point of view the advantage is that one can place these things together in the shop to increase sales. In this research work, data mining is effectively applied to a domain called placement chance prediction, since taking wise career decision is so crucial for anybody for sure. In India technical manpower analysis is carried out by an organization named National Technical Manpower Information System (NTMIS), established in 1983-84 by India's Ministry of Education & Culture. The NTMIS comprises of a lead centre in the IAMR, New Delhi, and 21 nodal centres located at different parts of the country. The Kerala State Nodal Centre is located at Cochin University of Science and Technology. In Nodal Centre, they collect placement information by sending postal questionnaire to passed out students on a regular basis. From this raw data available in the nodal centre, a history data base was prepared. Each record in this data base includes entrance rank ranges, reservation, Sector, Sex, and a particular engineering. From each such combination of attributes from the history data base of student records, corresponding placement chances is computed and stored in the history data base. From this data, various popular data mining models are built and tested. These models can be used to predict the most suitable branch for a particular new student with one of the above combination of criteria. Also a detailed performance comparison of the various data mining models is done.This research work proposes to use a combination of data mining models namely a hybrid stacking ensemble for better predictions. A strategy to predict the overall absorption rate for various branches as well as the time it takes for all the students of a particular branch to get placed etc are also proposed. Finally, this research work puts forward a new data mining algorithm namely C 4.5 * stat for numeric data sets which has been proved to have competent accuracy over standard benchmarking data sets called UCI data sets. It also proposes an optimization strategy called parameter tuning to improve the standard C 4.5 algorithm. As a summary this research work passes through all four dimensions for a typical data mining research work, namely application to a domain, development of classifier models, optimization and ensemble methods.

Applying Data mining using Statistical Techniques for Career Selection

For years, choosing the right career by monitoring the trends and scope for different career paths have been a requirement for all youngsters all over the world. In this paper we provide a scientific, data mining based method for job absorption rate prediction and predicting the waiting time needed for 100% placement, for different engineering courses in India. This will help the students in India in a great deal in deciding the right discipline for them for a bright future. Information about passed out students are obtained from the NTMIS ( National technical manpower information system ) NODAL center in Kochi, India residing in Cochin University of science and technology

A Data Mining Approach for the Identification of Adverse Drug Events (ADE) resulting from Drug-Drug Interactions (DDI) to improve pharmacovigilance

In the current study, epidemiology study is done by means of literature survey in groups identified to be at higher potential for DDIs as well as in other cases to explore patterns of DDIs and the factors affecting them. The structure of the FDA Adverse Event Reporting System (FAERS) database is studied and analyzed in detail to identify issues and challenges in data mining the drug-drug interactions. The necessary pre-processing algorithms are developed based on the analysis and the Apriori algorithm is modified to suit the process. Finally, the modules are integrated into a tool to identify DDIs. The results are compared using standard drug interaction database for validation. 31% of the associations obtained were identified to be new and the match with existing interactions was 69%. This match clearly indicates the validity of the methodology and its applicability to similar databases. Formulation of the results using the generic names expanded the relevance of the results to a global scale. The global applicability helps the health care professionals worldwide to observe caution during various stages of drug administration thus considerably enhancing pharmacovigilance

Multichannel Remote Data Acquisition and Analysing Systems For Fisheries / Environmental Investigations

Remote Data acquisition and analysing systems developed for fisheries and related environmental studies have been reported. It consists of three units. The first one namely multichannel remote data acquisition system is installed at the remote place powered by a rechargeable battery. It acquires and stores the 16 channel environmental data on a battery backed up RAM. The second unit called the Field data analyser is used for insitue display and analysis of the data stored in the backed up RAM. The third unit namely Laboratory data analyser is an IBM compatible PC based unit for detailed analysis and interpretation of the data after bringing the RAM unit to the laboratory. The data collected using the system has been analysed and presented in the form of a graph. The system timer operated at negligibly low current, switches on the power to the entire remote operated system at prefixed time interval of 2 hours.Data storage at remote site on low power battery backedupRAM and retrieval and analysis of data using PC are the special i ty of the system. The remote operated system takes about 7 seconds including the 5 second stabilization time to acquire and store data and is very ideal for remote operation on rechargeable bat tery. The system can store 16 channel data scanned at 2 hour interval for 10 days on 2K backed up RAM with memory expansion facility for 8K RAM.