Re-engineering and Re-defining University Libraries in the context of modern Information and Communication Technologies: a study with special reference to the University Libraries in Kerala

Information and communication technologies are the tools that underpin the emerging “Knowledge Society”. Exchange of information or knowledge between people and through networks of people has always taken place. But the ICT has radically changed the magnitude of this exchange, and thus factors such as timeliness of information and information dissemination patterns have become more important than ever.Since information and knowledge are so vital for the all round human development, libraries and institutions that manage these resources are indeed invaluable. So, the Library and Information Centres have a key role in the acquisition, processing, preservation and dissemination of information and knowledge. ln the modern context, library is providing service based on different types of documents such as manuscripts, printed, digital, etc. At the same time, acquisition, access, process, service etc. of these resources have become complicated now than ever before. The lCT made instrumental to extend libraries beyond the physical walls of a building and providing assistance in navigating and analyzing tremendous amounts of knowledge with a variety of digital tools. Thus, modern libraries are increasingly being re-defined as places to get unrestricted access to information in many formats and from many sources.The research was conducted in the university libraries in Kerala State, India. lt was identified that even though the information resources are flooding world over and several technologies have emerged to manage the situation for providing effective services to its clientele, most of the university libraries in Kerala were unable to exploit these technologies at maximum level. Though the libraries have automated many of their functions, wide gap prevails between the possible services and provided services. There are many good examples world over in the application of lCTs in libraries for the maximization of services and many such libraries have adopted the principles of reengineering and re-defining as a management strategy. Hence this study was targeted to look into how effectively adopted the modern lCTs in our libraries for maximizing the efficiency of operations and services and whether the principles of re-engineering and- redefining can be applied towards this.Data‘ was collected from library users, viz; student as well as faculty users; library ,professionals and university librarians, using structured questionnaires. This has been .supplemented by-observation of working of the libraries, discussions and interviews with the different types of users and staff, review of literature, etc. Personal observation of the organization set up, management practices, functions, facilities, resources, utilization of information resources and facilities by the users, etc. of the university libraries in Kerala have been made. Statistical techniques like percentage, mean, weighted mean, standard deviation, correlation, trend analysis, etc. have been used to analyse data.All the libraries could exploit only a very few possibilities of modern lCTs and hence they could not achieve effective Universal Bibliographic Control and desired efficiency and effectiveness in services. Because of this, the users as well as professionals are dissatisfied. Functional effectiveness in acquisition, access and process of information resources in various formats, development and maintenance of OPAC and WebOPAC, digital document delivery to remote users, Web based clearing of library counter services and resources, development of full-text databases, digital libraries and institutional repositories, consortia based operations for e-journals and databases, user education and information literacy, professional development with stress on lCTs, network administration and website maintenance, marketing of information, etc. are major areas need special attention to improve the situation. Finance, knowledge level on ICTs among library staff, professional dynamism and leadership, vision and support of the administrators and policy makers, prevailing educational set up and social environment in the state, etc. are some of the major hurdles in reaping the maximum possibilities of lCTs by the university libraries in Kerala. The principles of Business Process Re-engineering are found suitable to effectively apply to re-structure and redefine the operations and service system of the libraries. Most of the conventional departments or divisions prevailing in the university libraries were functioning as watertight compartments and their existing management system was more rigid to adopt the principles of change management. Hence, a thorough re-structuring of the divisions was indicated. Consortia based activities and pooling and sharing of information resources was advocated to meet the varied needs of the users in the main campuses and off campuses of the universities, affiliated colleges and remote stations. A uniform staff policy similar to that prevailing in CSIR, DRDO, ISRO, etc. has been proposed by the study not only in the university libraries in kerala but for the entire country.Restructuring of Lis education,integrated and Planned development of school,college,research and public library systems,etc.were also justified for reaping maximum benefits of the modern ICTs.

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.

Design of a Knowledge-Based Geographic Information System

A GIS has been designed with limited Functionalities; but with a novel approach in Aits design. The spatial data model adopted in the design of KBGIS is the unlinked vector model. Each map entity is encoded separately in vector fonn, without referencing any of its neighbouring entities. Spatial relations, in other words, are not encoded. This approach is adequate for routine analysis of geographic data represented on a planar map, and their display (Pages 105-106). Even though spatial relations are not encoded explicitly, they can be extracted through the specially designed queries. This work was undertaken as an experiment to study the feasibility of developing a GIS using a knowledge base in place of a relational database. The source of input spatial data was accurate sheet maps that were manually digitised. Each identifiable geographic primitive was represented as a distinct object, with its spatial properties and attributes defined. Composite spatial objects, made up of primitive objects, were formulated, based on production rules defining such compositions. The facts and rules were then organised into a production system, using OPS5