Boston College Law School Magazine Spring 1995

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Protection of Farmers’ Rights in India-Challenges for Law in the Context of Plant Breeders’ Rights

School of Legal Studies, Cochin University of Science and Technology

International Initiatives for Regulating Access to Genetic Resources

In light of the various international instruments and international agencies that are actively engaged in resolving the issue of ABS, the present work tries to find an answer to the larger question how far the above agencies have succeeded in regulating access and make sure of benefit sharing. In this process, the work comprehensively analyses the work of different agencies involved in the process. It tries to find out the major obstacles that stand in the way of fulfilment of the benefit sharing objective and proposes the ways and means to tackle them. The study first traces the legal foundations of the concept of property in GRs and associated TK.For this, it starts with analysis of the nature of property and the questions related to ownership in GRs as contained in the CBD as well as in various State legislations. It further examines the notion of property before and after the enactment of the CBD and establishes that the CBD contains strong private property jurisprudence.Based on the theoretical foundation of private property right,Chapter 3 analyses the benefit sharing mechanism of the CBD, i.e. the Nagoya Protocol. It searches for a theoretical convergence of the notion of property as reflected in the two instruments and successfully establishes the same. It makes an appraisal of the Nagoya regime to find out how far it has gone beyond the CBD in ensuring the task of benefit sharing and the impediments in its way.Realizing that the ITPGRFA forms part of the CBD system, Chapter 4 analyses the benefit sharing structure of ITPGRFA as revealed through its multilateral system. This gives the work the benefit of comparing two different benefit sharing models operating on the same philosophy of property. This chapter tries to find out whether there is conceptual coherence in the notion of property when the benefit sharing model changes. It alsocompares the merits and demerits of both the systems and tries to locate the hurdles in achieving benefit sharing. Aware of the legal impediments caused by IPRs in the process of ABS, Chapter 5 tries to explore the linkages between IPRs and GRs and associated TK and assesses why contract-based CBD system fails before the monopoly rights under TRIPS. Chapter 6 analyses the different solutions suggested by the international community at the TRIPS Council as well as the WIPO (World Intellectual property Organisation) and examines their effectiveness. Chapter 7 concludes that considering the inability of the present IP system to understand the grass root realities of the indigenous communities as well as the varying situations of the country of origin, the best possible way to recognise the CBD goals in the TRIPS could be better achieved through linking the two instruments by means of the triple disclosure requirement in Article 29 as suggested by the Disclosure Group during the TRIPS Council deliberations. It also recommends that considering the nature of property in GR, a new section/chapter in the TRIPS dealing with GRs would be another workable solution.

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.

Exceptions and Limitations to Intellectual Property Rights with Special Reference to Patent and Copyright Law

This thesis entitled Exceptions and limitations to intellectual property rights with special reference to patent and copyright law.The study on the limitations and exceptions to copyright and patent was mainly characterized by its diversity and flexibility. The unique feature of limited monopoly appended to intellectual property was always a matter of wide controversy.The historical analysis substantiated this instrumentalist philosophy of intellectual property.the study from a legal space characterized by diversity and flexibility and end up in that legal space being characterized by homogeneity and standardization. The issue of flexibility and restrictiveness in the context of TRIPS is the next challenging task. Before devising flexibility to TST, the question to be answered is whether such a mechanism is desirable in the context of TRIPS.In conclusion it is submitted to reorient the intellectual property framework in the context of the noble public interest objectives.

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.