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.

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.

Design and Development of a Performance Evaluation Prototype for IT Organizations in the Context of Outsourcing

The present study shows design and development of a performance evaluation prototype for IT organizations in the context of outsourcing. The main objective of this research is to help an IT organization in the context of outsourcing to realize its current standing, so it can take corrective steps where ever necessary and strive for continuous improvement. Service level management (SLM) process plays a crucial role in controlling the quality provision for IT service. Out sourcing is the process of entrusting the responsibility of providing certain goods and services to an external party. We have tried to identify as many as twenty complexities and categorized in to four headings. Complexities associated with contracts and SLAs,SLM process,SLM organization and complexities due to intrinsic characteristics. In this study it is possible to measure the quality of the performance of an IT organization in an outsourcing environment effectively

Performance Evaluation of Commercially Important Indian and Imported Fishing Hooks

Fishing Technology Division, Central Institute of Fisheries Technology

Some Studies and New Results on Multi Microprocessor Applications

One of the fastest expanding areas of computer exploitation is in embedded systems, whose prime function is not that of computing, but which nevertheless require information processing in order to carry out their prime function. Advances in hardware technology have made multi microprocessor systems a viable alternative to uniprocessor systems in many embedded application areas. This thesis reports the results of investigations carried out on multi microprocessors oriented towards embedded applications, with a view to enhancing throughput and reliability. An ideal controller for multiprocessor operation is developed which would smoothen sharing of routines and enable more powerful and efficient code I data interchange. Results of performance evaluation are appended.A typical application scenario is presented, which calls for classifying tasks based on characteristic features that were identified. The different classes are introduced along with a partitioned storage scheme. Theoretical analysis is also given. A review of schemes available for reducing disc access time is carried out and a new scheme presented. This is found to speed up data base transactions in embedded systems. The significance of software maintenance and adaptation in such applications is highlighted. A novel scheme of prov1d1ng a maintenance folio to system firmware is presented, alongwith experimental results. Processing reliability can be enhanced if facility exists to check if a particular instruction in a stream is appropriate. Likelihood of occurrence of a particular instruction would be more prudent if number of instructions in the set is less. A new organisation is derived to form the basement for further work. Some early results that would help steer the course of the work are presented.

Natural Rubber Latex Filler Masterbatch by Soap Sensitised Coagulation-Preparation, Processing and Evaluation

Sensitisation of natural rubber latex by addition of a small quantity of an anionic surfactant prior to the addition of a coacervant results in quick coagulation. The natural rubber prepared by the novel coagulation method shows improved raw rubber characteristics, better cure characteristics in gum and carbon black filled compounds and improved mechanical properties as compared to the conventionally coagulated natural rubber. Compounds based on dried masterbatches prepared by the incorporation of fluffy carbon black in different forms of soap sensitised natural rubber latices such as fresh latex, preserved field latex, centrifuged latex and a blend of preserved field latex and skim latex show improved cure characteristics and vucanizate properties as compared to an equivalent conventional dry rubber-fluffy carbon black based compound. The latex masterbatch based vulcanizates show higher level of crosslinking and better dispersion of filler. Vulcanizates based on fresh natural rubber latex- dual filler masterbatches containing a blend of carbon black and silica prepared by the modified coagulation process shows very good mechanical and dynamic properties that could be correlated to a low rolling resistance. The carbon black/silica/nanoclay tri-filler - fresh natural rubber latex masterbatch based vulcanizates show improved mechanical properties as the proportion of nanoclay increased up to 5 phr. The fresh natural rubber latex based carbon black-silica masterbatch/ polybutadiene blend vulcanizates show superior mechanical and dynamic properties as compared to the equivalent compound vulcanizates prepared from the dry natural rubber-filler (conventional dry mix)/polybutadiene blends

Design, Development & Performance Evaluation of an Anechoic Chamber for

The design, erection and evalution procedures for it microwave anechuic chamber competed at Cochin University for antenna studies are presented the chamber has an average reflectivity level of - 32 dB on at X-band frequencies, and it i, comparable to international standards.

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

Biochemical Characterization and Bio-evaluation of Collagen and Collagen Peptides Extracted and Purified from Fish Skin: In vitro and In vivo Studies on Antiarthritic and Wound Healing Properties

In fish processing plants, there is huge amount of skin that is left as the waste. When this skin is taken and processed into fish collagen, it will save large amount of money that is used for extraction of collagen from other animal s.Fish collagen can be used as an alternative to replace mammalian collagen, especially collagen extracted from bovine, when we consider the outbreak of bovine spongiform encephalopathy (BSE), transmissible spongiform encephalopathy (TSE) and the foot - and-mouth disease (FMD) issues. BSE and TSE are progressive neurological disorders affecting cattles caused by proteinacious infectious particles called prions.The study aims in producing collagen that has been extracted from fish skin to replace other animal collagen so as to overcome the problem of other animal collagen issues. Also the study utilized the abandoned fish waste produced by fish processing industry since bone, skin, fin and scales of fish can be a useful source of collagen.

Product and Process Innovation: Antecedents and Performance outcomes in small IT firms in India

Innovation is a strategic necessity for the survival of today’s organizations. The wide recognition of innovation as a competitive necessity, particularly in dynamic market environments, makes it an evergreen domain for research. This dissertation deals with innovation in small Information Technology (IT) firms in India. The IT industry in India has been a phenomenal success story of the last three decades, and is today facing a crucial phase in its history characterized by the need for fundamental changes in strategies, driven by innovation. This study, while motivated by the dynamics of changing times, importantly addresses the research gap on small firm innovation in Indian IT.This study addresses three main objectives: (a) drivers of innovation in small IT firms in India (b) impact of innovation on firm performance (c) variation in the extent of innovation adoption in small firms. Product and process innovation were identified as the two most contextually relevant types of innovation for small IT firms. The antecedents of innovation were identified as Intellectual Capital, Creative Capability, Top Management Support, Organization Learning Capability, Customer Involvement, External Networking and Employee Involvement.Survey method was adopted for data collection and the study unit was the firm. Surveys were conducted in 2014 across five South Indian cities. Small firm was defined as one with 10-499 employees. Responses from 205 firms were chosen for analysis. Rigorous statistical analysis was done to generate meaningful insights. The set of drivers of product innovation (Intellectual Capital, Creative Capability, Top Management Support, Customer Involvement, External Networking, and Employee Involvement)were different from that of process innovation (Creative Capability, Organization Learning Capability, External Networking, and Employee Involvement). Both product and process innovation had strong impact on firm performance. It was found that firms that adopted a combination of product innovation and process innovation had the highest levels of firm performance. Product innovation and process innovation fully mediated the relationship between all the seven antecedents and firm performance The results of this study have several important theoretical and practical implications. To the best of the researcher’s knowledge, this is the first time that an empirical study of firm level innovation of this kind has been undertaken in India. A measurement model for product and process innovation was developed, and the drivers of innovation were established statistically. Customer Involvement, External Networking and Employee Involvement are elements of Open Innovation, and all three had strong association with product innovation, and the latter twohad strong association with process innovation. The results showed that proclivity for Open Innovation is healthy in the Indian context. Practical implications have been outlined along how firms can organize themselves for innovation, the human talent for innovation, the right culture for innovation and for open innovation. While some specific examples of possible future studies have been recommended, the researcher believes that the study provides numerous opportunities to further this line of enquiry.

Studies on Synthesis And Nutritional Evaluation of Prawn Feeds

Chemical Oceanography Division, School of Marine Sciences, Cochin University of Science and Technology

Optimization of Process Parameters for the Extraction of Gelatin from the Skin of Freshwater Fish and the Evaluation of Physical anal Chemical Characteristics

Faculty of Marine Sciences,Cochin University of Science and Technology