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.

Semantic web mining

Semantic Web Mining aims at combining the two fast-developing research areas Semantic Web and Web Mining. This survey analyzes the convergence of trends from both areas: an increasing number of researchers is working on improving the results of Web Mining by exploiting semantic structures in the Web, and they make use of Web Mining techniques for building the Semantic Web. Last but not least, these techniques can be used for mining the Semantic Web itself. The Semantic Web is the second-generation WWW, enriched by machine-processable information which supports the user in his tasks. Given the enormous size even of today’s Web, it is impossible to manually enrich all of these resources. Therefore, automated schemes for learning the relevant information are increasingly being used. Web Mining aims at discovering insights about the meaning of Web resources and their usage. Given the primarily syntactical nature of the data being mined, the discovery of meaning is impossible based on these data only. Therefore, formalizations of the semantics of Web sites and navigation behavior are becoming more and more common. Furthermore, mining the Semantic Web itself is another upcoming application. We argue that the two areas Web Mining and Semantic Web need each other to fulfill their goals, but that the full potential of this convergence is not yet realized. This paper gives an overview of where the two areas meet today, and sketches ways of how a closer integration could be profitable.

Benefits of Implementing Lean Practices and the Impact of the Lean Aerospace Initiative in the Defense Aerospace Industry and Government Agencies

In the 1980’s, many United States industrial organizations started developing new production processes to improve quality, reduce cost, and better respond to customer needs and the pressures of global competition. This new paradigm was coined Lean Production (or simply “Lean”) in the book The Machine That Changed The World published in 1990 by researchers from MIT’s International Motor Vehicle Program. In 1993, a consortium of US defense aerospace firms and the USAF Aeronautical Systems Center, together with the AFRL Materials and Manufacturing Directorate, started the Lean Aircraft Initiative (LAI) at MIT. With expansion in 1998 to include government space products, the program was renamed the Lean Aerospace Initiative. LAI’s vision is to “Significantly reduce the cost and cycle time for military aerospace products throughout the entire value chain while continuing to improve product performance.” By late 1998, 23 industry and 13 government organizations with paying memberships, along with MIT and the UAW were participating in the LAI.

What is wrong with the global support facility for small-scale mining?

The World Bank, United Nations and UK Department for International Development (DfID) have spearheaded a recent global drive to regularize artisanal and small-scale mining (ASM), and provide assistance to its predominantly impoverished participants. To date, millions of dollars have been pledged toward the design of industry-specific policies and regulations; implementation of mechanized equipment; extension; and the launch of alternative livelihood (AL) programmes aimed at diversifying local economies. Much of this funding, however, has failed to facilitate marked improvements, and in many cases, has exacerbated problems. This paper argues that a poor understanding of artisanal, mine-community dynamics and operators’ needs has, in a number of cases, led to the design and implementation of inappropriate industry support schemes and interventions. The discussion focuses upon experiences from sub-Saharan Africa, where ASM is in the most rudimentary of states.

Championing the rhetoric: corporate social responsibility in the Ghanaian mining sector

This paper contributes to a growing body of literature that critically examines how mining companies are embracing community development challenges in developing countries, drawing on experiences from Ghana. Despite receiving considerable praise from the donor and industry communities, the actions being taken by Ghana's major mining companies to foster community development are facilitating few improvements in the rural regions where activities take place. Companies are generally implementing community development programmes that are incapable of alleviating rural hardship and are coordinating destructive displacement exercises. The analysis serves as a stark reminder that mining companies are not charities and engage with African countries strictly for commercial purposes.

Challenges with minimising mercury pollution in the small-scale gold mining sector: Experiences from the Guianas

This paper examines the barriers to mitigating mercury pollution at small-scale gold mines in the Guianas (Guyana, French Guiana and Suriname), and prescribes recommendations for overcoming these obstacles. Whilst considerable attention has been paid to analysing the environmental impacts of operations in the region, minimal research has been undertaken to identify appropriate policy and educational initiatives for addressing the mounting mercury problem. Findings from recent fieldwork and selected interviews with operators from Guyanese and Surinamese gold mining regions reveal that legislative incapacity, the region's varied industry policy stances, various technological problems, and low environmental awareness on the part of communities are impeding efforts to facilitate improved mercury management at small-scale gold mines in the Guianas. Marked improvements can be achieved, however, if legislation, particularly that pertaining to mercury, is harmonised in the region; educational seminars continue to be held in important mining districts; and additional outlets for disseminating environmental equipment and mercury-free technologies are provided.

Understanding the indoor environment through mining sensory data - A case study

A wireless sensor network (WSN) is a group of sensors linked by wireless medium to perform distributed sensing tasks. WSNs have attracted a wide interest from academia and industry alike due to their diversity of applications, including home automation, smart environment, and emergency services, in various buildings. The primary goal of a WSN is to collect data sensed by sensors. These data are characteristic of being heavily noisy, exhibiting temporal and spatial correlation. In order to extract useful information from such data, as this paper will demonstrate, people need to utilise various techniques to analyse the data. Data mining is a process in which a wide spectrum of data analysis methods is used. It is applied in the paper to analyse data collected from WSNs monitoring an indoor environment in a building. A case study is given to demonstrate how data mining can be used to optimise the use of the office space in a building.

Child labour in African Artisanal Mining Communities: experiences from Northern Ghana

The issue of child labour in the artisanal and small-scale mining (ASM) economy is attracting significant attention worldwide. This article critically examines this ‘problem’ in the context of sub-Saharan Africa, where a lack of formal sector employment opportunities and/or the need to provide financial support to their impoverished families has led tens of thousands of children to take up work in this industry. The article begins by engaging with the main debates on child labour in an attempt to explain why young boys and girls elect to pursue arduous work in ASM camps across the region. The remainder of the article uses the Ghana experience to further articulate the challenges associated with eradicating child labour at ASM camps, drawing upon recent fieldwork undertaken in Talensi-Nabdam District, Upper East Region. Overall, the issue of child labour in African ASM communities has been diagnosed far too superficially, and until donor agencies and host governments fully come to grips with the underlying causes of the poverty responsible for its existence, it will continue to burgeon.

Livelihood diversification and the expansion of artisanal mining in rural Tanzania: drivers and policy implications

This paper provides an extended analysis of livelihood diversification in rural Tanzania, with special emphasis on artisanal and small-scale mining (ASM). Over the past decade, this sector of industry, which is labour-intensive and comprises an array of rudimentary and semi-mechanized operations, has become an indispensable economic activity throughout Sub-Saharan Africa, providing employment to a host of redundant public sector workers, retrenched large-scale mine labourers and poor farmers. In many of the region’s rural areas, it is overtaking subsistence agriculture as the primary industry. Such a pattern appears to be unfolding within the Morogoro and Mbeya regions of southern Tanzania, where findings from recent research suggest that a growing number of smallholder farmers are turning to ASM for employment and financial support. It is imperative that national rural development programmes take this trend into account and provide support to these people.

Restructuring and rescaling water governance in mining contexts: the co-production of waterscapes in Peru

The governance of water resources is prominent in both water policy agendas and academic scholarship. Political ecologists have made important advances in reconceptualising the relationship between water and society. Yet, while they have stressed both the scalar dimensions, and the politicised nature, of water governance, analyses of its scalar politics are relatively nascent. In this paper, we consider how the increased demand for water resources by the growing mining industry in Peru reconfigures and rescales water governance. In Peru, the mining industry’s thirst for water draws in, and reshapes, social relations, technologies, institutions and discourses that operate over varying spatial and temporal scales. We develop the concept of waterscape to examine these multiple ways in water is co-produced through mining, and become embedded in changing modes and structures of water governance, often beyond the watershed scale. We argue that an examination of waterscapes avoids the limitations of thinking about water in purely material terms, structuring analysis of water issues according to traditional spatial scales and institutional hierarchies, and taking these scales and structures for granted.

Strengthening artisanal mining research and policy through baseline census activities

Over the past 10-15 years, several governments have implemented an array of technology, support-related, sustainable livelihoods (SL) and poverty-reduction projects for artisanal and small-scale mining (ASM). In the majority of cases, however, these interventions have failed to facilitate improvements in the industry's productivity and raise the living standards of the sector's subsistence operators. This article argues that a poor understanding of the demographics of target populations has precipitated these outcomes. In order to strengthen policy and assistance in the sector, governments must determine, with greater precision, the number of people operating in ASM regions, their origins and ethnic backgrounds, ages, and educational levels. This can be achieved by carrying out basic and localized census work before promoting ambitious sector-specific projects aimed at improving working conditions in the industry.

PDM 2006: Proceedings of the International Workshop on Data Mining

Recently major processor manufacturers have announced a dramatic shift in their paradigm to increase computing power over the coming years. Instead of focusing on faster clock speeds and more powerful single core CPUs, the trend clearly goes towards multi core systems. This will also result in a paradigm shift for the development of algorithms for computationally expensive tasks, such as data mining applications. Obviously, work on parallel algorithms is not new per se but concentrated efforts in the many application domains are still missing. Multi-core systems, but also clusters of workstations and even large-scale distributed computing infrastructures provide new opportunities and pose new challenges for the design of parallel and distributed algorithms. Since data mining and machine learning systems rely on high performance computing systems, research on the corresponding algorithms must be on the forefront of parallel algorithm research in order to keep pushing data mining and machine learning applications to be more powerful and, especially for the former, interactive. To bring together researchers and practitioners working in this exciting field, a workshop on parallel data mining was organized as part of PKDD/ECML 2006 (Berlin, Germany). The six contributions selected for the program describe various aspects of data mining and machine learning approaches featuring low to high degrees of parallelism: The first contribution focuses the classic problem of distributed association rule mining and focuses on communication efficiency to improve the state of the art. After this a parallelization technique for speeding up decision tree construction by means of thread-level parallelism for shared memory systems is presented. The next paper discusses the design of a parallel approach for dis- tributed memory systems of the frequent subgraphs mining problem. This approach is based on a hierarchical communication topology to solve issues related to multi-domain computational envi- ronments. The forth paper describes the combined use and the customization of software packages to facilitate a top down parallelism in the tuning of Support Vector Machines (SVM) and the next contribution presents an interesting idea concerning parallel training of Conditional Random Fields (CRFs) and motivates their use in labeling sequential data. The last contribution finally focuses on very efficient feature selection. It describes a parallel algorithm for feature selection from random subsets. Selecting the papers included in this volume would not have been possible without the help of an international Program Committee that has provided detailed reviews for each paper. We would like to also thank Matthew Otey who helped with publicity for the workshop.

An overview of interactive visual data mining techniques for knowledge discovery

n the past decade, the analysis of data has faced the challenge of dealing with very large and complex datasets and the real-time generation of data. Technologies to store and access these complex and large datasets are in place. However, robust and scalable analysis technologies are needed to extract meaningful information from these datasets. The research field of Information Visualization and Visual Data Analytics addresses this need. Information visualization and data mining are often used complementary to each other. Their common goal is the extraction of meaningful information from complex and possibly large data. However, though data mining focuses on the usage of silicon hardware, visualization techniques also aim to access the powerful image-processing capabilities of the human brain. This article highlights the research on data visualization and visual analytics techniques. Furthermore, we highlight existing visual analytics techniques, systems, and applications including a perspective on the field from the chemical process industry.