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.

Significance Of Classification Techniques In Prediction Of Learning Disabilities

The aim of this study is to show the importance of two classification techniques, viz. decision tree and clustering, in prediction of learning disabilities (LD) of school-age children. LDs affect about 10 percent of all children enrolled in schools. The problems of children with specific learning disabilities have been a cause of concern to parents and teachers for some time. Decision trees and clustering are powerful and popular tools used for classification and prediction in Data mining. Different rules extracted from the decision tree are used for prediction of learning disabilities. Clustering is the assignment of a set of observations into subsets, called clusters, which are useful in finding the different signs and symptoms (attributes) present in the LD affected child. In this paper, J48 algorithm is used for constructing the decision tree and K-means algorithm is used for creating the clusters. By applying these classification techniques, LD in any child can be identified

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.

Attribute reduction and missing value imputing with ANN: prediction of learning disabilities

Learning disability (LD) is a neurological condition that affects a child’s brain and impairs his ability to carry out one or many specific tasks. LD affects about 10% of children enrolled in schools. There is no cure for learning disabilities and they are lifelong. The problems of children with specific learning disabilities have been a cause of concern to parents and teachers for some time. Just as there are many different types of LDs, there are a variety of tests that may be done to pinpoint the problem The information gained from an evaluation is crucial for finding out how the parents and the school authorities can provide the best possible learning environment for child. This paper proposes a new approach in artificial neural network (ANN) for identifying LD in children at early stages so as to solve the problems faced by them and to get the benefits to the students, their parents and school authorities. In this study, we propose a closest fit algorithm data preprocessing with ANN classification to handle missing attribute values. This algorithm imputes the missing values in the preprocessing stage. Ignoring of missing attribute values is a common trend in all classifying algorithms. But, in this paper, we use an algorithm in a systematic approach for classification, which gives a satisfactory result in the prediction of LD. It acts as a tool for predicting the LD accurately, and good information of the child is made available to the concerned

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.

Smart electricity networks based on large integration of renewable sources and distributed generation

Das Grünbuch 2006 der Europäischen Kommission "Eine Europäische Strategie für nachhaltige, wettbewerbsfähige und sichere Energie" unterstreicht, dass Europa in ein neues Energie-Zeitalter eingetreten ist. Die vorrangigen Ziele europäischer Energiepolitik müssen Nachhaltigkeit, Wettbewerbsfähigkeit und Versorgungssicherheit sein, wobei sie eine zusammenhängende und logische Menge von Taktiken und Maßnahmen benötigt, um diese Ziele zu erreichen. Die Strommärkte und Verbundnetze Europas bilden das Kernstück unseres Energiesystems und müssen sich weiterentwickeln, um den neuen Anforderungen zu entsprechen. Die europäischen Stromnetze haben die lebenswichtigen Verbindungen zwischen Stromproduzenten und Verbrauchern mit großem Erfolg seit vielen Jahrzehnten gesichert. Die grundlegende Struktur dieser Netze ist entwickelt worden, um die Bedürfnisse großer, überwiegend auf Kohle aufgebauten Herstellungstechnologien zu befriedigen, die sich entfernt von den Verbraucherzentren befinden. Die Energieprobleme, denen Europa jetzt gegenübersteht, ändern die Stromerzeugungslandschaft in zwei Gesichtspunkten: die Notwendigkeit für saubere Kraftwerkstechnologien verbunden mit erheblich verbesserten Wirkungsgraden auf der Verbraucherseite wird es Kunden ermöglichen, mit den Netzen viel interaktiver zu arbeiten; andererseits müssen die zukünftigen europaweiten Stromnetze allen Verbrauchern eine höchst zuverlässige, preiswerte Energiezufuhr bereitstellen, wobei sowohl die Nutzung von großen zentralisierten Kraftwerken als auch kleineren lokalen Energiequellen überall in Europa ausgeschöpft werden müssen. In diesem Zusammenhang wird darauf hingewiesen, dass die Informationen, die in dieser Arbeit dargestellt werden, auf aktuellen Fragen mit großem Einfluss auf die gegenwärtigen technischen und wirtschaftspolitischen Diskussionen basieren. Der Autor hat während der letzten Jahre viele der hier vorgestellten Schlussfolgerungen und Empfehlungen mit Vertretern der Kraftwerksindustrie, Betreibern von Stromnetzen und Versorgungsbetrieben, Forschungsgremien und den Regulierungsstellen diskutiert. Die folgenden Absätze fassen die Hauptergebnisse zusammen: Diese Arbeit definiert das neue Konzept, das auf mehr verbraucherorientierten Netzen basiert, und untersucht die Notwendigkeiten sowie die Vorteile und die Hindernisse für den Übergang auf ein mögliches neues Modell für Europa: die intelligenten Stromnetze basierend auf starker Integration erneuerbarer Quellen und lokalen Kleinkraftwerken. Das neue Modell wird als eine grundlegende Änderung dargestellt, die sich deutlich auf Netzentwurf und -steuerung auswirken wird. Sie fordert ein europäisches Stromnetz mit den folgenden Merkmalen: – Flexibel: es erfüllt die Bedürfnisse der Kunden, indem es auf Änderungen und neue Forderungen eingehen kann – Zugänglich: es gestattet den Verbindungszugang aller Netzbenutzer besonders für erneuerbare Energiequellen und lokale Stromerzeugung mit hohem Wirkungsgrad sowie ohne oder mit niedrigen Kohlendioxidemissionen – Zuverlässig: es verbessert und garantiert die Sicherheit und Qualität der Versorgung mit den Forderungen des digitalen Zeitalters mit Reaktionsmöglichkeiten gegen Gefahren und Unsicherheiten – Wirtschaftlich: es garantiert höchste Wirtschaftlichkeit durch Innovation, effizientes Energiemanagement und liefert „gleiche Ausgangsbedingungen“ für Wettbewerb und Regulierung. Es beinhaltet die neuesten Technologien, um Erfolg zu gewährleisten, während es die Flexibilität behält, sich an weitere Entwicklungen anzupassen und fordert daher ein zuversichtliches Programm für Forschung, Entwicklung und Demonstration, das einen Kurs im Hinblick auf ein Stromversorgungsnetz entwirft, welches die Bedürfnisse der Zukunft Europas befriedigt: – Netztechnologien, die die Stromübertragung verbessern und Energieverluste verringern, werden die Effizienz der Versorgung erhöhen, während neue Leistungselektronik die Versorgungsqualität verbessern wird. Es wird ein Werkzeugkasten erprobter technischer Lösungen geschaffen werden, der schnell und wirtschaftlich eingesetzt werden kann, so dass bestehende Netze Stromeinleitungen von allen Energieressourcen aufnehmen können. – Fortschritte bei Simulationsprogrammen wird die Einführung innovativer Technologien in die praktische Anwendung zum Vorteil sowohl der Kunden als auch der Versorger stark unterstützen. Sie werden das erfolgreiche Anpassen neuer und alter Ausführungen der Netzkomponenten gewährleisten, um die Funktion von Automatisierungs- und Regelungsanordnungen zu garantieren. – Harmonisierung der ordnungspolitischen und kommerziellen Rahmen in Europa, um grenzüberschreitenden Handel von sowohl Energie als auch Netzdienstleistungen zu erleichtern; damit muss eine Vielzahl von Einsatzsituationen gewährleistet werden. Gemeinsame technische Normen und Protokolle müssen eingeführt werden, um offenen Zugang zu gewährleisten und den Einsatz der Ausrüstung eines jeden Herstellers zu ermöglichen. – Entwicklungen in Nachrichtentechnik, Mess- und Handelssystemen werden auf allen Ebenen neue Möglichkeiten eröffnen, auf Grund von Signalen des Marktes frühzeitig technische und kommerzielle Wirkungsgrade zu verbessern. Es wird Unternehmen ermöglichen, innovative Dienstvereinbarungen zu benutzen, um ihre Effizienz zu verbessern und ihre Angebote an Kunden zu vergrößern. Schließlich muss betont werden, dass für einen erfolgreichen Übergang zu einem zukünftigen nachhaltigen Energiesystem alle relevanten Beteiligten involviert werden müssen.

Development and Integration of Biocontrol Products in Branched Broomrape (Phelipanche ramosa) management in Tomato

Parasitic weeds of the genera Striga, Orobanche, and Phelipanche pose a severe problem for agriculture because they are difficult to control and are highly destructive to several crops. The present work was carried out during the period October, 2009 to February, 2012 to evaluate the potential of arbuscular mycorrhizal fungi (AMF) to suppress P. ramosa on tomatoes and to investigate the effects of air-dried powder and aqueous extracts from Euphorbia hirta on germination and haustorium initiation in Phelipanche ramosa. The work was divided into three parts: a survey of the indigenous mycorrhizal flora in Sudan, second, laboratory and greenhouse experiments (conducted in Germany and Sudan) to construct a base for the third part, which was a field trial in Sudan. A survey was performed in 2009 in the White Nile state, Sudan to assess AMF spore densities and root colonization in nine fields planted with 13 different important agricultural crops. In addition, an attempt was made to study the relationship between soil physico-chemical properties and AMF spore density, colonization rate, species richness and other diversity indices. The mean percentage of AMF colonization was 34%, ranging from 19-50%. The spore densities (expressed as per 100 g dry soil) retrieved from the rhizosphere of different crops were relatively high, varying from 344 to 1222 with a mean of 798. There was no correlation between spore densities in soil and root colonization percentage. A total of 45 morphologically classifiable species representing ten genera of AMF were detected with no correlation between the number of species found in a soil sample and the spore density. The most abundant genus was Glomus (20 species). The AMF diversity expressed by the Shannon–Weaver index was highest in sorghum (H\= 2.27) and Jews mallow (H\= 2.13) and lowest in alfalfa (H\= 1.4). With respect to crop species, the genera Glomus and Entrophospora were encountered in almost all crops, except for Entrophospora in alfalfa. Kuklospora was found only in sugarcane and sorghum. The genus Ambispora was recovered only in mint and okra, while mint and onion were the only species on which no Acaulospora was found. The hierarchical cluster analysis based on the similarity among AMF communities with respect to crop species overall showed that species compositions were relatively similar with the highest dissimilarity of about 25% separating three of the mango samples and the four sorghum samples from all other samples. Laboratory experiments studied the influence of root and stem exudates of three tomato varieties infected by three different Glomus species on germination of P. ramosa. Root exudates were collected 21or 42 days after transplanting (DAT) and stem exudates 42 DAT and tested for their effects on germination of P. ramosa seeds in vitro. The tomato varieties studied did not have an effect on either mycorrhizal colonization or Phelipanche germination. Germination in response to exudates from 42 day old mycorrhizal plants was significantly reduced in comparison to non-mycorrhizal controls. Germination of P. ramosa in response to root exudates from 21 day old plants was consistently higher than for 42 day-old plants (F=121.6; P<.0001). Stem diffusates from non-mycorrhizal plants invariably elicited higher germination than diffusates from the corresponding mycorrhizal ones and differences were mostly statistically significant. A series of laboratory experiments was undertaken to investigate the effects of aqueous extracts from Euphorbia hirta on germination, radicle elongation, and haustorium initiation in P. ramosa. P. ramosa seeds conditioned in water and subsequently treated with diluted E. hirta extract (10-25% v/v) displayed considerable germination (47-62%). Increasing extract concentration to 50% or more reduced germination in response to the synthetic germination stimulants GR24 and Nijmegen-1 in a concentration dependent manner. P. ramosa germlings treated with diluted Euphorbia extract (10-75 % v/v) displayed haustorium initiation comparable to 2, 5-Dimethoxy-p-benzoquinon (DMBQ) at 20 µM. Euphorbia extract applied during conditioning reduced haustorium initiation in a concentration dependent manner. E. hirta extract or air-dried powder, applied to soil, induced considerable P. ramosa germination. Pot experiments were undertaken in a glasshouse at the University of Kassel, Germany, to investigate the effects of P. ramosa seed bank on tomato growth parameters. Different Phelipanche seed banks were established by mixing the parasite seeds (0 - 32 mg) with the potting medium in each pot. P. ramosa reduced all tomato growth parameters measured and the reduction progressively increased with seed bank. Root and total dry matter accumulation per tomato plant were most affected. P. ramosa emergence, number of tubercles, and tubercle dry weight increased with the seed bank and were, invariably, maximal with the highest seed bank. Another objective was to determine if different AM fungi differ in their effects on the colonization of tomatoes with P. ramosa and the performance of P. ramosa after colonization. Three AMF species viz. GIomus intraradices, Glomus mosseae and Glomus Sprint® were used in this study. For the infection, P. ramosa seeds (8 mg) were mixed with the top 5 cm soil in each pot. No mycorrhizal colonization was detected in un-inoculated control plants. P. ramosa infested, mycorrhiza inoculated tomato plants had significantly lower AMF colonization compared to plants not infested with P. ramosa. Inoculation with G. intraradices, G. mosseae and Glomus Sprint® reduced the number of emerged P. ramosa plants by 29.3, 45.3 and 62.7% and the number of tubercles by 22.2, 42 and 56.8%, respectively. Mycorrhizal root colonization was positively correlated with number of branches and total dry matter of tomatoes. Field experiments on tomato undertaken in 2010/12 were only partially successful because of insect infestations which resulted in the complete destruction of the second run of the experiment. The effects of the inoculation with AMF, the addition of 10 t ha-1 filter mud (FM), an organic residues from sugar processing and 36 or 72 kg N ha-1 on the infestation of tomatoes with P. ramosa were assessed. In un-inoculated control plants, AMF colonization ranged between 13.4 to 22.1% with no significant differences among FM and N treatments. Adding AMF or FM resulted in a significant increase of branching in the tomato plants with no additive effects. Dry weights were slightly increased through FM application when no N was applied and significantly at 36 kg N ha-1. There was no effect of FM on the time until the first Phelipanche emerged while AMF and N application interacted. Especially AMF inoculation resulted in a tendency to delayed P. ramosa emergence. The marketable yield was extremely low due to the strong fruit infestation with insects mainly whitefly Bemisia tabaci and tomato leaf miner (Tuta absoluta). Tomatoes inoculated with varied mycorrhiza species displayed different response to the insect infestation, as G. intraradices significantly reduced the infestation, while G. mosseae elicited higher insect infestation. The results of the present thesis indicate that there may be a potential of developing management strategies for P. ramosa targeting the pre-attachment stage namely germination and haustorial initiation using plant extracts. However, ways of practical use need to be developed. If such treatments can be combined with AMF inoculation also needs to be investigated. Overall, it will require a systematic approach to develop management tools that are easily applicable and affordable to Sudanese farmers. It is well-known that proper agronomical practices such as the design of an optimum crop rotation in cropping systems, reduced tillage, promotion of cover crops, the introduction of multi-microbial inoculants, and maintenance of proper phosphorus levels are advantageous if the mycorrhiza protection method is exploited against Phelipanche ramosa infestation. Without the knowledge about the biology of the parasitic weeds by the farmers and basic preventive measures such as hygiene and seed quality control no control strategy will be successful, however.

How do domestic policies affect the integration of Ethiopian fertiliser markets with world markets?

Frequent shifts in policy on fertiliser markets have occurred in Ethiopia with the aim of facilitating both physical and economic access of farmers to fertiliser. The last shift was the introduction of a monopoly on each stage of the supply chain in 2008. Furthermore, government control of prices and margins as well as stockholding programmes are also present on the markets. This paper evaluates the effect of these policies on the integration of domestic with world markets of fertiliser, using cointegration methods. Time series data of diammonium phosphate (DAP) and urea prices on world, import and retail markets between 1971 and 2012 are used. The findings show high transmission of price signals from world markets to import prices for both DAP and urea. However, between import and retail prices there is no evidence of cointegration for urea, while for DAP full price transmission is concluded. In the retail market, domestic transaction costs associated with storing large volumes of fertiliser act as a buffer between import and retail prices, especially for urea. Therefore, economic benefits could be achieved by reducing the size of stocks and revising the demand estimation process.

Explorations of the Practical Issues of Learning Prediction-Control Tasks Using Temporal Difference Learning Methods

There has been recent interest in using temporal difference learning methods to attack problems of prediction and control. While these algorithms have been brought to bear on many problems, they remain poorly understood. It is the purpose of this thesis to further explore these algorithms, presenting a framework for viewing them and raising a number of practical issues and exploring those issues in the context of several case studies. This includes applying the TD(lambda) algorithm to: 1) learning to play tic-tac-toe from the outcome of self-play and of play against a perfectly-playing opponent and 2) learning simple one-dimensional segmentation tasks.

The Informational Complexity of Learning from Examples

This thesis attempts to quantify the amount of information needed to learn certain tasks. The tasks chosen vary from learning functions in a Sobolev space using radial basis function networks to learning grammars in the principles and parameters framework of modern linguistic theory. These problems are analyzed from the perspective of computational learning theory and certain unifying perspectives emerge.

Vector-Based Integration of Local and Long-Range Information in Visual Cortex

Integration of inputs by cortical neurons provides the basis for the complex information processing performed in the cerebral cortex. Here, we propose a new analytic framework for understanding integration within cortical neuronal receptive fields. Based on the synaptic organization of cortex, we argue that neuronal integration is a systems--level process better studied in terms of local cortical circuitry than at the level of single neurons, and we present a method for constructing self-contained modules which capture (nonlinear) local circuit interactions. In this framework, receptive field elements naturally have dual (rather than the traditional unitary influence since they drive both excitatory and inhibitory cortical neurons. This vector-based analysis, in contrast to scalarsapproaches, greatly simplifies integration by permitting linear summation of inputs from both "classical" and "extraclassical" receptive field regions. We illustrate this by explaining two complex visual cortical phenomena, which are incompatible with scalar notions of neuronal integration.

Designing context-aware adaptive units of learning based on IMS-LD standard

Learning contents adaptation has been a subject of interest in the research area of the adaptive hypermedia systems. Defining which variables and which standards can be considered to model adaptive content delivery processes is one of the main challenges in pedagogical design over e-learning environments. In this paper some specifications, architectures and technologies that can be used in contents adaptation processes considering characteristics of the context are described and a proposal to integrate some of these characteristics in the design of units of learning using adaptation conditions in a structure of IMS-Learning Design (IMS-LD) is presented. The key contribution of this work is the generation of instructional designs considering the context, which can be used in Learning Management Systems (LMSs) and diverse mobile devices

Models of Learning and Motivation

A brief skim through educational theory intended for students registered on a single module in Technology Enhanced Learning. Startes with Blooms taxonomy, travles through instructivism and constructivism and on to theories of motivation/

Accessibility in the Design of Learning Resources

These slides accompany a seminar delivered on 20 May 2016 by Jane Warren (Southampton Education School) and Adam Warren (Institute for Learning Innovation and Development). A recording of the lecture can be viewed here: http://tinyurl.com/zp8u3lq