Relationships Among Preschool Attendance, Type, and Quality and Early Mathematical Literacy

As students enter kindergarten, some students are more academically prepared than others. This study looked at the relationships among preschool attendance, preschool type (i.e., public, private, Head Start, and home-based technology providers) and preschool quality and early mathematical literacy skills for diverse students. The study sought to answer three research questions: What is the relationship between preschool attendance and early mathematical literacy? What is the relationship between preschool type and early mathematical literacy? What is the relationship between preschool quality and early mathematical literacy? Within each research question, there was also an investigation to see if there were differing effects for diverse student demographics. Data was obtained from the USBE in relation to preschool enrollment records and kindergarten entry scores on the state mandated Kindergarten Entry and Exit Profile (KEEP) assessment for all kindergarten students enrolled in the 2017-18 school year. The researcher conducted a 2x2 Factor ANOVA, independent group means t-tests, and multiple regression analysis to determine relationships among preschool attendance, type, and quality and early mathematical literacy. In general, the independent variables of attending preschool and the quality of the preschool did not seem to have the positive influence expected on early mathematical literacy as a whole, but when looking more specifically at the demographic covariates, there were some positive influences. Students who participated in online preschool programming on average experienced the highest early mathematical literacy scores. Overall, the results suggested that students from diverse backgrounds experience improved early mathematical literacy when they attended preschool. Therefore, with the limited funding available for preschool, policymakers should consider which students might most benefit from preschool experience and target limited resources to such populations

English character recognition algorithm by improving the weights of MLP neural network with dragonfly algorithm

Character Recognition (CR) is taken into consideration for years. Meanwhile, the neural network plays an important role in recognizing handwritten characters. Many character identification reports have been publishing in English, but still the minimum training timing and high accuracy of handwriting English symbols and characters by utilizing a method of neural networks are represents as open problems. Therefore, creating a character recognition system manually and automatically is very important. In this research, an attempt has been done to incubate an automatic symbols and character system for recognition for English with minimum training and a very high recognition accuracy and classification timing. In the proposed idea for improving the weights of the MLP neural network method in the process of teaching and learning character recognition, the dragonfly optimization algorithm has been used. The innovation of the proposed detection system is that with a combination of dragonfly optimization technique and MLP neural networks, the precisions of the system are recovered, and the computing time is minimized. The approach which was used in this study to identify English characters has high accuracy and minimum training time

A proposal on reasoning methods in fuzzy rule-based classification systems

AbstractFuzzy Rule-Based Systems have been succesfully applied to pattern classification problems. In this type of classification systems, the classical Fuzzy Reasoning Method (FRM) classifies a new example with the consequent of the rule with the greatest degree of association. By using this reasoning method, we lose the information provided by the other rules with different linguistic labels which also represent this value in the pattern attribute, although probably to a lesser degree. The aim of this paper is to present new FRMs which allow us to improve the system performance, maintaining its interpretability. The common aspect of the proposals is the participation, in the classification of the new pattern, of the rules that have been fired by such pattern. We formally describe the behaviour of a general reasoning method, analyze six proposals for this general model, and present a method to learn the parameters of these FRMs by means of Genetic Algorithms, adapting the inference mechanism to the set of rules. Finally, to show the increase of the system generalization capability provided by the proposed FRMs, we point out some results obtained by their integration in a fuzzy rule generation process

A proposal on reasoning methods in fuzzy rule-based classification systems

Fuzzy Rule-Based Systems have been succesfully applied to pattern classification problems. In this type of classification systems, the classical Fuzzy Reasoning Method (FRM) classifies a new example with the consequent of the rule with the greatest degree of association. By using this reasoning method, we lose the information provided by the other rules with different linguistic labels which also represent this value in the pattern attribute, although probably to a lesser degree. The aim of this paper is to present new FRMs which allow us to improve the system performance, maintaining its interpretability. The common aspect of the proposals is the participation, in the classification of the new pattern, of the rules that have been fired by such pattern. We formally describe the behaviour of a general reasoning method, analyze six proposals for this general model, and present a method to learn the parameters of these FRMs by means of Genetic Algorithms, adapting the inference mechanism to the set of rules. Finally, to show the increase of the system generalization capability provided by the proposed FRMs, we point out some results obtained by their integration in a fuzzy rule generation process.CICYT TIC96-077

Employing dynamic fuzzy membership functions to assess environmental performance in the supplier selection process

The proposed system illustrates that logic fuzzy can be used to aid management in assessing a supplier's environmental performance in the supplier selection process. A user-centred hierarchical system employing scalable fuzzy membership functions implement human priorities in the supplier selection process, with particular focus on a supplier's environmental performance. Traditionally, when evaluating supplier performance, companies have considered criteria such as price, quality, flexibility, etc. These criteria are of varying importance to individual companies pertaining to their own specific objectives. However, with environmental pressures increasing, many companies have begun to give more attention to environmental issues and, in particular, to their suppliers’ environmental performance. The framework presented here was developed to introduce efficiently environmental criteria into the existing supplier selection process and to reflect on its relevant importance to individual companies. The system presented attempts to simulate the human preference given to particular supplier selection criteria with particular focus on environmental issues when considering supplier selection. The system considers environmental data from multiple aspects of a suppliers business, and based on the relevant impact this will have on a Buying Organization, a decision is reached on the suitability of the supplier. This enables a particular supplier's strengths and weaknesses to be considered as well as considering their significance and relevance to the Buying OrganizationPeer reviewe

A K Nearest Classifier design

This paper presents a multi-classifier system design controlled by the topology of the learning data. Our work also introduces a training algorithm for an incremental self-organizing map (SOM). This SOM is used to distribute classification tasks to a set of classifiers. Thus, the useful classifiers are activated when new data arrives. Comparative results are given for synthetic problems, for an image segmentation problem from the UCI repository and for a handwritten digit recognition problem

Fusions of CNN and SVM Classifiers for Recognizing Handwritten Characters

© Xiaoxiao Niu, 2011 CONCORDIA UNIVERSITY School of Graduate Studies This is to certify that the thesis prepare

Utopian Visions toward a Grand Unified Global Income Tax

Over the past several decades, many countries that historically relied on progressive taxes on income, wealth, and decedents’ estates for much governmental revenue have shifted to less progressive and regressive taxes on labor and consumption. Reasons for the shift are many but include international tax competition as world economies have become increasing global. This paper assumes that progressive taxes remain the best and fairest choice for providing governmental revenue, so that a fundamental change in the scope of the progressive income tax is essential to protect progressivity. The paper argues that the rapid growth of international cooperation on economic and tax matters makes a shift to a unified global income tax possible. A GUGIT would make far better sense than separate national income taxes. The paper goes on to describe a vision of a GUGIT with a global taxing authority and a uniform tax base. For business entities, the GUGIT would apportion the uniform tax base among countries in which a taxpayer is active based upon a four factor formula that includes sales, property but not intangible property with respect to which physical location has little meaning, labor based upon adjusted payroll that eliminates the distortions of wage differentials or upon person hours, and beneficial ownership. The GUGIT would include a robust related taxpayer definition in order to prevent taxpayers from artificially shifting income to countries that choose to impose lower rates of tax on their shares of the uniform base through transfer pricing and similar devices. At individual level, the GUGIT would allocate income to the jurisdiction into which the taxpayer intends his or her services to have their impact or upon relative periods of residence during the year. Either method diminishes the benefit of expatriation to avoid taxes but a continuation tax or exit tax may remain necessary to combat tax expatriation