Hasil pencarian

Answer Set Programming

International Developments in Computer Science

Visual Modeling With Ibm Rational Software Architect And Uml"

Directory of primary journals in computer science

Proceedings

Pemrograman berorientasi objek

Struktur Matematik Diskret - untuk Sains Komputer

Proceedings of the International Workshop in Conjunction with AVI 2006 on Context in Advanced Interfaces

Research-based Education in Computer Science at the ANU

This project examines research-based education, which includes research-led education, in the discipline of computer science at the Australian National University. It traces the University's position on the issue over recent years, and it explores and evaluates current practices of research-based education. Of particular interest is the newly-introduced Bachelor of Computer Science program, which is found to have many issues in common with the University's flagship research-oriented degree, the Bachelor in Philosophy (PhB) program. From the analysis of data from semi-structured interviews with both BCS students and academics either involved in the BCS and/or whose teaching practices relate to research-based education, this project explores the range of staff and student attitudes and experiences in this area. It then identifies challenges and opportunities in creating an expanded and more coherent use of research-based education through the computer science curriculum. This project makes the following main conclusions. Firstly, that the range of staff and student attitudes and experiences concur with what is found in recent literature. Secondly, there are several important lessons for the BCS program and research-based education in general that can be gleaned from the PhB experience. Thirdly, that there are in fact already a large range of practices of research-based education in computer science (but there has previously been little overall awareness of this). Finally, that if applied judiciously, the potential benefits of research-based education are considerable, in terms of higher quality pedagogical outcomes, enhanced student and academic experience and a positive future impact in computing-related industry.

Data Normalization and Clustering for Big and Small Data and an Application to Clinical Trials

The purpose of this thesis is to propose new methodology for data normalization and cluster prediction in order to help us unravel the structure of a data set. Such data may come from many different areas, for example clinical responses, genomic multivariate data such as microarray, educational test scores, and so on. In addition and more specifically for clinical trials this thesis proposes a new cohort size adaptive design method that will adapt cohort size eventually and finally will save time and cost while still keep the accuracy to find the target maximum tolerate dose. The new normalization method is called Fishe-Yates normalization and it has the advantage of being computationally superior than the standard quantile normalization and it improved the power of the following statistical analysis. Once the data has been normalized the observations are clustered by their pattern of response and cluster prediction is used to validate the findings. We propose a new method for cluster prediction which is a natural way to predict for hierarchical clustering. Our prediction method using nonlinear boundaries between clusters. Normalization method and clustering prediction method can help to identify subgroups of patients which has positive treatment effect. For clinical trial study, this thesis also proposes a new adaptive design which will adapt cohort size thus save time and cost to locate the target maximum tolerated dose.

Uncertainty Analysis for the Unknown Function Using Artificial Neural Network (ANN) Approximated Function

This thesis deals with the finding of uncertainty analysis for the unknown function from experimental data by using Neural Network Approximation. The objective of this thesis is to estimates the uncertainty value for the unknown function where Artificial Neural Network (ANN) approximated function join together with sequential perturbation method will be applied. The thesis describes the uncertainty analysis techniques which are analytical (Newton Approximation) method and numerical (Sequential Perturbation) method to predict the uncertainty value and build up the new function from the experimental data via Fortran program using non-linear regression. The approach in analyzing uncertainty of Nusselt number is approximate the function via ANN using feed-forward and backpropagation network with four inputs and output were randomly generated. Finally, uncertainty outcome through sequential perturbation with ANN will be compare with the outcome using analytical method. Percentage error between both methods shall be compute to prove that uncertainty analysis for unknown function using sequential perturbation with ANN can also be use. From the results, average percentage error between Newton approximation (analytical method) and sequential perturbation (numerical method) retrieved is 5.52395×10-4 %. Meanwhile, the average percentage error between actual Nusselt number produced and approximated Nusselt number is 0.955373 %. However the main focus of this study is to determine whether sequential perturbation with ANN approximated function can be apply or not to estimate the uncertainty for the unknown function. The average percentage error between sequential perturbation with ANN and Newton approximation (analytical method) is 3.563%. Therefore, the objective is achieved.

Sistem penasihatan muamalat (jual beli dan pajak gadai) secara online

Laptops and Mobile Devices Made Easy

With illustrated step-by-step tutorials, jargon busters and plenty of helpful tips, this book helps you to get the most out of your technology. Starting from the very beginning and assuming no prior knowledge, this guide teaches you all the basics.

System Software