In this study, we introduces a classification approach using Multi-Layer Perceptron (MLP)with BackPropagation learning algorithm and a feature selection algorithm along with biomedical test values to diagnose heart disease.Clinical diagnosis is done mostly by doctor's expertise and experience.But still cases are reported of wrong diagnosis and treatment.Patients are asked to take number of tests for diagnosis.In many cases,not all the tests contribute towards effective diagnosis of a disease.Our work is to classify the presence of heart disease with reduced number of attributes.Original,13 attributes are involved in classify the heart disease.We use Information Gain to determine the attributes which reduces the number of attributes which is need to be taken from patients.The Artificial neural networks is used to classify the diagnosis of patients.Thirteen attributes are reduced to 8 attributes.The accuracy differs between 13 features and 8 features in training data set is 1.1% and in the validation data set is 0.82%.

This study investigated the use of Artificial Neural Network (ANN) and Genetic Algorithm (GA) for prediction of Thailand's SET50 index trend. ANN is a widely accepted machine learning method that uses past data to predict future trend, while GA is an algorithm that can find better subsets of input variables for importing into ANN, hence enabling more accurate prediction by its efficient feature selection. The imported data were chosen technical indicators highly regarded by stock analysts, each represented by 4 input variables that were based on past time spans of 4 different lengths: 3-, 5-, 10-, and 15-day spans before the day of prediction. This import undertaking generated a big set of diverse input variables with an exponentially higher number of possible subsets that GA culled down to a manageable number of more effective ones. SET50 index data of the past 6 years, from 2009 to 2014, were used to evaluate this hybrid intelligence prediction accuracy, and the hybrid's prediction results were found to be more accurate than those made by a method using only one input variable for one fixed length of past time span.

Successful management of an information technology (IT) project is the most desirable for all organisations and stakeholders. Many researchers elaborated that risk management is a key part of project management for any project size. Risk management is so critical because it provides project managers with a forward-looking view of both threats and opportunities to improve the project success. The objectives of this research are to explore organisational factors affecting IT project success and risk management practices influencing IT project success. Risk management practices include risk identification, risk analysis, risk response planning, and risk monitoring and control. The IT project success is measured by process performance and product performance. Data are collected from 200 project managers, IT managers, and IT analysts in IT firms through questionnaires and analysed using Independent Sample t-test, One-way ANOVA, and Multiple Linear Regression at the statistical significance level of 0.05. The results show that the differences in organisational types affect IT project success in all aspects, while the differences on organisational sizes affect IT project success in the aspect of product performance and total aspects. Risk identification and risk response planning influence the process performance and the total aspects of IT project success. Risk identification has the highest positive influence on product performance, followed closely by risk response, while risk analysis negatively influences product performance.

No abstract