RETINA FUNDUS IMAGE MASK GENERATION USING PSEUDO PARAMETRIC MODELING TECHNIQUE

The use of vascular intersection as one of the symptoms for monitoring and diagnosis of diabetic retinopathy from fundus images have been widely reported in literatures. In this work, a new hybrid approach that makes use of three different methods of vascular intersection detection namely Modified cross-point number (MCN), Combine Cross Points (CNN) and Artificial Neural Network (ANN) is hereby proposed. Result obtained from the application of this technique to both simulated and experimental shows a very high accuracy and precision value in detecting both bifurcation and cross over points. Thus an improvement in bifurcation and vascular point detection and a good tool in the monitoring and diagnosis of diabetic retinopathy

Building Information Modelling Implementation in Practice: Lessons learned from a housing project in the Netherlands

Real-world implementations of BIM can serve as use cases to demonstrate BIM implementation strategy in practice. This paper presents the findings from a case study of BIM implementation on a housing project in the Netherlands. It describes how BIM approach was used to facilitate the delivery of the project. The benefits and challenges encountered are discussed. The role of BIM process management, BIM activities and key enabling technologies are examined as well as the impact of procurement on BIM implementation. The paper highlights how BIM activities was structured to deliver the project faster (time), cheaper (cost) and better (quality and performance). The analysis is based on project documents and interview with those involved in managing the BIM process. One of the major implications of the findings is that: BIM implementation is a set of interrelated activities and processes. Organisations seeking to work using BIM approach need to actively engage with the process and, in an ongoing basis, learn from their experiences as well as improve based on the lessons learned

Legal framework for alternative dispute resolution: Examination of the Singapore national legal system for arbitration

10.1061/(ASCE)1052-3928(2007)133:2(148)Journal of Professional Issues in Engineering Education and Practice1332148-157JPEP

Explaining cooperative behavior in building and civil engineering projects' claims process: Interactive effects of outcome favorability and procedural fairness

10.1061/(ASCE)0733-9364(2008)134:9(681)Journal of Construction Engineering and Management1349681-691JCEM

Performance Analysis of ANN based YCbCr Skin Detection Algorithm

AbstractSkin detection from acquired images has various areas of applications especially in automatic facial and human recognition system. The performance analysis of artificial neural network based –YcbCr skin recognition and three other techniques is evaluated in this work. Results obtained show that the use of YCbCr color model performs better than RGB colour model and the use of artificial neural network further improves the accuracy of the system

Use of artificial intelligence to predict the accuracy of pre-tender building cost estimate

Pre-tender estimates are susceptible to inaccuracies (biases) because they are often prepared within a limited timeframe, and with limited information about project scope. Inaccurate estimation of project uncertainties is the underlying cause of project cost overruns in construction. Typically, cost engineers and quantity surveyors would add contingency reserve to a pretender estimate in order to account for any unforeseen cost that may arise between the date of the estimate and the projected completion date of the project. The traditional 10% rule of thumb for estimating contingency is subjective - based on experience and expert judgment, and are often inadequate. In the research reported in this paper, we propose that learning algorithms trained to use the known characteristic of completed projects could allow quantitative and objective estimation of the inaccuracies in pretender building cost estimates of new projects. The study assumes that the accuracy in the initial estimate (bias) of a completed project is the difference between the actual project completion costs minus the pre-tender cost forecast expressed as a percentage of the actual project completion costs. A three-layer ANN model of feed- forward type with one output node was constructed and trained to generalise nine characteristics of 100 completed projects and the cost data from those projects. The nine input variables of the model are project size (measured by number of storeys and gross floor area), principal structural material, procurement route, project type, location, sector, estimating method, and estimated sum. Estimate accuracy (bias) was used as the output variable. The prediction power stands at 73% correlation coefficient, 3% of Mean Absolute Error and 0.2% Mean Squared Error. It was found that in more than 73% of the test cases the predicted estimate bias did not differ by more than 8.2% from the expected (Maximum Absolute Error). This means that amount of estimate bias predicted by the ANN are similar to what actually occurred. The trained ANN model can be used as a decision making tool by cost advisors when forecasting building cost at the pretender stage. The model can be queried with the characteristics of a new project in order to quickly predict the error in the estimate of the new project. The predicted error represents the additional contingency reserve that must be set aside for the project in order to cater for possible cost overruns. The model can also be extended to forecast the likely cost of a project