Azurah A. Samah

Recommended factors for triage categories using Simulation Modelling

Malaysia is in advancement towards the establishment of effective and efficient energy mix plan within its Nuclear Power Programme (NPP) resulting from current governance acceptance and support. Prior precautions have been inculcated in order to comprehend nuclear energy accountability in striving high income economic growth and energy security. This paper is attempting to prescribe nuclear safety recommendations encompassing radiation and nuclear (RN) event recovery phase within integrated disaster operations management (DOM) activities from Soft System Methodology (SSM) perspective by using Simulation Modelling. Possibly, this method is introduced in order to recommend triage categories for vulnerable population groups which are commonly at-risks during RN emergencies, incidents and disasters. Therefore, this paper will proposed some factors of triage categories which may affect these groups whereby representing an initial system map. It is concluded that prioritized suggestions given could possibly ascertain the characteristics of other interventions involved in order to achieve a sustainable and secure nuclear safety in the future.

Cooperative enhanced scatter search with opposition-based learning schemes for parameter estimation in high dimensional kinetic models of biological systems

Abstract Industrial bioprocesses development nowadays is concerned with producing chemicals using yeast, bacteria and therapeutic proteins in mammalian cells. This involves the utilization of microorganism cells as factories and re-engineering them in silico. The tools that could facilitate this process are known as the kinetic models. Kinetic models of cellular metabolism are important in assisting researchers to understand the rational design of biological systems, predicting metabolites production, and improving bio-products development. However, the most challenging task in model development is parameter estimation, which is the process of identifying an unknown value of model parameters which provides the best fit between the model output and a set of experimental data. Due to the increased complexity and high dimensionality of the models, which are extremely nonlinear and contain large numbers of kinetic parameters, parameter estimation is known to be difficult and time-consuming. This study proposes a cooperative enhanced scatter search with opposition-based learning schemes (CeSSOL) for parameter estimation in large-scale biology models. The method was executed in parallel with the proposed cooperative mechanism in order to exchange information (kinetic parameters) between individual threads. Each thread consists of different parameters settings that enhance the systemic properties in obtaining the global minimum. The performance of the proposed method was assessed against two large-scale microorganisms models using mammalian and bacteria cells. The results revealed that the proposed method recorded faster computation time compared to other methods. The study has also demonstrated that the proposed method can be used to provide more accurate and faster estimation of kinetic models, indicating the potential benefits of utilizing this method for expert systems of industrial biotechnology.

Block matching algorithm for moving object detection in video forensic

The most challenging issues in video forensic is about detecting tampered moving objects from video sequences. Approaches based on moving object are more complex because making a distinction between tampered and non-tampered regions is difficult. Hence, this study proposed block matching algorithm (BMA) in video copy-move forgery detection scheme to detect regions that represent either tampered or non-tampered moving object. Various search methods have been compared in this study by using standard video forensic dataset to determine the most suitable search method for video copy-move forgery detection scheme. Adaptive rood pattern search (ARPS) has been chosen as the most suitable BMA to detect regions of moving object in video forensic due to its performance.

Optimization of green building design to achieve green building index (GBI) using genetic algorithm (GA)

Designing a feasible green building that complies with national green building assessment is one of the main problems faced by architects. The existing Building Information Modelling (BIM) software, is used by architects to assist them to design a green building. Green building is a concept where the building is environmentally friendly to the surrounding, does not use too much electricity and have low Overall Thermal Transfer Value (OTTV). Architects having some tough time to design green building because OTTV of the building majorly affected by wall and window material used. Hence in this study, single objective Genetic Algorithm (GA) is proposed to optimize green building designed by BIM. A decision support system using GA optimization is proposed to optimize green building designed using BIM. The goal is to find an optimum material combination that produces nearly optimum OTTV. The initial result shows that GA manages to find optimum materials combination with a reduced 16% OTTV.

Decision Support System Using System Dynamics Simulation Modelling for Projection of Dentist Supply

This paper describes the development of a decision support system (DSS) using System Dynamics (SD) simulation approach to project and analyse the supply of dentist at both local and private Malaysian healthcare provider from 2015 to 2030. The project was driven by the need to provide sustainable, quality dental services in a complex environment given fluctuations in dental graduate numbers, migrations, attritions and activities. The developed DSS using VENSIM software described in this paper is flexible and expandable. It can be used to monitor future changes in dental workforce and provide a comprehensive and structured policy analysis to policy-makers and health managers, within the Ministry of Health Malaysia.

A Study on Rostering On-call Doctor Using Genetic Algorithm with Enhanced Genetic Operator

This paper presents a study on enhanced genetic operator within genetic algorithm for rostering on-call doctor. We review the literature on genetic operator approach method which focused on genetic operator. The enhanced genetic operator is to be used within genetic algorithm to generate Oncall doctor rostering. On call doctor rostering problems represent a subclass of scheduling problems that are complicated to be solved. The main goal is to propose new algorithm for genetic operator and also to find satisfying shift and resource assignments, satisfying the needs and requirements of employees as well as the employers in specific health care institutions.

An Overview on Warranty Cost Modelling in Two-Dimensional Warranty Using Neuro-Fuzzy Approach

In early study of manufacturing, warranty studies were mainly carried out by researchers in the legal profession and, thus, such articles appeared only in the law journals. Subsequently, warranty studies attracted researchers from such field as economics, accounting, management, marketing engineering, operations research and statistics. There are two types of warranty namely one-dimensional warranty, which is based on product usage or product age, and two-dimensional warranty which considers both product age and usage. This paper is mainly concerned about two-dimensional warranty. We applied neuro-fuzzy system approach of AI to optimize warranty cost and period. In this paper, neuro-fuzzy framework apply in warranty cost modelling is proposed.