Burairah Hussin

Exam timetabling using graph colouring approach

Timetabling at large covering many different types of problems which have their own unique characteristics. In education, the three most common academic timetabling problems are school timetable, university timetable and exam timetable. Exam timetable is crucial but difficult to be done manually due to the complexity of the problem. The main problem includes dual academic calendar, increasing student enrolments and limitations of resources. This study presents a solution method for exam timetable problem in centre for foundation studies and extension education (FOSEE), Multimedia University, Malaysia. The method of solution is a heuristic approach that include graph colouring, cluster heuristic and sequential heuristic.

A Framework of an Intelligent Maintenance Prognosis Tool

The technology of prognosis has become a significant approach but its implementation in maintenance has a major extension. The ability prognosis in the medical area has been established to estimate the future of human health. However, in maintenance, application of prognosis is not yet seen as a practical use for making better maintenance decision. To date, research in this area has been done in proposing prognosis techniques or model but leaving the implementation of prognosis as their future work. In this paper, an overview of prognosis in maintenance is presented. By using the data-driven approach, a framework for implementing of an intelligent maintenance prognosis tool is introduced. The framework utilizes the existing equipment operating performance data in the industry for prognosis process. Next, the framework combines the ability of prognosis in estimating remaining useful life (RUL) of equipment with the maintenance action knowledge to generate a well-received maintenance plan.

A Comparison Of Stepwise And Fuzzy Multiple Regression Analysis Techniques For Managing Software Project Risks: Analysis Phase

Risk is not always avoidable, but it is controllable. The aim of this study is to identify whether those techniques are effective in reducing software failure. This motivates the authors to continue the effort to enrich the managing software project risks with consider mining and quantitative approach with large data set. In this study, two new techniques are introduced namely stepwise multiple regression analysis and fuzzy multiple regression to manage the software risks. Two evaluation procedures such as MMRE and Pred (25) is used to compare the accuracy of techniques. The model’s accuracy slightly improves in stepwise multiple regression rather than fuzzy multiple regression. This study will guide software managers to apply software risk management practices with real world software development organizations and verify the effectiveness of the new techniques and approaches on a software project. The study has been conducted on a group of software project using survey questionnaire. It is hope that this will enable software managers improve their decision to increase the probability of software project success.

A Data Mining Approach for Developing Quality Prediction Model in Multi-Stage Manufacturing

prediction model has been developed in various industries to realize the faultless manufacturing. However, most of quality prediction model is developed in single-stage manufacturing. Previous studies show that single-stage quality system cannot solve quality problem in multi-stage manufacturing effectively. This study is intended to propose combination of multiple PCA+ID3 algorithm to develop quality prediction model in MMS. This technique is applied to a semiconductor manufacturing dataset using the cascade prediction approach. The result shows that the combination of multiple PCA+ID3 is manage to produce the more accurate prediction model in term of classifying both positive and negative classes.

Application of multi-step time series prediction for industrial equipment prognostic

The use of prognostics is critically to be implemented in industrial. This paper presents an application of multi-step time series prediction to support industrial equipment prognostic. An artificial neural network technique with sliding window is considered for the multi-step prediction which is able to predict the series of future equipment condition. The structure of prognostic application is presented. The feasibility of this prediction application was demonstrated by applying real condition monitoring data of industrial equipment.

Replacement Model for Higher Education Institution Hostel Building Maintenance in Malaysia

 Abstract—This paper is part of an on-going research on the development of maintenance management cost model for Higher Education Institution Hostel Buildings in Malaysia where the case study is conducted at Kolej Universiti Islam Melaka (KUIM). The model is developed to analyse the total cost curve for various values of the uncertain parameter, and noting the effect of this variation on the optimal solution. The decision areas addressed based on the replacement action that are assumed to be known with certainty. This is due to the item is not subject to failure but consider the operating cost with use. The study is aimed to assist engineers in deciding an appropriate replacement policy. This is usually useful to plot the total cost per unit time curve. The advantage of the curve is that, along with giving the optimal value, it shows the total cost around the optimum. If the curve is fairly flat around the optimum, it is not really very important that engineers should plan for the replacements exactly at the optimum. The model is proposed to guide and facilitate when dealing with optimization problems. If there is uncertainty about the value of the particular parameter required during the analysis, then the replacement cost is unsure. Furthermore the evaluation of the total cost curve for various values of the uncertain parameter could in consequence affect the optimal solution.

Towards Incorporation of Software Security Testing Framework in Software Development

The aim of this paper is to provide secure software using security testing approach. The researchers have reviewed and analyzed the software testing frameworks and software security testing frameworks to efficiently incorporate both of them. Later, the researchers proposed to fully utilize the acceptance testing in software testing framework to achieve by incorporating it in software security testing framework. This incorporation is able to improve the security attribute needed during requirement stage of software development process. The advantage of acceptance test is to expose the system of the real situation, including vulnerability, risk, impacts and the intruders which provide a various set of security attribute to the requirement stage. This finding is recommended to establish a baseline in formulating the test pattern to achieve effective test priority.

Efficient cluster head selection method to improve the lifetime in wireless sensor networks

In wireless sensor networks (WSNs), environmental event monitoring and tracking systems require efficient routing protocols in order to offer reliable controlling of data traffic. This could help in an efficient utilisation of knowledge obtained from data to provide proper decision under abnormal environment conditions. The efficiency and reliability are the main challenges of routing data in WSNs. LEACH routing protocol is found to provide better performance compared to other protocols due to its hierarchical nature. However, since LEACH is a single-hop protocol, sensors located far from the base station (BS) consume more energy for data transmission and die early, causing a serious reduction in the network performance in terms of lifetime. Moreover, it cannot ensure that sensors have a uniform distribution in the sensing area. In this paper, an improved version of LEACH is presented based on node energy, distance, density, and the use of gravity centre and mass centre for selecting cluster heads (CHs). Simulation results show that the enhanced version is able to improve the network lifetime in comparison with the traditional LEACH protocol. The results confirmed that the proposed enhancement provides a proper decision in selecting CHs in high density areas.

Ant Colony-Dijkstra’s Algorithm for Evacuation Preparedness in High Rise Buildings

The critical tasks during evacuation process is how to find the right ways in order to escape from the danger place to a safe place. In process of finding the right ways, most of the evacuees are panicked. Subsequently, make the process more difficult. With that occurrence, the main objectives of this research study are to identify the suitable shortest path algorithm for evacuation in high rise building, then design and develop an evacuation route via shortest path algorithm in order to obtain an exit route to evacuate by using Optimization and Artificial Intelligence Technique. The objectives that involved are to help the evacuees to find the best routes during evacuation process. Six phases of methods are raised to accomplish the objectives by utilizing the Dijkstra and Ant Colony Optimization Algorithm. The first step is started from the original building layout. Then transform the layout into 2D layout plan. After that, import the matrix data to generate graph theory. Next step is utilizing the both approaches to achieve the shortest path. The preliminary result has shown positive result which can deliver the shortest path to help evacuees.

Enhancement of Nurse Scheduling Steps Using Particle Swarm Optimization

Allocating of working schedule, especially for shift approach is hard to ensure its fairness among them. In the case of nurse scheduling, to set up the time table for each available nurse is time consuming and complicated, which consider many factors including rules, regulation and human factor. Moreover, most nurses are women, which have personnel constraints and maternity leave factors. The undesirable schedule can affect the nurse productivity, social life and the absenteeism can significantly as well affect the patient’s life. This paper aimed to enhance the scheduling process by utilizing the particle swarm optimization in order to generate an intelligent nurse schedule. The result shows that the multiple initial schedules can be generated and can be selected with the lowest cost of constraint violation.