Mohd Rizam Abu Bakar

Multi-objective method for divisible load scheduling in multi-level tree network

There is extensive literature concerning the divisible load theory. Based on the divisible load theory (DLT) the load can be divided into some arbitrary independent parts, in which each part can be processed independently by a processor. The divisible load theory has also been examined on the processors that cheat the algorithm, i.e., the processors do not report their true computation rates. According to the literature, if the processors do not report their true computation rates, the divisible load scheduling model fails to achieve its optimal performance. This paper focuses on the divisible load scheduling, where the processors cheat the algorithm. In this paper, a multi-objective method for divisible load scheduling is proposed. The goal is to improve the performance of the divisible load scheduling when the processors cheat the algorithm. The proposed method has been examined on several function approximation problems. The experimental results indicate the proposed method has approximately 66% decrease in finish time in the best case. We have proposed a multi-objective divisible load scheduling method.The proposed method is able to estimate the actual computation rate of the processors.The proposed method improves the performance of divisible load when the processors cheat the algorithm.We investigate the effects of cheating problem on the total finish time.The proposed method has approximately 66% decrease in finish time in the best case.

Multi-Criteria Based Algorithm for Scheduling Divisible Load

Divisible load theory has become a popular area of research during the past two decades. Based on divisible load theory the computations and communications can be divided into some arbitrarily independent parts and each part can be processed independently by a processor. Existing divisible load scheduling algorithms do not consider any priority for allocating fraction of load. In some situation the fractions of load must be allocated based on some priorities. In this paper we propose a multi criteria divisible load scheduling algorithm. The proposed model considers several criteria with different priorities for allocating fractions of load to processors. Experimental result indicates the proposed algorithm can handle the priority of processors.

Fraud detection in telecommunication industry using Gaussian mixed model

The prevalence of fraud activities in telecommunication industry has reached a critical point so that efficient algorithms to identify such activities are greatly needed. In this article, we propose a new fraud detection algorithm using Gaussian mixed model (GMM), a probabilistic model successfully used in speech recognition problem. The expectation maximization algorithm is used to estimate the parameter of the model such that the initial values of the algorithm is determined using the kernel method. Using data obtained from one of the leading telecommunication companies in Malaysia, we show that the proposed algorithm has successfully not only detected fraud calls as suspected by the company, but also to identify suspicious calls which can be candidates of fraud call. The proposed algorithm is easy to implement with a great potential to be extended to detect (billed/outgoing) fraud calls and hence reduces the lost incurred by the telecommunication companies.

Nonparametric versus Parametric Estimation of the Cure Fraction Using Interval Censored Data

This article discusses estimation of the cure rate by means of the bounded cumulative hazard (BCH) model using interval censored data. The parametric and nonparametric estimation methods within the framework of the EM algorithm were employed for cure rate estimation and their results compared. The Turnbull estimator was used in the nonparametric estimation while in parametric method both the exponential and Weibull distributions were considered. We show via simulation that the nonparametric method is a viable alternative to the parametric one when the censoring rate is rapidly increasing.

Enhanced simulated annealing for solving aggregate production planning

Simulated annealing () has been an effective means that can address difficulties related to optimisation problems. is now a common discipline for research with several productive applications such as production planning. Due to the fact that aggregate production planning () is one of the most considerable problems in production planning, in this paper, we present multiobjective linear programming model for APP and optimised by . During the course of optimising for the APP problem, it uncovered that the capability of was inadequate and its performance was substandard, particularly for a sizable controlled problem with many decision variables and plenty of constraints. Since this algorithm works sequentially then the current state will generate only one in next state that will make the search slower and the drawback is that the search may fall in local minimum which represents the best solution in only part of the solution space. In order to enhance its performance and alleviate the deficiencies in the problem solving, a modified () is proposed. We attempt to augment the search space by starting with solutions, instead of one solution. To analyse and investigate the operations of the MSA with the standard and harmony search (), the real performance of an industrial company and simulation are made for evaluation. The results show that, compared to and , offers better quality solutions with regard to convergence and accuracy.

Rank-based inference for the accelerated failure time model in the presence of interval censored data

Semiparametric analysis and rank-based inference for the accelerated failure time model are complicated in the presence of interval censored data. The main difficulty with the existing rank-based methods is that they involve estimating functions with the possibility of multiple roots. In this paper a class of asymptotically normal rank estimators is developed which can be aquired via linear programming for estimating the parameters of the model, and a two-step iterative algorithm is introduce for solving the estimating equations. The proposed inference procedures are assessed through a real example.

A Bayesian estimation on right censored survival data with mixture and non-mixture cured fraction model based on Beta-Weibull distribution

Models for survival data that includes the proportion of individuals who are not subject to the event under study are known as a cure fraction models or simply called long-term survival models. The two most common models used to estimate the cure fraction are the mixture model and the non-mixture model. in this work, we present mixture and the non-mixture cure fraction models for survival data based on the beta-Weibull distribution. This four parameter distribution has been proposed as an alternative extension of the Weibull distribution in the analysis of lifetime data. This approach allows the inclusion of covariates in the models, where the estimation of the parameters was obtained under a Bayesian approach using Gibbs sampling methods.

Jackknife and bootstrap inferential procedures for censored survival data

Confidence interval is an estimate of a certain parameter. Classical construction of confidence interval based on asymptotic normality (Wald) often produces misleading inferences when dealing with censored data especially in small samples. Alternative techniques allow us to construct the confidence interval estimation without relying on this assumption. In this paper, we compare the performances of the jackknife and several bootstraps confidence interval estimates for the parameters of a log logistic model with censored data and covariate. We investigate their performances at two nominal error probability levels and several levels of censoring proportion. Conclusions were then drawn based on the results of the coverage probability study.

Improved Expectation Maximization Algorithm for Gaussian Mixed Model Using the Kernel Method

Fraud activities have contributed to heavy losses suffered by telecommunication companies. In this paper, we attempt to use Gaussian mixed model, which is a probabilistic model normally used in speech recognition to identify fraud calls in the telecommunication industry. We look at several issues encountered when calculating the maximum likelihood estimates of the Gaussian mixed model using an Expectation Maximization algorithm. Firstly, we look at a mechanism for the determination of the initial number of Gaussian components and the choice of the initial values of the algorithm using the kernel method. We show via simulation that the technique improves the performance of the algorithm. Secondly, we developed a procedure for determining the order of the Gaussian mixed model using the log-likelihood function and the Akaike information criteria. Finally, for illustration, we apply the improved algorithm to real telecommunication data. The modified method will pave the way to introduce a comprehensive method for detecting fraud calls in future work.

Effect of Tumor Microenvironmental Factors on the Stability of Tumor Growth Dynamics with Nonzero Correlation Time

The effect of tumor microenvironmental factors on tumor growth dynamics modeled by multiplicative colored noise is investigated. Using the Novikov theorem and Fox approach, an approximate Fokker--Planck equation for the non-Markovian stochastic equation is obtained and an analytic expression for the steady-state probability distribution \(P_{st} (x)\) is derived. We found that the strength of the microenvironmental factors \(\theta\) have a negative effect on the stability of tumor growth at weak correlation time \(\tau\) and at strong correlation time, the effect of \(\theta\) is opposed and instead a positive growth stability is noticed for the tumor growth dynamics which in other words corresponds to growth effect. The result indicated that the growth effect exerted by the non-immunogenic components of tumor microenvironmental depend on the strength of the correlation time \(\tau\).