Musa Mammadov

Federation University of Australia · School of Science, Information Technology and Engineering

In this paper, the turnpike property is established for a nonconvex optimal control problem in discrete time. The functional is defined by the notion of the ideal convergence and can be considered as an analogue of the terminal functional defined over infinite-time horizon. The turnpike property states that every optimal solution converges to some...

In this paper the turnpike property is established for a non-convex optimal control problem in discrete time. The functional is defined by the notion of the ideal convergence and can be considered as an analogue of the terminal functional defined over infinite time horizon. The turnpike property states that every optimal solution converges to some...

The superparent one-dependence estimators (SPODEs) is a popular family of semi-naive Bayesian network classifiers, and the averaged one-dependence estimators (AODE) provides efficient single pass learning with competitive classification accuracy. All the SPODEs in AODE are treated equally and have the same weight. Researchers have proposed to apply...

Machine learning techniques have shown superior predictive power, among which Bayesian network classifiers (BNCs) have remained of great interest due to its capacity to demonstrate complex dependence relationships. Most traditional BNCs tend to build only one model to fit training instances by analyzing independence between attributes using conditi...

Over recent decades, the rapid growth in data makes ever more urgent the quest for highly scalable Bayesian networks that have better classification performance and expressivity (that is, capacity to respectively describe dependence relationships between attributes in different situations). To reduce the search space of possible attribute orders, k...

Bayesian network classifiers (BNCs) have demonstrated competitive classification performance in a variety of real-world applications. A highly scalable BNC with high expressivity is extremely desirable. This paper proposes Redundant Dependence Elimination (RDE) for improving the classification performance and expressivity of k-dependence Bayesian c...

To mine significant dependencies among predictiveattributes, much work has been carried out to learn Bayesian netwrok classifiers (BNC\(_\mathcal {T}\)s) from labeled training data set \(\mathcal {T}\). However, if BNC\(_\mathcal {T}\) does not capture the “right” dependencies that would be most relevant to unlabeled testing instance, that will res...

Within the landscape of Personal Injury Compensation, building of Decision Support Tools that can be used at different stages of a client’s journey, from accident to rehabilitation, and which have various targets is important. The challenge considered in this paper is concerned with finding outliers amongst Health/Medical Providers (providers) serv...

Identifying multivariate distributions related to practical scenarios became an interesting research area among scholars recently as most of the practical scenarios are associated with more than one variable. Analytical methods have been introduced by many investigators to estimate parameters of multivariate distributions. However, they involve hea...

Many real world applications are associated with more than one variable and hence, identifying multivariate distributions associated with real world problems portrays great importance today. Many studies can be found in the literature in this aspect and most of them are associated with two variables/dimensions and the maximum dimension of multivari...

Many real world applications are associated with more than one variable and hence, identifying multivariate distributions associated with real world problems portrays great importance today. Many studies can be found in the literature in this aspect and most of them are associated with two variables/dimensions and the maximum dimension of multivari...

The paper presents a generalization of a known density theorem of Arrow, Barankin, and Blackwell for properly efficient points defined as support points of sets with respect to monotonically increasing sublinear functions. This result is shown to hold for nonconvex sets of a partially ordered reflexive Banach space.

Stock market forecasting models attract many parties in the financial world as they provide vital information for making appropriate decisions. Financial indices play a major role in controlling dynamics of the recent financial world predictions. Many forecasting models including stock market forecasting models consider other financial indices as p...

An optimal control problem for continuous time systems described by a special class of multi-valued mappings and quasi-concave utility functions is considered. The objective is defined as an analogue of the terminal functional defined over an infinite time horizon. An upper bound of this functional over all solutions to the system is established. T...

This paper introduces a new optimal control model to describe and control the dynamics of infectious diseases. In the present model, the average time of isolation (i.e. hospitalization) of infectious population is the main time-dependent parameter that defines the spread of infection. All the preventive measures aim to decrease the average time of...

Reliability-based design optimization (RBDO) is an important area in structural optimization. A principal step of the RBDO process is to solve a reliability analysis problem. This problem has been considered in inner loop of double-loop RBDO approaches. Although many algorithms have been developed for solving this problem, there are still some chal...

Many researchers were interested in predicting stock markets nowadays. When building prediction models, use of most appropriate multivariate distribution depicts prodigious impotence in terms of prediction accuracy. Therefore, scholars focus on identifying most appropriate multivariate distributions related to stock market. The main objective of th...

In the dynamic global economy, forecasting demonstrates crucial importance for many future investments. World major commodities influence most of the financial sectors and are considered as predictors in many forecasting models. Identifying return distributions of predictors of forecasting models exhibit immense interest among researchers recentl...

This paper investigates the dynamics of Ebola virus transmission in West Africa during 2014. The reproduction numbers for the total period of epidemic and for different consequent time intervals are estimated based on a newly suggested linear model. It contains one major variable - the average time of infectiousness (time from onset to hospitalizat...

Bayesian Networks are increasingly popular methods of modeling uncertainty in artificial intelligence and machine learning. A Bayesian Network consists of a directed acyclic graph in which each node represents a variable and each arc represents probabilistic dependency between two variables. Constructing a Bayesian Network from data is a learning p...

An optimization model that is widely used in engineering problems is Reliability-Based Design Optimization (RBDO). Input data of the RBDO is non-deterministic and constraints are probabilistic. The RBDO aims at minimizing cost ensuring that reliability is at least an accepted level. Reliability analysis is an important step in two-level RBDO approa...

With modern data-acquisition equipment and on-line computers used during production, it is now common to monitor several correlated quality characteristics simultaneously in multivariate processes. Multivariate control charts (MCC) are important tools for monitoring multivariate processes. One difficulty encountered with multivariate control charts...

World major commodities play a major role in controlling dynamics of the recent financial world predictions. Many forecasting models consider world commodity prices as predictors. Return distributions of predictors of forecasting models exhibit immense interest nowadays. Identifying return distributions of two major world commodities, Oil index and...

Loss function plays an important role in data classification. Manyloss functions have been proposed and applied to differentclassification problems. This paper proposes a new so called thesmoothed 0-1 loss function, that could be considered as anapproximation of the classical 0-1 loss function. Due to thenon-convexity property of the proposed loss...

Reliability-based design optimization (RBDO) is an important area in structural optimization. A principal step of the RBDO process is to solve a reliability anal-ysis problem. This problem has been considered in inner loop of double-loop RBDO approaches. Although many algorithms have been developed for solving this problem, there are still some cha...

The Naive Bayes classifier is a popular classification technique for data mining and machine learning. It has been shown to be very effective on a variety of data classification problems. However, the strong assumption that all attributes are conditionally independent given the class is often violated in real-world applications. Numerous methods ha...

An optimal control problem for systems described by a special class of non-linear differential equations with time delay is considered. The cost functional adopted could be considered as an analogue of the terminal functional defined over infinite time horizon. The existence of optimal solutions as well as the asymptotic stability of optimal trajec...

This paper investigates the population dynamics of a system of identically prepared B cells whose proliferation trajectories have been individually tracked using live-cell imaging techniques. The main goal is to investigate whether the system behavior can be determined using an optimality criterion. In order to achieve this goal we assume the exist...

In this paper the turnpike property is established for convex optimal control problems, involving undiscounted utility function and differential inclusions defined by multi-valued mapping having convex graph. Utility function is concave but not neces-sarily strictly concave. The turnpike theorem is proved under the main assumption that over any giv...

An optimal control problem for systems described by a special class of nonlinear differential equations with time delay is considered. The cost functional adopted could be considered as an analogue of the terminal functional defined over an infinite time horizon. The existence of optimal solutions as well as the asymptotic stability of optimal traj...

Reliability-Based Design Optimisation (RBDO) is an optimisation model including probabilistic constraints that is widely used in engineering problems. Input data of the RBDO is non-deterministic. The RBDO aims at minimising the cost ensuring that the reliability is at least an accepted level. Reliability analysis is an important step in the double-...

Naive Bayes is among the simplest probabilistic classifiers. It often performs surprisingly well in many real world applications, despite the strong assumption that all features are conditionally independent given the class. In the learning process of this classifier with the known structure, class probabilities and conditional probabilities are ca...

Reliability-based design optimisation (RBDO) and robust design optimisation (RDO) are two parts in structural optimisa-tion. A principal step of the RBDO process is to solve a reliability analysis problem. This problem has been considered in inner loop of double-loop RBDO approaches. Although many algorithms have been developed for solving this pro...

A class of scalar nonlinear difference equations with delay is considered. Suffi-cient conditions for the global asymptotic stability of a unique equilibrium are given. Appli-cations in economics and other fields lead to consideration of associated optimal control problems. An optimal control problem of maximizing a consumption functional is stated...

An appropriate distance is an essential ingredient in various real-world learning tasks. Distance metric learning proposes to study a metric, which is capable of reflecting the data configuration much better in comparison with the commonly used methods. ...

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a b s t r a c t In this paper, we propose a new method for solving large-scale ill-posed problems. This method is based on the Karush–Kuhn–Tucker conditions, Fisher–Burmeister function and the discrepancy principle. The main difference from the majority of existing methods for solving ill-posed problems is that, we do not need to choose a regulariz...

The least-squares method is a standard approach used in data fitting that has important applications in many areas in science and engineering including many finance problems. In the case when the problem under consideration involves large-scale sparse matrices regularization methods are used to obtain more stable solutions by relaxing the data fitt...

Phishing activity has recently been focused on social networking sites as a more effective way of exploiting not only the technology but also the trust that may exist between members in a social network. In this paper, a novel method for profiling phishing activity from an analysis of phishing emails is proposed. Profiling is useful in determining...

Solving systems of nonlinear equations is a relatively complicated problem for which a number of different approaches have been presented. In this paper, a new algorithm is proposed for the solutions of systems of nonlinear equations. This algorithm uses a combination of the gradient and the Newton's methods. A novel dynamic combinatory is develope...

Drug--drug interaction is one of the important problems of Adverse Drug Reaction (ADR). This presentation describes a data mining approach to this problem developed at the University of Ballarat. This approach is based on drug--reaction relationships represented in the form of a vector of weights; each vector related to a particular drug can be con...

Naive Bayes classifier is the simplest among Bayesian Network classifiers. It has shown to be very efficient on a variety of data classification problems. However, the strong assumption that all features are conditionally independent given the class is often violated on many real world applications. Therefore, improvement of the Naive Bayes classif...

The quadratic knapsack problem (QKP) maximizes a quadratic objective function subject to a binary and linear capacity constraint. Due to its simple structure and challenging difficulty, it has been studied intensively during the last two decades. This paper first presents some global optimality conditions for (QKP), which include necessary conditio...

An essentially nonlinear differential equation with delay serving as a mathematical model of several applied problems is considered. Sufficient conditions for the global asymptotic stability of a unique equilibrium are de-rived. An application to a physiological model by M.C. Mackey is treated in detail.

In this paper a Human–Vehicle–Road (HVR) model, comprising a quarter-car and a biomechanical representation of the driver, is employed for the analysis. Differential equations are provided to describe the motions of various masses under the influence of a harmonic road excitation. These equations are, subsequently, solved to obtain a closed form ma...

In this paper we study optimality conditions for optimization problems described by a special class of directionally differentiable functions. The well-known necessary and sufficient optimality condition of nonsmooth convex optimization, given in the form of variational inequality, is generalized to the nonconvex case by using the notion of weak su...

In this article, we first propose a method to obtain an approximate feasible point for general constrained global optimization problems (with both inequality and equality constraints). Then we propose an auxiliary function method to obtain a global minimizer or an approximate global minimizer with a required precision for general global optimizatio...

In text categorization, different supervised term weighting methods have been applied to improve classification performance by weighting terms with respect to different categories, for example, Information Gain, χ 2 statistic, and Odds Ratio. From the literature there are three term ranking methods to summarize term weights of different categories...

Problems of data classification can be studied in the framework of regularization theory as ill-posed problems. In this framework, loss functions play an important role in the application of regularization theory to classification. In this paper, we review some important convex loss functions, including hinge loss, square loss, modified square loss...

In this paper, we analyze an improved suspension system with the incorporated inerter device of the quarter-car model to obtain optimal design parameters for maximum comfort level for a driver and passengers. That is achieved by finding the analytical solution for the system of ordinary differential equations, which enables us to generate an optimi...

A nonlinear differential equation with delay serving as a mathematical model of several applied problems is considered. Sufficient conditions for the global asymptotic stability and for the existence of periodic solutions are given. Two particular applications are treated in detail. The first one is a blood cell production model by Mackey, for whic...

In this paper, a novel method for profiling phishing activity from an analysis of phishing emails is proposed. Profiling is useful in determining the activity of an individual or a particular group of phishers. Work in the area of phishing is usually aimed at detection of phishing emails. In this paper, we concentrate on profiling as distinct from...

In macro investment, an investment decision model is established by using an improved back propagation (BP) artificial neural network (ANN). In this paper, the relations between elements of investment and output of products are determined, and then the optimal distribution of investment is determined by adjusting the distributions rationally. This...

In this paper we consider the problem of finding optimal parameters of the two mass model that represents vehicle suspension systems. The analysis of the problem is based on finding analytical solution of the system of coupled Ordinary Differential Equations (ODE). Such a technique allows us to generate optimization problem, where an objective func...

We propose and analyze an inexact version of the modified subgradient (MSG) algorithm, which we call the IMSG algorithm, for nonsmooth and nonconvex optimization over a compact set. We prove that under an approximate, i.e. inexact, minimization of the sharp augmented Lagrangian, the main convergence properties of the MSG algorithm are preserved for...

An essentially nonlinear differential equation with delay serving as a math-ematical model of several applied problems is considered. Sufficient conditions for the global asymptotic stability of a unique equilibrium are derived. An application to a physiological model by M.C. Mackey is treated in detail.

In this paper we study the turnpike property for nonconvex op-timal control problems described by differential inclusion ˙ x ∈ a(x). We study the infinite horizon problem of maximizing the functional T 0 u(x(t), ˙ x(t)) dt as T grows to infinity. The purpose of this paper is to avoid the convexity conditions, which are usually assumed in the turnpi...

In this paper, a novel learning strategy for radial basis function networks (RBFN) is proposed. By adjusting the parameters of the hidden layer, including the RBF centers and widths, the weights of the output layer are adapted by local optimization methods. A new local optimization algorithm based on a combination of the gradient and Newton methods...

In this paper, a new global optimization approach based on the filled function method is proposed for solving box-constrained systems of nonlinear equations. We first convert the nonlinear system into an equivalent global optimization problem, and then propose a new filled function method to solve the converted global optimization problem. Several...

In this paper we consider the problem of finding optimal parameters of the Kelvin element in vibration analysis. This problem is based on finding analytical solution of the initial ODE for development of the optimization model. Such technique allows us to compute optimal parameters of Kelvin element.

A general approach to designing multiple classifiers represents them as a combination of several binary classifiers in order to enable correction of classification errors and increase reliability. This method is explained, for example, in Witten and Frank (Data Mining: Practical Machine Learning Tools and Techniques, 2005, Sect.7.5). The aim of thi...

Optimization of multiple classifiers is an important problem in data mining. We introduce additional structure on the class sets of the classifiers using string rewriting systems with a convenient matrix representation. The aim of the present paper is to develop an efficient algorithm for the optimization of the number of errors of individual class...

The aim of this paper is to present modified neural network algorithms to predict whether it is best to buy, hold, or sell shares (trading signals) of stock market indices. Most commonly used classification techniques are not successful in predicting trading signals when the distribution of the actual trading signals, among these three classes, is...

In this paper we study relations between the directional derivatives, the weak subdifferentials, and the radial epiderivatives for nonconvex real-valued functions. We generalize the well-known theorem that represents the directional derivative of a convex function as a pointwise maximum of its subgradients for the nonconvex case. Using the notion o...

Keywords and Phrases Introduction Definitions Turnpike Theorems for Terminal Functionals Functional \( { \lim \inf_{t \to \infty u(x(t))} } \) Functional \( { \lim \inf_{t \to \infty u(x(t),\dot{x}(t))} } \) Turnpike Theorems for Integral Functionals Convex Problems Other Results See also References

In this paper, based on level sets we define the limit inferior and limit superior of a bounded sequence of fuzzy numbers and prove some properties. We extend the concept of the core of a sequence of complex numbers, first introduced by Knopp in 1930, to a bounded sequence of fuzzy numbers and prove that the core of a sequence of fuzzy numbers is t...

The aim of this paper is to develop new neural network algorithms to predict trading signals: buy, hold and sell, of stock market indices. Most commonly used classification techniques are not suitable to predict trading signals when the distribution of the actual trading signals, among theses three classes, is imbalanced. In this paper, new algorit...

Introducing a new concept of (α,β)-fairness, which allows for a bounded fairness compromise, so that a source is allocated a rate neither less than 0⩽α⩽1, nor more than β⩾1, times its fair share, this paper provides a framework to optimize efficiency (utilization, throughput or revenue) subject to fairness constraints in a general telecommunication...

A new learning strategy is proposed for training of radial basis functions (RBF) network. We apply two different local optimization methods to update the output weights in training process, the gradient method and a combination of the gradient and Newton methods. Numerical results obtained in solving nonlinear integral equations show the excellent...

In this paper, we propose an auxiliary function method to solve constrained systems of nonlin- ear equations. By introducing an auxiliary function, an unconstrained (box-constrained) optimization problem is constructed for a given constrained system of nonlinear equations. It is shown that any local minimizer of the constructed unconstrained optimi...

Keywords and Phrases Introduction Turnpike Theory Statistical Cluster Points and Statistical Convergence Problem 1 Problem 2 A Challenging Problem See also References

In this paper, we will present a new approach of using link information to improve the accuracy and efficiency of web classification. However, different from others, we only use the mappings between linked documents and their own class or classes. In this case, we only need to add a few features called linked-class features into the datasets. We ap...

In this paper, we propose a new global optimization approach based on the filled function method for solving box-constrained systems of nonlinear equations. The special properties of optimization problem are employed to construct a novel filled function. The objective function value can be reduced by half in each iteration of our filled function al...

We quantify the influence from the US S&P 500 Index, along with those from major European and Asian stock market indices, on the Australian All Ordinary Index (AORD). Weights were derived to optimise the average rank correlation between the current day's relative return of the AORD and the weighted sum of the lagged relative returns of the potentia...

When designing wireless communication systems, it is very important to know the optimum numbers of access points (APs) in order to provide a reliable design. In this paper we describe a mathematical model developed for finding the optimal number and location of APs. A new Global Optimiza-tion Algorithm (AGOP) is used to solve the problem. Results o...

In this paper we develop an optimization approach for the study of adverse drug reaction (ADR) problems. This approach is based on drug–reaction relationships represented in the form of a vector of weights, which can be defined as a solution to some global optimization problem. Although it can be used for solving many ADR problems, we concentrate o...

In this paper, we present a global optimization method based on the filled function method to solve systems of nonlinear equations. Formulating a system of nonlinear equation into an equivalent global optimization problem, we manage to find a solution or an appropriate solution of the system of nonlinear equations by solving the formulated global o...

Abstract This paper investigates the use of influence from for- eign stock markets (intermarket influence) to predict the trading signals, buy, hold and sell, of the of a given stock market. Australian All Ordinary Index was se- lected as the stock market whose trading signals to be predicted. Influence is taken into account as a set of input varia...

Multi-label classification is an important and difficult problem that frequently arises in text categorization. The accurate identification of drugs which are responsible for reactions that have occurred is one of the important problems of adverse drug reactions (ADR). In this chapter we consider the similarities of these two problems and analyze t...

Facility location problems are one of the commonest applications to optimisation. Traditionally these problems have been formulated as combinatorial problems, where the facilities can only be placed at a finite number of locations. However, many applications do not require this constraint, and in such a case, continuous optimisation formulations ar...

This study forecasts trading signals of the Australian All Ordinary Index (AORD), one day ahead. These forecasts were based on the current day's relative return of the Close price of the US S&P 500 Index, the UK FTSE 100 Index, French CAC 40 Index and German DAX Index as well as the AORD. The forecasting techniques examined were feedforward and pro...

In this paper we study classification problems on datasets characterized by a small number of features and a high level of multi-labelness. We call them Shorter Featured Multi-Label (SFML) datasets. These types of datasets arise in many areas, including medicine and text classification. We introduce a new classification algorithm for classification...