Search

Unintentional poisoning is on the rise globally and thus a statistical analysis has been conducted. This study focuses on implementing a binary logistic regression analysis to study the factors that lead significantly to unintentional and intentional poisoning in Malaysia. The data consists of information from 592 patients with 7 predictor variables and a binary response variable. Preliminary analysis and chi-square test are conducted before performing the analysis. To obtain a good logistic model, there are three main steps: model building, diagnostic checking and drawing inferences from the parameters. The model provides parameter inferences such as odds ratio and estimation of predicted probabilities. Based on the results, unintentional poisoning is significantly influenced by physical form of poison, type of natural toxin and exposure route. Odds ratio estimation indicates that patients exposed to poisoning through bites or stings are more likely to have unintentional poisoning compared to other exposure routes. Generally, estimation of predicted probability are high for unintentional poisoned patients when they are bitten by animals. [ABSTRACT FROM AUTHOR]

The parametric method is often applied to model income data and to assess income inequality. Parametric estimation of income distribution is convenient because it facilitates subsequent inferences about the measured inequality. In this study, we conduct a comparative assessment of four parametric distribution candidates in terms of model fitting for the income of poor households in Malaysia on the basis of survey data of household incomes for the years of 2007, 2009, 2012 and 2014. The considered models involve the reverse Pareto, shifted reverse exponential, shifted reverse stretched exponential and shifted reverse lognormal. We also derive the explicit expression of Lorenz curve and Gini coefficient for all four considered models. Finally, on the basis of the best parametric model, income inequality analysis of poor households in Malaysia is assessed using Lorenz curve and Gini coefficient. [ABSTRACT FROM AUTHOR]

2 Satisfiability (2SAT) is a language that bridges real life application to neural network optimization. 2SAT is an interesting paradigm because the outcome of this logical rule is always positive. Hopfield Neural Network (HNN) is a type of neural network that finds the solution based on energy minimization. Interesting intelligent behavior has been observed when logical rule is embedded to HNN. Increasing the storage capacity during learning phase of HNN has been a challenging problem for most neural network researchers. Ant Colony Optimization (ACO) is a population-based and nature-inspired algorithm to decipher several combinatorial optimization problems. In this paper, metaheuristic algorithm ACO will be proposed in learning 2SAT programming. To this end, all the learning model will be tested in a new restricted learning environment. In circumstances of where HNN model was not given unlimited iteration until all 2SAT clauses were fully satisfied, we would like to determine the better performing search method between exhaustive search and ACO. Results acquired from the computer simulation showed that ACO outperformed exhaustive search in restricted learning environment. [ABSTRACT FROM AUTHOR]

This paper discusses a prey-predator model with a Type II functional response and environmental suitability components. The stability of equilibrium points and the emergence of bifurcation are analyzed using techniques from dynamical systems. It is discovered that the transcritical and Hopf bifurcations appear as some ecologically- relevant parameters change. In particular, our two-parameter bifurcation analysis shows how the interaction of transcritical and Hopf bifurcations can lead to different dynamics of species interaction e.g. coexistence of species, species exclusion and oscillatory dynamics. Overall, our findings suggest significant roles of predation and abiotic environments in mediating species biodiversity. [ABSTRACT FROM AUTHOR]

Everybody has at least a smartphone and the technology keep on evolving from time to time, same goes to mobile applications. Mobile applications are currently more towards learning, business and entertainment purposes, but why not we extend the use of the technology to forensic investigation? In this paper, we are highlighting the potential of a mobile application on smartphones to support investigation in crime scenes. As we know, crimes involving firearms has been a threat to humanity since time immemorial. The mass production of handguns and other firearms since the nineteenth century has similarly expanded crime numbers globally. Thus, it is critical to identify the firearm used whenever a firearm crime has been reported, which calls for expertise in forensic ballistics. Firearms identity is a crucial goal of firearm analysis. From previous research, the firing pin impact impression on a cartridge case from a fired bullet is one of the most extensive clues in firearms identification. Since nowadays a lot of crime cases involve guns, a portable device should help the police and the forensic teams for immediate firearm identification, whereby they can pre-analyze the firearm impression on the spot to gain some prior information, which is cost, space and time saving. In this paper, mobile application for firearm identification is proposed, named Firearm Identification Mobile Application (FIMA). This firearm identification application will analyze numerical features extracted from the firing pin impression image as the unique features, and immediately catalogues the features using the backpropagation neural network classification technique. It is found that 'trainlm' performed the best compared to the other training algorithms based on average overall correct classification rates for all features. On the other hand, 'trainscg' performed the best based on centre firing pin impression images. In the near future, a portable firearm analysis device will be developed, namely Portable Firearm Analysis Device (PAFAD) to further assist in forensic investigation activities. [ABSTRACT FROM AUTHOR]

The association between dietary habits and cardiovascular diseases (CVDs) has been established. However, the dietary habits vary among countries, therefore, there is scarcity in the literature for a standardized instrument to assess the dietary habits. This paper is aimed to evaluate the psychometric properties of modified dietary habits instrument, originally based on a food frequency questionnaire (FFQ). In this case-control study, it was used to find the most prevalent dietary habits in CVDs patients and their corresponding controls. The modified instrument consisted of 13 items representing thirteen food groups identified from CVDs related studies and FFQ. A sample of 180 (90 cases and 90 controls) was selected from the largest cardiac hospital of Pakistan through systematic sampling. Data was collected through modified instrument using direct investigation method. Twelve food groups were validated which further constituted two main factors labelled; healthy and unhealthy dietary habits. Exploratory factor analysis explained 70% and 68% variation through these two main identified dietary habits in cases and control, respectively. Internal consistency reliability for healthy and unhealthy dietary habits in cases were 0.905 and 0.879 and almost similar coefficient of reliability was recorded in the control group. Further, the mean score of healthy dietary habits was significantly less than unhealthy dietary habits (p<0.001) in the cases and vice versa in controls. This study concludes that the modified instrument is valid and reliable and should be used to assess the dietary habits in CVDs related studies. Further, mean score of unhealthy dietary habits can help in disease prediction models and early diagnosis of cardiac problems. [ABSTRACT FROM AUTHOR]

It is noting that the response variable and the explanatory variables are highly correlated in high dimension data. Hence, the selection of informative variables is important in order to achieve a better model interpretation and concomitantly improve the accuracy of the prediction. In this study, the variable selection in stock market price using statistical approach was carried out. It is pertinent since most of the previous study only concerns on the financial interests of the stock market. Therefore, this study considers the homogeneity structure in the highly correlated data on yearly stock market price by applying ordered homogenous pursuit lasso (OHPL) method. The performance results of OHPL were compared with lasso and elastic net. As a result, OHPL a had higher number of selected variables and a better prediction power than of lasso and elastic net. In conclusion, OHPL shows its capability to enhance variable selection while increasing the prediction power of the selected variables than its counterpart. [ABSTRACT FROM AUTHOR]

The field of survival analysis has made a tremendous progress during the latter half of the twentieth century. Weibull distribution and parametric models (proportional hazards models) have played important roles in the analysis of survival data. In this paper, a two-parameter modified Weibull distribution, three-parameter modified Weibull distri- butions and a four-parameter modified Weibull distribution are demonstrated that can be adopted in the proportional hazards models. Smoothed hazard estimate is used to indicate that Weibull and modified Weibull models are preferred to apply in the coronary heart disease (CHD) data. Weibull and modified Weibull proportional hazards models are fitted to the CHD data. It is found that the two-parameter modified Weibull proportional hazards model has a better fit to the CHD data than Weibull and other modified Weibull proportional hazards models. [ABSTRACT FROM AUTHOR]

There were many cases reported for ovarian cysts and it was difficult to recognize the size of cyst whether it is a large or a small cyst. Thus, this research was conducted to predict the size of benign cysts according to the patient's symptoms and its risk factors, to analyze the relationship between the size of benign cyst and the type of surgical procedures that the patient needs to undergo and to obtain which variable gives most important effect to the model. Retrospective observation study has been applied in this research since the data was collected from the monthly census file for year 2016 in Ward of Obstetrics and Gynecology from Hospital A, Johor Bahru. From the 89 patients of benign ovarian cysts, only 26 of the patient have small cysts and the other 63 patients have large benign cysts. From analysis, it found that the significant symptoms towards the predicting of Size of Benign Cyst were Abdominal Pain and Intermenstrual Bleeding. There were no significant for the risk factors. There was a significant relationship between the Size of Benign Cysts and the Type of Surgical Procedure. From the association, it can be concluded, if the patient had a large size of cysts, they will need to undergo laparotomy surgery. By using log-linear analysis, it is found that the most important effect in model was Type of Operation. [ABSTRACT FROM AUTHOR]

Portfolio investment is one of the important topics in financial management. Its aim of having the wealth in a period of time with maximum return and minimum risk has been well-defined in the literature. Specifically, the portfolio selection is arisen to be an interesting investment issue. Since the risk is defined as the probability of losses relative to the expected return in the portfolio investment, the existing risk would influence the rate of return and affect the investment decision. In this paper, the portfolio selection problem is discussed. The aim is to provide an effective quantitative study in managing the portfolio. In the beginning, the rates of return are calculated and it is followed by computing the expected rates of return and the related covariance matrix. Accordingly, the mean-variance optimization model, which is the quadratic programming model, is formulated and solved by the quadratic programming technique. Here, the objective function to be minimized represents the risk of the portfolio selection, while the weights of the portfolio selection, which are defined as the decision variables, are normalized with the total amount of investment, and the expected return is assigned to a certain targeted return. For illustration, a set of historical data on the stock prices in the Financial Times Stock Exchange (FTSE) Bursa Malaysia Kuala Lumpur Composite Index (KLCI) is considered. Through simulation and optimization of the mean-variance portfolio model, the result shows the relationship between risk and return with the various targeted returns. Moreover, by applying the Sharpe ratio, the optimal weight of the portfolio selection, which is the optimal portfolio decision, is properly suggested. In conclusion, the simulation optimization model for the portfolio selection problem is highly recommended. [ABSTRACT FROM AUTHOR]

Income inequality is a matter of concern for any society and an interesting discussion topic, particularly for developing countries, since high-income inequality could be detrimental to economic growth. In Malaysia, upper-income earners are classified as the T20 group. This study analyzes the demographic profile of members of the T20 group living in urban areas in Malaysia. The upper-income distribution is described using the Pareto model, and a robust and efficient estimator called the probability integral transform statistic estimator is utilized to estimate the shape parameter of this model. Thereafter, a Gini index and Lorenz curve are derived to analyze the severity of income inequality among T20 group members. [ABSTRACT FROM AUTHOR]

A copula-based analysis is usually performed in hydrology for the purpose of water management and flood prevention by analyzing the hydrological attributes, such as rainfall volume and duration. The primary goal of this study is to fit the rainfall volume and duration into copulas. The analysis was conducted on six selected rainfall stations in Malaysia with hourly rainfall data. The correlation between volume and duration is measured by Kendalls correlation coefficient. Clayton, Gumbel-Hougard (GH), Frank and Joe copulas of Archimedean family are used and the parameter of the copulas is estimated using Maximum Likelihood Estimation (MLE). Akaikes Information Criterion (AIC), three graphical methods, and Goodness-of-Fit (GOF) tests are used to find the best parameter estimation of copulas. The three graphical methods used are the conditional distribution function of Y|X, the distribution function of the copula, and a nonparametric estimate of the distribution of the copula approach. The GOF tests are The Kolmogorov-Smirnov (K-S) and Chi-Square (χ2) tests. Results estimate that Frank copula is found to be suitable to represent the rainfall volume and duration for all stations. [ABSTRACT FROM AUTHOR]

Workforce size problem is a common issue in manufacturing, which is to determine the optimal number of workers required such that the productivity of a product is ensured. As such, the situation of understaffing and overstaffing in the manufacturing could be handled properly, in turn, to save the labour cost and to fulfil the economic supply and demand. In this paper, the workforce size problem in manufacturing is described, and the dynamic programming approach is applied to solve the workforce size problem. In the discussion, the return function of the workforce size problem and the state transition equation are identified. Then, the backward recursion equation of dynamic programming is formulated. According to each stage considered, the possible states are determined, and the related value of the return functions are calculated. It is followed by establishing the optimal decision policy for having the optimal number of workers. For illustration, an example of the workforce size problem in manufacturing, which is on a weekly basis, is studied. The result obtained showed that the minimum recruitment cost is RM33, where the optimal number of workers for the respective weeks is four workers, eight workers, eight workers, six workers, and six workers. In conclusion, the efficiency of the dynamic programming method in solving the workforce size problem in manufacturing is highly demonstrated. [ABSTRACT FROM AUTHOR]

Pertussis (whooping cough) is a high incidence contagious respiratory disease which continues to be a major public health concern in both developed and developing countries. Its reemergence has been particularly recorded despite high rates of vaccination. In this paper, a nonlinear mathematical model for the transmission dynamics of pertussis (whooping cough) with maternally derived immunity is studied. The basic reproduction number for both disease free and endemic equilibrium is obtained using the next generation method. The stability of the equilibrium points is analyzed and bifurcation theory is used to observe the behavior of the stability of the system. Numerical simulations of the nonlinear differential equations were carried out using Mathematica and relevant results are displayed. [ABSTRACT FROM AUTHOR]

This study aims to analyse the trend of Malay voters' support for Barisan Nasional during the Malaysian General Elections which spans from year 1995 until year 2018. Data of registered Malay voters during the respective general election at the Parliament level is obtained via a secondary source, which is the Malaysia Election Commission's portal. The pattern of support from the Malay voters for Barisan Nasional shows a diminishing trend over the years which shows that there is decline of support from Malay registered voters for Barisan Nasional over the election years. From the regression analysis, the results show that there is a significant positive relationship between the percentage of registered Malay voters and the percentage of votes for Barisan Nasional's party. This results suggest that the support of Malay voters is crucial for Barisan Nasional to win the election at the Parliamentary level. [ABSTRACT FROM AUTHOR]

Seabed Logging (SBL) is a technique that employs high-powered electric dipole source to emit electromagnetic (EM) signal to detect hydrocarbon (HC) reservoirs beneath the seabed. This application is based on electrical resistivity contrasts between target reservoirs and its surrounding. SBL analysis can become a challenging task when the target reservoirs are thin, and the contrasts of resistivity are not very significant. As HC reservoirs are getting thinner, target responses and reference (non-HC) responses are difficult to be distinguished. Addressing this problem, we propose a simple statistical method, Gaussian Process (GP), to model one-dimensional (1-D) SBL data with uncertainties quantification. In this paper, Computer Simulation Technology (CST) software was used to replicate SBL models with five different thicknesses of HC. Some characteristics of the SBL models such as seawater depth, reservoir thickness and reservoir depth were imitated as the case study of Troll West Oil Province, North Sea. We developed 1-D forward GP model for all the SBL responses. Both modelled responses, target and reference, were compared and mean percentage differences between the responses were then calculated. For every comparison, confidence bars for each modelled response were observed to confirm the existence of thin HC. For model validation, root mean square errors (RMSEs) between modelled and generated (CST software) data were calculated. The confidence intervals revealed that the target and reference responses are distinguishable for all HC thicknesses, and the calculated RMSEs showed that GP is reliable to be applied in SBL data to provide uncertainties quantification. [ABSTRACT FROM AUTHOR]

Undoubtedly, early detection and characterization of brain tumor is critical in clinical practices. Automated diagnosis using neuroimaging tool like MRI guided by machine learning approaches has been the focus of numerous researches. In this study, various feature extraction, dimensionality reduction and supervised classification models are explored, evaluated and compared under different finite number of features to identify the optimal pathway/pipeline for classification of types of brain tumor, namely meningioma, glioma and pituitary tumor. The performance metrics utilized include accuracy, Kappa statistic, sensitivity, precision, F-measure, training time and test time. Results show that RBF SVM (pairwise coupling) under 80 PLS features achieved the highest average accuracy (95.02% ± 0.19%) among all other machine learning pipelines. [ABSTRACT FROM AUTHOR]

Big Data phenomenon has risen questions concerning the capabilities of classical methods of statistical inference, most of which were rendered obsolete. This problem exists in the fields of geostatistics for a long time but have not been properly addressed. Adaptive Spatial Compression Clustering (ASDC), a new spatial data compression method, has done well where methods using full dataset prove futile. Presence of global outliers in the spatial data affects the formation of spatial dispersion function which subsequently affects the outcome of the spectral clustering; this, in effect, affects spatial contiguity. With the aim of neutralizing the effect of the global outliers, absolute difference of all pairs of spatial observations is used. Hampel's outlier test was adopted before taking local spatial dispersion. Simulation results of synthetic spatial fields reveal worthwhile in treating the effect of outliers. [ABSTRACT FROM AUTHOR]

The deer mouse, Peromyscus maniculatus, is one of the carriers for the Sin Nombre Virus (SNV). Contact with the SNV infected mice's saliva, urea or excreta may cause fatal disease among humans. In this paper, the study analyzed the biodiversity effect on the SNV prevalence in the deer mouse population from a stochastic perspective. The effects of carrying capacity and interspecific strength were analyzed and compared with Peixoto and Abramson [9] deterministic model. This study observed that the infected deer mouse population in the stochastic model went extinct at different rates depending on the initial population size; whereas, the infection persisted in the deterministic model irrespective of the initial population size. Based on these results, it should be noted that the deterministic model may have overestimated the prevalence of the infection and perhaps the stochastic model is much more favorable in representing the dynamical system. [ABSTRACT FROM AUTHOR]

This paper presents the application of simulation in modeling the food manufacturing system at XYZ factory. The problems faced by the factory are the bottlenecks that happen in their food manufacturing system which consist of four processes i.e. rolling and fermentation process, drying process, sorting and grading process and packaging process. From the data collection provided by the management of the company showed that the drying process and packaging process has high waiting processing times and lead to the entities from the previous process waiting for a long time. Moreover, the imbalance workers for each process can affect the productivity and output of the production. Simulation method was used for modeling the system. The results of the simulation model are analyzed for the suggestion of improvement. The scoring model was used in this study to select the best improvement model from nine improvement models that have been developed. For the scoring model, the company has selected five criteria. The criteria are total production, average worker utilization, and average total production time, the average number of entities in the system and total number of workers involved in the manufacturing system. Each of the criteria has been weighted by the company in terms of its relative importance. In this study, IP4 is the best improvement model where the model achieves the highest score in the scoring model and can maximize the total production and average resource utilization. IP4 has recommended the reallocation of workers. The application of the simulation method and the results from this research can help the management of the factory to make better decisions thus enhance the food manufacturing system. [ABSTRACT FROM AUTHOR]

In this note, we consider a supersingular integral equations (SuperSIEs) of the first kind on the interval [−1,1] with the assumption that kernel of the hypersingular integral is constant on the diagonal of the domain D = [−1,1]×[−1,1]. Projection method together with Chebyshev polynomials of the first, second, third and fourth kinds are used to find bounded, unbounded and semi-bounded solutions of SuperSIEs respectively. Exact calculations of singular integrals for Chebyshev polynomials allow us to obtain high accurate approximate solution. Gauss- Chebyshev quadrature formulas are used for high accurate computations of regular kernel integrals. Two examples are provided to verify the validity and accuracy of the proposed method. Comparisons with other methods are also given. Numerical examples reveal that approximate solutions are exact if solution of SuperSIEs is of the polynomial forms with corresponding weights. It is worth to note that proposed method works well for large value of node points and errors are drastically decreases. [ABSTRACT FROM AUTHOR]

In biology, DNA splicing system models the recombination behaviours of DNA molecules which require the presence of restriction enzymes. Since it is quite incompetent to describe DNA splicing system in one-dimensional string, hence graph theory is used in describing the complexity of DNA splicing system. Thus by graph splicing system, a type of semigraph can be obtained, called as n-cut spliced semigraph. Various concepts of graph theory have been applied on semigraph to study its characteristics and properties. In this study, the focus is on the application of the folding technique on n-cut spliced semigraph.It is observed that the maximum number of folding for every n-cut spliced semigraph is n+1 times. Also, the final form of new semigraphs obtained are determined. [ABSTRACT FROM AUTHOR]

The study on probability theory in finite rings has been an interest of various researchers. One of the probabilities that has caught their attention is the probability that two elements of a ring have product zero. In this study, the probability is determined for a finite ring R of matrices over integers modulo four. First, the annihilators of R are determined with the assistance of Groups, Algorithms and Programming (GAP) software and then the probability is calculated using the definition. Next, by using the results obtained, the zero divisor graph of the ring R is constructed. A zero divisor graph is defined as a graph in which the zero divisors of R are its vertices and two vertices are connected by an edge if their product is zero. [ABSTRACT FROM AUTHOR]

There are various types of matrices associated with graph in the field of graph theory. A graph can be represented in the form of its adjacency matrix or its incidence matrix. These matrices have their own way of representing a complex graph which have many vertices, edges and directions. In this paper, the incidence matrices of the non-normal subgroup graphs are determined for some dihedral groups. [ABSTRACT FROM AUTHOR]

The mathematical modelling of DNA splicing systems is developed from the biological process of recombinant DNA where DNA molecules are cut and reassociated with the presence of a ligase and restriction enzymes. The molecules resulting from the splicing system generate a language which is known as a splicing language using formal language theory. In previous research, the splicing languages from different splicing systems have been generalised based on the sequences of restriction enzymes. In this research, the molecular aspects on the generalisations of splicing languages are discussed to validate the splicing languages through wet lab experiment. The initial string used in this model is taken from bacteriophage lambda. From the model, the predicted molecules resulting from the combination of the initial string and the chosen restriction enzymes are determined. The actual results from the experiment will be compared with the modelled results from the generalisations of splicing languages from DNA splicing system with palindromic and non-palindromic sequences of restriction enzymes with different crossings. [ABSTRACT FROM AUTHOR]

The coprime graph is defined as a graph where two distinct vertices are adjacent if and only if the order of both vertices are coprime. The Szeged index is the summation of the products of the number of vertices which are lying closer to x than y and vice versa. Meanwhile, the Wiener index is the half-sum of the distances for all vertices of the graph. In this paper, the Szeged index and the Wiener index of the coprime graph for certain order of dihedral groups are computed. Then, the general form of the Szeged and Wiener indices of the coprime graph for the dihedral groups are determined. [ABSTRACT FROM AUTHOR]

In DNA computing, a sticker system is a computing mechanism involving the Watson-Crick complementarity of DNA molecules. The sticker system is known as a language generating device based on the sticker operation which is analyzed through the concept of formal language theory. The grammar of a formal language can be described by determining finite sets of variables, terminal symbols and production rules. Research on the grammar which uses the Watson-Crick complementarity has been done previously, known as Watson-Crick grammars. As an improvement to the Watson-Crick grammars, the static Watson-Crick grammars have been proposed as an analytical counterpart of sticker system which consist of regular grammar, linear grammar and context-free grammar. In this research, the closure properties of static Watson-Crick linear and context-free grammars are investigated. The result shows that the families of languages generated by static Watson-Crick linear and context-free grammars are closed under different operations. [ABSTRACT FROM AUTHOR]

Head in 1987 was the first person to introduce the concept of splicing system as a theoretical model for DNA based computation using splicing operation. Splicing operation is a method of cutting and recombining DNA molecules under the influence of restriction enzymes such as ligase. Previous researches have proven that splicing systems with finite sets of axioms and rules generate only regular languages. Hence, in order to increase the computational power of the languages generated by splicing systems, several restrictions in the use of rules have been considered. In this paper, simple splicing systems controlled by permutation groups are defined and the computational power of the languages generated by this variable is explored. [ABSTRACT FROM AUTHOR]

Mathematical modelling of splicing system has been introduced to initiate a linkage between the study of informational macromolecules that includes DNA and formal language theory. The ability to present the nitrogenous base which is a component in a nucleotide of DNA, as a series of alphabet, ignites this interdisciplinary study. Over the years, researchers have developed models to match their need. In addition, product of splicing system is called splicing language. Through some development, second order limit language is derived from other type of languages known as limit language. Its existence and characteristics have been vastly discussed. Beside, some types of splicing system can produce second order limit language. In this research, the characteristics of varieties of splicing system are studied and their relations with second order limit language are established. [ABSTRACT FROM AUTHOR]

The homological functors of a group have its foundation in homotopy theory and algebraic K-theory. The Schur multiplier of a group is one of the homological functors while the Schur multiplier of pairs of groups is a continuation of the Schur multiplier of a group. Meanwhile, a pair of groups is capable if the precise center or epicenter of the pair of group is trivial. In this research, the Schur multiplier and capability of pairs of all abelian groups of order p4 are computed. [ABSTRACT FROM AUTHOR]

A direct product graph is a graph that is formed from the direct product of two different graphs for two groups G and H, labelled as ΓG and ΓH. Suppose x1 and y1 be the elements in ΓG and, x2 and y2 be the elements in ΓH. Then, two vertices (x1, x2) and (y1, y2) are connected if x1 and y1 are connected in ΓG, and x2 and y2 are connected in ΓH. In this research, a new type of graph is introduced and constructed, namely the pi-Cayley graph. This graph is constructed for the symmetric group of order 24 and alternating group of order 12. The graphs obtained are the regular graphs. Then, the direct product of the graphs obtained is also found. [ABSTRACT FROM AUTHOR]

Dengue is the most prevalent vector-borne disease, transmitted primarily by the bite of the Aedes aegypti mosquito. The rapid spread of the dengue viruses indicates a strong dependency on the seasonal variation, particularly changes in temperature. In this study, a new model for the dengue transmission is proposed as our baseline model by incorporating the immature stage of the mosquito population and considering the dependence of the entomological parameters of Aedes aegypti with respect to the temperature. The stage-structured mosquito population is analyzed to simulate the effect of the temperature on the vectorial capacity of the mosquitoes. The qualitative analysis of the stage-structured model is performed in terms of the parameter, Rm, which is defined as the basic offspring of mosquitoes. Finally, the numerical simulations of the autonomous model with temperature variation ranging from (20°C−36°C) are presented. [ABSTRACT FROM AUTHOR]

A nonhomogeneous forced Korteweg-de Vries (fKdV) equation with some certain forcing term is investigated using a semi-analytic method called Homotopy Analysis Method (HAM). HAM is a summation of infinite series whereby its approximation solution converges immediately to the exact solution. Theoretical model, fKdV incorporating forcing term representing underwater bottom topography is used to analyze the wave interaction patterns over different inclination plane. The relationship between the three underwater sloping regions corresponding to wave interaction patterns are investigated. HAM solution found the higher inclination plane triggers higher amplitude wave interaction patterns. [ABSTRACT FROM AUTHOR]

Murabahah is an Islamic financial tool of a purchase and a sale of an asset where the purchase price, selling price and profit margin must be clearly made transparent between the seller and the purchaser. The evaluation process for selecting successful credit worthy applicants is crucial to mitigating risks against probable payment default in the future where such risk can fall under a credit risk taxonomy. Payment default under Murabahah financing has become an increasing concern for Islamic banks due to the potential inability of the respective banks to impose additional charges post execution of Murabahah contract. Hence, an effective evaluation technique in minimizing the default risks is essential. The assessment of applications is usually made by human and subjective evaluation will be involved due to different and varying levels of knowledge, experience and confidence of the evaluators. This justifies a fuzzy method as a suitable approach to be adopted. In this paper, a Fuzzy Inferior Ratio (FIR) procedure using generalized fuzzy numbers is proposed to be used for such evaluation of Murabahah applications. The level of confidence of the evaluators shall be represented in the form of generalized fuzzy numbers, and the compromise solution by FIR will be offered. The existing FIR and the modified FIR are compared with and analyzed to determine consistency of the ranking of the applicants. The proposed method has an advantage over the original FIR as the former does incorporate the element of the confidence level of the decision makers in the calculation to determine the rank of the successful applicants whereas the latter may not. [ABSTRACT FROM AUTHOR]

The urgency in demand for global warming alleviation has necessitated more research into carbon-dioxide diffusion (CO2) both for sequestration and enhanced oil recovery. Captured atmospheric CO2 can be stored underground to leave a haven for the populace. CO2 diffusivity in oil-brine-rock systems at various pressures and temperatures and also solely in brine with different ions but at a fixed temperature and pressure are studied. Increases in pressure and temperature leads to increase in diffusion coefficient of CO2 in oil-brine-rock systems. On the other hand, in CO2-brine system, the variations of ions affect CO2 diffusion while Ca ion appears to result in the highest diffusivity of CO2 in brine thereby, suggesting saline aquifers with Ca ion as the best choice for CO2 sequestration. [ABSTRACT FROM AUTHOR]

Competition between species with asymmetrical interaction strength is thought to have a significant force in determining species biodiversity. Generally, competitive interaction has received a lot of attention among ecologists due to the important influences that interspecific competition played in the advancement of ecological theory and the development of population dynamics field. Mathematical modelling has been employed widely in the study of population dynamics and this tool can be used to better understand the influential roles of asymmetrical interaction in structuring community dynamics. To achieve this aim, we examine a multispecies Lotka-Volterra competition model consisting of asymmetrical interactions and environmental carrying capacity components. The significant influences of competitive interactions on multispecies community dynamics are investigated; in particular, we examine how the variation in competitive strength between multiple species leads to different outcomes of biotic interactions. To do this, we conduct a detailed bifurcation analysis of the model, as the strength of asymmetrical interaction varies, by means of dedicated continuation software. This allows us to discover regions of different types of attractors (e.g., limit cycles, alternative stable state communities, species coexistence and species exclusion) in the parameter space and to understand its bifurcation structures. Transcritical and Hopf bifurcations are observed in this ecological system and these bifurcations correspond to threshold values for asymmetrical interactions, which can lead to certain dynamics occur and disappear. Our bifurcation analysis also explains several previously published results, highlights parameter space with qualitatively different dynamics and uncovers some intriguing ecological phenomena concerning the interplay between asymmetrical competition and the maintenance of species biodiversity. [ABSTRACT FROM AUTHOR]

A simple definition of a peer tutor is a student that tutors a fellow student. 148 answered questionnaires pertaining peer tutoring relationships are visualized as directed and weighted networks using graph theory. Social network analysis is used to analyze and highlight top peer tutors amongst the student. Measures such as in-degree and eigenvector centrality are juxtaposed with their final exam results as a measure of academic performance on the network. The main observation is that peer tutoring does not always correlate with academic achievements. Some low achievers are also taken as peer tutors. It is hoped that, the visualization of peer tutoring as a network will be helpful for teachers to target the influencers (the top peer tutors) amongst the students, in order to take effective action that will have a significant positive ripple effect on the entire network. [ABSTRACT FROM AUTHOR]

Tuberculosis is a global threat that is found worldwide. This paper is a modification of the existing SEIR epidemic model of tuberculosis infection by incorporating educational counselling, treatment and vaccination as an intervention strategies. The model was analyzed by using the system of first order linear equations. The disease free equilibrium represented by Ef was established and effective basic reproduction number generally represented by Rc has been obtained by using the next generation operator. It was found that Rc>1 when there is no intervention strategies and the disease persist and also turned to die out wit Rc<1 whenever the intervention strategies are implemented. Numerical simulation reveals that educational counselling and treatment offered a significantly reduction in the tuberculosis burden than vaccination. [ABSTRACT FROM AUTHOR]

Fuzzy time series forecasting model has been proposed to cater for data in linguistic values. One of the crucial factors that influence the performance of fuzzy time series is the partition of interval length. This paper compares the effect of several interval lengths to the performance of fuzzy time series forecasting model. The interval length considered are the average based, frequency density based and randomly chosen length methods. The data are represented in trapezoidal fuzzy numbers and the accuracy of the forecasting model is calculated using the distance, area, height and perimeter ratio similarity measure. The model is applied in a numerical example of Malaysian unemployment rate. The findings show that the average based length outperforms the other two types of interval length. [ABSTRACT FROM AUTHOR]

The main objective of this research is to develop an effective intelligent system that can be used by medical practitioners (physicians) to accelerate diagnosis and treatment processes. In this paper, the sparse matrix approach was incorporated in neural network learning algorithm for scalability, minimize higher memory usage/storage capacity, enhancing implementation time and speed up the analysis of the data. The proposed intelligent classification system maximizes the intelligently classification of data and minimizes the number of trends inaccurately identified. For robustness, the proposed method was tested with three different datasets, namely, Hepatitis, SPECT Heart and Cleveland Heart. Therefore, an attempt was made to determine the performance indicators efficacy. Compared to some similar existing methods, the approach presented achieves improved performance. The program used for implementation of the proposed model is MATLAB R2016a (version 9.0) and executed in the 4.0 GB RAM processor of PC Intel Pentium Quad Core N3700. [ABSTRACT FROM AUTHOR]

In data fitting, researchers use various methods to determine the quality of a fitting. Visualization of images is crucial in observing the behaviour of data obtained. The problem in judging the accuracy of a result obtained through visual observation are commonly faced by researchers when handling contaminated data such as noisy data, missing data or outliers. In this research, study has been conducted to deal with missing data using Radial Basis Function (RBF). Error evaluation using RBF with thin plate spline (TPS) of different parameter value, c is obtained. The approach is applied to a real life data of a head injured patient on the skull using bootstrap method in RBF. This study has performed well on craniofacial reconstruction to estimate the best fit curve for the missing part of fractured region of a skull. This provides a significant contribution that could be an alternative approach in medical application especially on craniofacial reconstruction. [ABSTRACT FROM AUTHOR]

Due to difference pressure beneath the earth, oil and gas or known as hydrocarbon most probably rises to the surface. This presence of oil and gas can be identified through their seepage. The hydrocarbon seepage can lead to an abnormality of soil and dynamism of vegetation health on land surface. This phenomenon can be act as indicator for detecting potential onshore of oil and gas reservoir. Through remote sensing spectral reflectance vegetation index, the abnormality and dynamism of plant growth can be detected. In this study, spectral reflectance data originating from Unmanned Aerial Vehicle (UAV) Multispectral is used to monitor and determine the hydrocarbon seepage detection in oil palm vegetation stress. Based on the experimental design, 12 oil palm trees were simulated with crude oil and remaining three trees were left as control sample which these trees are divided into five sample. The spectra acquisition was taken on the first day, 30th day, 60th day and 90th day of experiment. From the data, the potential visible region and Near - Infrared (NIR) reflectance region being investigated by exerted the statistical analysis to vegetation index which is Normalised Difference Vegetation Index (NDVI) data trends to study the impact of hydrocarbon seepage towards oil palm tree. Detecting hydrocarbon seepage using vegetation reflectance is very useful in oil and gas field as this can be one of the alternative approaches in exploring new oil and gas resources. [ABSTRACT FROM AUTHOR]

In 1990, Perona and Malik introduced diffusion model which is known as Perona and Malik (P-M) model to preserve the edge or boundaries. The edge indicator used as the variable exponent controls the diffusion mode as edge stopping between P-M diffusion and Gaussian smoothing depending on the image. The application of nonlinear second-order PDE model of P-M model is designed such that smooth areas diffused faster than the other areas and consequently may cause the presence of blocky effect. Hence, numerous modified diffusion functions have been developed to enhance image denoising results in order to overcome the loss of detail and blur in images. The objectives of this paper are to propose modified diffusion functions and compare with existing functions in order to provide alternative diffusion functions with edge and details preservation. Besides that, the modified functions were implemented on an image with different level of noise to made visual comparison with existing functions The proposed functions must be a positive function, monotonically decreasing and also convergent to zero. Results shows that the modified functions were converged rapidly as compared to the existing diffusion functions and able to remove different level of noise. In addition, the quality of image and their details are also preserved after the denoising process. [ABSTRACT FROM AUTHOR]

This study investigates on the stagnation point flow and viscous dissipation over a slippery stretching/shrinking cylinder in copper nanofluid. A system of nonlinear partial differential equations is modeled and transformed into ordinary differential equations using similarity transformations. The governing equations with corresponding boundary conditions are analyzed numerically using a bvp4c in MATLAB. The solutions are found to be dependent on the governing parameters including shrinking parameter, slip parameter, curvature parameter and Prandtl number with copper nanoparticle. The results are shown in the velocity and temperature profiles as well as the skin friction coefficient and Nusselt number. Dual solutions are observed for the shrinking cylinder in the presence of Eckert number. The temperature decreases smoothly at the first solution, however the temperature increases at a certain point and then decreases as the slip parameter increases for the second solution. [ABSTRACT FROM AUTHOR]

League of Legends (LoL) has been on a rise in becoming an extremely favored multiplayer online battle arena (MOBA) game ever since its release in 2009. This paper presents a logic mining technique to model the results (Win / Lose) of the LoL games played in the professional league of South Korea, most commonly known as LCK. In this research, a method, namely k satisfiability based reverse analysis method (kSATRA) is brought forward to obtain the logical relationship among the gameplays and objectives in the game. The logical rule obtained from the LoL games is used to categorize the results of future games. kSATRA made use of the advantages of Hopfield Neural Network and k Satisfiability representation. The data set used in this study included the data of all 10 teams in LCK, which composed of all games from Spring Season 2018. The effectiveness of kSATRA in obtaining logical rule in LoL games is tested based on root mean square error (RMSE), mean absolute error (MAE) and CPU time. The main issue was to determine the relationship among gameplays or objectives and how they affect the outcome of the game. Results acquired from the computer simulation shows the effectiveness of kSATRA in exhibiting the performance of the LoL teams. [ABSTRACT FROM AUTHOR]

Restricted Boolean 2 Satisfiability (2SAT) is a variant of discrete constraint satisfaction problem, commonly being applied with Hopfield neural network (HNN) as logic programming. In recent years, the binary Ant colony optimization has been formulated to solve various satisfaction and optimization problem due to the power onlooker bee phase in attaining the global convergence. The core motivation of this research is to propose a hybrid Hopfield neural network (HNN) incorporated ACO in enhancing the learning phase of new type of 2SAT logic programming. The developed hybrid model will be compared with the other conventional learning method in hybrid HNNs models such as genetic algorithm (GA) and exhaustive search (ES). The simulations were conducted by training and testing the developed model and the other counterparts with randomized simulated data sets. Therefore, the results manifested the capability of ACO in improving the learning phase of HNN as compared with GA and ES under different complexities. [ABSTRACT FROM AUTHOR]

The dynamical behaviors of logic mining in real datasets are strongly dependent by its logical structure. In this case, logical rule that has been embedded to neural network has long suffered from a lack of interpretability and accuracy. This has severely limited the practical usability of logic mining. Logical permutation is a definitive finite arrangement of attributes that makes 2SAT became true. It was believed that the effect of permutation will increase the accuracy of the system. In this paper, we presented the effect of logical permutation in logic mining (2SATRA) integrated with recurrent Hopfield Neural Network (HNN). Several benchmark datasets will be used to validate the effect of logical permutation. It has been shown that 2SATRA with different permutation will results in improvement in terms of accuracy value. This finding will lead to a better understand of 2SATRA in doing real life datasets. [ABSTRACT FROM AUTHOR]

In this paper, continuous curves using different levels of continuity are being evaluated and compared as the degree of Bézier curve increases. Unfortunately, the ordinary Bézier curve lacks flexibility in controlling the shape of the curve. Therefore, quintic trigonometric Bézier curve is introduced as an alternative to produce a flexible continuous curve. The smooth continuous curve will act as a curve generator into producing desired shapes. Additionally, by applying quintic trigonometric Bézier curve on an object, the curve generator will undergo a sweeping technique in order to form a surface. [ABSTRACT FROM AUTHOR]

The paper presents the block hybrid method for solving delay differential equations (DDEs) in constant step size. The derivation of the order six block hybrid method involves the divided difference relative to main and off-step points. It is implemented together with Lagrange interpolation to estimate the delay term. The new method is proven to be more accurate and efficient via comparison with existing method. [ABSTRACT FROM AUTHOR]

Imperialist Competitive Algorithm (ICA) is an evolutionary algorithm inspired by the phenomenon of human's socio-political evolution among human empires in the real world, known as imperialistic competition. Meanwhile, logic programming in data mining can explore the underlying relationship in real life data sets. In this paper, enhanced Imperialist Competitive Algorithm is incorporated in the training phase of Hopfield Neural Network to perform the 2-Satisfiability logic mining. Then, the performance of ICA algorithm will be compared with the widely used conventional method, Exhaustive Search (ES) algorithm. The hybrid network will be tested in bank marketing data set. The performance of both algorithms will be evaluated in term of performance evaluation metrics such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Sum of Squared Error (SSE), Schwarz Bayesian Criterion (SBC), accuracy and CPU time to determine the effectiveness of the hybrid model. ICA is expected to outperform ES algorithm in doing 2-SAT logic programming. [ABSTRACT FROM AUTHOR]