In this research paper, the numerical simulation of a Susceptible-Exposed-Infected-Recovered- Susceptible (SEIRS) epidemic model with saturated incidence rate with the saturated terms for the susceptible individuals was analyzed. We established the disease-free equilibrium and endemic equilibrium states of the model. We also investigated the local and global stabilities of the disease free equilibrium using matrix and Lyapunov function methods when the basis reproduction number 10<1. The work is an extension Kolawole and Olayiwola (2016) to investigate the effect of saturation term on the susceptible individual. We used maple for the simulation of the analysis and the result we obtained are in good agreement with other results in the literature.

Reproductive number, SEIRS, saturated incidence rate, susceptible individual.

If the auxiliary variable coefficient of variation c_x is known but negligible, then what comes of the mean square error (mse) of our conventional product and mean per unit estimators. In this study, if c_x is known but negligible, that is, if it tend towards zero (c_x→0), the mse of both the conventional product and mean per unit estimator becomes equal. The question that will quickly come to mind here is, how often does known becomes negligible?. It at times do occur in sample survey, so there must be statistical awareness on this in case we experience such in our researches, just like when the population size (N) is large, then, the finite population correction (fpc) becomes negligible and hence, it tends towards zero (f=n/N→0). If c_x→0, then virtually the mse of some of the existing proposed alternatives may tend towards the mse of the mean per unit estimator. When this occur, over estimation problem in product estimation does not arise but if known c_x is not negligible, Adewara (2016) proposed an alternative product estimator, ӯ_aaap, which utilizes c_x which was found to minimize over estimation of ӯ_p on ӯ whenever ρ_xy < [c_y²*(1-α²*(Ẍ/(Ẍ+c_x)²)-α²*(Ẍ/(Ẍ+c_x))²*c_x²]/[2*c_y*c_x*α²*(Ẍ/(Ẍ+c_x))²], 0.1 <= α <0.7.

product, coefficient of variation, estimator, mean square error, bias.

In this research article, the Variational Iteration method coupled with the polynomial approximation is used to find numerical solution to some homogenous and non-homogenous ordinary differential equations arising in mechanics.

variational iteration method, boundary value problem, polynomial.

In this paper, robustness of suggested modified factor-type estimators have been study under non-response model in relation to other existing related estimators of population mean. Mean square error (MSE) of suggested estimators under non-response model has been obtained and empirical study was done using Data 1, 2, 3 and 4. The robustness of the considered estimators were obtained by averaging their expected MSE and ranked accordingly to their level of efficiency. The results revealed that the efficiency of all the estimators considered increases as the proportion of non-respondent who responded after been re-interviewed increases and the suggested estimators compete favourably with almost the estimators considered in the study.

Estimator, Robustness, Mean square error (MSE), Efficiency.

Twenty-first century mathematics education is about facing novel real-world problems, nurturing creative thinking skills and cultivating productive ways of learning. In attempting to innovate teaching and learning in order to prepare a new generation for the demands of this new era, many educators have discovered the value of metacognition. This paper presents the importance of metacognition to the learning of mathematics, employed metacognitive skills in the process of solving mathematics problems.

Metacognitive skills, higher-order thinking skill, mathematics problem solving, mathematics teaching methods.

This paper investigates properties of some dynamic panel data estimators including Ordinary Least Squares (OLS), the Anderson-Hsiao IV (AH), Arellano-Bond Generalized Method of Moment (GMM) first-step, Blundell- Bond System (SYS1) first-step, M and MM estimators in the presence of serial correlation. Absolute Bias and Root Mean Squares Error were used to evaluate finite properties of the estimators and revealed improving performances asymptotically under different sample sizes and varying degrees of autocorrelation. The results showed that in small and large sample situations, irrespective of time dimension, Anderson–Hsiao IV estimator (AH) outperforms all other estimators. Arellano-Bond Generalized Method of Moment (GMM) one-step proves to be relatively superior among the estimators when the time period (T) is small, though it may not be the best but performance improved drastically as the number of cross-sectional units increases. However, Ordinary Least Squares estimator has least performance among all the estimators considered in this study.

Dynamic Panel Data, Serial Correlation, Cross-Sections, Time Periods, Absolute Bias and Root Mean Squares Error.

Educational Data Mining (EDM) is concerns with developing and modeling methods that discover knowledge from data originating from educational environments. This paper presents the use of data mining approach to study students’ performance in CSC207 (Internet Technology and Programming I) a 200 level course in the department of Computer, Library and Information Science. Data mining provides many approaches that could be used to study the students’ performance, classification task is used in this work to evaluate the student’s performance and as there are numbers of approaches that can be used for data classification, including decision tree method. In this work, decision trees were used which include BFTree, J48 and CART. Students’ attribute such as Attendance, Class test, Lab work, Assignment, Previous Semester Marks and End Semester Marks were collected from the students’ management system, to predict the performance at the end of semester examination. This paper also investigates the accuracy of different Decision tree algorithms used. The experimental results show that BFtree is the best algorithm for classification with correctly classified instance of 67.07% and incorrectly classified instance of 32.93%.

Classification, Decision tree, Students’ Performance, Educational Data Mining.

In this paper, two modified factor-type estimators with two auxiliary variables for population mean have been suggested. Bias and MSE of the suggested estimators have been derived up to first order approximation using Tailor’s series expansion and the conditions for their efficiency over some existing estimators have been established theoretically. Empirical study was conducted using three dataset and the results revealed that the suggested estimators are more efficient.

Estimator, Auxiliary variable, Mean square error (MSE), Two-phase sampling.

The research work examined the statistical modelling of Nigeria GDP of some selected sectors in the presence of autocorrelation. It examines the effects and contribution of some economic sectors to the Gross Domestic Production of Nigeria. The data set in (N million) covered a period of 20 years from 1990 to 2009 for the economic variables of interest. The statistical methods employed were regression analysis, correlation and residual analysis. The estimated regression equation is given as: Y = -77203 + 6.43816X1 + 1.26391X2 + 6. 99025X3 1. 01914X4.The Coefficient of Determination (R2) of 0.9806 showed that the four economic variables considered explained about 98% of the variation in the nations GDP. A further analysis revealed the Variance Inflation Factor with the independent variables been highly correlated which denotes the presence of multicollinearity. Durbin-Watson and Goldfeld-Quandt tests revealed that there was no autocorrelation in the error terms but there was evidence of heteroscedasticity respectively. It was found that Agriculture sector contribute the highest to the growth of the economy, follow by the manufacturing and oil sector respectively, while the least contribution was experienced from building and construction. The work therefore advice the government to invest more on the agriculture sector and non oil sector in order to increase GDP.

GDP, Durbin-Watson, Multiple.

This paper presents an overview of intrusion detection and a hybrid classification algorithm based on ensemble method (stacking) which uses decision tree (J48) and Bayesian network as base classifiers and functional tree algorithm as the meta-learner. The data set is passed through the decision tree and node Bayesian network for classification. The meta-learner (Functional tree classifier) will then select the value of the base classifier that has the higher accuracy based on majority voting. The key idea here is to always pick the value with higher accuracy since both base classifier (decision tree and Bayesian network) will always classify all instances. A performance evaluation was performed using a 10-fold cross validation technique on the individual base classifiers (decision tree and Bayesian network) and the ensemble classifier (DT-BN) using the KDD Cup 1999 dataset on WEKA tool. Experimental results show that the hybrid classifier (DT-BN) gives the best result in terms of accuracy and efficiency compared with the individual base classifiers (decision tree and BN). The decision tree gave a result of (99.9974% for DoS, 100% for Normal, 98.8069% for probing, 97.6021% for U2R and 73.0769% for R2L), the Bayesian network (99.6410% for DoS, 100% for Normal, 97.1756% for probing, 97.0693% for U2R and 69.2308% for R2L),while the ensemble method gave a result of (99.9977% for DoS, 100% for Normal, 98.8069% for probing, 97.6909% for U2R and 73.0769% for R2L).

Network security, Intrusion detection system, classifiers, Bayesian network, Functional tree Decision Tree, meta-learner.

This paper presents the application of some data mining techniques in the field of health care and computer network security. The selected classifiers were used individually and also, they were ensemble methods using four different combinations for the purpose of classification. Naïve Bayes, Radial Basis Function and Ripper algorithms were selected and the ensemble methods were majority voting, multi-scheme, stacking and Minimum Probability. The KDDCup’99 dataset was used as the benchmark for computer network security, while for the health care, breast cancer and diabetes dataset from the WEKA repository were used. All experiments and simulations were carried out, analyzed and evaluated using the WEKA tool. The Multi-scheme ensemble method gave the best accuracy result for the KDD dataset (99.81%) and the breast cancer dataset (73.08%) but its value of (75.65%) on breast cancer is the least of them all. Ripper algorithm gave the best result accuracy (99.76%) on KDD dataset amongst the base classifier but it was slightly behind in the breast cancer and diabetes dataset.

Ensemble, Data mining, Classification, KDD Dataset, Machine Learning.

Data mining is an important stage in knowledge discovery in database (KDD) of clinical data due to its ability to extract concealed (hidden) patterns from huge amount of datasets to produce more useful and understandable information. One of the most important applications of data mining is the prediction of heart disease. Techniques in data mining can be employed for management, diagnosis and prediction of heart disease in healthcare establishments. This paper discusses review on different approaches in data mining that have been employed by several researchers to predict heart disease.

Prediction, Data mining, Diagnosis, Healthcare, Knowledge discovery in database.

An efficient recognition algorithm for human face is a technique discovered to be based on good facial feature representation. A two-dimensional Gabor represents a group of wavelets which capture optimally frequency information and local orientation from a digital image. Gabor filters have been employed greatly and highly considered to be one of the best performing techniques for feature extraction in face recognition owing to its invariant against local distortion initiated by changes in expression, lighting and pose. This paper discusses some reviews on 2-Dimensional Gabor-based facial recognition techniques. The huge feature dimensionality problem associated with Gabor feature is stated and several techniques to reduce this problem are suggested.

Gabor-filters, face recognition, feature extraction, wavelets, Gabor feature, dimensionality.

A pairwise balanced design of index λ is a way of selecting blocks from a set of treatments (support set) such that any two treatments have covalency λ. If there are v treatments and if every block size is a member of some set K of positive integers, the design is designated a PB (v;K; λ).{5} Therefore, this research research work attempts to proof the existence of PBDs, shows the relationship that exists among the PBDs parameters (v, b, r, k, λ), and specify conditions for the construction of PBDs when λ > 0, k is odd and λ = 1.Thus, v − k and r − λ are proved to be nonnegative therefore, r − k ≥ 0, and b ≥ v. Also, a PBD(15; {4, 3}; 3) was constructed from the PBD(15; {5, 3}; 1).

Regular Design, Balanced Designs, Blocks Designs and Pairwise Balanced Designs.

Nigeria voting system is characterized with violence and malpractices, where some people used another person voting card to vote and many have more than one voter’s card in their possession, which allow them to vote more than one time. These problems does not peculiar to Nigeria alone but includes other countries that are using manual method of voting system. In order to combat the aforementioned problems the paper therefore proposed a secure voting system using bimodal identification and verification system for voting and registration of voters. The purpose is to uniquely identify voters and to eliminate the possibility of double registration at the polling booth by the voters. Also the issue of double voting will also be abolished. Further study can look into web based and mobile based automated voting system, which allow voter to catch their vote anywhere and anytime.

Voting system, automated, recognition, Verification, Bimodal.

Resolvable Balanced Incomplete Block Designs (RBIBDs) are important combinatorial designs that have useful applications in various fields of human endeavour. In this paper, Nim addition tables, from the game of Nim, of order 2ⁿ, 2≤n≤5 were used as the basis for the construction of some (k², b, r, k, 1) RBIBDs. Nim addition tables of order 2ⁿ, 2≤n≤5 were constructed. These tables were special Latin squares that obeyed the Finite Groups theory and closed n-nim-regularity conditions for closed n-nim-regular games. The Bose’s generalized method of constructing Mutually Orthogonal Latin Squares (MOLS) was used to obtain 2ⁿ-1 MOLS for each n. The MOLS were super-imposed on one another and successive diagonalization algorithm was used to obtain the RBIBDs from the super-imposed MOLS. The RBIBDs constructed were (16, 20, 5, 4, 1), (64, 72, 9, 8, 1), (256, 272, 17, 16, 1) and (1024, 1056, 33, 32, 1) RBIBDs. These RBIBDs are all in existence and a link was thus established between the game of Nim and RBIBDs.

Mutually Orthogonal Latin Squares; Successive diagonalization algorithm; Impartial Combinatorial games; closed n-nim-regular matrix; Finite groups.

As transactions, data communication and information systems are drastically increasing in the society, so many people are connected through internet for e-commerce and other electronic activities. The introduction of internet technology in business brings about great relief in reaching the end users. Also this technology invites numerous security threats of misuses and intrusions. Intrusion detection systems are significant element for network security infrastructure which plays key role in the detection of several attacks along the network. They are several techniques being employed in intrusion detection, but these methods are not completely flawless. In quest for an efficient Intrusion Detection System (IDS), this study employs hybridization technique which involves the Genetic Algorithm and Tabu-search to produce a robust Intrusion Detection System. Evaluation of the system on KKD 99 intrusion database, shows that the performance of proposed hybridized IDS is better than that of Genetic algorithm or tabu search method alone which can significantly detect almost all anomaly data in the computer network.

Intrusion detection, data communication, Genetic Algorithm, Tabu Search, Information System, Electronic Transaction.

Violation of homoscedasticity assumption in a Panel Data Regression Model (PDRM) implies unequal variability of error terms, and this creates heterogeneity problem in estimation. This research thus attempts to investigate the presence and effect of heteroscedasticity in panel data through the estimation of a specified audit fees PDRM using Pooled ordinary least square (POLS, Least square dummy variable (LSDV) technique where all coefficients vary across individual and Random Effect estimator (REM). A conditional Lagrange multiplier test was developed via a two-way error components model, to examine the presence of heteroscedasticity in the fitted POLS model while Hausman test was used to ascertain the suitability of the LSDV Model over Random effect model and vice-versa. The conditional LM test gave a value of 7.1462 with P-value of 0.000000000000446 which shows that there is presence of unequal variance of MA(1) errors among the residuals of the fitted Pooled OLS model, thereby rendered the estimator inconsistent. Both LSDV and RE models were fitted to take care of the challenges posed by the presence of heteroscedasticity and both models captured the goodness of fit better when compared to the Pooled OLS model. However, the Hausman test revealed that random effect model will not be preferable since p-value of the former is less than 0.05.

Heterogeneity, Heteroscedasticity, Conditional Lagrange Multiplier, Panel Data, Audit Fees Model, Panel Data Regression Model.

L’objectif de cet article est d’analyser la stabilité transitoire (ST) du réseau IEEE- 5 nœuds par deux methodes differentes, suite à un défaut triphasé. Les méthodes proposées sont le critère des aires égales (CAE) et la méthode du plan de phase (pp). Pour analyser cette stabilité, nous avons fait appel au modèle classique dans lequel les machines sont representées électriquement, par des f.e.ms constantes en module en série avec leurs réactances transitoires d'axe direct. Nous avons procédé essentiellement à la réduction de l’ensemble des nœuds du réseau aux seuls nœuds producteurs par la méthode de kron. Les résultats de simulations obtenus sont validés par comparaison avec ceux obtenus en utilisant l’intégration numérique.

Stabilité transitoire, Méthode de Kron, Méthode du Plan de phase, Critère des aires égales.

The traditional system of identifying and recognising Nigerian Naira currencies in Sokoto Metropolis during daily cash transactions is done by manually checking for specific features on each currency. This approach poses a lot of challenges such as error in differentiating between the original and fake copies of the currency which can easily occur due to some salient details of the currency that cannot be genuinely identified by the concerned individuals. This research work aims at developing a currency recognition system using image-based processing technique to identify and recognize the different kinds of currencies. The application was implemented using Visual Basic and the MS Access relational database management system. The result of the experimental evaluation using some sample users of the selected domains has demonstrated that, the average users score of the overall system is 77.7%. This implies that, the proposed system improved the effectiveness of the currency recognition processes based on the result obtained during evaluation.

Image-Based Processing, Currency Recognition, Naira.

The stability of economy of any nation is at risk if inflation is not properly checked through constant analysis and study, hence this research work attempts to model inflation rate in Nigeria, test for the adequacy of the fitted model, forecast future inflation rate. Descriptive statistics, Box-Jenkins Methodology, Augmented Dickey Fuller Unit Root test was employed, the model of the inflation rate was determined using the correlogram. The order of the models and parameter of the models were confirmed from the information criterion computation. It was discovered to be normal from the Box-Pierce test statistic with value of 0.754. The inflation rate had an all-time high value of 28.20 and an all-time low value of 3.00 with a mean of 10.92. The series was found to be stationary from the Augmented Dickey Fuller Unit Root test. However, correlogram, autoregressive Moving average was also detected. ARIMA (0,1,1), ARIMA (1,1,1) and ARIMA (1,1,0) was compared. The best model was picked using the AIC, BIC and AICC. Therefore, the best model is ARIMA (0,1,1). Also, the inflation rate forecasted for a period of 36 months shows a parallel movement.

Box-Jenkins Methodology, Augmented Dickey Fuller Unit Root test, Autoregressive Moving average, inflation, correlogram.

Assessment is an important procedure for teaching in the high schools. Evaluation results may affect curricula and methods. It also affects students, teachers and managers. This article proposes to build the capacity of teachers to evaluate the academic performance of students at the high schools in Vietnam.

Teachers’ assessment competency, classroom assessment, learning outcomes, mathematics teaching methods.

Automatic Speech Recognition has found its application on various aspects of our daily lives such as automatic phone answering service, dictating text and issuing voice commands to computers. Speech recognition is one of the fastest developing fields in the framework of speech science and engineering. Also, in computing technology, it comes as the next major innovation in human computer interaction. However, in speech signal processing, Pre-processing of speech plays a vital role in development of an efficient automatic speech recognition system. Nowadays, Humans are able to interact with computer hardware and other machines through human language. In view of the above, researchers are putting efforts to develop a perfect and efficient speech recognition system but machines are unable to match the performance of human utterances in terms of accuracy of matching and speed of response. Therefore, preprocessing of signal is based on number of applications and drawback of the available techniques of ASR systems. Hence, the process of preprocessing in speech recognition discussed in the study includes: Noise removal, Voice Activity Detection, Pre-emphasis, Framing and Windowing.

Automatic Speech Recognition (ASR), Human Computer Interaction (HCI), Pre-processing.