World of mathematical concept, which is where the model is built. We then manipulate the model using techniques or computer aided numerical computation. Finally we re-enter the real world, taking with us the solution to the mathematical problems, which is translated into a useful solution to the real problems. The application of first order differential equation in temperature have been studied the method of separation of variables Newton’s law of cooling were used to find the solution of the temperature problems that requires the use of first order differential equation and these solution are very useful in mathematics, biology, and physics especially in analyzing problems involving temperature which requires the use of Newton’s law of cooling.

Differential equation, temperature.

This study used some probability distribution (Gamma, Beta and Chi-square distributions) to assess the performance of partial least square regression (PLSR), ridge regression (RR) and LASSO regression (LR) methods. Ordinary Least Squares may fail if the variables are almost collinear or related. As such, this methods (PLSR, RR, AND LR) were compared using simulated data that follows gamma, beta and chi-square distributions with number variables (P=4 and 10) and sample sizes (n=60 and 90). The comparison was carried out using Mean Square Log Error (MSLE), Mean Absolute Error (MAE) and R-Square (R2) which shows that the results of RR is better when P=4 and n=60 using gamma distribution, but using chi square distribution PLRS is better methods. Also, when P=4 and n=90, RR shows better results with both gamma and beta distributions but with chi square distribution all methods have equal predictive ability. However, at P=10 and n=60 RR performed better with both gamma and chi square distributions while when data follows beta distribution all distributions have equal predictive ability. RR shows better results at both gamma and chi square distributions when P=10 and n=90 while PLSR performed better with beta distribution.

Regression; multicollinearity; ridge (RR); partial least square (PLSR); lasso regressions (LR).

In this paper, an Independent Binary Model (IBM) is proposed. It is aimed at estimating cell probabilities in an r x c contingency table when some of the cells have zero count. Existing methods in this situation are either subjective or based on arbitrary decision of the researcher. The IBM is applied to sets of simulated data for various combinations of categorical variables. It is pointed out that the IBM could be an alternative for such situations especially when the result is needed for further analysis.

Cell probabilities, Categorical variables, Independent Binary Model, Zero count.

The emerging proliferation of fragmented healthcare applications has accounted for an overwhelming high cost of maintaining healthcare services and facilities, clinical data security and privacy concerns, decentralized data redundancy leading to snail-speed of improvement in healthcare services’ delivery efficiency and more often than not, a total shutdown of health facilities. Due to lack of communication among these decentralized and often similar health applications, their relative characteristics and advantages are either misaligned and/or duplicated in most cases. Hence, needs be that robust and integrated healthcare services management applications, that can collaboratively deliver consolidated healthcare objectives and facilitate huge cost savings, seamless clinical information workflow, optimum access and sharing of centralized clinical data, highly-efficient quality of service (QoS) and real-time accountability, be developed especially for the healthcare systems in low-and-middle-income economies. In this paper, an integrated web-based electronic HealthcAre Management System (e-HAMS) for low-and-middle income economies was developed. e-HAMs is a composite mobile-compliant system with a collaborative framework that accommodates electronic patients’ health record, electronic cash flow audit, electronic personnel record management, electronic pharmacy services and the electronic healthcare services payment management systems. Prior to its development, a users’ needs assessment was conducted and the possibility that e-HAMS would offer some cost-savings benefit was also modelled. The collaborative model when evaluated based on Closed World Assumption (CWA) reveals that cost-savings benefit is huge. An underlying architecture for the integrated development plan was also developed. Unified modeling language and V-shaped software development model were employed to co-design and orient the development of e-HAMS. Interactive graphical user interfaces were developed to implement e-HAMS’ designs in a visual studio integrated development environment using C# language. The system database was developed using a SQL server application. At the testing stage, a qualitative users’ assessment of e-HAMS was conducted to evaluate the performance of the developed e-HAMS system. Results obtained reveal that cost savings benefit of e-HAMS is rated at 96%, reliability (92%), availability (90%), privacy (90%), security (89%), quality assurance (86%), user interface design (82%), ease of use (78%) and user friendliness (76%) in that order. Thus, e-HAMS is best suited for low-and-middle income economies to realize improved Quality of healthcare services delivery and facilitated efficiency of operations while offering timely generation of informed decisions.

Unified modeling design, co-design, cost, integrated healthcare services management system, pharmacy, audit, patient, low and middle income countries.

Maintainability is a key factor in measuring the quality of developed software and it becomes important due to dynamism of software. Partially, maintainability is a function of source code understandability on the part of developers. Therefore, cognitive complexity of software is relevant to its maintainability. In fact it is not an overemphasis to state that, quality of software in general can hardly be control if the code is complex (Banker, Datar and Zweig, 2009;francalanci and Merlo, 2010). Hence as a result of strong impact that cognitive complexity has on the software quality this research work investigates the effect of some implementation languages on cognitive complexity. Three earlier and recent implementation languages were sampled in term of Procedural Programming Languages and Object Oriented Languages then implemented on a unique algorithm and appraised using Procedural Cognitive Complexity Metric[P.C.C.M] and Multiparadigm Cognitive Complexity Metrics [ M.C.C.M] respectively. The experiment results have shown that among the procedural programming languages, Fortran has least cognitive complexity with sixty six while among Object Oriented Languages C++ has the least with one hundred and thirty eight. Cross assessment of Fortran and C++ using both [P.C.C.M] and [M.C.C.M] reveal that Fortran has the least cognitive complexity among all the implementation languages used. The research results has shown that Fortran 77 is the best for implementation of Dijkstra algorithm among the selected languages to have the least cognitive complexity and has reaffirmed that some languages are more appropriate for easy understandability of source code than others.

Cognitime metric, Software Complexity, Dijdstra Algorithm, Objict Programming Language , Procedural Programming Language.

Several sectors including engineering, health, academics and so on deals with very large number of information and few specimens. This highlight the need of a technique to improve data accuracy in order to enable professionals such as biologists, clinicians and so on to comprehend the structure of a complex microarray dataset and the gene expression in cells when reduced. This study employs Independent Component Analysis for feature extraction before using k-Nearest Neighbor algorithm to classify colon cancer dataset which contains DNA microarray gene expression data with 2000 features and 62 samples. The experiment was performed using MATLAB 2015a. The result shows that the dimensionality reduction applied improve the classification performance in terms of accuracy, sensitivity, specificity and precision by 11.3%, 25.2%, 36.3% and 12.8% respectively.

Microarray, k-NN, Dimensionality Reduction, ICA, Colon Cancer.

The United Nation has its number one Sustainable Development Goal (SDG #1) of No Poverty Global World which is, Agriculture and Food Security. The question to be asked is how do we make production of food like maize, rice, and wheat (the three – world stable food) available in abundant, in the face of excessive temperature, limited or excessive rainfall, and low or sometimes high humidity? World maize, rice, and wheat production must increase by approximately 1% annually to meet the growing demand for food that will result from population growth and economic development. Global mean surface air temperature increased by ≈0.5°C in the 20th century and is projected to further increase by 1.5°C to 4.5°C this century. The seasonal and spatial variation of temperature, rainfall and humidity have been an important climatic factor determining the cropping pattern, regulates the agricultural activities and quality of food production all over the world. This research work examines the effect of these climatic elements on Food Production in Africa: establishes standard Temperature level, amount of Rainfall and relative Humidity required for the optimal yields of Maize, Rice and Wheat; Predicts the optimal yields of these global crops; Investigates degree of loss to food productions in Africa. A 3 by 3 factorial experiment was used for data analysis, further test was carried out using Duncan Multiple Range Test to detect the most significance of the various levels of climatic elements used. Multiple regression analysis was adopted to obtain the normal levels for the three main factors. Thus, the derived Quadratic regression model: Y_i=µ+αT_i+βH_i+ẟR_i+λT²_i+πH²_i+τR²_i+ξ_i was used to predict yields of these crops. The result of the various data analysis shows that all the three climatic elements contributed significantly to the yields of the three crops. In some region of Africa, Temperature, Rainfall and Humidity exceed normal while in some region the three climatic elements were below normal which affected the yields of the crops. Thus, there is reduction in the food production in Africa between 2015 and 2016: Maize 9.489%, Rice 11.482%, Wheat 14.827%.

Climate Change, Global food production, Quadratic Regression Analysis and factorial Experiment.

The level of urbanization in developing nations indicates that more people live in cities than before. This increase heaviness on traffic flow and makes living in urban area complex. Traffic control at road junction which was done purely by human effort and expansion of roads remain inefficient owing to the increasing rate of both motorists as well as the complexity of road networks. This paper proposes that an intelligent traffic light in addition to existing traffic management techniques should be put in place to monitor traffic congestions. The traffic light system is designed using arduino uno microcontroller, ultrasonic sensor, liquid crystal display and light emitting diode (LED). For effective traffic control, the controller was programmed using C language. The designed traffic light control system was simulated on cardboard and using toy cars as model of the real vehicle.

Arduino Uno Controller, LCD, LED, Ultrasonic Sensor, Traffic Simulation.

In this article, we propose the use of information color to authenticate face; several spaces of colors were used for the transformation of colorimetric components RGB of the original images. The results obtained in different spaces/or component colorimetric are combined by the use of a nonlinear fusion with a simple neuron network type MLP (Multi layer perceptron). We applied the method of linear discriminant analysis (LDA) to extract the characteristic vector of the face image. To validate this work we tested this approach on frontal images of the data base XM2VTS according to its associated protocol (protocol of Lausanne).

Linear discriminant analysis (LDA), face authentication, color spaces, neural network.

Mobile Network Operators (MNOs) permit subscribers to gain access to data, voice calls or short messaging services offered by their networks. Payments for any of these services could be via online payments platforms, short codes provided by financial institutions or the acquisition of recharge vouchers. Of these payment methods, the recharge voucher acquisition option remains the most widely used. However, illegal access to the recharge codes has not been totally eradicated and this has marred the recharge vouchers payment method. Therefore, this paper examined and demonstrated the feasibility of employing Quick Response (QR) code as a more secured payment alternative to recharge vouchers. Twitter fabric’s crashlytics tool was used to evaluate the performance of the developed QR code mobile payment method for a three month periods. A successful payment attempt and crash free sessions were experienced among 48 volunteers who used the application in 125 sessions.

Mobile Payment Systems, Mobile Network Operators, Quick Response codes, Recharge Vouchers.

A mobile ad hoc network (MANET) nodes move arbitrarily and as a result the networks experience a rapid and unpredictable topology changes. The mobile nodes can receive and forward packets as router which leads to superfluous energy consumption. Routing being critical MANET makes this paper focus on one of the routing protocols i.e. Ad hoc On-Demand Distance Vector (AODV).To make the AODV more efficient with an enhanced route discovery procedure that will yield a reduction in the transmission delay that is caused due to instability of the path. Mobile nodes in MANET are battery dependant then suffers from limited energy level and as a result makes it difficult to recharge /replaced. Energy constraint in MANET lead to the development of an enhanced AODV (E-AODV) that reduces the variance in residual energy of the nodes, it also proposed a link stability parameter while selecting the path in order to increase the energy efficiency using a formulation for the link strength of the path that have the highest residual energy between the nodes. The enhanced AODV selects the path with the highest reliability and stability. Simulation was carried out using OPNET modeler for a different number of nodes in terms of delay, traffic sent and traffic received and the results shows that in any way the enhanced E-AODV outperforms the AODV.

AODV, E-AODV, OPNET riverbed, MANET.

This study aimed at obtaining variance component estimators for all effects of Sudoku square models. The analysis of variance (ANOVA) method was used for the derivation of the variance components for the four Sudoku models.

Sudoku Square design, Variance components, ANOVA.

Complexity metric is used to estimate various parameters such as software development cost, amount of time needed for implementation and effort required in understanding the software. In this paper, different software complexity models are critically studied and compared. For application, heap sort algorithm is considered. The programs are written in three object oriented languages: C++, C# and Java. Software complexity for each program is found using the four popular Line of Code (LOC), McCabe Cyclomatic Complexity Metric, Halstead Metric and Cognitive model (Improved Cognitive Complexity Metric (ICCM)). The results are compared, according to Halstead Program Difficulty and ICCM, program in C++ has complexity higher than that of program in Java and program in Java has complexity higher than that of program in C#.

Software Complexity Metric, Line of Code, Cyclomatic Number, Halstead metric, Cognitive Complexity, Heap sort algorithm.

There are different facets of software complexity some of which have been computed using widely accepted metrics like cognitive complexity metric such as Improved cognitive complexity measure (ICCM), Cognitive functional size (CFS), and Cognitive information complexity measure (CICM), Cognitive complexity metric reflects difficulty for programmers to understand the code and the information packed in it. In this research work, the strength and weakness of existing cognitive complexity metric such as Improved Cognitive Complexity Measure (ICCM), Cognitive Functional Size (CFS), and Cognitive Information Complexity Measure (CICM) on breadth first search code implemented in C++, C# and java language were examined.

Breadth first search algorithm, Improved Cognitive Complexity Metric, Cognitive Functional Size, Cognitive Information Complexity Metric.

Due to the problem that normally occur when colors are needed to be chosen for industrial uses, color are needed to be mixed together to generate new difference color. The algorithms were developed in two phases which consist of illustration algorithm and the other aspect which is the working algorithm which focus on the pattern of the mixing of any two colors in order to generated new color. Java programming language is used to develop the color mixture template as a guide for the users in industrial painting in order to know the likely match color, when two colors where to mixed together. This color mixture techniques algorithm will go a long way in generating the new color and will surely eliminate the issue of color riot when trying to use for industrial purpose and also help to showcase the beauty of color mixture.

Algorithm, Color, Generated, Light, Mixture.

One of the major causes of death in Ilorin metropolis which is the capital of Ilorin Kwara State is the issue of environmental waste. Wastes are unused and rejected materials from household, schools, industries and highways. The waste materials are increasing as the population also increases. This paper aimed at developing a software model that is capable of predicting the waste generation and number of RORO bin needed based on population. Visual Basic programming was used to interpret the model developed and with this, we can predict the waste generation and number of RORO bin in One Dimensional representation. This will help both the government of Kwara state and Nigerian as a whole to tackle the environmental treat causes by waste generation by predetermine the number of RORO bin a particular environment or city we be needed. It will also assist the government to plan ahead especially in procurement of RORO bin and also reduce the number of disease (such as Malaria, Typhiod) which the government spends millions of Naira on yearly.

Waste, RORO bin, Population, Generation.

The introduction of R software into the statistical computing space has provided comprehensive language for managing and manipulating multidimensional data. Developing the capacity and skills of students and actuarial analysts is essential for actuarial practices. In this study, the use of R is proposed as a decision support tool for in the field of actuarial teaching and practice, as a complement to the existing excel platform. Count data is fitted using six regression models out of which zero-inflated Poisson model is considered to be most suitable model for the count data based on information criteria; also procedure for reserving is demonstrated. It is expected that this would promote the use of R among academia and practitioners.

R, Decision support tool, Actuarial Analysis, Valuation, Reserving.

Medical problems are often experienced during child delivery in a poor place of delivery and health care. The use of immediate health indicator as direct proxies for choice making biases estimates that these proxies are poor correlates. This study is aimed at investigating the hypothesized relationship and effect between choice of place of delivery and among socio demographic risk factors by using single factor mode in Structural Equation Modeling(SEM). Result of our findings shows that Place of residence, mother education, mother age, mother occupation, marital status and religion are hypothetically significantly correlated with choice of place of delivery. Overall, the measurement model perfectly fits the covariance (χ2 = 8451.279 with p value =0 .000, df =14). The model achieved a good fit for a dataset with the sample size adequate for the Chi-Square test. Thus, the measurement model in structural equation modeling is appropriate for the analysis. The importance of the choice of place of delivery cannot be overemphasized. Therefore, government at all levels should provide a means of informing women about it.

Measurement model; single factor model and structural equation modeling.

The paper examined the rainfall distribution of Enugu state in Nigeria. Box-Jenkins methodology was used to build ARIMA model to analyze data and forecast for the period of 15 years, from January, 2002 to December, 2016 and to predict for the future. We observed that the average annual rainfall of Enugu state ranges from 124mm to 179mm. The irregularity in annual rainfall of Enugu State one and half decades ago is a bit large, indicating that climate stability is high in the state. Different time series models were diagnostically checked, and tested for Enugu state and at last an SARIMA (0, 0, 0) (1, 0, 1)12 model is chosen as the proposed best model. The proposed model was used to forecast two years’ monthly rainfall value for the state. The results indicated that relatively there is a tendency of increasing in trend of future rainfall values in the state.

Modelling; Box and Jenkins; ARIMA; Rainfall; SARIMA; Forecasting; Enugu State.

The study investigates asymptotic classification of high dimensional data by adopting Gaussian Process, five different kernels(covariance functions) were employed and compared to showcase the outperformed kernel asymptotically. Log marginal likelihood, Accuracy and log loss were the measurement criteria adopted to measure classification performances. The study therefore observed that the classification observed asymptotically and found out that gpml had overall best model improvement asymptotically and across the covariance structures. K3 and K4 had the best accuracy in classification paradigm at the lower sample sizes but gpml and learned kernel had best model accuracy as the sample sizes tend to large scales.

Bayesian, Kernels, Classification and Gaussian Process..

This study aimed at identifying and retaining factors that contributed immensely to economic growth in Nigeria based on some variable selection methods. Stepwise regressions are often not efficient when there is multicollinearity. It was observed that the model suffers the problem of multicollinearity and this necessitate the use of other variable selection based on Partial least squares and Lasso. In this study, twelve factors were available to predict economic growth. It was observed that oil revenue, non-oil revenue and Capital Expenditure on Transfers have positive impacts on Nigeria economic growth and should be retained.

Variable selection methods, Oil revenue, Non-oil revenue, Capital expenditure, Economic growth.

This study is aimed at analysing the effects of Poultry manure and NPK fertilizer on fresh weight, dry weight and yield of Maize at the Institute of Agricultural Research and Training, Moore Plantation, Apata, Ibadan in 2015 and 2016 Cropping Sessions. The study adopted the statistical model for analysing the effects of Poultry manure and NPK fertilizer applications on the growth and yield of Maize (Zea Mays). This model was based on factorial experiment and the results obtained showed that poultry manure and NPK fertilizers equally contributed significantly to the fresh plant weights of maize for a bumper harvest while the interaction of the two factors did not indicate any significant difference. In the analysis of dry weight of maize the contribution of the two factors considered was the same to the quality of dry weight of maize.The results also showed that the two factors contributed greatly to the quality and bumper harvest of maize. However, the interaction of the factors gave the impression that poultry manure and NPK fertilizer have equal contributions to the quality and bumper harvest of seed yield of maize.

Analysis, Factorial experiment, Maize, Model, Yield.

Learning Management System (LMS) is an Internet based software system for educational or learning environment. Most Learning Management System not only has features for creation and distribution of content but also has features to track the level of learning or training. Being internet based, it becomes imperative to offer the learning environment in any such smart device like mobile phone, computer etc. LMS can be a learning environment for any setup be it formal academic educational institutions like schools, colleges and universities or informal coaching/training institutions/organizations. LMS provides interactive and evaluative environment for learning. Without doubt LMS is a cost effective and convenient learning platform. This paper presents a survey of various popular LMS and brings out the comparison of the identified and important features.

Survey, Learning Management System, LMS,MOODLE, BlackBoard, Cloud LMS, Opensource LMS

Skin diseases are common diseases that exist between the children and adults in the society. The issue of finding and proffering a better skin disease predictive model in the health care system has been identified to be major problem. Thus, this study provides a comparative evaluation on two data mining classification techniques; Decision Tree and Multi-layer Neural Network apply for the prediction of skin diseases. All experimental analysis were carried out in WEKA data mining tool environment. Each individual classifier was put through training and testing using the N-fold cross validation technique (N value was set to 10). The two classifiers are Decision Tree and Neural Network family respectively. The predictive model obtained from the J48 and Multi-layer Perceptron (MLP) was measured and evaluated accordingly with the use of basic parameters such as accuracy, kappa statistics, TP Rate, FP Rate, Precision, Recall, ROC area. Multi-layer neural network presented accuracy of 96.9945 % while J48 gave an accuracy of 93.9891%.

Skin Disease, Prediction, Decision Tree, Artificial Neural Network, Multi-layer Perceptron (MLP)

One of the most dangerous diseases in the modern society is diabetes mellitus and it is not only a medical problem but also a socio-economy. Artificial Intelligence techniques have been successfully employed in diabetes disease diagnosis, risk evaluation, patient monitoring, and medicine. Using single techniques in diagnosis of diabetes has been comprehensively investigated showing some level of accuracy. Researchers have investigated the effect of hybridizing more than one technique to show enhance results in the diagnosis of diabetes. However, using the combination of two or more technique to identify a suitable treatment for diagnosis of diabetes patients has received less attention. Therefore, in this work, a framework for three intelligent approaches will be used to develop an expert system using Genetic, Neural network and Fuzzy logic techniques. The framework will further be used to diagnosis and management of Diabetes Mellitus and compare the model with existing work to determine it performance.

Diabetes mellitus, Artificial intelligence, expert system, Diagnosis, Genetic algorithm.

Some recent cross-country cross-sectional analyses have employed Bayesian Model Averaging to tackle the issue of model uncertainty. Bayesian model averaging has become an important tool in empirical settings with large numbers of potential regressors and relatively limited number of observations. We examine the effect of a variety of prior assumptions on the inference, posterior inclusion probabilities of regressors and on predictive performance. Bayesian model averaging (BMA) has become a widely accepted way of accounting for model uncertainty in regression models. However, to implement BMA, a prior is usually specified in two parts: prior for the regression parameters and prior over the model space. Hence, the choice of prior specification becomes paramount in Bayesian inference, unfortunately, in practice, most Bayesian analyses are performed with the so-called non-informative priors (i.e. priors constructed by some formal rule). The arbitrariness in the choice of prior or choosing inappropriate priors often lead to badly behaved posteriors. It is therefore imperative to study the effect of choice of priors in Bayesian model averaging. Six candidate parameter priors namely, Unit information prior (UIP), Risk inflation criterion (RIC), Bayesian Risk Inflation criterion (BRIC), Hannan-Quinn criterion (HQ), Empirical Bayes (EBL) and hyper-g and three model priors: uniform, beta-binomial and binomial were examined in this study. The performances of the resulting eighteen cases were judged using posterior inference, posterior inclusion probabilities of regressors and predictive performance. Analyses were carried out using datasets with 8-potential drivers of growth for 126 countries from 2010 to 2014. Finally, our analysis shows that the EBL parameter prior with random model prior robustly identifies far more growth determinants than other priors.

Prior specification, Bayesian Model Averaging, Economic growth, Predictive performance.