Miss OHORIEMU OKEOGHENE BLESSING
Publication Title ROLE OF STATISTICS IN NIGERIA POLITICS
Publication Type journal
Publisher Global Journal of Applied and Physical Sciences
Publication Authors Okeoghene Blessing Ohoriemu
Year Published 2023-11-03
Abstract This paper attempts to analyze the role of Statistic in our society today through comprehensive and
timely collection of data from the grass root to the urban and utilizing same for efficient and
adequate elections. Attempt was made to critically justify how elections in politic dispensation can
be improved upon. The discussion for considering the use of statistics in politics as a useful resource
for democracy in the society have also been advanced. The quest for democracy and good
governance has been a global phenomenon over the years and Nigeria which is still green in
democracy is not an exception. Evidence has shown that both in the theoretical and empirical
literature, democracy, good governance and statistics mutually reinforces one another and are
supportive of economic development. In fact, this probably underscores the popular saying that the
worst form of democracy is better than a military regime no matter how good it may be. The
indispensability of statistics for economic development cannot be overemphasized. Statistics
provides the ingredient for planning and policy execution. It provides the numbers for matching
resources with needs, particularly, ensuring equity and justice. The paper aimed at demonstrating
how democracy and good governance are appraised using statistics in Nigeria.
Publication Title COMPUTER SIMULATIONS AS A TOOL FOR ENHANCING ALGEBRA RETENTION IN JUNIOR SECONDARY SCHOOLS: AN ANALYSIS
Publication Type journal
Publisher
Publication Authors *1Justin Onyarin Ogala and 2Ohoriemu Blessing Okeoghene
Year Published 2024-08-04
Abstract Over the years, Nigerian students have consistently performed poorly in mathematics. The West African
Examination Council (WAEC) reports a steady decline in math performance among examinees outside the
school system. Research papers and Chief Examiners' Reports have highlighted that students struggle with
certain math tasks, indicating general deficiencies in mathematical skills. This research aimed to investigate
the impact of computer simulation on students' retention of algebraic mathematics. The study was guided by
two research questions and two hypotheses. A quasi-experimental design was employed, involving fifty-four
students from two different schools. Each school had two junior secondary streams, with experimental and
control groups assigned randomly. The lessons were conducted by the students' regular math teachers, who
also served as research assistants. The Algebra Achievement Test (AAT) was used to measure retention, with
a reliability coefficient 0.98. Data analysis was performed using SPSS software, with a significance level set
at 0.05. The results revealed no statistically significant differences in the mean retention scores of male and
female students who received algebra instruction through computer simulation.
Publication Title MULTIPLE LINEAR REGRESSION MODEL: A STATISTICAL TOOL FOR PREDICTION OF SCORES OF FINAL YEAR MATHEMATICS DEGREE STUDENTS OF THE COLLEGE OF EDUCATION, AGBOR IN AFFILIATION WITH DELTA STATE UNIVERSITY, ABRAKA
Publication Type journal
Publisher abacus
Publication Authors Ohoriemu Blessing Okeoghene, Osemeke Reuben Friday and Aghamie Sunday Osiebun
Year Published 2022-08-02
Abstract Multiple linear regression is one of the most widely used statistical tools in diagnosing the
performance of students in any examination. It is defined as a multivariate technique for
assessing the correlation between a dependent variable(Y) and some combination of two or more
independent variables(X1, X2, X3 . . Xp). In this paper, a multiple linear regression model
comprises of three independent variables(X1, X2, X3) is developed to analyses the performance
of final year mathematics Degree students of the College of Education, Agbor in affiliation with
Delta State University, Abraka. The model is based on the data of student’s scores in first tests,
second test, and class attendance. The estimates both of the magnitude and statistical
significance of relationships between the variables have been provided. Several statistical
measures such as descriptive statistics, F calculated, T calculated, coefficient of
determination(r2), adjusted coefficient of determination, Mallows Cp Statistic, multicollinearity
diagnostics and graphical residual plots, were used as a benchmark for selection of best subsets
optimal regression models in a multiple regression diagnostics and a statistical tool for the
analyzing the performance of final year mathematics Degree students of the College of
Education, Agbor in affiliation with Delta State University, Abraka
Publication Title INTEGRATING ARTIFICIAL INTELLIGENCE AND MATHEMATICAL MODELS FOR PREDICTIVE MAINTENANCE IN INDUSTRIAL SYSTEMS
Publication Type journal
Publisher
Publication Authors *1Justin Onyarin Ogala and 2Ohoriemu Blessing Okeoghene
Year Published 2024-06-03
Abstract Predictive maintenance is a critical task for ensuring the reliability and efficiency of industrial systems. The
integration of artificial intelligence (AI) and mathematical models has shown great potential in improving the
accuracy and efficiency of predictive maintenance. This study provides an overview of the different types of
mathematical models used for predictive maintenance, including physics-based, data-driven, and hybrid
models. The study also discusses how AI techniques, such as machine learning and deep learning, can be used
to enhance the accuracy and efficiency of predictive maintenance models. Additionally, the article highlights
some of the challenges and limitations of integrating AI and mathematical models for predictive maintenance
in industrial systems. Finally, this study provides a case study to demonstrate the practical application of the
integrated approach for predictive maintenance in an industrial setting. This article aims to provide a
comprehensive overview of the state-of-the-art in integrating AI and mathematical models for predictive
maintenance and to provide guidance for researchers and practitioners working in this field.
Publication Title A HYBRID APPROACH TO SOLVING COMPLEX OPTIMIZATION PROBLEMS USING EVOLUTIONARY ALGORITHMS AND MATHEMATICAL MODELING
Publication Type journal
Publisher
Publication Authors *1Justin Onyarin Ogala and 2Ohoriemu Blessing Okeoghene
Year Published 2024-06-03
Abstract T
It can be difficult to optimize complex issues, and doing so frequently calls for the application of cutting-edge
methods like mathematical modelling and evolutionary algorithms. Our proposal in this work is to address
complex optimization issues using a hybrid strategy that integrates both approaches. The suggested method
builds a surrogate model of the issue by mathematical modelling, which is subsequently optimized through the
application of evolutionary algorithms. The hybrid methodology is tested against other optimization methods,
such as particle swarm optimization and genetic algorithms, on a series of benchmark tasks. The experimental
findings demonstrate that in terms of both computing time and solution quality, the suggested hybrid strategy
performs better than various alternative methods. The suggested methodology exhibits great potential as a
means of resolving intricate optimization issues across diverse fields, such as engineering, finance, and
healthcare