Papers by Kennedy E Ehimwenma, Ph.D.
International Conference on Internet of Everything, 2023
Execution costs are broadly used in the evaluation of the scalability of IoT systems. A well-know... more Execution costs are broadly used in the evaluation of the scalability of IoT systems. A well-known concern in their use is the extent to which their scalability desiderata best predicts Quality of Service (QoS). At first, past studies did not ratify a relationship between the scalability approaches and QoS in IoT systems. More recently, however, the correlations between these have begun to emerge. In this paper, we extend those findings and open up new avenues to further research by proposing a statistical testing approach for scrutinizing this relationship. The initial findings delineate that there is a significant correlation between the scalability approach employed and QoS in IoT systems. Our results strengthen the use of execution costs in the scalability of IoT systems confirming that QoS can be successfully predicted.
Educational Dimension
A red-black (RB) tree is a data structure with red and black nodes coloration. The red and black ... more A red-black (RB) tree is a data structure with red and black nodes coloration. The red and black color of nodes make up the principal component for balancing a RB tree. A balanced tree has an equal number of black nodes on any simple path. But when a black leaf node is deleted, a double-black (DB) node is formed, thus, causing a reduction in black heights and the tree becomes unbalanced. Rebalancing a RB tree with a DB node is a fairly complex process. Teaching and learning the removal of DB nodes is also challenging. This paper introduces a simplified novel method which is a symbolic-algebraic arithmetic procedure for the removal of DB nodes and the rebalancing of black heights in RB trees. This simplified approach has enhanced student learning of the DB node removal in RB trees. Feedback from students showed the learnability, workability and acceptance of the symbolic-algebraic method in balancing RB trees after a delete operation.
International Journal of Artificial Intelligence and Applications (IJAIA), 2022
The probability of an event is in the range of [0, 1]. In a sample space S, the value of probabil... more The probability of an event is in the range of [0, 1]. In a sample space S, the value of probability determines whether an outcome is true or false. The probability of an event Pr(A) that will never occur = 0. The probability of the event Pr(B) that will certainly occur = 1. This makes both events A and B thus a certainty. Furthermore, the sum of probabilities Pr(E1) + Pr(E2) + … + Pr(En) of a finite set of events in a given sample space S = 1. Conversely, the difference of the sum of two probabilities that will certainly occur is 0. Firstly, this paper discusses Bayes’ theorem, then complement of probability and the difference of probability for occurrences of learning-events, before applying these in the prediction of learning objects in student learning. Given the sum total of 1; to make recommendation for student learning, this paper submits that the difference of argMaxPr(S) and probability of student-performance quantifies the weight of learning objects for students. Using a dataset of skill-set, the computational procedure demonstrates: i) the probability of skill-set events that has occurred that would lead to higher level learning; ii) the probability of the events that has not occurred that requires subject-matter relearning; iii) accuracy of decision tree in the prediction of student performance into class labels; and iv) information entropy about skill-set data and its implication on student cognitive performance and recommendation of learning [1].
Environmental Science and Pollution Research, 2021
Human societies develop rapidly through the advancement of technology; however, with these advanc... more Human societies develop rapidly through the advancement of technology; however, with these advancements, many problems are emerging. The topic chosen for this study surrounds the e-waste, which has become a major problem around the world. Secondhand and unused mobile phones are a big part of globally generated e-waste. If these devices are properly recycled, they can generate substantial economic and resource value. Yet if they are indiscriminately discarded, they cause a profound environmental impact. Given the current low recovery rate of mobile phones, an increase in recovery rates becomes critical in lessening economic and environmental impacts. Based on the status quo of secondhand mobile phone recycling processes in China, this article analyzes the behavior of individuals and recyclers through a comprehensive static information game theory and finds ways to increase the recycling rate of secondhand mobile phones. The study helps the customers, to clearly identify the recycle price. In case of market, the government policy can be introduced with a reward and punishment mechanism. Furthermore, under the ideological guidance of game theory, this paper also establishes a corresponding price model of secondhand mobile phone recycling based on best response dynamics like search, variable neighborhood search, and hybrid meta-heuristic method. This model shows that the recovery time differences have a significant impact on the recovery price. Moreover, to an extent, this model can promote the possibility and initiative of customers choosing cell phone recycling.
A red-black (RB) tree is a data structure with red and black nodes coloration. The red and black ... more A red-black (RB) tree is a data structure with red and black nodes coloration. The red and black color of nodes make up the principal component for balancing a RB tree. A balanced tree has an equal number of black nodes on any simple path. But when a black leaf node is deleted, a double-black (DB) node is formed, thus, causing a reduction in black heights and the tree becomes unbalanced. Rebalancing a RB tree with a DB node is a fairly complex process. Teaching and learning the removal of DB nodes is also challenging. This paper introduces a simplified novel method which is a symbolic-algebraic arithmetic procedure for the removal of DB nodes and the rebalancing of black heights in RB trees. This simplified approach has enhanced student learning of the DB node removal in RB trees. Feedback from students showed the learnability, workability and acceptance of the symbolic-algebraic method in balancing RB trees after a delete operation.
The probability of an event is in the range of [0, 1]. In a sample space S, the value of probabil... more The probability of an event is in the range of [0, 1]. In a sample space S, the value of probability determines whether an outcome is true or false. The probability of an event Pr(A) that will never occur = 0. The probability of the event Pr(B) that will certainly occur = 1. This makes both events A and B thus a certainty. Furthermore, the sum of probabilities Pr(E1) + Pr(E2) + … + Pr(En) of a finite set of events in a given sample space S = 1. Conversely, the difference of the sum of two probabilities that will certainly occur is 0. Firstly, this paper discusses Bayes' theorem, then complement of probability and the difference of probability for occurrences of learning-events, before applying these in the prediction of learning objects in student learning. Given the sum total of 1; to make recommendation for student learning, this paper submits that the difference of argMaxPr(S) and probability of student-performance quantifies the weight of learning objects for students. Using a dataset of skill-set, the computational procedure demonstrates: i) the probability of skill-set events that has occurred that would lead to higher level learning; ii) the probability of the events that has not occurred that requires subject-matter relearning; iii) accuracy of decision tree in the prediction of student performance into class labels; and iv) information entropy about skill-set data and its implication on student cognitive performance and recommendation of learning [1].
Educational Dimension Vol. 59, pp. 112-129 , 2022
A red-black (RB) tree is a data structure with red and black nodes coloration. The red and black ... more A red-black (RB) tree is a data structure with red and black nodes coloration. The red and black color of nodes make up the principal component for balancing a RB tree. A balanced tree has an equal number of black nodes on any simple path. But when a black leaf node is deleted, a double-black (DB) node is formed, thus, causing a reduction in black heights and the tree becomes unbalanced. Rebalancing a RB tree with a DB node is a fairly complex process. Teaching and learning the removal of DB nodes is also challenging. This paper introduces a simplified novel method which is a symbolic-algebraic arithmetic procedure for the removal of DB nodes and the rebalancing of black heights in RB trees. This simplified approach has enhanced student learning of the DB node removal in RB trees. Feedback from students showed the learnability, workability and acceptance of the symbolic-algebraic method in balancing RB trees after a delete operation.
International Journal of Artificial Intelligence and Applications (IJAIA), Vol.13, No.5,, 2022
The probability of an event is in the range of [0, 1]. In a sample space S, the value of probabil... more The probability of an event is in the range of [0, 1]. In a sample space S, the value of probability determines whether an outcome is true or false. The probability of an event Pr(A) that will never occur = 0. The probability of the event Pr(B) that will certainly occur = 1. This makes both events A and B thus a certainty. Furthermore, the sum of probabilities Pr(E1) + Pr(E2) + … + Pr(En) of a finite set of events in a given sample space S = 1. Conversely, the difference of the sum of two probabilities that will certainly occur is 0. Firstly, this paper discusses Bayes' theorem, then complement of probability and the difference of probability for occurrences of learning-events, before applying these in the prediction of learning objects in student learning. Given the sum total of 1; to make recommendation for student learning, this paper submits that the difference of argMaxPr(S) and probability of student-performance quantifies the weight of learning objects for students. Using a dataset of skill-set, the computational procedure demonstrates: i) the probability of skill-set events that has occurred that would lead to higher level learning; ii) the probability of the events that has not occurred that requires subject-matter relearning; iii) accuracy of decision tree in the prediction of student performance into class labels; and iv) information entropy about skill-set data and its implication on student cognitive performance and recommendation of learning [1].
IInternational Journal of Artificial Intelligence and Applications (IJAIA), Vol.13, No.5, 2022
The probability of an event is in the range of [0, 1]. In a sample space S, the value of probabil... more The probability of an event is in the range of [0, 1]. In a sample space S, the value of probability determines whether an outcome is true or false. The probability of an event Pr(A) that will never occur = 0. The probability of the event Pr(B) that will certainly occur = 1. This makes both events A and B thus a certainty. Furthermore, the sum of probabilities Pr(E1) + Pr(E2) + … + Pr(En) of a finite set of events in a given sample space S = 1. Conversely, the difference of the sum of two probabilities that will certainly occur is 0. Firstly, this paper discusses Bayes' theorem, then complement of probability and the difference of probability for occurrences of learning-events, before applying these in the prediction of learning objects in student learning. Given the sum total of 1; to make recommendation for student learning, this paper submits that the difference of argMaxPr(S) and probability of student-performance quantifies the weight of learning objects for students. Using a dataset of skill-set, the computational procedure demonstrates: i) the probability of skill-set events that has occurred that would lead to higher level learning; ii) the probability of the events that has not occurred that requires subject-matter relearning; iii) accuracy of decision tree in the prediction of student performance into class labels; and iv) information entropy about skill-set data and its implication on student cognitive performance and recommendation of learning [1].
The probability of an event is in the range of [0, 1]. In a sample space S, the value of probabil... more The probability of an event is in the range of [0, 1]. In a sample space S, the value of probability determines whether an outcome is true or false. The probability of an event Pr(A) that will never occur = 0. The probability of the event Pr(B) that will certainly occur = 1. This makes both events A and B thus a certainty. Furthermore, the sum of probabilities Pr(E1) + Pr(E2) + … + Pr(En) of a finite set of events in a given sample space S = 1. Conversely, the difference of the sum of two probabilities that will certainly occur is 0. Firstly, this paper discusses Bayes' theorem, then complement of probability and the difference of probability for occurrences of learning-events, before applying these in the prediction of learning objects in student learning. Given the sum total of 1; to make recommendation for student learning, this paper submits that the difference of argMaxPr(S) and probability of student-performance quantifies the weight of learning objects for students. Using a dataset of skill-set, the computational procedure demonstrates: i) the probability of skill-set events that has occurred that would lead to higher level learning; ii) the probability of the events that has not occurred that requires subject-matter relearning; iii) accuracy of decision tree in the prediction of student performance into class labels; and iv) information entropy about skill-set data and its implication on student cognitive performance and recommendation of learning [1].
First-order logic based data structure have knowledge representations in Prolog-like syntax. In a... more First-order logic based data structure have knowledge representations in Prolog-like syntax. In an agent based system where beliefs or knowledge are in FOL ground fact notation, such representation can form the basis of agent beliefs and inter-agent communication. This paper presents a formal model of classification rules in first-order logic syntax. In the paper, we show how the conjunction of boolean [Passed, Failed] decision predicates are modelled as Passed(N) or Failed(N) formulas as well as their implementation as knowledge in agent oriented programming for the classification of students' skills and recommendation of learning materials. The paper emphasizes logic based contextual reasoning for accurate diagnosis of students' skills after a number of prior skills assessment. The essence is to ensure that students attain requisite skill competences before progressing to a higher level of learning.
The probability computation of events is in the interval of [0, 1], which are values that are det... more The probability computation of events is in the interval of [0, 1], which are values that are determined by the number of outcomes of events in a sample space S. The probability Pr(A) that an event A will never occur is 0. The probability Pr(B) that event B will certainly occur is 1. This makes both events A and B a certainty. Furthermore, the sum of probabilities Pr(E₁) + Pr(E₂) + … + Pr(Eₙ) of a finite set of events in a given sample space S equals 1. Conversely, the difference of the sum of two probabilities that will certainly occur is 0. This paper first discusses Bayes, the complement of probability, and the difference of probability for occurrences of learning-events before applying them in the prediction of learning objects in student learning. Given the sum of 1; to make a recommendation for student learning, this paper proposes that the difference of argMaxPr(S) and the probability of student-performance quantifies the weight of learning objects for students. Using a dataset of skill-set, the computational procedure demonstrates i) the probability of skill-set events that have occurred that would lead to higher-level learning; ii) the probability of the events that have not occurred that requires subject-matter relearning; iii) accuracy of the decision tree in the prediction of student performance into class labels and iv) information entropy about skill-set data and its implication on student cognitive performance and recommendation of learning.
Human societies develop rapidly through the advancement of technology; however, with these advanc... more Human societies develop rapidly through the advancement of technology; however, with these advancements, many problems are emerging. The topic chosen for this study surrounds the e-waste, which has become a major problem around the world. Secondhand and unused mobile phones are a big part of globally generated e-waste. If these devices are properly recycled, they can generate substantial economic and resource value. Yet if they are indiscriminately discarded, they cause a profound environmental impact. Given the current low recovery rate of mobile phones, an increase in recovery rates becomes critical in lessening economic and environmental impacts. Based on the status quo of secondhand mobile phone recycling processes in China, this article analyzes the behavior of individuals and recyclers through a comprehensive static information game theory and finds ways to increase the recycling rate of secondhand mobile phones. The study helps the customers, to clearly identify the recycle price. In case of market, the government policy can be introduced with a reward and punishment mechanism. Furthermore, under the ideological guidance of game theory, this paper also establishes a corresponding price model of secondhand mobile phone recycling based on best response dynamics like search, variable neighborhood search, and hybrid meta-heuristic method. This model shows that the recovery time differences have a significant impact on the recovery price. Moreover, to an extent, this model can promote the possibility and initiative of customers choosing cell phone recycling.
Agent unified modeling languages (AUML) are agent-oriented approaches that supports the specifica... more Agent unified modeling languages (AUML) are agent-oriented approaches that supports the specification, design, visualization and documentation of an agent-based system. This paper presents the use of prometheus AUML approach for the modeling of a pre-assessment system of five interactive agents. The pre-assessment system, as previously reported, is a multi-agentbased e-learning system that is developed to support the assessment of prior learning skills in students so as to classify their skills and make recommendation for their learning. This paper discusses the detailed design approach of the system in a step-by-step manner; and domain knowledge abstraction and organization in the system. In addition, the analysis of the data collated and models of prediction for future pre-assessment results are also presented.
International Journal of Artificial Intelligence & Applications, 2016
Objects or structures that are regular take uniform dimensions. Based on the concepts of regular ... more Objects or structures that are regular take uniform dimensions. Based on the concepts of regular models, our previous research work has developed a system of a regular ontology that models learning structures in a multiagent system for uniform pre-assessments in a learning environment. This regular ontology has led to the modelling of a classified rules learning algorithm that predicts the actual number of rules needed for inductive learning processes and decision making in a multiagent system. But not all processes or models are regular. Thus this paper presents a system of polynomial equation that can estimate and predict the required number of rules of a non-regular ontology model given some defined parameters.
IAES International Journal of Artificial Intelligence, 2021
Agent unified modeling languages (AUML) are agent-oriented approaches that supports the specifica... more Agent unified modeling languages (AUML) are agent-oriented approaches that supports the specification, design, visualization and documentation of an agent-based system. This paper presents the use of prometheus AUML approach for the modeling of a Pre-assessment System of five interactive agents. The Pre-assessment System, as previously reported, is a multi-agent-based e-learning system that is developed to support the assessment of prior learning skills in students so as to classify their skills and make recommendation for their learning. This paper discusses the detailed design approach of the system in a step-by-step manner; and domain knowledge abstraction and organization in the system. In addition, the analysis of the data collated and models of prediction for future pre-assessment results are also presented.
International Journal of Information Technology and Computer Science
First-order logic based data structure have knowledge representations in Prolog-like syntax. In a... more First-order logic based data structure have knowledge representations in Prolog-like syntax. In an agent based system where beliefs or knowledge are in FOL ground fact notation, such representation can form the basis of agent beliefs and inter-agent communication. This paper presents a formal model of classification rules in first-order logic syntax. In the paper, we show how the conjunction of boolean [Passed, Failed] decision predicates are modelled as Passed(N) or Failed(N) formulas as well as their implementation as knowledge in agent oriented programming for the classification of students' skills and recommendation of learning materials. The paper emphasizes logic based contextual reasoning for accurate diagnosis of students' skills after a number of prior skills assessment. The essence is to ensure that students attain requisite skill competences before progressing to a higher level of learning.
Int.J. Information Technology and Computer Science, 2018
First-order logic based data structure have knowledge representations in Prolog-like syntax. In a... more First-order logic based data structure have knowledge representations in Prolog-like syntax. In an agent based system where beliefs or knowledge are in FOL ground fact notation, such representation can form the basis of agent beliefs and inter-agent communication. This paper presents a formal model of classification rules in first-order logic syntax. In the paper, we show how the conjunction of boolean [Passed, Failed] decision predicates are modelled as Passed(N) or Failed(N) formulas as well as their implementation as knowledge in agent oriented programming for the classification of students' skills and recommendation of learning materials. The paper emphasizes logic based contextual reasoning for accurate diagnosis of students' skills after a number of prior skills assessment. The essence is to ensure that students attain requisite skill competences before progressing to a higher level of learning.
Uploads
Papers by Kennedy E Ehimwenma, Ph.D.