Browsing by Author "OGUNDE, ADEWALE OPEOLUWA"

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    A Decision Tree Algorithm Based System for Predicting Crime in the University
    (Machine Learning Research, 2017) OGUNDE, ADEWALE OPEOLUWA
    CRIME is one of the major problems encountered in any society and universities together with other higher institutions of learning are not exceptions. Thus, there is an urgent need for security agents and agencies to battle and eradicate crime. The Directorate of Students and Services Development (DSSD) are responsible for investigating and detecting criminals of any crime committed within the Redeemer’s University. DSSD faces major challenges when it comes to detecting the real perpetrators of several crimes. An improvement in their strategy can produce positive results and high success rates, which is the basic objective of this project. Several methods have been applied to solve similar problems in the literature but none was tailored to solving the problem in Redeemer’s University and other universities. This work therefore applied classification rule mining method to develop a system for detecting crimes in universities. Past data for both crimes and criminals were collected from DSSD. In order to develop and test the proposed model, the data was pre-processed to get clean and accurate data. The Iterative Dichotomiser 3 (ID3) decision tree algorithm obtained from WEKA mining software was used to analyze and train the data. The model obtained was then used to develop a system that showed the hidden relationships between the crime-related data, in form of decision trees. This result was then used as a knowledge base for the development of the crime prediction system. The developed system could effectively predict a list of possible suspects by simply analyzing data retrieved from the crime scene with already existing data in the database. This system has all the potentials of helping the students’ affairs department and security apparatus of any university and other institutions to quickly detect either the real or possible perpetrators of crimes in the system.
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    A K-Nearest Neighbour Algorithm-Based Recommender System for the Dynamic Selection of Elective Undergraduate Courses
    (Science Publishing Group, 2019) OGUNDE, ADEWALE OPEOLUWA
    The task of selecting a few elective courses from a variety of available elective courses has been a difficult one for many students over the years. In many higher institutions, guidance and counsellors or level advisers are usually employed to assist the students in picking the right choice of courses. In reality, these counsellors and advisers are most times overloaded with too many students to attend to, and sometimes they do not have enough time for the students. Most times, the academic strength of the student based on past results are not considered in the new choice of electives. Recommender systems implement advanced data analysis techniques to help users find the items of their interest by producing a predicted likeliness score or a list of top recommended items for a given active user. Therefore, in this work, a collaborative filtering-based recommender system that will dynamically recommend elective courses to undergraduate students based on their past grades in related courses was developed. This approach employed the use of the k-nearest Neighbour algorithm to discover hidden relationships between the related courses passed by students in the past and the currently available elective courses. Real-life students’ results dataset was used to build and test the recommendation model. The new model was found to outperform existing results in the literature. The developed system will not only improve the academic performance of students; it will also help reduce the workload on the level advisers and school counsellors.
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    A Knowledge Sharing Architectural Model for Inter State e-Governement in Nigeria
    (Nigeria Computer Society, 2010) OGUNDE, ADEWALE OPEOLUWA
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    A partition enhanced mining algorithm for distributed association rule mining systems
    (Elsevier., 2015) OGUNDE, ADEWALE OPEOLUWA
    The extraction of patterns and rules from large distributed databases through existing Distributed Association Rule Mining (DARM) systems is still faced with enormous challenges such as high response times, high communication costs and inability to adapt to the constantly changing databases. In this work, a Partition Enhanced Mining Algorithm (PEMA) is presented to address these problems. In PEMA, the Association Rule Mining Coordinating Agent receives a request and decides the appropriate data sites, partitioning strategy and mining agents to use. The mining process is divided into two stages. In the first stage, the data agents horizontally segment the databases with small average transaction length into relatively smaller partitions based on the number of available sites and the available memory. On the other hand, databases with relatively large average transaction length were vertically partitioned. After this, Mobile Agent-Based Association Rule Mining-Agents, which are the mining agents, carry out the discovery of the local frequent itemsets. At the second stage, the local frequent itemsets were incrementally integrated by the from one data site to another to get the global frequent itemsets. This reduced the response time and communication cost in the system. Results from experiments conducted on real datasets showed that the average response time of PEMA showed an improvement over existing algorithms. Similarly, PEMA incurred lower communication costs with average size of messages exchanged lower when compared with benchmark DARM systems. This result showed that PEMA could be efficiently deployed for efficient discovery of valuable knowledge in distributed databases.
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    A Prototype System for Mining Frequent Citizens’ Demand Patterns from E-Government Databases
    (Computing and Information Systems Journal, 2017) OGUNDE, ADEWALE OPEOLUWA
    Purpose: The objective of this study is to propose a prototype system for collecting, analyzing, understanding and mining citizens pressing needs and demands from e-government databases. Design/Methodology/Approach: Common citizens demands were obtained from published literature and used to populate a database which was mined with the Apriori algorithm to obtain the most frequent citizens demands. Findings: Mining a collection of citizens’ pressing demands data from e-government databases is very feasible. Research limitations/implications: The developed system is a prototype designed and tested with a randomized synthetic dataset. The prototype can greatly assist the government to make informed decisions regarding the needs of the citizens. Practical implications: This will serve as a framework to the government and interested organizations for deploying such an important system that will provide a reliable link between the government and the citizens. The results should provide improved governance and also enhance citizens' satisfaction. Originality/value: For any government to succeed, citizens’ relationship management and satisfaction must be given a top priority. The developed prototype is practicable, achievable and promises great benefits to any interested government, researchers and system developers.
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    A recommender system for selecting potential industrial training organizations
    (John Wiley & Sons Ltd., 2019) OGUNDE, ADEWALE OPEOLUWA
    The difficulty in securing students industrial work experience scheme (SIWES) placements has negatively affected the final grades of some undergraduates. A recommender system is hereby proposed to solve this problem. This systemwill use past SIWES data to recommend potential organizations to future SIWES students. Research data collection was through an online questionnaire, with 200 respondents. The data was divided into a training data set (70%) and test data set (30%). Collaborative filtering recommendation approach was employed using the C4.5 algorithm to classify the data and generate a decision tree model from the training data set. The model generated was used to predict the class label of the test data set. Results from the data analysis carried out revealed that Kappa statistics was 0.7839, mean absolute error = 0.0058, and root mean squared error = 0.0586. In addition, true positive rate = 0.788, recall = 0.788, precision = 0.749, F-measure = 0.755,MCC = 0.760, ROC = 0.985, and PRC = 0.813. The high values obtained indicates that the model was predicting with a high level of accuracy, ie, 78.84% correctly classified instances. The developed model was used as a knowledge base to develop a very beneficial front-end web application, tagged “RecommendIT” where students can enter their preferences and view company recommendations.
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    A Review of Some Issues and Challenges in Current Agent-Based Distributed Association Rule Mining
    (Asian Journal of Information Technology, 2011) OGUNDE, ADEWALE OPEOLUWA
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    Development Of A Mobile-Based Accident Detection And Notification System With Multi Modal Data
    (Corpus Intellectual, 2024) OGUNDE, ADEWALE OPEOLUWA
    Road accidents is one of the factors responsible for fatalities and lifelong disabilities globally. The predominant increase in mortality rate on the highway has been attributed to the late arrival of emergency authorities, which could be because of delays in reporting time. The literature has suggested a number of approaches to deal with this significant issue. Some of these approaches include the use of machine learning algorithms. However, many of these works have been limited to single-mode (video, audio, or image) accident detection methods. In addition, some of the existing study explore the use of intelligence transportation system, which could be considered expensive to use. This research has developed a mobile-based accident detection and notification system with multi-modal data. The detection system has incorporated trained simulated data that is validated by unseen data online. The simulated data was characterized by some selected modal data (accelerometer, sound, and gravitational force magnitude) equivalent to the online features. The simulated data was trained using the multilayered perceptron artificial neural network (ANN) model. The trained model was tested using online data from the data world platform. The observation with the simulated data showed that the model achieved an accuracy of 99.5%. The result of the experiment on the online data showed 99.8% accuracy. At the end of the modelling, the ANN model was integrated into an android mobile-based accident detection and notification system. Furthermore, the system was tested with several case study and the result showed that the system performed as expected. Keywords: accelerometer, accident, artificial neural network, audio, G-force, android mobile application.
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    Knowledge Sharing for Academic Enhancement among Computer Science Undergraduates in Nigeria
    (2011) OGUNDE, ADEWALE OPEOLUWA
    Knowledge sharing is the process by which individuals collectively and iteratively refines a thought, an idea or a suggestion in the light of experiences and the socialization process through which people share knowledge with one another. Fallen standard of education in most African countries had been attributed to many reasons, one of which is that of gaining little or insufficient knowledge from these academic institutions. Very few students and schools have been able to overcome this barrier through internet and knowledge sharing and exchange to get up-to-date knowledge and skills from all around the world. There is a need for these few to share their knowledge with others for academic enhancement. Many knowledge sharing and social networking sites abound today on the internet, where users of these sites share and exchange information but there is rarely anyone specifically designed for academic knowledge sharing among Nigerian students. Most students wastes a lot of time, energy and other resources navigating existing sites and engaging in social interactions but not gaining any particular academic knowledge that will be of use in their discipline. A web-based student knowledge sharing system (SKSS) is therefore designed in this work to specifically facilitate academic knowledge sharing and interaction between Nigerian computer science undergraduates. Utilizing the system built in this work will help to generally enhance academic knowledge and improve the academic performance of the undergraduates, as valuable knowledge in the areas of algorithms, programming languages, software development, computer networking and engineering and other important topics could be easily shared and solved together in a timely and efficient manner by removing the obstacles that could be imposed by the geographical location of the students and their universities. SKSS can easily be extended to incorporate all other categories of students and disciplines in the country.
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    PROFESSOR
    (Journal of Computer Science and Information Technology, 2014-03) OGUNDE, ADEWALE OPEOLUWA
    The desire of every organization is to extract hidden but useful knowledge from their data through data mining tools. Also, the recent decline in the standard of education in most developing countries has necessitated researches that will help proffer solutions to some of the problems. From the literature, different analysis has been carried out on university data, which includes student’s university entrance examination and Ordinary level results but the relationship between these entry results and students’ final graduation grades has been in isolation. Therefore, in this work, a new system that will predict students’ graduation grades based on entry results data using the Iterative Dichotomiser 3 (ID3) decision tree algorithm was developed. ID3 decision tree algorithm was used to train the data of the graduated sets. The knowledge represented by decision trees were extracted and presented in form of IF-THEN rules. The trained data were then used to develop a model for making future prediction of students’ graduation grades. The developed system could be very useful in predicting students’ final graduation grades even from the point of entry into the university. This will help management staff, academic planners to properly counsel students in order to improve their overall performance. Keywords: Data mining, Decision trees, Prediction, ID3 algorithm, Knowledge extraction