Adesesan Barnabas Adeyemo

Work place: University of Ibadan, Ibadan, Nigeria



Research Interests: Computer systems and computational processes, Computer Architecture and Organization, Computer Networks, Data Mining, Data Structures and Algorithms


Dr. Adesesan Barnabas ADEYEMO is senior lecturer at the Computer Science Department of the University of Ibadan, Nigeria. He obtained his PhD and M.Sc degrees at the Federal University of Technology, Akure. His research activities are in Data Mining, Data Warehousing & Computer Networking. He is a member of the Nigerian Computer Society and the Computer Professionals Registration Council of Nigeria. Dr. Adeyemo is a Computer Systems and Network Administration specialist with expertise in Data Analysis and Data Management.

Author Articles
A Data Mining-Based Response Model for Target Selection in Direct Marketing

By Eniafe Festus Ayetiran Adesesan Barnabas Adeyemo

DOI:, Pub. Date: 8 Feb. 2012

Identifying customers who are more likely to respond to new product offers is an important issue in direct marketing. In direct marketing, data mining has been used extensively to identify potential customers for a new product (target selection). Using historical purchase data, a predictive response model with data mining techniques was developed to predict a probability that a customer in Ebedi Microfinance bank will respond to a promotion or an offer. To achieve this purpose, a predictive response model using customers’ historical purchase data was built with data mining techniques. The data were stored in a data warehouse to serve as management decision support system. The response model was built from customers’ historic purchases and demographic dataset.

Bayesian algorithm precisely Naïve Bayes algorithm was employed in constructing the classifier system. Both filter and wrapper feature selection techniques were employed in determining inputs to the model.

The results obtained shows that Ebedi Microfinance bank can plan effective marketing of their products and services by obtaining a guiding report on the status of their customers which will go a long way in assisting management in saving significant amount of money that could have been spent on wasteful promotional campaigns.

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Application of Data Mining Techniques in Weather Prediction and Climate Change Studies

By Folorunsho Olaiya Adesesan Barnabas Adeyemo

DOI:, Pub. Date: 8 Feb. 2012

Weather forecasting is a vital application in meteorology and has been one of the most scientifically and technologically challenging problems around the world in the last century. In this paper, we investigate the use of data mining techniques in forecasting maximum temperature, rainfall, evaporation and wind speed. This was carried out using Artificial Neural Network and Decision Tree algorithms and meteorological data collected between 2000 and 2009 from the city of Ibadan, Nigeria. A data model for the meteorological data was developed and this was used to train the classifier algorithms. The performances of these algorithms were compared using standard performance metrics, and the algorithm which gave the best results used to generate classification rules for the mean weather variables. A predictive Neural Network model was also developed for the weather prediction program and the results compared with actual weather data for the predicted periods. The results show that given enough case data, Data Mining techniques can be used for weather forecasting and climate change studies.

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