Ayodeji O.J Ibitoye

Work place: Department of Computer Science and Information Technology, Bowen University, Iwo, Osun State, Nigeria

E-mail: Ibitoye_ayodeji@yahoo.com


Research Interests: Information Systems, Information Retrieval, Multimedia Information System, Data Structures and Algorithms


Ayodeji O.J Ibitoye lectures at the Department of Computer Science and Information Technology, Bowen University, Iwo, Nigeria. He obtained his B.Sc. and M.sc Computer Science from the prestigious University of Ilorin, Ilorin Kwara State and University of Ibadan, Ibadan, Oyo state in 2009 and 2014 respectively. He is a young innovative and resourceful researcher with great analytic and programming skills. His Research Interest is in Big Data Analytics, Information Retrieval, Biometrics intelligent Applications, Fuzzy learning, and Knowledge Organization. He has several peered reviewed publications in journals and conferences in his field of expertise.

Author Articles
Predictive Analytic Game-based Model for Yoruba Language Learning Evaluation

By Ayodeji O.J Ibitoye Opeyemi T. I Olaifa

DOI: https://doi.org/10.5815/ijmecs.2018.02.05, Pub. Date: 8 Feb. 2018

Be it indigenous or foreign language, languages are core for communicating messages from one person to another or group of persons. Primarily, it is learnt at home, schools, through the media like television and radio programmes. However, most of these language-teaching approaches do not measure the percentage growth of people who have gained the knowledge of the language over the years; they also lack the capacity to foretell the range of people that will acquire the knowledge of the language in the latest future. This is because several of the language teaching aids do not have the required dataset to describe and effectively predict the state of the language (a category of people who can speak and write the language) now, and against the future. Here, the research proposed an analytic game based model for Yoruba language evaluation. The essence is first to ascertain the user’s initial knowledge of a language, train users through difference fun filled game stages and levels, evaluate the user at the end of every level and analyse the clustered dataset of users game points to describe and predict the state of the language by using a dual level predictive analytics technique.

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Fuzzy Latent Semantic Query Expansion Model for Enhancing Information Retrieval

By Olufade F. W. Onifade Ayodeji O.J Ibitoye

DOI: https://doi.org/10.5815/ijmecs.2016.02.06, Pub. Date: 8 Feb. 2016

One natural and successful technique to have retrieved documents that is relevant to users’ intention is by expanding the original query with other words that best capture the goal of users. However, no matter the means implored on the clustered document while expanding the user queries, only a concept driven document clustering technique has better capacity to spawn results with conceptual relevance to the users’ goal. Therefore, this research extends the Concept Based Thesaurus Network document clustering techniques by using the Latent Semantic Analysis tool to identify the Best Fit Concept Based Document Cluster in the network. The Fuzzy Latent Semantic Query Expansion Model process achieved a better precision and recall rate values on experimentation and evaluations when compared with some existing information retrieval approaches.

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