Browsing by Author "Awotunde, J. B."
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Item Customer Churn Prediction in Telecommunications Using Ensemble Technique(Department of Computer Science, University of Ibadan, Ibadan, Nigeria, 2023) Oladipo, I. D.; Awotunde, J. B.; AbdulRaheem, M.; Taofeek-Ibrahim, F. A.; Obaje, O.; Ndunagu, J. N.Item Design and Implementation of Information Retrieval System for Laboratory Equipments(Journal of Mathematics Association of Nigeria, ABACUS, 2016) Oladipo, I. D.; Adewole, K. S.; Babatunde, A. O.; Abati, A. I.; Tomori, A. R.; Awotunde, J. B.Getting information relating to user's search criteria on equipment within a University laboratory has posed many challenges since there is no specific system to serve this purpose. It is difficult to handle the whole system manually. The aim of this paper is therefore to design and implement a web-based information retrieval system for University Laboratory equipment. This system offers a powerful internet-based search engine for locating and identifying equipment within a University laboratory while eliminating all unrelated content that a general-purpose search engine would retrieve. The method used involved collecting information needed from the technologist on various equipment in the laboratory and inserted into the retrieval system using SQL server to make query on the database to manage the data and a PHP programming language was used as the server-side scripting language for establishing an efficient querying of information from the database. Java scripting was also used as client-side scripting language for the purpose of adding more interactions to the proposed system. The developed system having been evaluated and assessed thoroughly was found to be efficient, easy to use for locating laboratory equipment in a University.Item DESIGN AND IMPLEMENTATION OF INFORMATION RETRIEVAL SYSTEM FOR LABORATORY EQUIPMENTS(Published by Mathematics Association of Nigeria (MAN). Kwali, Abuja., 2016-09-22) Oladipo, I. D; Adewole, K. S.; Babatunde, A. O.; Abati, A. I.; Tomori, A. R.; Awotunde, J. B.Getting information relating to user's search criteria on equipment within a University laboratory has posed many challenges since there is no specific system to serve this purpose. It is difficult to handle the whole system manually. The aim of this paper is therefore to design and implement a web-based information retrieval system for University Laboratory equipment. This system offers a powerful internet-based search engine for locating and identifying equipment within a University laboratory while eliminating all unrelated content that a general-purpose search engine would retrieve. The method used involved collecting information needed from the technologist on various equipment in the laboratory and inserted into the retrieval system using SQL server to make query on the database to manage the data and a PHP programming language was used as the server-side scripting language for establishing an efficient querying of information from the database. Java scripting was also used as client-side scripting language for the purpose of adding more interactions to the proposed system. The developed system having been evaluated and assessed thoroughly was found to be efficient, easy to use for locating laboratory equipment in a University.Item Diagmal: A Malaria Coactive Neuro-Fuzzy Expert System(Computational Science and Its Applications – ICCSA 2020, 2020) Oladele, Tinuke Omolewa; Ogundokun, R. O.; Awotunde, J. B.; Adebiyi, M. O.; Adeniyi, J. K.In the process of clarifying whether a patient or patients is suffering from a disease or not, diagnosis plays a significant role. The procedure is quite slow and cumbersome, and some patients may not be able to pursue the final test results and diagnosis. The method in this paper comprises many fact-finding and data-mining methods. Artificial Intelligence techniques such as Neural Networks and Fuzzy Logic were fussed together in emerging the Coactive Neuro-Fuzzy Expert System diagnostic tool. The authors conducted oral interviews with the medical practitioners whose knowledge were captured into the knowledge based of the Fuzzy Expert System. Neuro-Fuzzy expert system diagnostic software was implemented with Microsoft Visual C# (C Sharp) programming language and Microsoft SQL Server 2012 to manage the database. Questionnaires were administered to the patients and filled by the medical practitioners on behalf of the patients to capture the prevailing symptoms. The study demonstrated the practical application of neuro-fuzzy method in diagnosis of malaria. The hybrid learning rule has greatly enhanced the proposed system performance when compared with existing systems where only the back-propagation learning rule were used for implementation. It was concluded that the diagnostic expert system developed is as accurate as that of the medical experts in decision making. DIAGMAL is hereby recommended to medical practitioners as a diagnostic tool for malaria.Item Explainable artificial intelligence (XAI) in medical decision systems (MDSSs): healthcare systems perspective(The Institute of Engineering Technology (IET), 2022) Ayoade, O. B.; Oladele, Tinuke Omolewa; Imoize, A. L.; Awotunde, J. B.; Adeloye, A. J.; Olorunyomi, S. O.; Idowu, A. O.The healthcare sector is very interested in machine learning (ML) and artificial intelligence (AI). Nevertheless, applying AI applications in scientific contexts is difficult due to explainability issues. Explainable AI (XAI) has been studied as a potential remedy for the problems with current AI methods. The usage of ML with XAI may be capable of both explaining models and making judgments, in contrast to AI techniques like deep learning. Computer applications called medical decision support systems (MDSS) affect the decisions doctors make regarding certain patients at a specific moment. MDSS has played a crucial role in systems’ attempts to improve patient safety and the standard of care, particularly for noncommunicable illnesses. They have moreover been a crucial prerequisite for effectively utilizing electronic healthcare (EHRs) data. This chapter offers a broad overview of the application of XAI in MDSS toward various infectious diseases, summarizes recent research on the use and effects of MDSS in healthcare with regard to non-communicable diseases, and offers suggestions for users to keep in mind as these systems are incorporated into healthcare systems and utilized outside of contexts for research and development.