SHANU RILWAN OLANIYI

Meet SHANU RILWAN OLANIYI, an Academic Staff of Lagos State University.

Specialization

Software Engineering, Health Informatics, Machine Learning, Soft Computing, Expert System

Designation

Assistant Lecturer

Department

Computer Science

Office

At the Computer Science department office

Visiting Hour

Appointment on Visitation important

Research Interest

Topic: Expert System Using Fuzzy Logic FCM

Description: My research interest and focus is on Intuitionistic fuzzy similarity measure which gives Boolean value 0-1 but considers both membership and non-membership functions. This has been used to resolve classification problems but with shortcomings. The interest in this area is to research into resolving some of these lapses. Also, my interest on Neural Network, Data Science and Analysis; including application of statistical, mathematical modeling and machine learning analyses for solving real life complex problems.

Qualifications

# Certificate SchoolYear
1. M.Sc (Computer Science) Computer Science, Lagos State University, Ojo Lagos. 2018

Current Research

DEVELOPMENT OF A MACHINE LEARNING-BASED DECISION SUPPORT SYSTEM FOR PREDICTING CAUSE OF DEATH FROM AUTOPSY DATA

Research Details

An autopsy report, also known as a postmortem examination, is a medical procedure carried out to determine the cause of death (CoD) in deceased individuals. The investigation takes into account information from the death scene, statements from witnesses, and the medical history to formulate a final conclusion. This process includes both internal and external examinations of the deceased body. All data collected from the body, such as autopsy findings, toxicology, and histopathology reports, are analyzed to determine the CoD. The initial version of the report is usually prepared within 2-3 days, while the final report may take anywhere from 30-45 days, or even up to 90 days in complex cases. Additionally, forensic autopsy procedures can be costly due to the high-dimensional and complex nature of autopsy data. Moreover, manual analysis may introduce inconsistencies due to the manual handling of the procedures. Aim/Objectives: The aim of this study is to develop a ML-Based DSS for predicting the CoD from autopsy data, in order to improve accuracy and efficiency of death investigation and the methodology Methodology include Data collection and pre-processing; Feature engineering and selection; Machine learning techniques and model training; Decision Support System design and implementation. The Expected Results: from the articles reviewed revealed that machine learning approach offers a viable method to analyze autopsy reports., thus, Machine learning-based algorithms offers improved accuracy in CoD determination and also enhances efficiency in autopsy data analysis.

Biography

SHANU RILWAN is a Assistant Lecturer at the Department of Computer Science

SHANU has a M.Sc in Computer Science from Computer Science, Lagos State University, Ojo Lagos.

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