RAJI-LAWAL HANAT YETUNDE

Computer Science

At the Computer Science department office

Appointment on Visitation important

Topic: Intuitionistic Fuzzy Similarity Measure

Description: Similarity measure gives the numerical evaluation of the common features between entities. Lot of researches had been conducted in this area, it ranges from Crisp similarity measure which gives Boolean value, to Fuzzy similarity measure which gives a value between the range of o-1, but it considers only membership function. Intuitionistic fuzzy similarity measure IFSM gives value between the range of 0-1, but considers both membership and non-memmbership functions. IFSM had been applied to lots of domain to resolve classification problems. Lot of problems still exhibit the lapses covered by IFSM. The interest in this area is to research into such domains and use IFSM to resolve such weaknesses.

#	Certificate	School	Year
1.	Ph.D (Computer Science)	Federal University of Agriculture Abeokuta	2022

Machine Learning for Dementia Detection

Experiment was conducted to determine the diagnostic impact of clinical decision support system CDSS and predict ND tool for differential diagnosis of dementia in memory clinics. This showed that there is increase in confidence in the diagnosis. This can be improved by expanding the number of biomarkers used for the diagnosis Bruun et al., 2019 . A cognition detection technique for dementia patient was developed using type-1 intuitionistic fuzzy similarity measure. The research showed that some reasoning features can be used to diagnose dementia. The research was limited to only text modality, it can be improved by expanding it s features and including more modalities Hanat .Y. Raji-Lawal, 2020 . The baseline clinical, neuropsychiatric and structural MRI data of non demented older adults were combined, machine learning framework was used to predict feature cognitive category. The research can be improved by extending it with other data sets and exploring more machine learning approaches. Research was conducted on biomarkers and risk assessment of alzhemer s disease in low and middle-income countries LMICs . Research revealed that prevalence of dementia in LMICs will increase over the next couple of years. Potential biomarkers were identified by considering factors like sensivity, specifivity, invasiveness and affordability. Areas of risk assessment tools and the potential use of artificial intelligence and machine learning solutions for diagnosing, assessing risks and monitoring the progression of Alzheimer Disease AD in low resource setting were explored. Research revealed that clinical diagnosis of AD will remain the main stay in LMICs for the foreseeable feature. It was recommended that efforts should be made towards the development of low cost, easily administered risk assessment tools to identify individuals who are at risk of AD Adewale et al., 2023 . The goal of this research is to develop a multi-modal risk assessment tool for Alzheimer/dementia detection

RAJI-LAWAL HANAT is a Lecturer I at the Department of Computer Science

RAJI-LAWAL has a Ph.D in Computer Science from Federal University of Agriculture Abeokuta