Meet AJAGBE TAOFIK TOLA, an Academic Staff of Lagos State University.


Artificial Intelligence, Machine Learning And Health Informatics


Assistant Lecturer


Computer Science


At the Computer Science department office

Visiting Hour

Appointment on Visitation important

Research Interest

Topic: An Investigation Into The Effects Of Ablution On Visage Categorization Using Machine Learning Techniques

Description: Neurotheology encompasses areas of research that investigate the nexus between science and religion . In Neurotheology there is a believe that performing ablution usually enhance brightness of faces. In line with this assertion, there has not been proper scientific proof in determining facial conditions of Muslims in respect of ablution effect to the brightness of their faces. Most of the existing systems on facial image categorization were implemented using Principal Component Analysis PCA , K-Nearest Neighbour or Extreme Learning Machine ELM techniques, thereby causing low rate of accuracy, longer time of processing, high false acceptance and false rejection rates which make facial categorization analysis difficult in practice. In this work, I intend to use hybridized Convolutional Neural Network CNN , Local Binary Pattern LBP and Support Vector Machine SVM to solve the stated problem and also to classify facial complexion to determine the effect of ablution.


# Certificate SchoolYear
1. Ph.D (Computer Science in View) Lagos State University 2025

Current Research

Investigating The Effect Of Some Selected Distance Measures On The Performance Of Query-By-Image-Content Over Mammogram Images

Research Details

Query by image content QBIC is art of generating signatures of images and comparing such signatures with those stored in a database for the purpose of retrieval of similar content and is helpful for detection of various diseases such asbreast cancer, brain tumor, spine disorder among others. The image data are acquired through Computerized Tomography CT scan, Magnetic Resonance Imaging MRI and mammogram. In this paper, a QBIC was experimented using selected distance measures to detect abnormality in mammogram images. The system was benchmarked with mini mammographic image analysis society mini-MIAS and breast cancer digital repository BCDR dataset. The experimental process includes thresholding and extraction of Region of Interest ROI from the mammogram using gray level co-occurrence matrix GLCM . The extracted features were tested on Euclidean distance, Minkowski distance, Hamming distance, Mahalanobis distance, Cosine Similarity and Manhattan distance measures. The performance of the system on the distance measure was compared and evaluated on the datasets to determine the distance metric that could best identify abnormality in the samples. The empirical results reveal that Mahalanobis distance measure outperforms the others in terms of retrieval time 1.26s and 1.14s and minimal error 0.004 and 0.002 respectively for both the mini-MIAS dataset as well as the BCDR dataset, based on the similarity of images retrieved when compared to queried images. The implication from this research is that for a QBIC system, the choice of distance measure is an advantage over the use of classification algorithms which always requires train/test splits and validation.


AJAGBE TAOFIK is a Assistant Lecturer at the Department of Computer Science

AJAGBE has a Ph.D in Computer Science in View from Lagos State University

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