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A. S. Falohun

Publications (4)

Iris Texture Analysis for Ethnicity Classification Using Self-Organizing Feature Maps

B. M. Latinwo, A. S. Falohun, E. O. Omidiora & B. O. Makinde · Journal of Advances in Mathematics and Computer Science · 2018

Ethnicity Classification from iris texture is a notable research in the field of pattern recognition that differentiates groups of people as distinct community by certain characteristics and attributes. Several ethnicity classification systems have been developed using Supervised...

Open access Research Article 10.9734/JAMCS/2017/29634

Radio Frequency Identification and Internet of Things: A Fruitful Synergy

O. T. Arulogun, A. S. Falohun & N. O. Akande · Current Journal of Applied Science and Technology · 2017

The interconnection of devices, mechanical and digital machines, objects, animals or people with the ability to transfer data over a network without requiring human-to-human or human-to-computer interaction known as Internet of Things (IoT) can only be successfully achieved with...

Open access Research Article 10.9734/BJAST/2016/30737

Principal Component Analysis - Based Ethnicity Prediction Using Iris Feature

B. M. Latinwo, A. S. Falohun & E. O. Omidiora · Current Journal of Applied Science and Technology · 2016

This paper presents the effectiveness of Principal Component Analysis (PCA) technique in analyzing iris texture by performing dimensionality reduction and extracting unique feature codes of images for efficient ethnicity classification using iris images from African and two Asian...

Open access Research Article 10.9734/BJAST/2016/26131

A Wireless Sensor Network for Examination Attendance Management System

I. F. Obayemi, O. T. Arulogun & A. S. Falohun · Journal of Advances in Mathematics and Computer Science · 2016

Every academic institute has special concerns for student’s attendance in examination halls. At present, in developing countries attendance is usually taken using paper sheets and the old file system. The effectiveness of this style is low because it is highly labor-intensive, er...

Open access Research Article 10.9734/BJMCS/2016/24661