Lare Adeola

This paper presents a novel hybrid deep learning model that combines Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) for accurate age and gender classification from iris images. Using the GMBAMU-IRIS dataset, the model leverages CNNs for spatial feature extraction and RNNs for sequential pattern recognition. The proposed model achieved an age classification accuracy of 85.68% and a gender classification accuracy of 98.95%, outperforming traditional methods. These findings highlight the potential of hybrid models in enhancing biometric recognition systems. 1 Introduction Biometric recognition systems have emerged as critical components in various security and authentication applications, providing reliable and efficient means of identifying individuals based on their physiological and behavioral traits. Among the array of biometric modalities, the human iris stands out due to its unique patterns and remarkable stability throughout an individual's life. Iris recognition systems leverage the distinct textural patterns within the iris to achieve high levels of accuracy and reliability in personal identification and verification tasks [1][2]. Beyond the primary goal of identification, there is a growing interest in using iris images for soft biometric traits, such as age and gender classification. Accurate age and gender classification can significantly enhance the functionality of biometric systems by providing additional contextual information [3]. This additional layer of data can improve user experience , enable demographic-specific applications, and enhance the robust-ness of security systems by adding another factor of authentication [4].

Maternal health has become an increasing health problem, especially in the Global South, where the risks, although reducing but not fast enough, continue to register high maternal mortality rates. This research work sought to provide an ergonomic and easily accessible solution by creating a machine learning web application that will be able to detect maternal health with an accuracy of 84%. The web application is a simple form where the user has a set list of questions and answers, based on those choices, a prediction is made. The research hopes this tool will be deployed in areas with high maternal mortality rates in order to bring down the avoidable risks of deaths experienced by pregnant women.