Awoyelu T. M.

Work place: Department of Computer Sciences, Faculty of Natural Sciences, Ajayi Crowther University, Oyo, Nigeria

E-mail: tm.awoyelu@acu.edu.ng

Website:

Research Interests: Artificial Intelligence, Data Analysis

Biography

Awoyelu T. M. is an Assistant Lecturer in the Department of Computer Sciences, Faculty of Natural Sciences, Ajayi Crowther University, Oyo Township, Oyo State. She obtained her B.Sc. (Hons) Degree of Computer Science from the Department of Computer Science and Engineering, Osun State University, Osogbo in 2014. She also holds M.Sc. degree in Intelligent Systems Engineering from the Department of Computer Science and Engineering, Obafemi Awolowo University, Ile-Ife in 2018. She has over 5 years of teaching and research experience. Her research interests are in Artificial Intelligence and Data Analytics. She has successfully supervised 10 Undergraduate Theses. She has five publications to her credit. She is a member of Nigeria Computer Society (NCS) and Nigerian Women in Information Technology (NIWIIT).

Author Articles
An Optimized Convolutional Neural Network Model for Detecting Depressive Symptoms from Image Posts

By Awoyelu T. M. Iyanda A. R. Mosaku S. K.

DOI: https://doi.org/10.5815/ijitcs.2024.04.03, Pub. Date: 8 Aug. 2024

This paper presents an optimized model that uses an optimized CNN to detect depressive symptoms from image posts. This is with a view to detecting depression symptoms in individuals. Visual data were collected in their raw form and assessed as having or not having a mental condition. The images were processed, and the relevant features retrieved from them. An optimized convolutional neural network (CNN) was used to simulate the defined classification model of the image posts. The model was implemented using Python Programming Language. Precision, recall, accuracy, and the area under the Receiver Operating Characteristics (ROC) curve were used as performance indicators to assess the model's efficacy. The collected findings indicate that 77% accuracy is achieved by the optimized model. As a result, 77% of the cases were accurately predicted by the model, suggesting that the model is generally accurate in its predictions. The research will contribute to a decrease in the incidence, prevalence, and recurrence of mental health illnesses as well as the disabilities they cause.

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