Browsing by Author "Babatunde A. N."

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    Effectiveness of Contraceptive Usage among Reproductive Ages in Nigeria Using Artificial Neural Network (ANN)
    (University of the West of Scotland, School of Computing, 2018) Dauda K. A.; Babatunde A. N.; Olorede K. O.; Abdulsalam S. O.; Ogundokun O. R.
    Purpose: Contraceptive usage among women of reproductive age is a fertile area of research for the medical scientists, social scientists, and medical practitioners. However, not much work has been done in Nigeria to identify some of the contraceptive methods used in preventing unwanted pregnancy and sexually transmitted infection (STI) diseases, particularly to explicitly determine which of them are most effective. This paper tackles this task. Methodology: In this paper, we examine different types of contraceptive methods used by Nigeria women to prevent unwanted pregnancy. Datasets on contraceptive usage among 28,647 women of reproductive age from the 2008 Nigerian Demographic Health Survey (NDHS) were analyzed using Artificial Neural Network (ANN) approach in the R language. Finding/Results: The results of the analysis revealed that approximately 1%, 3% and 9% of the study population were using Folkloric, Traditional and Modern methods, respectively. Additionally, 12%, 3% and 9% of the women were found to be currently using the methods since last birth and before last birth. Furthermore, the ANN results through the Garson algorithm revealed that using the modern methods (IUD, Norplant and Female condom) and the traditional methods prevents the unwanted pregnancy negatively, while other modern methods (pill, male condom, female sterilizer, periodic abstinence, withdrawal, locational amenorrhea and foam or jelly) were found to be effective in preventing unwanted pregnancy positively. Interestingly, women that used the modern methods (i.e. Pill, IUD, Injection, Male condom, Female sterilization, Periodic abstinence, Withdrawal and Lactational amenorrhea) were found to be effective in preventing unwanted pregnancy. Research Limitation: The main limitation of this study is the inability to access the current data from NDHS. Therefore, the conclusion of this study is only based on the 2008 NDHS data. Originality/Value: This research work introduces artificial neural network (ANN) to determine and identify the effect of contraceptive methods usage among Nigeria women, and to determine their level of important using Garson's algorithm variable important. This introduction measures the nonlinear effect that exists between the methods and response variable (pregnant and no pregnant) which existing research approaches do not. Keywords: Contraceptive methods, Variable Important, Garson Algorithm, Artificial Neural Network (ANN) and Data mining.
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    Human-Computer Interaction-Based Yorùbá Language using Recurrent Neural Network of Deep Learning
    (Faculty of Computing and Information Technology and Faculty of Natural and Applied Science, Sule LAmido University, Kafin Hausa, 2025-03) Babatunde A. N.; Ajao J. F.; Babatunde R. S.; Olawuyi H. O.; Yusuff, S. R.
    Background: This paper presents the development of a Human-Computer Interaction-Based Yorùbá to English Language Translator using a Recurrent Neural Network (RNN), specifically a Bidirectional Gated Recurrent Unit (GRU) encoder-decoder model. This study presents a novel contribution to existing studies by systematically addressing the setbacks of Yoruba tonal variations with larger corpus and offering a user -friendly interface both underexplored in prior research. Aim: The research aims to develop a machine translation system for translating Yoruba to English and English back to Yoruba applying Bidirectional Gated Recurrent Unit (GRU) algorithm. Methodology: 10054 instances of bilingual Yorùbá and English parallel corpus were collected from Kaggle. The dataset was split into 80% and 20% for training and testing respectively. The Bidirectional GRU model was trained to learn the relationships between the languages with optimization techniques such as gradient descent and backward propagation. Results: The model was evaluated using the BLEU score achieving 93% and 96% accuracy on the training and test sets respectively indicating its robustness in translating between Yorùbá and English and vice versa. A user-friendly web interface was integrated for easy real-time translation. The final system was deployed on a cloud server for remote access. The findings demonstrates that models lik e the Bidirectional GRU can ef fectively handle Yorùbá’s linguistic complexities, improving translation accuracy and facilitating cross-cultural communication. The system implemented in this paper performs well with standardized text but its performance may reduce for high idiomatic expressions. Notwithstanding, the system holds promise for applications in education, multilingual content localization, and assistive communication tools, particularly for Yorùbá-speaking communities.