Browsing by Author "Ronke Seyi Babatunde"

Now showing 1 - 9 of 9
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    A Logistic Regression-Based Technique for Predicting Type II Diabetes
    (Journal of The Faculty of Computational Sciences & Informatics, 2024) Ronke Seyi Babatunde; Akinbowale Nathaniel Babatunde; Shuaib Babatunde Mohammed
    In recent years, diabetes has emerged as one of the main causes of death for people. The spread of unhealthy foods, sedentary lifestyles, and eating habits have all contributed to the annual increase in the incidence of diabetes. A diabetes prediction model can help with clinical management decision-making. Diabetes prevention may be aided by being aware of potential risk factors and early detection of high-risk individuals. Numerous diabetes prediction models have been created. The size of the data set to be used was an issue in earlier research, but more recent studies have incorporated the use of high-quality, trustworthy data sets, such as the Vanderbilt and PIMA India data sets. Recent research has demonstrated that a few variables, including glucose, pregnancy, body mass index (BMI), the function of the diabetic pedigree, and age, can be used to predict Type II diabetes. Machine learning models of these parameters can be used to accurately predict the chance of the disease occurring as it was investigated in this study. In order to predict Type II diabetes, this study used the machine learning method Logistic Regression.
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    A Neuro-Fuzzy-based Approach to Detect Liver Diseases
    (Pakistan Journal of Engineering and Technology, 2024) Ronke Seyi Babatunde; Shuaib Babatunde Mohammed; Akinbowale Nathaniel Babatunde
    The liver is a crucial organ in the human body and performs vital functions essential for overall health, including metabolism, immunity, digestion, detoxification, and vitamin storage. Detecting liver diseases at an early stage poses challenges due to the liver's ability to function adequately despite partial damage. Early detection is crucial as liver diseases have significant clinical and socio-economic impacts, affecting other organ systems and requiring timely intervention to improve patient survival rates. Classical diagnostic methods for liver disorders may not always produce better results, thus necessitating more advanced and accurate diagnostic systems. Intelligent systems like predictive modeling and decision support systems, have shown promising results in recent years in disease detection and are aiding medical practitioners. In this research, a neuro fuzzy-based system integrating neural networks and fuzzy logic (FL) was implemented. Based on the risk factors present in the dataset that was used to benchmark the algorithm, this system offered a classification accuracy of 97% which is comparable with existing systems in the literature. This study creates a neuro-fuzzy system for early liver disease identification, solving diagnostic issues and offering healthcare improvements. The proposed system is validated by presenting the simulation results.
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    A Neuro-Fuzzy-based Approach to Detect Liver Diseases
    (Pakistan Journal of Engineering and Technology, 2024) Ronke Seyi Babatunde; Akinbowale Nathaniel Babatunde; Shuaib Babatunde Mohammed
    The liver is a crucial organ in the human body and performs vital functions essential for overall health, including metabolism, immunity, digestion, detoxification, and vitamin storage. Detecting liver diseases at an early stage poses challenges due to the liver's ability to function adequately despite partial damage. Early detection is crucial as liver diseases have significant clinical and socio-economic impacts, affecting other organ systems and requiring timely intervention to improve patient survival rates. Classical diagnostic methods for liver disorders may not always produce better results, thus necessitating more advanced and accurate diagnostic systems. Intelligent systems like predictive modeling and decision support systems, have shown promising results in recent years in disease detection and are aiding medical practitioners. In this research, a neuro fuzzy-based system integrating neural networks and fuzzy logic (FL) was implemented. Based on the risk factors present in the dataset that was used to benchmark the algorithm, this system offered a classification accuracy of 97% which is comparable with existing systems in the literature. This study creates a neuro-fuzzy system for early liver disease identification, solving diagnostic issues and offering healthcare improvements. The proposed system is validated by presenting the simulation results
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    Development of an Intrusion Detection System using Mayfly Feature Selection and Artificial Neural Network Algorithms
    (LAUTECH Journal of Engineering and Technology, 2024-06-24) Sulaiman Olaniyi Abdulsalam; Rasheed Abiodun Ayofe; Michael Favour Edafeajiroke; Jumoke Falilat Ajao; Ronke Seyi Babatunde
    Protecting the privacy and confidentiality of information and devices in computer networks requires reliable methods of detecting intrusion. However, effective intrusion detection is made more difficult by the enormous dimensions of data available in computer networks. To boost intrusion detection classification performance in computer networks, this study proposed a feature selection mode for the classification task. The proposed model utilized the Mayfly feature selection algorithm and ANN as the classifier. The model was also tested without a Mayfly Algorithm (MA). The efficiency of the model was determined through a comparison of its Accuracy, Specificity, Precision, Negative predictive value, False positive rate, False discovery rate, False negative rate, Sensitivity, and F1- score. The experimental outcomes revealed that the proposed model is more efficient than existing models when implemented on the Canadian Institute for Cybersecurity Intrusion Detection System 2017 (CIC-IDS 2017) dataset. Accuracy of 99.94% (using Data+mayfly+ANN) and 90.17% (using Data+ANN) were attained after experimentation. The proposed model demonstrated superior accuracy compared to existing studies. Its robustness is due to employing mayfly techniques that combine the strengths of PSO, GA, and FA for optimal feature selection. This research presents a dependable dimensionality reduction model useful for intrusion detection and improving security in computer networks.
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    Enhanced Interpretable Deep Learning Framework for Dengue Fever Prediction: A Rule Extraction Approach
    (co-hosted by Baze University Big Data Analytics and Innovation Research Group, American University Yola, and Sule Lamido University, 2026-04-23) Kolawole Muhammed Abdulsalam; Hamidat Ayodeji Arikewuyo; Ismail Idowu Akuji; Abdulmumeen Sekinat Shaban; Islamiyat Oluwakemi Giwa; Muhammad Sharafadeen Jimoh; Taofik Abiodun Ahmed; Shakirat Ronke Yusuff; Ronke Seyi Babatunde
    Dengue fever poses a critical public health challenge worldwide, particularly in developing countries with laboratory-constrained healthcare systems. Existing methods show promise but are prone to overfitting, which can be mitigated by LSTM-based deep learning models. However, LSTM inherently lack interpretability, a major barrier to clinical adoption in disease prediction. This study bridges robustness and clinical interpretability by integrating rule extraction with LSTM for dengue fever prediction, using a Mendeley dengue fever dataset comprising 1,003 instances and 9 features, preprocessed with label encoding, simple imputation, and synthetic minority oversampling (SMOTE).The LSTM algorithm is optimized using GridSearchCV and RandomizedSearchCV and combined with G-REX for transparent generation, evaluated using accuracy, precision, recall, F1 score, and ROC AUC. All three LSTM variants, including Baseline, GridSearchCV, and RSCV (20 iterations), achieve 100% across Accuracy, Precision, Recall, F1-Score, and ROC-AUC. However, G-REX rules deliver exceptional practical performance: 98.99% Accuracy, 98.53% Precision, 100% Recall, 99.26% F1-Score, and 99.09% ROC-AUC, while maintaining 93.3% fidelity to LSTM predictions, affirming medical validity. These findings position LSTM models as a strong reference standard for dengue classification, enabling resource-limited clinics to achieve high-quality diagnostics through transparent, rulebased support systems. This study can be strengthened in future by exploring other rule-extraction techniques and incorporating diverse, time-series datasets to enhance reliability, interpretability, and generalizability across varied clinical settings.
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    Implementation of Yorùbá Unicode generation for an indigenous keyboard
    (Kwara State University, 2020) Jumoke Falilat Ajao; Ronke Seyi Babatunde; Shakirat Ronke Yusuff; Suliyat Oyindamola Asapetu
    Characters are generally represented on standard keyboards by the use of coding schemes such as ASCII and Unicode but some characters of the Yorùbá language were not captured because of the accent and diacritic signs inherent in Yorùbá language. Coding representation for Yorùbá characters has become a key problem in circuit implementations of keyboards for typing Yorùbá documents correctly. The standard keyboard layouts do not have a simple key combination for all the characters. To represent Yorùbá characters in human-computer interaction, this paper proposes the use of Unicode for the deployment of Yorùbá keyboard for effective and efficient typing of documents in Yorùbá language. The approach used for the development of the Yorùbá keyboard uses both binary and hexadecimal representation of the coding standards to codify Yorùbá characters with their diacritic signs and the under dot. This dimension introduces the complex Yorùbá character coding representations using a single code point standard. The generated Unicode point was transformed into HTML entities. The HTML entities generated were converted to its character equivalent for the Yorùbá keyboard. The new Unicode was compared with the results of the two encoding standard scheme using the bit relationships of upper and lower case characters to ascertain conformity of the new Unicode with the standard encoding scheme. This provided some insights about the efficient representation of Yorùbá character scheme. The representation is expected to quickly identify solutions to the design of Yorùbá keyboards and signage.
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    Optimized Convolution Neural Network-based Model for Detection and Classification of Pulmonary Diseases
    (LAUTECH Journal of Engineering and Technology, 2024-06-15) Anthony Taiwo Olajide; Ronke Seyi Babatunde; Sulaiman Olaniyi Abdulsalam; Jumoke Falilat Ajao; Rafiu Mope Isiaka; A. J. Kehinde
    Pelican Optimization Algorithm-based Convolutional Neural Network (POA-CNN) method for the automated identification of pulmonary disorders such as COVID-19 and pneumonia is proposed in this research. The study aims to enhance the efficiency of CNN models in diagnosing lung diseases by using the Pelican Optimization Algorithm (POA), and by addressing drawbacks like a lack of flexibility in hyperparameter modifications. The three primary phases of the model are feature extraction via POA-based hyperparameter optimization, image classification, and image pre-processing. This approach improves existing systems' performance in detecting pulmonary diseases, highlighting the potential of deep learning in identifying and categorizing human diseases. The study uses resizing, grayscale, and augmentation methods to optimize an existing CNN model. A Convolutional Neural Network (CNN) is then applied to classify Pneumonia and COVID-19 cases. The proposed model achieves an accuracy rate of 97.28% and 97.00%, outperforming existing models. This technique is effective in detecting and classifying other pulmonary diseases and can be used to automatically detect and classify these diseases. Higher accuracy findings show how successful the model is, making it a useful tool for pulmonary illness identification.
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    Towards adoption of information and communication technology in higher education - a structural equation model approach
    (Published by Inderscience Publishers, 2021) Rafiu Mope Isiaka; Ronke Seyi Babatunde; Kennedy Arebamen Eiriemiokhale; Damilola David Popoola
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    Use of White Shark Optimization for Improving the Performance of Convolution Neural Network in Classification of Infected Citrus
    (University of Ibadan Journal of Science and Logics in ICT Research, 2023) Ronke Seyi Babatunde; Abdulrafiu Mope Isiaka; Jumoke F. Ajao; Sulaiman Olaniyi Abdulsalam; Bukola Fatimah Balogun
    Citrus plant diseases are major causes of reduction in the production of citrus fruits and their usage. Early detection of the onset of the diseases is very important to curb and reduce its spread. A number of researches have been done on the detection and classification of the diseases, most of which have identified poor labelling of symptoms which result into improper classification. Some researchers have also experimented on the effectiveness of convolution neural network and other deep learning techniques, most of which results into a faster convergence but suffers from low accuracy, computational overhead and overfitting as fundamental issues. To reduce the effects of overfitting, this research developed a White Shark Optimization-Convolution Neural Network (WSO-CNN) technique to address the aforementioned problem by introducing a regularization strategy via feature selection which selects more useful and distinguishing features for classification. As a result, the developed technique was able to detect and classify various types of citrus fruit diseases and label them accordingly with low false positive rate, high sensitivity, specificity, increased accuracy and reduced recognition time, based on all the experiments performed with the dataset used in the research. Hence WSOCNN performed better than CNN in classifying citrus plant disease having a reduced FPR of 3.57%, 8.34%, 3.89% and 9.00% for black spot, greasy spot, canker and healthy/non healthy dataset respectively.