Oyebayo Ridwan Olaniran

@unilorin.edu.ng

Lecturer, Faculty of Physical Sciences
University of Ilorin

Oyebayo Ridwan Olaniran


                              Download Compact PDF  
https://researchid.co/olaniranor
Oyebayo Ridwan OLANIRAN (PhD) is a lecturer in the Department of Statistics, Faculty of Physical Sciences, University of Ilorin. He has over nine years of university teaching, research, and administrative experience.
He has to his credit many publications in reputable outlets covering journals and edited conference proceedings. He has successfully supervised several undergraduate projects, postgraduate diploma and Master dissertations.
Dr. Olaniran is a member of professional bodies within and outside Nigeria, including the Nigeria Mathematical Society (NMS), International Society of Clinical Biostatistics (ISCB), International Biometrics Society-Group Nigeria (IBS-Gni), International Society for Bayesian Analysis (ISBA), and American Society for Clinical Oncology (ASCO).

EDUCATION

EDUCATION
Universiti Tun Hussein Onn, Malaysia, Batu Pahat, Johor Malaysia. Sept, 2016 – Oct, 2019
PhD Science, Statistics,

University of Ilorin, Ilorin, Kwara State, Nigeria. Oct, 2014 – April, 2016
Master of Science, Statistics, 79.63/100, Distinction.

University of Ilorin, Ilorin, Kwara State, Nigeria. Oct, 2009 – June, 2013
Bachelor of Science, Statistics, 4.82/5.00, First Class.

RESEARCH, TEACHING, or OTHER INTERESTS

Statistics, Probability and Uncertainty, Statistics and Probability
28

Scopus Publications

376

Scholar Citations

13

Scholar h-index

15

Scholar i10-index

Scopus Publications

  • Mixed effect gradient boosting for high-dimensional longitudinal data
    Oyebayo Ridwan Olaniran, Saidat Fehintola Olaniran, Jeza Allohibi, Abdulmajeed Atiah Alharbi, Nada MohammedSaeed Alharbi
    Scientific Reports, 2025
  • Legendre polynomial transformation and energy-weighted random forests for sequential data classification
    Oyebayo Ridwan Olaniran, Fatimah M. Alghamdi, Nada MohammedSaeed Alharbi, Gamal A. Abd-Elmougod, Samirah Alzubaidi, et al.
    Scientific Reports, 2025
  • Random Forest Adaptation for High-Dimensional Count Regression
    Oyebayo Ridwan Olaniran, Saidat Fehintola Olaniran, Ali Rashash R. Alzahrani, Nada MohammedSaeed Alharbi, Asma Ahmad Alzahrani
    Mathematics, 2025
    The analysis of high-dimensional count data presents a unique set of challenges, including overdispersion, zero-inflation, and complex nonlinear relationships that traditional generalized linear models and standard machine learning approaches often fail to adequately address. This study introduces and validates a novel Random Forest framework specifically developed for high-dimensional Poisson and Negative Binomial regression, designed to overcome the limitations of existing methods. Through comprehensive simulations and a real-world genomic application to the Norwegian Mother and Child Cohort Study, we demonstrate that the proposed methods achieve superior predictive accuracy, quantified by lower root mean squared error and deviance, and critically produced exceptionally stable and interpretable feature selections. Our theoretical and empirical results show that these distribution-optimized ensembles significantly outperform both penalized-likelihood techniques and naive-transformation-based ensembles in balancing statistical robustness with biological interpretability. The study concludes that the proposed frameworks provide a crucial methodological advancement, offering a powerful and reliable tool for extracting meaningful insights from complex count data in fields ranging from genomics to public health.
  • Smart Health Monitoring for Predicting Heart Disease using IoT-Fog-Cloud Computing Model
    Hafsat Jalo Suleiman, Isredza Rahmi A. Hamid, Oyebayo Ridwan Olaniran
    Engineering Technology and Applied Science Research, 2025
    Cloud computing enables access to various resources online, supporting services across numerous sectors. However, meeting real-time demands in IoT-based computing is challenging due to high latency issues. This is particularly problematic for low-latency applications, such as health monitoring and traffic surveillance, which require fast processing of large datasets. Performance drop occurs when data moves between central databases and cloud data centers. Edge and fog computing have emerged as new solutions to address this. These models place computing resources closer to users, significantly reducing latency and energy consumption while improving data processing efficiency. This paper presents a prediction system utilizing a fog-cloud framework, combining machine learning and deep learning with wearable IoT devices for real-time cardiovascular disease prediction. The system is trained using cardiovascular data from Gombe State, Nigeria, and evaluated based on energy consumption, precision, accuracy, recall, F1 score, and AUC. The proposed Optimized Naïve Bayes Random Forest (ONBRF) model offers a reliable and energy efficient approach to predicting heart disease.
  • Bayesian Tapered Narrowband Least Squares for Fractional Cointegration Testing in Panel Data
    Oyebayo Ridwan Olaniran, Saidat Fehintola Olaniran, Ali Rashash R. Alzahrani, Nada MohammedSaeed Alharbi, Asma Ahmad Alzahrani
    Mathematics, 2025
    Fractional cointegration has been extensively examined in time series analysis, but its extension to heterogeneous panel data with unobserved heterogeneity and cross-sectional dependence remains underdeveloped. This paper develops a robust framework for testing fractional cointegration in heterogeneous panel data, where unobserved heterogeneity, cross-sectional dependence, and persistent shocks complicate traditional approaches. We propose the Bayesian Tapered Narrowband Least Squares (BTNBLS) estimator, which addresses three critical challenges: (1) spectral leakage in long-memory processes, mitigated via tapered periodograms; (2) precision loss in fractional parameter estimation, resolved through narrowband least squares; and (3) unobserved heterogeneity in cointegrating vectors (θi) and memory parameters (ν,δ), modeled via hierarchical Bayesian priors. Monte Carlo simulations demonstrate that BTNBLS outperforms conventional estimators (OLS, NBLS, TNBLS), achieving minimal bias (0.041–0.256), near-nominal coverage probabilities (0.87–0.94), and robust control of Type 1 errors (0.01–0.07) under high cross-sectional dependence (ρ=0.8), while the Bayesian Chen–Hurvich test attains near-perfect power (up to 1.00) in finite samples. Applied to Purchasing Power Parity (PPP) in 18 fragile Sub-Saharan African economies, BTNBLS reveals statistically significant fractional cointegration between exchange rates and food price ratios in 15 countries (p<0.05), with a pooled estimate (θ^=0.33, p<0.001) indicating moderate but resilient long-run equilibrium adjustment. These results underscore the importance of Bayesian shrinkage and spectral tapering in panel cointegration analysis, offering policymakers a reliable tool to assess persistence of shocks in institutionally fragmented markets.
  • Unraveling the Impact of Climate Change on Food Security in Malaysia: Insights from Vector Error Correction Modeling
    Nur Fazlin Ibrahim, Mohd Asrul Affendi Abdullah, Oyebayo Ridwan Olaniran
    Engineering Technology and Applied Science Research, 2025
    This study examines the influence of climate variables on paddy production in Malaysia, focusing on historical data from 1980 to 2016. The employed methodology incorporates Multiple Linear Regression (MLR) to identify the critical predictors, Johansen cointegration tests to explore the long-term relationships, and Vector Error Correction Models (VECMs) alongside Granger causality tests to analyze the dynamic interactions among variables. The performed analysis reveals consistent patterns in mean rainy days and rainfall amounts, indicating a relatively stable climate. In contrast, mean 24-hour temperatures show an upward trend, while mean 24-hour relative humidity exhibits a decline. The findings identify the mean rainfall amount and 24-hour relative humidity as significant predictors of the paddy production. The advanced analytical techniques confirm two long-term cointegrating relationships among the variables. Granger causality tests reveal a bidirectional relationship between the mean rainfall amount and paddy production, suggesting mutual predictability. Conversely, the mean 24-hour relative humidity exhibited a unidirectional relationship, predicting paddy production but not vice versa. These findings underscore the critical role of climate variables, particularly rainfall and humidity, in shaping the paddy cultivation outcomes in Malaysia.
  • Random Generalized Additive Logistic Forest: A Novel Ensemble Method for Robust Binary Classification
    Oyebayo Ridwan Olaniran, Ali Rashash R. Alzahrani, Nada MohammedSaeed Alharbi, Asma Ahmad Alzahrani
    Mathematics, 2025
    Ensemble methods have proven highly effective in enhancing predictive performance by combining multiple models. We introduce a novel ensemble approach, the Random Generalized Additive Logistic Forest (RGALF), which integrates generalized additive models (GAMs) within a random forest framework to improve binary classification tasks. Unlike traditional random forests, which rely on piecewise constant predictions in terminal nodes, RGALF fits GAM logistic regression (LR) models to the data in each terminal node, enabling it to capture complex nonlinear relationships and interactions among predictors. By aggregating these node-specific GAMs, RGALF addresses multicollinearity, enhances interpretability, and achieves superior bias–variance tradeoffs, particularly in nonlinear settings. Theoretical analysis confirms that RGALF achieves Stone’s optimal rates for additive models (O(n−2k/(2k+d)) under appropriate conditions, outperforming the slower convergence of traditional random forests (O(n−2/3)). Furthermore, empirical results demonstrate RGALF’s effectiveness across both simulated and real-world datasets. In simulations, RGALF demonstrates superior performance over random forests (RFs), reducing variance by up to 69% and bias by 19% in nonlinear settings, with significant MSE improvements (0.032 vs. RF’s 0.054 at n=1000), while achieving optimal convergence rates (O(n−0.48) vs. RF’s O(n−0.29)). On real-world medical datasets, RGALF attains near-perfect accuracy and AUC: 100% accuracy/AUC for Heart Failure and Hepatitis C (HCV) prediction, 99% accuracy/100% AUC for Pima Diabetes, and 98.8% accuracy/100% AUC for Indian Liver Patient (ILPD), outperforming state-of-the-art methods. Notably, RGALF captures complex biomarker interactions (BMI–insulin in diabetes) missed by traditional models.
  • Bayesian Random Forest with Multiple Imputation by Chain Equations for High-Dimensional Missing Data: A Simulation Study
    Oyebayo Ridwan Olaniran, Ali Rashash R. Alzahrani
    Mathematics, 2025
    The pervasive challenge of missing data in scientific research forces a critical trade-off: discarding incomplete observations, which risks significant information loss, while conventional imputation methods struggle to maintain accuracy in high-dimensional settings. Although approaches like multiple imputation (MI) and random forest (RF) proximity-based imputation offer improvements over naive deletion, they exhibit limitations in complex missing data scenarios or sparse high-dimensional settings. To address these gaps, we propose a novel integration of Multiple Imputation by Chained Equations (MICE) with Bayesian Random Forest (BRF), leveraging MICE’s iterative flexibility and BRF’s probabilistic robustness to enhance the imputation accuracy and downstream predictive performance. Our hybrid framework, BRF-MICE, uniquely combines the efficiency of MICE’s chained equations with BRF’s ability to quantify uncertainty through Bayesian tree ensembles, providing stable parameter estimates even under extreme missingness. We empirically validate this approach using synthetic datasets with controlled missingness mechanisms (MCAR, MAR, MNAR) and dimensionality, contrasting it against established methods, including RF and Bayesian Additive Regression Trees (BART). The results demonstrate that BRF-MICE achieves a superior performance in classification and regression tasks, with a 15–20% lower error under varying missingness conditions compared to RF and BART while maintaining computational scalability. The method’s iterative Bayesian updates effectively propagate imputation uncertainty, reducing overconfidence in high-dimensional predictions, a key weakness of frequentist alternatives.
  • A multi-objective optimization algorithm for gene selection and classification in cancer study
    Alabi W. Banjoko, Waheed B. Yahya, Oyebayo R. Olaniran
    Applied Soft Computing, 2025
  • Hybrid Random Feature Selection and Recurrent Neural Network for Diabetes Prediction
    Oyebayo Ridwan Olaniran, Aliu Omotayo Sikiru, Jeza Allohibi, Abdulmajeed Atiah Alharbi, Nada MohammedSaeed Alharbi
    Mathematics, 2025
    This paper proposes a novel two-stage ensemble framework combining Long Short-Term Memory (LSTM) and Bidirectional LSTM (BiLSTM) with randomized feature selection to enhance diabetes prediction accuracy and calibration. The method first trains multiple LSTM/BiLSTM base models on dynamically sampled feature subsets to promote diversity, followed by a meta-learner that integrates predictions into a final robust output. A systematic simulation study conducted reveals that feature selection proportion critically impacts generalization: mid-range values (0.5–0.8 for LSTM; 0.6–0.8 for BiLSTM) optimize performance, while values close to 1 induce overfitting. Furthermore, real-life data evaluation on three benchmark datasets—Pima Indian Diabetes, Diabetic Retinopathy Debrecen, and Early Stage Diabetes Risk Prediction—revealed that the framework achieves state-of-the-art results, surpassing conventional (random forest, support vector machine) and recent hybrid frameworks with an accuracy of up to 100%, AUC of 99.1–100%, and superior calibration (Brier score: 0.006–0.023). Notably, the BiLSTM variant consistently outperforms unidirectional LSTM in the proposed framework, particularly in sensitivity (98.4% vs. 97.0% on retinopathy data), highlighting its strength in capturing temporal dependencies.
  • Comparison of Multiple Regression and Model Averaging Model-Building Approach for Missing Data with Multiple Imputation
    Mohd Asrul Affendi Abdullah, Lai Jesintha, Gopal Pillay Khuneswari, Siti Afiqah Muhamad Jamil, Oyebayo Ridwan Olaniran
    Engineering Technology and Applied Science Research, 2024
  • A Novel Approach for Testing Fractional Cointegration in Panel Data Models with Fixed Effects
    Saidat Fehintola Olaniran, Oyebayo Ridwan Olaniran, Jeza Allohibi, Abdulmajeed Atiah Alharbi
    Fractal and Fractional, 2024
  • BAYESIAN NON-INFERIORITY TEST BETWEEN TWO BINOMIAL PROPORTIONS
    Reliability Theory and Applications, 2024
  • Eigenvalue Distributions in Random Confusion Matrices: Applications to Machine Learning Evaluation
    Oyebayo Ridwan Olaniran, Ali Rashash R. Alzahrani, Mohammed R. Alzahrani
    Mathematics, 2024
  • A Generalized Residual-Based Test for Fractional Cointegration in Panel Data with Fixed Effects
    Saidat Fehintola Olaniran, Oyebayo Ridwan Olaniran, Jeza Allohibi, Abdulmajeed Atiah Alharbi, Mohd Tahir Ismail
    Mathematics, 2024
  • Locoregional Breast Cancer Recurrence in the European Organisation for Research and Treatment of Cancer 10041/BIG 03-04 MINDACT Trial: Analysis of Risk Factors Including the 70-Gene Signature
    Sena Alaeikhanehshir, Taiwo Ajayi, Frederieke H. Duijnhoven, Coralie Poncet, Ridwan O. Olaniran, et al.
    Journal of Clinical Oncology, 2024
  • On the Oracle Properties of Bayesian Random Forest for Sparse High-Dimensional Gaussian Regression
    Oyebayo Ridwan Olaniran, Ali Rashash R. Alzahrani
    Mathematics, 2023
  • Bayesian weighted random forest for classification of high-dimensional genomics data
    Oyebayo Ridwan Olaniran, Mohd Asrul A. Abdullah
    Kuwait Journal of Science, 2023
  • Variational Bayesian inference for exponentiated Weibull right censored survival data
    Jibril Abubakar, Mohd Asrul Affendi Abdullah, Oyebayo Ridwan Olaniran
    Statistics Optimization and Information Computing, 2023
  • Bayesian Regularized Neural Network for Forecasting Naira-USD Exchange Rate
    Oyebayo Ridwan Olaniran, Saidat Fehintola Olaniran, Jumoke Popoola
    Lecture Notes in Networks and Systems, 2022
  • New two-way discrete frequency table with application to English Premier League data
    M. B. Mohammed, H. S. Zulkafli, N. Ali, O. R. Olaniran, H. Ahmed
    Research in Mathematics, 2022
  • Shrinkage based variable selection techniques for the sparse Gaussian regression model: A monte-carlo simulation comparative study
    Oyebayo Ridwan Olaniran
    Aip Conference Proceedings, 2021
  • Subset Selection in High-Dimensional Genomic Data using Hybrid Variational Bayes and Bootstrap priors
    O R Olaniran, M A A Abdullah
    Journal of Physics Conference Series, 2020
  • Generalized Self–Similar First Order Autoregressive Generator (GSFO–ARG) for Internet Traffic
    Jumoke Popoola, Waheed Babatunde Yahya, Olusogo Popoola, Oyebayo Ridwan Olaniran
    Statistics Optimization and Information Computing, 2020
  • Bayesian variable selection for multiclass classification using bootstrap prior technique
    Oyebayo Ridwan Olaniran, Mohd Asrul Affendi Abdullah
    Austrian Journal of Statistics, 2019
  • Bayesian Analysis of Extended Cox Model with Time-Varying Covariates Using Bootstrap Prior
    Oyebayo Ridwan Olaniran, Mohd Asrul Affendi Abdullah
    Journal of Modern Applied Statistical Methods, 2019
  • Simulation of parametric model towards the fixed covariate of right censored lung cancer data
    Siti Afiqah Muhamad Jamil, M. Asrul Affendi Abdullah, Sie Long Kek, Oyebayo Ridwan Olaniran, Syahila Enera Amran
    Journal of Physics Conference Series, 2017
  • Bayesian hypothesis testing of two normal samples using bootstrap prior technique
    Oyebayo Ridwan Olaniran, Waheed Babatunde Yahya
    Journal of Modern Applied Statistical Methods, 2017

RECENT SCHOLAR PUBLICATIONS

  • Adaptive Gaussian Process Search for Simulation-Based Sample Size Estimation in Clinical Prediction Models: Validation of the pmsims R Package
    OR Olaniran, D Shamsutdinova, S Markham, F Zimmer, D Stahl, G Forbes, ...
    arXiv preprint arXiv:2603.23688 , 2026
    2026
  • Sample size calculations for developing clinical prediction models: Overview and pmsims r package
    D Shamsutdinova, F Zimmer, OR Olaniran, S Markham, D Stahl, G Forbes, ...
    arXiv preprint arXiv:2602.23507 , 2026
    2026
    Citations: 1
  • Legendre polynomial transformation and energy-weighted random forests for sequential data classification
    OR Olaniran, FM Alghamdi, NMS Alharbi, GA Abd-Elmougod, S Alzubaidi, ...
    Scientific Reports 15 (1), 36984 , 2025
    2025
    Citations: 1
  • Random Forest Adaptation for High-Dimensional Count Regression
    OR Olaniran, SF Olaniran, ARR Alzahrani, NMS Alharbi, AA Alzahrani
    Mathematics 13 (18), 3041 , 2025
    2025
    Citations: 1
  • Mixed effect gradient boosting for high-dimensional longitudinal data
    OR Olaniran, SF Olaniran, J Allohibi, AA Alharbi, NMS Alharbi
    Scientific Reports 15 (1), 30927 , 2025
    2025
    Citations: 9
  • Smart Health Monitoring for Predicting Heart Disease using IoT-Fog-Cloud Computing Model
    HJ Suleiman, IRA Hamid, OR Olaniran
    Engineering, Technology & Applied Science Research 15 (3), 22565-22572 , 2025
    2025
    Citations: 6
  • Bayesian Tapered Narrowband Least Squares for Fractional Cointegration Testing in Panel Data
    OR Olaniran, SF Olaniran, ARR Alzahrani, NMS Alharbi, AA Alzahrani
    Mathematics 13 (10), 1615 , 2025
    2025
  • Random Generalized Additive Logistic Forest: A Novel Ensemble Method for Robust Binary Classification
    OR Olaniran, ARR Alzahrani, NMS Alharbi, AA Alzahrani
    Mathematics 13 (7), 1214 , 2025
    2025
    Citations: 3
  • Unraveling the Impact of Climate Change on Food Security in Malaysia: Insights from Vector Error Correction Modeling
    NF Ibrahim, MAA Abdullah, OR Olaniran
    Engineering, Technology & Applied Science Research 15 (2), 20811-20818 , 2025
    2025
    Citations: 1
  • Bayesian random forest with multiple imputation by chain equations for high-dimensional missing data: a simulation study
    OR Olaniran, ARR Alzahrani
    Mathematics 13 (6), 956 , 2025
    2025
    Citations: 6
  • A multi-objective optimization algorithm for gene selection and classification in cancer study
    AW Banjoko, WB Yahya, OR Olaniran
    Applied Soft Computing 172, 112911 , 2025
    2025
    Citations: 7
  • Hybrid random feature selection and recurrent neural network for diabetes prediction
    OR Olaniran, AO Sikiru, J Allohibi, AA Alharbi, NMS Alharbi
    Mathematics 13 (4), 628 , 2025
    2025
    Citations: 18
  • MEGB: An R package for Mixed Effect GradientBoosting for High-dimensional Longitudinal Data
    OR Olaniran, SF Olaniran, J Allohibi, A Alharbi, NMS Alharbi
    2025
  • Comparison of Multiple Regression and Model Averaging Model-Building Approach for Missing Data with Multiple Imputation
    MAA Abdullah, L Jesintha, GP Khuneswari, SAM Jamil, OR Olaniran
    Engineering, Technology & Applied Science Research 14 (6), 18502-18508 , 2024
    2024
    Citations: 2
  • A Novel Approach for Testing Fractional Cointegration in Panel Data Models with Fixed Effects
    SF Olaniran, OR Olaniran, J Allohibi, AA Alharbi
    Fractal and Fractional 8 (9), 527 , 2024
    2024
    Citations: 3
  • Robustness of Bayesian Random Forest in High-Dimensional Analysis with Missing Data
    OR Olaniran, ARR Alzahrani
    Preprints , 2024
    2024
    Citations: 1
  • Eigenvalue distributions in random confusion matrices: applications to machine learning evaluation
    OR Olaniran, ARR Alzahrani, MR Alzahrani
    Mathematics 12 (10), 1425 , 2024
    2024
    Citations: 23
  • A Generalized Residual-Based Test for Fractional Cointegration in Panel Data with Fixed Effects
    SF Olaniran, OR Olaniran, J Allohibi, AA Alharbi, MT Ismail
    Mathematics 12 (8), 1172 , 2024
    2024
    Citations: 3
  • Locoregional breast cancer recurrence in the European Organisation for Research and Treatment of Cancer 10041/BIG 03-04 MINDACT trial: analysis of risk factors including the 70 …
    S Alaeikhanehshir, T Ajayi, FH Duijnhoven, C Poncet, RO Olaniran, ...
    Journal of Clinical Oncology 42 (10), 1124-1134 , 2024
    2024
    Citations: 8
  • BAYESIAN NON-INFERIORITY TEST BETWEEN TWO BINOMIAL PROPORTIONS
    WB Yahya, CP Ezenweke, OR Olaniran, IA Adeniyi, K Jimoh, RB Afolayan, ...
    Reliability: Theory & Applications 19 (3 (79)), 689-703 , 2024
    2024

MOST CITED SCHOLAR PUBLICATIONS

  • Bayesian Hypothesis Testing of Two Normal Samples using Bootstrap Prior Technique
    OR Olaniran, WB Yahya
    Journal of Modern Applied Statistical Methods 16 (2), 618-638 , 2017
    2017
    Citations: 29
  • Safety of bread for human consumption in an urban community in Southwestern Nigeria.
    OT Afolabi, OO Aluko, O Olaniran, O Ajao, BK Ojumu, O Olawande
    2015
    Citations: 26
  • Bayesian weighted random forest for classification of high-dimensional genomics data
    OR Olaniran, MAA Abdullah
    Kuwait Journal of Science 50 (4), 477-484 , 2023
    2023
    Citations: 25
  • Eigenvalue distributions in random confusion matrices: applications to machine learning evaluation
    OR Olaniran, ARR Alzahrani, MR Alzahrani
    Mathematics 12 (10), 1425 , 2024
    2024
    Citations: 23
  • On Bayesian conjugate normal linear regression and ordinary least square regression methods: A Monte Carlo study
    WB Yahya, OR Olaniran, SO Ige
    Ilorin Journal of science 1 (1), 216–227-216–227 , 2014
    2014
    Citations: 23
  • Hybrid random feature selection and recurrent neural network for diabetes prediction
    OR Olaniran, AO Sikiru, J Allohibi, AA Alharbi, NMS Alharbi
    Mathematics 13 (4), 628 , 2025
    2025
    Citations: 18
  • Subset selection in high-dimensional genomic data using hybrid variational Bayes and bootstrap priors
    OR Olaniran, MAA Abdullah
    Journal of Physics: Conference Series 1489 (1), 012030 , 2020
    2020
    Citations: 17
  • Bayesian analysis of extended cox model with time-varying covariates using bootstrap prior
    OR Olaniran, MAA Abdullah
    Journal of Modern Applied Statistical Methods 18 (2), 7 , 2020
    2020
    Citations: 16
  • Bayesian variable selection for multiclass classification using Bootstrap Prior Technique
    OR Olaniran, MAA Abdullah
    Austrian Journal of Statistics 48 (2), 63-72 , 2019
    2019
    Citations: 16
  • Efficient support vector machine classification of diffuse large b-cell lymphoma and follicular lymphoma mRNA tissue samples
    AW Banjoko, WB Yahya, MK Garba, OR Olaniran, KA Dauda, KO Olorede
    Annals. Computer Science Series 13 (2), 69-79 , 2015
    2015
    Citations: 16
  • BayesRandomForest : An R Implementation of Bayesian Random Forest for Regression Analysis of High-Dimensional Data
    OR Olaniran, MAAB Abdullah
    Proceedings of the Third International Conference on Computing, Mathematics … , 2019
    2019
    Citations: 15
  • On the Oracle Properties of Bayesian Random Forest for Sparse High-Dimensional Gaussian Regression
    OR Olaniran, ARR Alzahrani
    Mathematics 11 (24), 4957 , 2023
    2023
    Citations: 14
  • Improved Bayesian feature selection and classification methods using bootstrap prior techniques
    OR Olaniran, SF Olaniran, WB Yahya, AW Banjoko, MK Garba, LB Amusa, ...
    Annals. Computer Science Series 14 (2) , 2016
    2016
    Citations: 13
  • Simulation of parametric model towards the fixed covariate of right censored lung cancer data
    SA Muhamad Jamil, MAA Abdullah, SL Kek, OR Olaniran, SE Amran
    Journal of Physics: Conference Series 890 (1), 012172 , 2017
    2017
    Citations: 11
  • Generalized self-similar first order autoregressive generator (gsfo-arg) for internet traffic
    J Popoola, WB Yahya, O Popoola, OR Olaniran
    Statistics, Optimization & Information Computing 8 (4), 810-821 , 2020
    2020
    Citations: 10
  • Mixed effect gradient boosting for high-dimensional longitudinal data
    OR Olaniran, SF Olaniran, J Allohibi, AA Alharbi, NMS Alharbi
    Scientific Reports 15 (1), 30927 , 2025
    2025
    Citations: 9
  • Locoregional breast cancer recurrence in the European Organisation for Research and Treatment of Cancer 10041/BIG 03-04 MINDACT trial: analysis of risk factors including the 70 …
    S Alaeikhanehshir, T Ajayi, FH Duijnhoven, C Poncet, RO Olaniran, ...
    Journal of Clinical Oncology 42 (10), 1124-1134 , 2024
    2024
    Citations: 8
  • A multi-objective optimization algorithm for gene selection and classification in cancer study
    AW Banjoko, WB Yahya, OR Olaniran
    Applied Soft Computing 172, 112911 , 2025
    2025
    Citations: 7
  • Gene selection for colon cancer classification using bayesian model averaging of linear and quadratic discriminants
    OR Olaniran, MAA Abdullah
    Journal of Science and Technology 9 (3) , 2017
    2017
    Citations: 7
  • Smart Health Monitoring for Predicting Heart Disease using IoT-Fog-Cloud Computing Model
    HJ Suleiman, IRA Hamid, OR Olaniran
    Engineering, Technology & Applied Science Research 15 (3), 22565-22572 , 2025
    2025
    Citations: 6