Oyebayo Ridwan Olaniran

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

Scopus Publications

A multi-objective optimization algorithm for gene selection and classification in cancer study
Alabi W. Banjoko, Waheed B. Yahya, and Oyebayo R. Olaniran
Elsevier BV



Hybrid Random Feature Selection and Recurrent Neural Network for Diabetes Prediction
Oyebayo Ridwan Olaniran, Aliu Omotayo Sikiru, Jeza Allohibi, Abdulmajeed Atiah Alharbi, and Nada MohammedSaeed Alharbi
MDPI AG
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, and Oyebayo Ridwan Olaniran
Engineering, Technology & Applied Science Research
Model construction is of significant importance for the extraction of information from datasets and the prediction of responses based on predictor variables. The objective of this study is to compare the Multiple Regression (MR) and model averaging approaches in the context of missing data and to validate the effectiveness of the Multiple Imputation (MI) method used to address missing data issues. A comparison was performed between the results obtained from the multiple-imputed data and those derived from the Complete Case (CC) data, using a diabetes dataset from Hospital Besar Alor Setar. Prior to the application of MI and model building, k-fold cross-validation was employed to partition the dataset, resulting in 90% of the data lacking complete covariates for training and 10% of the data comprising complete covariates for testing. Subsequently, MI was applied to the 90% training dataset. Model M115, derived from the multiple-imputed data, was identified as the optimal model for MR. In the model averaging approach, two models were identified as optimal: Model 1 (without interaction variables) and Model 2 (with interaction variables). The first one, exhibited the lowest values of Mean Square Error (MSE), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). These results indicate that model averaging, specifically Model 1, is the superior model-building approach for this study, demonstrating improved performance compared to MR and validating the effectiveness of the MI method.


BAYESIAN NON-INFERIORITY TEST BETWEEN TWO BINOMIAL PROPORTIONS




A Novel Approach for Testing Fractional Cointegration in Panel Data Models with Fixed Effects
Saidat Fehintola Olaniran, Oyebayo Ridwan Olaniran, Jeza Allohibi, and Abdulmajeed Atiah Alharbi
MDPI AG
Fractional cointegration in time series data has been explored by several authors, but panel data applications have been largely neglected. A previous study of ours discovered that the Chen and Hurvich fractional cointegration test for time series was fairly robust to a moderate degree of heterogeneity across sections of the six tests considered. Therefore, this paper advances a customized version of the Chen and Hurvich methodology to detect cointegrating connections in panels with unobserved fixed effects. Specifically, we develop a test statistic that accommodates variation in the long-term cointegrating vectors and fractional cointegration parameters across observational units. The behavior of our proposed test is examined through extensive Monte Carlo experiments under various data-generating processes and circumstances. The findings reveal that our modified test performs quite well comparatively and can successfully identify fractional cointegrating relationships in panels, even in the presence of idiosyncratic disturbances unique to each cross-sectional unit. Furthermore, the proposed modified test procedure established the presence of long-run equilibrium between the exchange rate and labor wage of 36 countries’ agricultural markets.


Eigenvalue Distributions in Random Confusion Matrices: Applications to Machine Learning Evaluation
Oyebayo Ridwan Olaniran, Ali Rashash R. Alzahrani, and Mohammed R. Alzahrani
MDPI AG
This paper examines the distribution of eigenvalues for a 2×2 random confusion matrix used in machine learning evaluation. We also analyze the distributions of the matrix’s trace and the difference between the traces of random confusion matrices. Furthermore, we demonstrate how these distributions can be applied to calculate the superiority probability of machine learning models. By way of example, we use the superiority probability to compare the accuracy of four disease outcomes machine learning prediction tasks.


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, and Mohd Tahir Ismail
MDPI AG
Asymptotic theories for fractional cointegrations have been extensively studied in the context of time series data, with numerous empirical studies and tests having been developed. However, most previously developed testing procedures for fractional cointegration are primarily designed for time series data. This paper proposes a generalized residual-based test for fractionally cointegrated panels with fixed effects. The test’s development is based on a bivariate panel series with the regressor assumed to be fixed across cross-sectional units. The proposed test procedure accommodates any integration order between [0,1], and it is asymptotically normal under the null hypothesis. Monte Carlo experiments demonstrate that the test exhibits better size and power compared to a similar residual-based test across varying sample sizes.


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, Esther H. Lips, Laura J. van 't Veer, Suzette Delaloge, Isabel T. Rubio, Alastair M. Thompson,et al.
American Society of Clinical Oncology (ASCO)
PURPOSE A number of studies are currently investigating de-escalation of radiation therapy in patients with a low risk of in-breast relapses on the basis of clinicopathologic factors and molecular tests. We evaluated whether 70-gene risk score is associated with risk of locoregional recurrence (LRR) and estimated 8-year cumulative incidences for LRR in patients with early-stage breast cancer treated with breast conservation. METHODS In this exploratory substudy of European Organisation for Research and Treatment of Cancer 10041/BIG 03-04 MINDACT trial, we evaluated women with a known clinical and genomic 70-gene risk score test result and who had breast-conserving surgery (BCS). The primary end point was LRR at 8 years, estimated by cumulative incidences. Distant metastasis and death were considered competing risks. RESULTS Among 6,693 enrolled patients, 5,470 (81.7%) underwent BCS, of whom 98% received radiotherapy. At 8-year follow-up, 189 patients experienced a LRR, resulting in an 8-year cumulative incidence of 3.2% (95% CI, 2.7 to 3.7). In patients with a low-risk 70-gene signature, the 8-year LRR incidence was 2.7% (95% CI, 2.1 to 3.3). In univariable analysis, adjusted for chemotherapy, five of 12 variables were associated with LRR, including the 70-gene signature. In multivariable modeling, adjuvant endocrine therapy and to a lesser extent tumor size and grade remained significantly associated with LRR. CONCLUSION This exploratory analysis of the MINDACT trial estimated an 8-year low LRR rate of 3.2% after BCS. The 70-gene signature was not independently predictive of LRR perhaps because of the low number of events observed and currently cannot be used in clinical decision making regarding LRR. The overall low number of events does provide an opportunity to design trials toward de-escalation of local therapy.


On the Oracle Properties of Bayesian Random Forest for Sparse High-Dimensional Gaussian Regression
Oyebayo Ridwan Olaniran and Ali Rashash R. Alzahrani
MDPI AG
Random forest (RF) is a widely used data prediction and variable selection technique. However, the variable selection aspect of RF can become unreliable when there are more irrelevant variables than relevant ones. In response, we introduced the Bayesian random forest (BRF) method, specifically designed for high-dimensional datasets with a sparse covariate structure. Our research demonstrates that BRF possesses the oracle property, which means it achieves strong selection consistency without compromising the efficiency or bias.


Bayesian weighted random forest for classification of high-dimensional genomics data
Oyebayo Ridwan Olaniran and Mohd Asrul A. Abdullah
Elsevier BV



Variational Bayesian inference for exponentiated Weibull right censored survival data
Jibril Abubakar, Mohd Asrul Affendi Abdullah, and Oyebayo Ridwan Olaniran
International Academic Press
The exponential, Weibull, log-logistic and lognormal distributions represent the class of light and heavy-tailed distributions that are often used in modelling time-to-event data. The exponential distribution is often applied if the hazard is constant, while the log-logistic and lognormal distributions are mainly used for modelling unimodal hazard functions. The Weibull distribution is on the other hand well-known for modelling monotonic hazard rates. Recently, in practice, survival data often exhibit both monotone and non-monotone hazards. This gap has necessitated the introduction of Exponentiated Weibull Distribution (EWD) that can accommodate both monotonic and non-monotonic hazard functions. It also has the strength of adapting unimodal functions with bathtub shape. Estimating the parameter of EWD distribution poses another problem as the flexibility calls for the introduction of an additional parameter. Parameter estimation using the maximum likelihood approach has no closed-form solution, and thus, approximation techniques such as Newton-Raphson is often used. Therefore, in this paper, we introduce another estimation technique called Variational Bayesian (VB) approach. We considered the case of the accelerated failure time (AFT) regression model with covariates. The AFT model was developed using two comparative studies based on real-life and simulated data sets. The results from the experiments reveal that the Variational Bayesian (VB) approach is better than the competing Metropolis-Hasting Algorithm and the reference maximum likelihood estimates.


New two-way discrete frequency table with application to English Premier League data
M. B. Mohammed, H. S. Zulkafli, N. Ali, O. R. Olaniran, and H. Ahmed
Informa UK Limited



Bayesian Regularized Neural Network for Forecasting Naira-USD Exchange Rate
Oyebayo Ridwan Olaniran, Saidat Fehintola Olaniran, and Jumoke Popoola
Springer International Publishing



Shrinkage based variable selection techniques for the sparse Gaussian regression model: A monte-carlo simulation comparative study
Oyebayo Ridwan Olaniran
AIP Publishing



Subset Selection in High-Dimensional Genomic Data using Hybrid Variational Bayes and Bootstrap priors
O R Olaniran and M A A Abdullah
IOP Publishing
Abstract In this study, the Variational Bayes (VB) approach was hybridized with the bootstrap prior procedure to improve the accuracy of subset selection as well as optimizing the algorithm time in modelling high-dimensional genomic data with inherent sparse structure. The new hybrid VB approach is shown to yields a minimal sufficient statistic which under mild regularity conditions converges to the true sparse structure. Simulation and real-life high-dimensional genomic data experiments revealed comparable empirical performance with other competing frequentist and Bayesian methods. In addition, a new fast algorithm that illustrates the procedure was developed and implemented in the environment of R statistical software as package “VBbootprior”.


Generalized Self–Similar First Order Autoregressive Generator (GSFO–ARG) for Internet Traffic
Jumoke Popoola, Waheed Babatunde Yahya, Olusogo Popoola, and Oyebayo Ridwan Olaniran
International Academic Press
Internet traffic data such as the number of transmitted packets and time spent on the transmission of Internet protocols (IPs) have been shown to exhibit self-similar property which can contain the long memory property, particularly in a heavy Internet traffic. Simulating this type of dataset is an important aspect of delay avoidance planning, especially when trying to mimic real-life processing of packets on the Internet. Most of the existing procedures often assumed the process follows a Gaussian distribution, and thus long memory processes such as Fractional Brownian Motion (FBM) and Fractional Gaussian Noise (FGN) among others are used. These approaches often result in estimation errors arising from the use of inappropriate distribution. However, it has been established that the distribution of Internet processes are heavy-tailed. Therefore, in this paper, a new method that is capable of generating heavy-tailed self-similar traffic is proposed based on the first-order autoregressive AR (1) process. The proposed method is compared with some of the existing methods at varying values of the self-similar index and sample sizes. The imposed self-similarity indices were estimated using the Range/Standard deviation statistic (R/S). Performance analysis was achieved using the absolute percentage errors. The results showed that the proposed method has a lower average error when compared with other competing methods.
  


Bayesian variable selection for multiclass classification using bootstrap prior technique
Oyebayo Ridwan Olaniran and Mohd Asrul Affendi Abdullah
Austrian Statistical Society
In this paper, the one-way ANOVA model and its application in Bayesian multi-class variable selection is considered. A full Bayesian bootstrap prior ANOVA test function is developed within the framework of parametric empirical Bayes. The test function developed was later used for variable screening in multiclass classification scenario. Performance comparison between the proposed method and existing classical ANOVA method was achieved using simulated and real life gene expression datasets. Analysis results revealed lower false positive rate and higher sensitivity for the proposed method.


Bayesian Analysis of Extended Cox Model with Time-Varying Covariates Using Bootstrap Prior
Oyebayo Ridwan Olaniran and Mohd Asrul Affendi Abdullah
Wayne State University Library System
A new Bayesian estimation procedure for extended cox model with time varying covariate was presented. The prior was determined using bootstrapping technique within the framework of parametric empirical Bayes. The efficiency of the proposed method was observed using Monte Carlo simulation of extended Cox model with time varying covariates under varying scenarios. Validity of the proposed method was also ascertained using real life data set of Stanford heart transplant. Comparison of the proposed method with its competitor established appreciable supremacy of the method.


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, and Syahila Enera Amran
IOP Publishing
In this study, simulation procedure was applied to measure the fixed covariate of right censored data by using parametric survival model. The scale and shape parameter were modified to differentiate the analysis of parametric regression survival model. Statistically, the biases, mean biases and the coverage probability were used in this analysis. Consequently, different sample sizes were employed to distinguish the impact of parametric regression model towards right censored data with 50, 100, 150 and 200 number of sample. R-statistical software was utilised to develop the coding simulation with right censored data. Besides, the final model of right censored simulation was compared with the right censored lung cancer data in Malaysia. It was found that different values of shape and scale parameter with different sample size, help to improve the simulation strategy for right censored data and Weibull regression survival model is suitable fit towards the simulation of survival of lung cancer patients data in Malaysia.


Bayesian hypothesis testing of two normal samples using bootstrap prior technique
Oyebayo Ridwan Olaniran and Waheed Babatunde Yahya
Wayne State University Library System

RECENT SCHOLAR PUBLICATIONS

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



A Multi-Objective Optimization Algorithm for Gene Selection and Classification in Cancer Study
AW Banjoko, WB Yahya, OR Olaniran
Applied Soft Computing, 112911 2025



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



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



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



Robustness of Bayesian Random Forest in High-Dimensional Analysis with Missing Data
OR Olaniran, ARR Alzahrani
2024



Eigenvalue distributions in random confusion matrices: applications to machine learning evaluation
OR Olaniran, ARR Alzahrani, MR Alzahrani
Mathematics 12 (10), 1425 2024



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



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



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



On the Oracle Properties of Bayesian Random Forest for Sparse High-Dimensional Gaussian Regression
OR Olaniran, ARR Alzahrani
Mathematics 11 (24), 4957 2023



Bayesian weighted random forest for classification of high-dimensional genomics data
OR Olaniran, MAA Abdullah
Kuwait Journal of Science 50 (4), 477-484 2023



A Novel Variable Selection Procedure for Binary Logistic Regression Using Akaike Information Criteria Testing: An Example in Breast Cancer Prediction: Methodological Study
OR Olaniran, SF Olaniran
Turkiye Klinikleri Journal of Biostatistics 15 (2) 2023



Variational bayesian inference for exponentiated weibullright-censored survnaldata
J Abubakar
Universiti Tun Hussein Onn Malaysia 2023



REVIEW OF SOME ROBUST ESTIMATORS IN MULTIPLE LINEAR REGRESSIONS IN THE PRESENCE OF OUTLIER (s)
T Alanamu, GM Oyeyemi, RO Olaniran, KO Adetunji
African Journal of Mathematics and Statistics Studies 2023



New two-way discrete frequency table with application to English Premier League data
MB Mohammed, HS Zulkafli, N Ali, OR Olaniran, H Ahmed
Research in Mathematics 9 (1), 2063538 2022



Modelling Internet Traffic Streams with Ga/M/1/K Queuing Systems under Self-similarity
J Popoola, OJ Popoola, OR Olaniran
Tanzania Journal of Science 48 (2), 394-401 2022



Bayesian regularized neural network for forecasting naira-USD exchange rate
OR Olaniran, SF Olaniran, J Popoola
International Conference on Soft Computing and Data Mining, 213-222 2022



Bayesian Additive Regression Trees for Predicting Colon Cancer
OR OLANIRAN, SF OLANIRAN, J POPOOLA, IV OMEKAM
Turkiye Klinikleri Journal of Biostatistics 14 (2), 103-109 2022

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
Citations: 29



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
Citations: 23



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: 20



Bayesian variable selection for multiclass classification using Bootstrap Prior Technique
OR Olaniran, MAA Abdullah
Austrian Journal of Statistics 48 (2), 63-72 2019
Citations: 17



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
Citations: 16



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
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
Faculty of Computer and Applied Computer Science, Tibiscus University of 2015
Citations: 15



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
Citations: 13



Simulation of parametric model towards the fixed covariate of right censored lung cancer data
SAM Jamil, MAA Abdullah, SL Kek, OR Olaniran, SE Amran
Journal of Physics: Conference Series 890 (1), 012172 2017
Citations: 13



Improved Bayesian feature selection and classification methods using bootstrap prior techniques
OR Olaniran, SF Olaniran, WB Yahya, AW Banjoko, MK Garba, LB Amusa, ...
Faculty of Computer and Applied Computer Science, Tibiscus University of 2016
Citations: 13



Bayesian weighted random forest for classification of high-dimensional genomics data
OR Olaniran, MAA Abdullah
Kuwait Journal of Science 50 (4), 477-484 2023
Citations: 12



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
Citations: 10



Eigenvalue distributions in random confusion matrices: applications to machine learning evaluation
OR Olaniran, ARR Alzahrani, MR Alzahrani
Mathematics 12 (10), 1425 2024
Citations: 8



On the Oracle Properties of Bayesian Random Forest for Sparse High-Dimensional Gaussian Regression
OR Olaniran, ARR Alzahrani
Mathematics 11 (24), 4957 2023
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
Citations: 7



An approximate performance of self-similar lognormal m 1 k internet traffic model
J Popoola, O Popoola, OR Olaniran
Journal of Science and Technology 11 (2), 36-42 2019
Citations: 5



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
Citations: 4



Variational bayesian inference for exponentiated weibullright-censored survnaldata
J Abubakar
Universiti Tun Hussein Onn Malaysia 2023
Citations: 4



Recent Advances on Soft Computing and Data Mining: Proceedings of the Third International Conference on Soft Computing and Data Mining (SCDM 2018), Johor, Malaysia, February 06
R Ghazali, MM Deris, NM Nawi, JH Abawajy
Springer 2018
Citations: 4



Bayesian Random Forest for the Classification of High-Dimensional mRNA Cancer Samples
OR Olaniran, MAAB Abdullah
Proceedings of the Third International Conference on Computing, Mathematics 2019
Citations: 3