Mr. Abbarapu Ashok, a passionate academician and researcher, hails from Madanapalle, Andhra Pradesh. He is currently in the final stages of completing his Ph.D. in Statistical Inference at VIT-AP University, where his research focuses on Bayesian estimation and continuous distributions. With a solid foundation in Statistics, Mr. Ashok has established himself as a dedicated educator and researcher in the field of Mathematics and Statistics.
EDUCATION
Currently an Internal full-time Research Scholar from VIT-AP University, INDIA. Ashok began his academic journey with a Bachelor’s degree in Mathematics, Statistics, and Computer Science from Shri Gnanambica Degree College, where he graduated with distinction. He further pursued an M.Sc. in Statistics from SDHR PG College, affiliated with Sri Venkateswara University, achieving an impressive academic record.
RESEARCH, TEACHING, or OTHER INTERESTS
Statistics and Probability, Applied Mathematics, Computational Mathematics, Discrete Mathematics and Combinatorics
7
Scopus Publications
10
Scholar Citations
2
Scholar h-index
Scopus Publications
Robust Parameter Estimation for Dual-population Weibull Models under Fuzzy Data Conditions Abbarapu Ashok, Nadiminti Nagamani Model Assisted Statistics and Applications, 2026 Estimating parameters accurately in the presence of uncertain and imprecise data is a key challenge in statistical analysis, particularly for complex models involving two populations. Fuzzy data provides a structured way to handle such uncertainties by effectively representing real-world ambiguity. While extensive research has been conducted on parameter estimation for single-population models using fuzzy data, extending these methods to dual populations remains a difficult task. This study addresses the issue by developing estimation techniques for two Weibull distributions that share a common scale parameter <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mi>σ</mml:mi> </mml:math> but have different shape parameters <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:msub> <mml:mi>k</mml:mi> <mml:mn>1</mml:mn> </mml:msub> </mml:math> and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:msub> <mml:mi>k</mml:mi> <mml:mn>2</mml:mn> </mml:msub> </mml:math> , under fuzzy data conditions. We apply the expectation-maximization (EM) algorithm for Maximum Likelihood estimation and utilize a Bayesian approach with TK approximation for parameter estimation. To further refine Bayesian estimates, Gibbs sampling is employed to derive posterior distributions. Through Monte Carlo simulations on both simulated and real-world datasets, we evaluate the accuracy and robustness of our estimators, demonstrating their effectiveness in handling imprecise data. Additionally, asymptotic and HPD confidence intervals are also obtained. This research highlights the importance of reliable statistical methods for dual-population Weibull models, contributing to improved analytical precision across various domains.
FUZZY DATA-DRIVEN ESTIMATION FOR DUAL WEIBULL POPULATIONS: EM AND BAYESIAN METHODS WITH GIBBS SAMPLING Abbarapu Ashok, Nadiminti Nagamani Reliability Theory and Applications, 2025 In statistical analysis, accurately estimating parameters in the presence of uncertain and imprecise data is critical, particularly when dealing with complex, dual population models. Fuzzy data, which effectively represents real-world ambiguity, provides a framework for handling such uncertainties. While parameter estimation for single-population models with fuzzy data has been explored extensively, extending these methods to dual populations remains challenging. This study addresses this gap by developing estimation techniques for two Weibull populations that share a common shape parameter but differ in scale parameters, under fuzzy data conditions. We employ the Expectation-Maximization (EM) algorithm for Maximum Likelihood estimation and a Bayesian framework with TK approximation for parameter estimation. To refine our Bayesian estimates, we use Gibbs sampling to compute posterior densities. Through Monte Carlo simulations, for generated data as well as for a real dataset, we evaluate the accuracy and robustness of the proposed estimators, demonstrating their practical utility in applications where data imprecision is a significant factor. This research highlights the importance of robust methodologies for dual-population Weibull models, contributing to enhanced reliability in statistical analysis across diverse fields.
ENHANCING LINDLEY DISTRIBUTION PARAMETER ESTIMATION WITH HYBRID BAYESIAN AVERAGE MODEL FOR FUZZY DATA Abbarapu Ashok, Nadiminti Nagamani Reliability Theory and Applications, 2025 With the ultimate goal of increasing parameter estimate accuracy, this study will examine and assess a number of estimating techniques used with the Lindley distribution in the context of fuzzy data. Gibbs sampling, Bootstrapping Sampling, MCMC, MH, and a unique hybrid methodology that combines these approaches via Bayesian model averaging were also studied. The research looks at several sample sizes ranging from 15 to 100 and repeats the estimate method 10,000 times for each size. Fuzzy data are created using established fuzzy systems, and the performance of each approach is measured using average values (AV), mean squared errors (MSE), coverage probabilities, and confidence interval lengths. The findings show that the hybrid technique consistently produces estimates closer to the genuine parameter value of one across all sample sizes, with smaller mean squared errors than individual methods. Furthermore, the hybrid method’s confidence intervals preserve coverage probabilities that are consistent with the targeted confidence level, demonstrating the method’s trustworthiness in statistical inference. Overall, the results show that the hybrid technique improves estimate accuracy and reliability, providing a strong foundation for parameter estimation in the Lindley distribution framework using fuzzy data.
Adaptive estimation: Fuzzy data-driven gamma distribution via Bayesian and maximum likelihood approaches Abbarapu Ashok, Nadiminti Nagamani Aims Mathematics, 2025 <p>Integrating fuzzy concepts into statistical estimation offers considerable advantages by enhancing both the accuracy and reliability of parameter estimations, irrespective of the sample size and technique used. This study specifically examined the improvement of parameter estimation accuracy when dealing with fuzzy data, with a focus on the gamma distribution. We explored and evaluated a variety of estimation techniques for determining the scale parameter $ \\eta $ and shape parameter $ \\rho $ of the gamma distribution, employing both maximum likelihood (ML) and Bayesian methods. In the case of ML estimates, the expectation-maximization (EM) algorithm and the Newton-Raphson (NR) method were applied, with confidence intervals constructed using the Fisher information matrix. Additionally, the highest posterior density (HPD) intervals were derived through Gibbs sampling. For Bayesian estimates, the Tierney and Kadane (TK) approximation and Gibbs sampling were used to enhance the estimation process. A thorough performance comparison was undertaken using a simulated fuzzy dataset of the lifetimes of rechargeable batteries to assess the effectiveness of these methods. The methods were evaluated by comparing the estimated parameters to their true values using mean squared error (MSE) as a metric. Our findings demonstrate that the Bayesian approach, particularly when combined with the TK method, consistently produces more accurate and reliable parameter estimates compared to traditional methods. These results underscore the potential of Bayesian techniques in addressing fuzzy data and enhancing precision in statistical analyses.</p>
ESTIMATING COMMON PARAMETERS OF DIFFERENT CONTINUOUS DISTRIBUTIONS Abbarapu Ashok, Nadiminti Nagamani Proceedings on Engineering Sciences, 2024 Estimating a common parameter is the most essential and quite fascinating task across various probability distributions. This article addresses the challenge of estimating this parameter through the application of Maximum Likelihood Estimation (MLE). Numeric determination of common parameters is conducted for several distributions, including the Lomax distribution, Gamma distribution, Rayleigh distribution, and Weibull distribution. In caseswhere distributions lack a closed-form solution, estimation of MLEs is achieved using the Newton-Raphson technique.Furthermore, asymptotic confidence intervals are computed utilizing the Fisher information matrix tailored to each distribution.The performance evaluation of these estimators centers on the assessment of bias and mean squared error.To enable a numerical comparison of these estimators, the Monte Carlo simulation method is employed.Finally, these techniques are applied to real-time rainfall data to assess parameter estimates for each distribution.
An Innovative Method for Fraud Detection in E-Commerce Using DCNN-Multiclass SVM Model Rafiya Banu, Abbarapu Ashok, Vijay Kumar Dwivedi, Kotte Amaranadha Reddy, Thulasimani T, Neerav Nishant International Conference on Intelligent Algorithms for Computational Intelligence Systems Iacis 2024, 2024 A growing number of monetary transactions are taking place through different E-commerce platforms in this age of big data. Theft of personal information for fraudulent reasons is one of the risks that could arise from these opportunities. Credit cards are a prime target for cybercriminals seeking to steal personal data or execute fraudulent transactions due to their extensive use for online shopping. Intelligent fraud detection systems have come a long way, yet the system still haven't solved the notorious problems brought on by data imbalances. Following this important sequence will ensure that the data is properly prepared for feature extraction and model training. Prior to ATF processing raw log files pertaining to online e-commerce activity, a preprocessor removes data noise, such as click records created by customers who have not registered. Data mining for features an evolving metaheuristic algorithm called the multi-verse optimizer imitates the laws of the popular theory called multi-verse. While training the model, the proposed approach utilized DCNN-Multiclass SVM. Two state-of-the-art approaches, CNN and SVM, are beaten by the suggested approach. After using the strategy, accuracy improved by 95.65%.
Machine Learning Approaches for Optimizing ERP Supply Chain Management with Ant Colony Optimization and GBDT Kanimozhi T, A. Thangam, D. Sai Chaitanya Kishore, Saranya R, Abbarapu Ashok, S. Kaliappan International Conference on Distributed Systems Computer Networks and Cybersecurity Icdscnc 2024, 2024 The impact of an ERP system's implementation on a company's supply chain system is investigated in this study. Supply chain design is emerging as a key competency in supply chain management (SCM), which is expected to integrate the enterprise resource planning (ERP) system as a basic component. Although Enterprise Resource Planning (ERP) software offers a range of capabilities to facilitate supply chain integration, there are some features that hinder ERP from integrating with other business operations. Preprocessing, feature extraction, and training the model are the three steps that make up the proposed technique. Data cleansing, data integrations, data transformation, data reduction, and data scaling are the five primary activities of data pre-processing. Principal component analysis (PCA) is utilized in component extraction. We used MACO-GBDT to train the model. This cutting-edge method outperforms MACO and GBDT with an average accuracy of 93.28 percent.
RECENT SCHOLAR PUBLICATIONS
Robust Parameter Estimation for Dual-population Weibull Models under Fuzzy Data Conditions A Ashok, N Nagamani Model Assisted Statistics and Applications, 15741699261424805 , 2025 2025
FUZZY DATA-DRIVEN ESTIMATION FOR DUAL WEIBULL POPULATIONS: EM AND BAYESIAN METHODS WITH GIBBS SAMPLING A Ashok, N Nagamani Reliability: Theory & Applications 20 (2 (84)), 479-492 , 2025 2025
ENHANCING LINDLEY DISTRIBUTION PARAMETER ESTIMATION WITH HYBRID BAYESIAN AVERAGE MODEL FOR FUZZY DATA A Ashok, N Nagamani Reliability: Theory & Applications 20 (1 (82)), 528-542 , 2025 2025 Citations: 2
Adaptive estimation: Fuzzy data-driven gamma distribution via Bayesian and maximum likelihood approaches A Ashok, N Nagamani AIMS Mathematics 10 (1), 438-459 , 2025 2025 Citations: 6
Machine Learning Approaches for Optimizing ERP Supply Chain Management with Ant Colony Optimization and GBDT T Kanimozhi, A Thangam, DSC Kishore, R Saranya, A Ashok, ... 2024 International Conference on Distributed Systems, Computer Networks and … , 2024 2024
Estimating Common Parameters of Different Continuous Distributions A Ashok, N Nagamani Proceedings on Engineering Sciences 6 (3), 1273-1286 , 2024 2024 Citations: 2
Enhancing fraud detection and risk assessment in financial services using machine learning and predictive analytics RVS ● Praveen, C Kumar, E Manigandan, A Ashok, P Rani, S & Pal Journal of Informatics Education and Research 4 (3), 2394 , 2024 2024
An Innovative Method for Fraud Detection in E-Commerce Using DCNN-Multiclass SVM Model R Banu, A Ashok, VK Dwivedi, KA Reddy, TTN Nishant International Conference on Intelligent Algorithms for Computational … , 2024 2024
Estimation of Parameters of Different Continuous Distributions to Obtain Best Estimates for a Rainfall Data: A Simulation Study A Ashok, N Nagamani 2023-ORSI and ICBAI , 2023 2023
MOST CITED SCHOLAR PUBLICATIONS
Adaptive estimation: Fuzzy data-driven gamma distribution via Bayesian and maximum likelihood approaches A Ashok, N Nagamani AIMS Mathematics 10 (1), 438-459 , 2025 2025 Citations: 6
ENHANCING LINDLEY DISTRIBUTION PARAMETER ESTIMATION WITH HYBRID BAYESIAN AVERAGE MODEL FOR FUZZY DATA A Ashok, N Nagamani Reliability: Theory & Applications 20 (1 (82)), 528-542 , 2025 2025 Citations: 2
Estimating Common Parameters of Different Continuous Distributions A Ashok, N Nagamani Proceedings on Engineering Sciences 6 (3), 1273-1286 , 2024 2024 Citations: 2
Robust Parameter Estimation for Dual-population Weibull Models under Fuzzy Data Conditions A Ashok, N Nagamani Model Assisted Statistics and Applications, 15741699261424805 , 2025 2025
FUZZY DATA-DRIVEN ESTIMATION FOR DUAL WEIBULL POPULATIONS: EM AND BAYESIAN METHODS WITH GIBBS SAMPLING A Ashok, N Nagamani Reliability: Theory & Applications 20 (2 (84)), 479-492 , 2025 2025
Machine Learning Approaches for Optimizing ERP Supply Chain Management with Ant Colony Optimization and GBDT T Kanimozhi, A Thangam, DSC Kishore, R Saranya, A Ashok, ... 2024 International Conference on Distributed Systems, Computer Networks and … , 2024 2024
Enhancing fraud detection and risk assessment in financial services using machine learning and predictive analytics RVS ● Praveen, C Kumar, E Manigandan, A Ashok, P Rani, S & Pal Journal of Informatics Education and Research 4 (3), 2394 , 2024 2024
An Innovative Method for Fraud Detection in E-Commerce Using DCNN-Multiclass SVM Model R Banu, A Ashok, VK Dwivedi, KA Reddy, TTN Nishant International Conference on Intelligent Algorithms for Computational … , 2024 2024
Estimation of Parameters of Different Continuous Distributions to Obtain Best Estimates for a Rainfall Data: A Simulation Study A Ashok, N Nagamani 2023-ORSI and ICBAI , 2023 2023
RESEARCH OUTPUTS (PATENTS, SOFTWARE, PUBLICATIONS, PRODUCTS)
His research contributions are noteworthy, with multiple publications in esteemed international journals. These include works on statistical inference, adaptive estimation, machine learning in fraud detection, and sustainable supply chain management. Mr. Ashok has presented his research at prestigious conferences such as the 56th ORSI-KA and 10th ICBAI at IISc Bangalore.
In addition to his teaching and research, Mr. Ashok possesses advanced technical skills in R-programming, MATLAB, and LaTeX, which he integrates into his teaching practices to provide students with hands-on experience in data analysis.
INDUSTRY EXPERIENCE
Mr. Ashok's professional career spans over five years, during which he has served as a Statistics Lecturer at reputed institutions, including Deeksha PU College, Bangalore, Shri Gnanambica Degree College, Madanapalle, and GATE Degree College, Tirupati. In these roles, he has inspired students with his engaging teaching methods and expertise in quantitative analysis. He has also been instrumental in organizing seminars and workshops aimed at enhancing students’ analytical skills and preparing them for competitive exams.
