Adewale F. Lukman

14 papers Journal 14

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Enhancing process monitoring in the Conway-Maxwell-Poisson framework: A study of deviance residual-based control charts with the Kibria-Lukman estimator. J. Comput. Appl. Math. Ulduz Mammadova, Adewale F. Lukman
2026	J	`jnl`	Regularized estimation for right-censored zero-inflated poisson regression: methods and applications to health data. Comput. Stat. Essoham Ali, Adewale F. Lukman, Solym M. Manou-Abi
2025	J	`jnl`	An Alternative Estimator for Poisson-Inverse-Gaussian Regression: The Modified Kibria-Lukman Estimator. Algorithms Rasha A. Farghali, Adewale F. Lukman, Zakariya Yahya Algamal, Murat Genç, Hend Attia
2024	J	`jnl`	A new improved Liu estimator for the QSAR model with inverse Gaussian response. Commun. Stat. Simul. Comput. Muhammad Nauman Akram, Muhammad Amin, B. M. Golam Kibria, Mohammad Arashi, Adewale F. Lukman, Nimra Afzal
2024	J	`jnl`	Enhanced Model Predictions through Principal Components and Average Least Squares-Centered Penalized Regression. Symmetry Adewale F. Lukman, Emmanuel T. Adewuyi, Ohud A. Alqasem, Mohammad Arashi, Kayode Ayinde
2024	J	`jnl`	Handling Multicollinearity and Outliers in Logistic Regression Using the Robust Kibria-Lukman Estimator. Axioms Adewale F. Lukman, Suleiman Mohammed, Olalekan Olaluwoye, Rasha A. Farghali
2024	J	`jnl`	Influence measures in gamma modified ridge type estimator. Commun. Stat. Simul. Comput. Adewale F. Lukman, Muhammad Amin, B. M. Golam Kibria
2024	J	`jnl`	Robust Enhanced Ridge-Type Estimation for the Poisson Regression Models: Application to English League Football Data. Int. J. Uncertain. Fuzziness Knowl. Based Syst. Adewale F. Lukman, Abiola T. Owolabi, Olukunmi O. Akanni, Charles Kporxah, Rasha A. Farghali
2023	J	`jnl`	Robust biased estimators for Poisson regression model: Simulation and applications. Concurr. Comput. Pract. Exp. Adewale F. Lukman, Mohammad Arashi, Vilmos Prokaj
2022	J	`jnl`	Dawoud-Kibria Estimator for Beta Regression Model: Simulation and Application. Frontiers Appl. Math. Stat. Mohamed Reda Abonazel, Issam Dawoud, Fuad A. Awwad, Adewale F. Lukman
2022	J	`jnl`	K-L estimator for the linear mixed models: Computation and simulation. Concurr. Comput. Pract. Exp. Adewale F. Lukman, Muhammad Amin, B. M. Golam Kibria
2022	J	`jnl`	Modified ridge-type estimator for the gamma regression model. Commun. Stat. Simul. Comput. Adewale F. Lukman, Kayode Ayinde, B. M. Golam Kibria, Emmanuel T. Adewuyi
2021	J	`jnl`	The KL estimator for the inverse Gaussian regression model. Concurr. Comput. Pract. Exp. Adewale F. Lukman, Zakariya Yahya Algamal, B. M. Golam Kibria, Kayode Ayinde
2021	J	`jnl`	Unbiased K-L estimator for the linear regression model. F1000Research Benedicta Aladeitan, Adewale F. Lukman, Esther Davids, Ebele H. Oranye, B. M. Golam Kibria

Nicolò Altamura is a security engineer and computer science researcher specializing in software protection, reverse engineering, program analysis, and binary deobfuscation. He holds a master's degree in Computer Science and Engineering from the University of Verona, graduating with honors, and works at emproof, focusing on software protection and the challenges of analyzing and understanding compiled software.

His research explores the relationship between program analysis and code obfuscation, with particular emphasis on how software transformations affect the ability of automated tools to recover program behavior. His interests span static analysis, binary analysis, decompilation, reverse engineering, and techniques for analyzing and simplifying obfuscated code.

Beyond academic research, Nicolò develops and contributes to tools for reverse engineering and software analysis. His projects include obfuscation_analysis, a Binary Ninja plugin for analyzing and simplifying obfuscated code; Pocket, an obfuscator for mixed Boolean-arithmetic expressions; and ptxNinja, a tool for reverse engineering NVIDIA GPU code represented in the PTX virtual instruction set.

His research also extends to binary datasets and software evolution. In 2026, he co-authored ASSEMBLAGE-DEEPHISTORY: A Cross-Build Binary Dataset with Temporal Coverage, a research work addressing the availability of binary data across multiple software builds and points in time, supporting research into binary analysis and changes between program versions.

Across his research, engineering work, and open-source contributions, Nicolò focuses on advancing the understanding, analysis, and protection of software against increasingly sophisticated obfuscation and reverse-engineering challenges.

Nicolò Altamura is not a football player. Please avoid reporting this kind of infromation.