Sahar Qaadan

16 papers C 1Journal 6Unranked 8

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Probabilities Feature Enrichment for Improved Early Diabetes Prediction. Algorithms Haneen Altartouri, Sahar Qaadan, Yousef K. Qawqzeh, Mohammed Hamdoun
2025	—	`conf`	A Comprehensive AI-Driven Approach Using EHG Signal Analysis for Delivery Type Prediction. RTSI Sahar Qaadan, Abdullah Ahmed, Reda AlSalman, Omar Obeidat, Suleimman Alsweedan, Aiman Alshare
2025	—	`conf`	A Conceptual Framework for Predicting Electric Vehicles Battery Remaining Useful Life used for Fleet Management Utilizing Survival Analysis. ICCMA Majsa Ammouriova, Sahar Qaadan
2025	—	`conf`	A Hybrid Weibull-Physics-Informed Neural Network Framework for Early Anomaly Detection in Lithium-Ion Batteries. ICCMA Haneen Altartouri, Sahar Qaadan, Rami Alazrai, Aiman Alshare
2025	—	`conf`	AI Meets Reliability: Predicting Li-Ion Battery SOH with Weibull and Wöhler S-N Models. RTSI Sahar Qaadan, Abdelrahman AlZamer, Moaz Mohamed, Aiman Alshare, Alexander Popp, Benedikt Schmuelling
2025	J	`jnl`	Causal Physics-Infused Hybrid Learning (CPIHL) Framework for Next-Gen Battery Health Forecasting. IEEE Access Sahar Qaadan, Aiman Alshare, Rami Alazrai, Alexander Popp, Benedikt Schmuelling
2025	J	`jnl`	Electroencephalogram Dataset of Visually Imagined Arabic Alphabet for Brain-Computer Interface Design and Evaluation. Data Rami Alazrai, Khalid Naqi, Alaa Elkouni, Amr Hamza, Farah Hammam, Sahar Qaadan, Mohammad I. Daoud, Mostafa Z. Ali, Hasan Al-Nashash
2025	—	`conf`	Introducing Feature Importance Consistency (FIC) in XAI Models: A Comprehensive Evaluation using Rank Correlation Coefficients. CIVEMSA Sahar Qaadan, Abdelrahman AlZamer, Moaz Mohamed, Aiman Alshare, Benedikt Schmuelling
2022	—	`conf`	Forecasting Solar Photovoltaic Power Output in the German Jordanian University in Amman Using Artificial Intelligence and Machine Learning Algorithms. ICCMA Sahar Qaadan, Aiman Alshare
2020	—	`—`	Budgeted stochastic coordinate ascent for large-scale kernelized dual support vector machine training Sahar Qaadan
2019	C	`conf`	Dual SVM Training on a Budget. ICPRAM Sahar Qaadan, Merlin Schüler, Tobias Glasmachers
2019	—	`conf`	Online Budgeted Stochastic Coordinate Ascent for Large-Scale Kernelized Dual Support Vector Machine Training. ICPRAM (Revised Selected Papers) Sahar Qaadan, Abhijeet Pendyala, Merlin Schüler, Tobias Glasmachers
2018	J	`jnl`	Dual SVM Training on a Budget. CoRR Sahar Qaadan, Merlin Schüler, Tobias Glasmachers
2018	J	`jnl`	Multi-Merge Budget Maintenance for Stochastic Gradient Descent SVM Training. CoRR Sahar Qaadan, Tobias Glasmachers
2018	—	`conf`	Speeding Up Budgeted Stochastic Gradient Descent SVM Training with Precomputed Golden Section Search. LOD Tobias Glasmachers, Sahar Qaadan
2018	J	`jnl`	Speeding Up Budgeted Stochastic Gradient Descent SVM Training with Precomputed Golden Section Search. CoRR Tobias Glasmachers, Sahar Qaadan

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.

In 2025, he co-authored Assessing the Effectiveness of the Tigress Obfuscator Against MOPSA and BinaryNinja, published at CheckMATE. The paper evaluates the effectiveness of obfuscation techniques and their impact on source-level static analysis and binary-level decompilation.

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.