Sana Alamgeer - RankMe

20 papers A 2B 4C 1Journal 8Unranked 5

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Dual-Stream Transformer with Kalman-Based Sensor Fusion for Wearable Fall Detection. Big Data Cogn. Comput. Abheek Pradhan, Sana Alamgeer, Rakesh Suvvari, Syed Tousiful Haque, Anne H. H. Ngu
2026	J	`jnl`	Personalized Fall Detection by Balancing Data with Selective Feedback Using Contrastive Learning. CoRR Awatif Yasmin, Tarek Mahmud, Sana Alamgeer, Anne H. H. Ngu
2026	J	`jnl`	TransConv-DDPM: Enhanced Diffusion Model for Generating Time-Series Data in Healthcare. CoRR Md Shahriar Kabir, Sana Alamgeer, Minakshi Debnath, Anne H. H. Ngu
2025	J	`jnl`	AI-Generated Fall Data: Assessing LLMs and Diffusion Model for Wearable Fall Detection. CoRR Sana Alamgeer, Yasine Souissi, Anne H. H. Ngu
2025	B	`conf`	Improving Time-Series Forecasting with Statistical and AI-Driven Feature Optimization. COMPSAC Minakshi Debnath, Sana Alamgeer, Anne H. H. Ngu
2025	J	`jnl`	No-reference underwater image quality assessment based on a convolutional neural network. J. Electronic Imaging Muhammad Irshad, Camilo Sánchez-Ferreira, Sana Alamgeer, Carlos H. Llanos, Mylène C. Q. Farias
2025	B	`conf`	TransConv-DDPM: Enhanced Diffusion Model for Generating Time-Series Data in Healthcare. COMPSAC Md Shahriar Kabir, Sana Alamgeer, Minakshi Debnath, Anne H. H. Ngu
2023	J	`jnl`	A survey on visual quality assessment methods for light fields. Signal Process. Image Commun. Sana Alamgeer, Mylène C. Q. Farias
2023	J	`jnl`	A two-stream cnn based visual quality assessment method for light field images. Multim. Tools Appl. Sana Alamgeer, Mylène C. Q. Farias
2023	C	`conf`	Using a Diverse Neural Network to Predict the Quality of Light Field Images. MMSP Sana Alamgeer, André H. M. Costa, Mylène C. Q. Farias
2022	—	`conf`	360RAT: A Tool for Annotating Regions of Interest in 360-degree Videos. WebMedia Lucas S. Althoff, Myllena Prado, Sana Alamgeer, Alessandro Rodrigues e Silva, Ravi Prakash, Marcelo M. Carvalho, Mylène C. Q. Farias
2022	B	`conf`	Deep Learning-Based Light Field Image Quality Assessment Using Frequency Domain Inputs. QoMEX Sana Alamgeer, Mylène C. Q. Farias
2022	—	`conf`	Designing an user-centric framework for perceptually-efficient streaming of 360° edited videos. IQSP Lucas S. Althoff, Henrique Domingues Garcia, Dario D. R. Morais, Sana Alamgeer, Myllena Prado, Gabriel De Castro Araújo, Ravi Prakash, Marcelo M. Carvalho, Mylène C. Q. Farias
2022	B	`conf`	Light Field Image Quality Assessment with Dense Atrous Convolutions. ICIP Sana Alamgeer, Mylène C. Q. Farias
2022	A	`conf`	Light field image quality assessment method based on deep graph convolutional neural network: research proposal. MMSys Sana Alamgeer, Muhammad Irshad, Mylène C. Q. Farias
2022	—	`conf`	No-Reference Light Field Image Quality Assessment Method Based on a Long-Short Term Memory Neural Network. ICME Workshops Sana Alamgeer, Mylène C. Q. Farias
2021	J	`jnl`	CNN-based no-reference video quality assessment method using a spatiotemporal saliency patch selection procedure. J. Electronic Imaging Sana Alamgeer, Muhammad Irshad, Mylène C. Q. Farias
2021	—	`conf`	No-reference Image Quality Assessment of Underwater Images Using Multi-Scale Salient Local Binary Patterns. IQSP Muhammad Irshad, Camilo Sánchez-Ferreira, Sana Alamgeer, Carlos H. Llanos, Mylène C. Q. Farias
2020	—	`conf`	Perceptual Quality Assessment of Enhanced Images Using a Crowd-Sourcing Framework. IQSP Muhammad Irshad, Alessandro Rodrigues e Silva, Sana Alamgeer, Mylène C. Q. Farias
2018	A	`conf`	Blind image quality assessment based on multiscale salient local binary patterns. MMSys Pedro Garcia Freitas, Sana Alamgeer, Welington Y. L. Akamine, Mylène C. Q. Farias

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.