Tayyaba Riaz

ORCID: 0009-0001-9711-0673
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About
Contact & Profiles
Research Areas
  • Environmental Education and Sustainability
  • Hearing Impairment and Communication
  • Digital Media Forensic Detection
  • Advanced Malware Detection Techniques
  • Environmental Sustainability in Business
  • Anomaly Detection Techniques and Applications
  • Energy and Environment Impacts
  • Gait Recognition and Analysis
  • Hand Gesture Recognition Systems

Quaid-i-Azam University
2023-2025

National University of Sciences and Technology
2022

In the evolving landscape of biometric authentication, integrity face recognition systems against sophisticated presentation attacks (PAD) is paramount. This study set out to elevate detection capabilities PAD by ingeniously integrating a teacher-student learning framework with cutting-edge methodologies. Our approach anchored in realization that conventional models, while effective degree, falter novel, unseen attack vectors and complex variations. As solution, we suggest novel architecture...

10.3390/s25072166 article EN cc-by Sensors 2025-03-28

Policymakers in developing countries like Pakistan mostly ignore the behavioral aspects of climate change mitigation, whereas literature is also deficient advocating evidence-based mitigation strategies. This study aims to analyze impact personality traits, social norms, and attitudes on energy conservation behavior. Face-to-face interviews 361 households are conducted capital city using random sampling. According characteristics data, ordered logistic regression model applied. The results...

10.1016/j.heliyon.2022.e11054 article EN cc-by-nc-nd Heliyon 2022-10-01

In an endeavor to bridge communication barriers for the Pakistani deaf community, this project presents a robust system capable of translating Sign Language (PSL) text and vice versa. The architecture leverages cutting-edge technologies: textual voice inputs are processed using Natural Processing (NLP) techniques, with voice-to-text translation facilitated by WebSpeechToolkit. For Sign-to-Text (S2T) module, images videos undergo rigorous preprocessing OpenCV, followed hand landmark detection...

10.1109/honet59747.2023.10374883 article EN 2023-12-04
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