So remember Me; I will remember you. And be grateful to Me and do not deny Me. (Quran 2:152) And He found you lost and guided [you], (Quran 93:7) Indeed, with hardship [will be] ease. (Quran 94:6) And do good; indeed, Allāh loves the doers of good.(Quran 2:195) Do not despair of the mercy of Allāh. Indeed, Allāh forgives all sins. Indeed, it is He who is the Forgiving, the Merciful.
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Ms. Maha Ijaz

Lecturer, Department of Computer Science

Ms. Maha Ijaz serves as a Lecturer in Computer Science at the Gujrat Institute of Management Sciences, affiliated with PMAS-Arid Agriculture University, Rawalpindi. Passionate about teaching and equipped with a robust academic foundation, Maha is committed to fostering an engaging and supportive learning environment. In her role as a lecturer, she emphasizes critical thinking and practical knowledge, empowering students to excel in both their academic and professional pursuits. Her lectures and mentorship equip students with the essential tools to thrive in the dynamic field of computer science.

Biography

Ms. Maha Ijaz is a dedicated Computer Science Lecturer at the Gujrat Institute of Management Sciences, affiliated with PMAS-Arid Agriculture University, Rawalpindi. With a deep passion for teaching and a robust academic background, she is committed to cultivating an engaging, supportive environment for her students. Maha is known for integrating critical thinking and practical knowledge into her teaching approach, preparing students to excel in the evolving field of computer science. Her classes are designed to inspire innovation, curiosity, and resilience, equipping students with the tools necessary for both academic and professional success.

Education Qualification and Experience

  • MS in Computer Science
  • BS (Hons) in Computer Science

Research Interests

Maha Ijaz's expertise spans programming, algorithms, AI, machine learning, and deep learning, with a research focus on transfer learning and sentiment analysis. She is particularly interested in domain adaptation within multi-realm sentiment classification through transfer learning algorithms, working towards enhancing sentiment analysis models to function effectively across various domains.

Publications and Contributions

Ijaz, M., Anwar, N., Safran, M., Alfarhood, S., Sadad, T., & Imran. (2024). Domain adaptive learning for multi-realm sentiment classification on big data. PLoS ONE, 19(4): e0297028. doi:10.1371/journal.pone.0297028

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