Research

Research

My thesis and peer-reviewed papers, with summaries, PDFs and citations you can copy.

MS thesis

MS thesis · 2026

Advancing Deep Learning and Neural Network Techniques for AI and Human Text Classification

MS Artificial Intelligence, The Islamia University of Bahawalpur · Supervisor: Prof. Dr. Najia Saher (Chairperson, Department of AI)

I built and compared deep learning models that separate AI-generated from human-written text on a balanced dataset of about 500,000 samples. A paper based on the thesis is in preparation.

Models compared

  1. LSTM
  2. Fine-tuned BERT
  3. TF-IDF + BERT + LightGBM
  4. Hybrid BERT + LSTM
99.65%Accuracy (BERT + LSTM)
F1-score
0.995
Text samples
~500,000

Peer-reviewed publications

Download all citations (.bib)
  1. First author20 June 2026

    Artificial Intelligence–Driven Fraud Detection in FinTech: Strengthening Cybersecurity Against Digital Financial Scams

    Khan, S. U., Zafar, U., Imran, S., & Ali, M. (2026). Journal of Business Insight and Innovation, 5(6), 72–82.

    Summary

    A survey of 385 professionals at FinTech firms, digital banks, payment providers and cybersecurity companies in Pakistan, analysed with SmartPLS 4. It finds that AI-based fraud detection, machine learning, real-time monitoring and anomaly detection together give strong protection against digital financial scams.

    • Fraud detection
    • Machine learning
    • Anomaly detection
    • FinTech
    • Cybersecurity
    DOI: Artificial Intelligence–Driven Fraud Detection in FinTech: Strengthening Cybersecurity Against Digital Financial Scams PDF: Artificial Intelligence–Driven Fraud Detection in FinTech: Strengthening Cybersecurity Against Digital Financial Scams
    BibTeX: Artificial Intelligence–Driven Fraud Detection in FinTech: Strengthening Cybersecurity Against Digital Financial Scams
    @article{khan2026fraud,
      title   = {Artificial Intelligence–Driven Fraud Detection in FinTech: Strengthening Cybersecurity Against Digital Financial Scams},
      author  = {Khan, Sana Ullah and Zafar, U. and Imran, S. and Ali, M.},
      journal = {Journal of Business Insight and Innovation},
      year    = {2026},
      volume  = {5},
      number  = {6},
      pages   = {72--82},
      doi     = {10.63544/jbii.v5i6.116}
    }
    
  2. First author31 March 2026

    Artificial Intelligence and Workplace Safety in the Oil and Gas Industry: Transforming Occupational Risk Assessment through Smart Safety Analytics

    Khan, S. U., Zafar, R., Alam, M. M., & Iqbal, M. (2026). Periodicals of Management Studies, 6(1), 46–60.

    Summary

    How AI can move occupational risk assessment in oil and gas from reacting to predicting. A survey of safety and AI professionals, analysed with regression and structural equation modelling, shows that AI adoption and predictive risk assessment are strongly linked to better safety outcomes (the model explains 69% of the variance), while data quality, cost and limited expertise remain barriers.

    • Artificial intelligence
    • Predictive analytics
    • Risk assessment
    • Workplace safety
    DOI: Artificial Intelligence and Workplace Safety in the Oil and Gas Industry: Transforming Occupational Risk Assessment through Smart Safety Analytics PDF: Artificial Intelligence and Workplace Safety in the Oil and Gas Industry: Transforming Occupational Risk Assessment through Smart Safety Analytics
    BibTeX: Artificial Intelligence and Workplace Safety in the Oil and Gas Industry: Transforming Occupational Risk Assessment through Smart Safety Analytics
    @article{khan2026safety,
      title   = {Artificial Intelligence and Workplace Safety in the Oil and Gas Industry: Transforming Occupational Risk Assessment through Smart Safety Analytics},
      author  = {Khan, Sana Ullah and Zafar, R. and Alam, M. M. and Iqbal, M.},
      journal = {Periodicals of Management Studies},
      year    = {2026},
      volume  = {6},
      number  = {1},
      pages   = {46--60},
      doi     = {10.65181/poms.06.01.0100}
    }
    
  3. Co-author30 March 2026

    GenAI Adoption in Entrepreneurial Education and Digital Entrepreneurial Intention

    Soomro, M. A., Khalid, M. A., Mangi, A. R., & Khan, S. U. (2026). International Journal of Advanced Social Studies, 6(1), 82–93.

    Summary

    A survey of 412 university students, analysed with structural equation modelling. Using generative AI in entrepreneurship education raises students' intention to start digital businesses, partly through their attitudes, and a supportive university climate makes the effect stronger (58% of variance explained).

    • Generative AI
    • Entrepreneurial education
    • Structural equation modelling
    DOI: GenAI Adoption in Entrepreneurial Education and Digital Entrepreneurial Intention PDF: GenAI Adoption in Entrepreneurial Education and Digital Entrepreneurial Intention
    BibTeX: GenAI Adoption in Entrepreneurial Education and Digital Entrepreneurial Intention
    @article{soomro2026genai,
      title   = {GenAI Adoption in Entrepreneurial Education and Digital Entrepreneurial Intention},
      author  = {Soomro, M. A. and Khalid, M. A. and Mangi, A. R. and Khan, Sana Ullah},
      journal = {International Journal of Advanced Social Studies},
      year    = {2026},
      volume  = {6},
      number  = {1},
      pages   = {82--93},
      doi     = {10.70843/ijass.2026.06107}
    }
    

Research interests

Working on now

  • AI-generated text detectionThe subject of my MS thesis.
  • Phishing & scam detectionMy current project, for English, Urdu and Roman Urdu messages.
  • Fraud detectionFirst-author paper on AI-driven fraud detection in FinTech.

Want to study next

  • Adversarial machine learningHow attackers fool ML models, and how to make them robust.
  • LLM securityPrompt injection, misuse and safe deployment of language models.
  • AI for the SOCUsing ML to triage alerts and logs in security operations.

Latest news

  1. Scored IELTS Academic 6.5 (B2) and started learning German.

  2. Completed "IoT for Everyone" (UET Peshawar / HEC, Coursera).

  3. Awarded the MS in Artificial Intelligence by The Islamia University of Bahawalpur (CGPA 3.66 / 4.00).