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Supervisor: Dr. Hafiz Ishfaq Ahmad

 

AI-Driven Optimization of Privacy-Preserving Association Rule Mining for Enhanced Predictive Analytics

 

Supervisor: Dr. Hafiz Ishfaq Ahmad, Snr Assistant Professor

Campus/School/Dept: BUIC – E8 Campus

Supervisory Record:  

  • PhD Produced: 0
  • PhD Enrolled: 0
  • MS/MPhil Produced: 01
  • MS/MPhil Enrolled: 4

 

Topic Brief Description:

This research aims to develop AI-driven methodologies for optimizing association rule mining while ensuring robust privacy protection. By integrating advanced machine learning techniques, the study will focus on enhancing the accuracy and efficiency of rule extraction processes while safeguarding sensitive data. The outcomes are expected to contribute to more secure and effective predictive analytics, enabling applications in domains such as healthcare, finance, and e-commerce where privacy and insight generation are critical.

 

Research Objectives/Deliverables:

  1. To design advanced AI-based methodologies to enhance the efficiency and accuracy of association rule mining processes.
  2. To integrate robust privacy-preserving mechanisms to protect sensitive data during rule mining and predictive analytics.
  3. To enhance the extraction and application of meaningful rules in healthcare domain for high utility and adaptability.

 

Research Questions: 

  1. How can AI techniques be utilized to improve the efficiency and accuracy of association rule mining methods?
  2. What privacy-preserving mechanisms can be integrated into association rule mining to protect sensitive data during analysis?
  3. How can AI-driven rule extraction methods be tailored for the domain of healthcare?

 

Candidate’s Eligibility Profile:

  1. The applicant must have an MS/MPhil/Equivalent degree in electrical engineering with CGPA >0. Besides, applicants must have a strong background in artificial intelligence, machine learning and related fields.
  2. A solid understanding of machine learning, data mining, and artificial intelligence techniques, particularly in the context of association rule mining and privacy-preserving methods
  3. Experience with programming languages such as Python is required with experience in machine learning frameworks (e.g., Tensorflow, pyTorch, scikit-learn)
  4. Previous experience in conducting independent research, especially in areas related to machine learning, data mining, or AI applications.
  5. Proficiency in spoken and written English is essential. We value independence and responsibility while promoting teamwork and collaboration among colleagues.