Md. Arifuzzaman

Lecturer
Computer Science & Engineering

Contact Information

Cell Phone: 01998740789

Biography

Academic Background
B.E : Computer Engineering (Gujarat Technological University, Ahmedabad, India)
ICCR Scholar : (2015-2019)
HSC : New Govt. Degree College, Rajshahi
SSC : Gangni Secondary School, Gangni

Area of Study

Subject Conducted By Me:

  • Fundamental of Basic Computer
  • C programming
  • Artificial Intelligence
  • Machine Learning
  • Nueral Networking and Fuzzy Logic
  • Software Engineering
  • Computer Architecture and Design
  • Digital Logic Design
  • Microprocessor: Assembly Language and Interfacing

 

Research Interests:

  1. Machine Learning Algorithms (ML)
  2. Artificial Intelligence (AI)
  3. Deep Learning
  4. Human Computer Interaction
  5. Medical Imaging

Publications:

1.

  • Title: "Heart Disease Prediction through Enhanced Machine Learning and Diverse Feature Selection Approaches,"
  • Published in: ICSIMA2024 (International Conference on Smart Instrumentation, Measurement, and Applications)
  • This study delves into leveraging advanced machine learning techniques alongside diverse feature selection methodologies to improve the accuracy of heart disease prediction. It's a step forward in harnessing technology for impactful healthcare solutions.
  • Paper Link: 10.1109/ICSIMA62563.2024.10675564

2.

  • Title: "Optimizing Facial Recognition: An Analytical Comparison of Traditional and Deep Learning Approaches"
  • Published In: 2024 International Conference on Data Science and Its Applications (ICoDSA)
  • This work presents a detailed exploration of traditional techniques like PCA and SVM alongside modern deep learning approaches, including CNNs and transfer learning using MobileNet. It highlights how these methods compare and complement each other in the quest for more efficient and accurate facial recognition systems.
  • Paper Link: [10.1109/ICoDSA62899.2024.10651632].

3.

  • Title: " A Novel Ensemble-Based Deep Learning Model with Explainable AI for Accurate Kidney Disease Diagnosis "
  • Published In: Advances in Artificial Intelligence and Machine Learning (Published 24-03-2025)
  • This work presents a detailed exploration of traditional techniques like PCA and SVM alongside modern deep learning approaches, including CNNs and transfer learning using MobileNet. It highlights how these methods compare and complement each other in the quest for more efficient and accurate facial recognition systems.
  • Paper Link: [https://www.oajaiml.com/uploads/archivepdf/416551196.pdf].

Advisor

Orpheus"bolt from the rock" (Oldest Musical club of Leading University)

From February,2023 To Till date