Rakin Sad Aftab graduated in 2023 from the American International University-Bangladesh (AIUB) with a Bachelor of Science degree in Computer Science and Engineering. As a researcher, he focuses on machine learning (ML), artificial intelligence (AI), and data science, with a particular interest in neural networks and their applications in deep learning (DL). His expertise includes working with convolutional neural networks (CNNs), artificial neural networks (ANNs), and deep hypercomplex neural networks. Rakin's work also extends to networking within AI frameworks and the software development life cycle (SDLC), aiming to optimize AI-driven software development and improve technological solutions in AI and analytics. He is eager to leverage his diverse skill set and innovative thinking to drive success and adapt to new challenges. Committed to problem-solving, continuous learning, and making positive contributions, Rakin seeks opportunities for growth and collaboration.
EDUCATION
AMERICAN INTERNATIONAL UNIVERSITY-BANGLADESH
Bachelor of Science, Computer Science and Engineering
Cumulative GPA: 3.68/4.00
Passing Year: 2023
DR. MAHBUBUR RAHMAN MOLLAH COLLEGE
Science, Higher Secondary School Certificate (HSC)
GPA: 4.33/5.00
Passing Year: 2019
KALIGANJ R. R. N. PILOT GOVT. HIGH SCHOOL
Science, Secondary School Certificate (SSC)
GPA: 5.00/5.00
Passing Year: 2016
Deep Facial Recognition: Unraveling Kinship Patterns Among Strangers Using CNN Md Masum Billah, Rakin Sad Aftab, Mir Maruf Ahmed, Mohammad Shorif Uddin 2024 IEEE Conference on Computing Applications and Systems Compas 2024, 2024 This study explores the application of deep facial recognition technology to identify kinship patterns among strangers using convolutional neural networks (CNNs). Utilizing the VGGFace2 dataset, a deep CNN model was developed and evaluated to determine its effectiveness in inferring familial relationships based on facial features. The model achieved an impressive accuracy of 95%, demonstrating its potential for accurately recognizing kinship. This research highlights the promising applications of facial feature analysis in various domains, including forensic science and social network research. Additionally, it addresses both technological and ethical considerations, contributing to the responsible development and application of facial recognition technology for establishing familial ties. The comprehensive evaluation provided in this study underscores the potential and implications of facial recognition technology in determining kinship, while also identifying areas for future research and improvement.