Rakin Sad Aftab

@aiub.edu

Faculty of Science and Technology, Department of Computer Science
Department of Computer Science, American International University-Bangladesh

Rakin Sad Aftab


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https://researchid.co/aftabrakinsad
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

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Science, Artificial Intelligence, Software, Computer Engineering
4

Scopus Publications

Scopus Publications

  • Leaf disease detection using convolutional neural networks: a proposed model using tomato plant leaves
    Md Masum Billah, Azmery Sultana, Rakin Sad Aftab, Mir Maruf Ahmed, Mohammad Shorif Uddin
    Neural Computing and Applications, 2024
  • APPLICATIONS OF IOT-ENABLED SMART MODEL: A MODEL FOR ENHANCING FOOD SERVICE OPERATION IN DEVELOPING COUNTRIES
    Azmery Sultana, Md Masum Billah, Mir Maruf Ahmed, Rakin Sad Aftab, Mohammed Kaosar, et al.
    Journal of Applied Engineering and Technological Science, 2024
    The dining sector in developing countries faces numerous challenges, including inefficiencies in order handling, resource management, and ensuring food quality and customer privacy. Traditional methods often lead to delays, errors, and dissatisfaction. This paper proposes a quick-witted, intelligent order-handling system utilizing the Internet of Things (IoT) to address these challenges and enhance the overall dining experience. We present a comprehensive approach to developing and implementing an IoT-based automated order-handling system tailored to restaurants' specific needs and challenges in developing countries, highlighting the importance of technology in enhancing operational efficiency and customer satisfaction. The proposed automated secure order-handling system using IoT demonstrates significant potential for improving efficiency and customer satisfaction in the dining sector. By addressing common problems through advanced technology, this system offers a sustainable solution that enhances the dining experience while ensuring food orders' validity, quality, and privacy. We analyzed the potential impact of implementing such a system in developing countries, focusing on economic and operational benefits.
  • Security Analysis in Online Transaction Systems: A Proposed Framework
    , Rakin S. Aftab, Md. Kais K. Emon, Sanjana F. Anny, Durjoy Sarker, Md. Mazid- Ul-Haque
    International Journal of Information Engineering and Electronic Business, 2024
    The safety of online transactions is paramount in the modern world, mainly since technology develops at a dizzying rate. This study aims to shed light on the numerous threats that users of online transaction systems face. The study used a mixed-methods research strategy to investigate the experiences and perspectives of 400 individuals from various backgrounds. Worryingly, the results show a significant knowledge gap on the many types of cyber hazards. The research reveals a troubling lack of awareness about various cyber risks, including fraud, phishing, and identity theft. It highlights the user's common functional difficulties. The study proposes a novel framework named COTSEF: A Comprehensive Framework for Enhancing Security in Online Transactions to enhance online transaction security alongside these findings. This comprehensive framework aims to provide a safer and more dependable environment for online commerce by mitigating the identified risks and challenges. The demographic breakdown of the users is also investigated, with the results indicating the increased vulnerability of some age groups and professions to various hazards. It also highlights the need for educational activities to address the significant need for more awareness about data protection rules. The study is a critical resource for policymakers, corporations, and educational institutions, offering actionable insights for developing more secure and user-friendly online transaction systems.
  • 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.