Ms. Shakila Rahman , American International University – Bangladesh (AIUB)
Shakila Rahman ๐ง๐ฉ is a Lecturer at the Department of Computer Science, AIUB ๐ซ. She earned her M.Sc. in AI & Computer Engineering ๐ค from the University of Ulsan, South Korea ๐ฐ๐ท, with a CGPA of 4.00/4.50 ๐. Her research spans UAV networking ๐, wireless sensor networks ๐, and machine learning ๐ง . She actively supervises undergraduate projects ๐ฉโ๐ป and has published in reputed journals ๐. With experience in academia and applied computing, she contributes passionately to AI and emerging technologies ๐ก. She is also active on ResearchGate, ORCID, and LinkedIn ๐.
Professional Profile:
Orcid
๐น Education & Experienceย
Shakila Rahman completed her M.Sc. in AI & Computer Engineering ๐ from the University of Ulsan, South Korea ๐ฐ๐ท, and her B.Sc. in Computer Science & Engineering from IIUC ๐ง๐ฉ. Currently, she serves as a Lecturer ๐ฉโ๐ซ at American International University-Bangladesh (AIUB), where she teaches core computing subjects ๐ป. She previously worked as a Graduate Research Assistant in South Korea ๐ฌ and as an Undergraduate Teaching Assistant at IIUC ๐ฉโ๐ฌ. Her experience blends international research, teaching, and real-world project supervision ๐. She is skilled in Python, Java, and C++ ๐งโ๐ป, and fosters student innovation through hands-on learning ๐งช.
๐น Professional Development
Shakila has enhanced her technical skillset through multiple workshops and training programs ๐ ๏ธ. She completed web development ๐ป at BASIS BITM, mobile game/app development ๐ฎ with Bangladesh ICT Division, and Android app development ๐ค with Affable Technology. As a competitive programmer, she participated in ICPC, NGPC, and UVA online judges ๐. During her B.Sc., she joined industrial events like the Tech Summit by LEADS Corporation ๐ฉโ๐ผ. These initiatives equipped her with a dynamic understanding of both academic theory and industry practices ๐. She is also an IELTS-certified English communicator with a score of 6.0 ๐ฌ๐ง๐ฃ๏ธ.
๐น Research Focus
Shakila Rahman’s research lies at the intersection of AI and networking technologies ๐ค๐. Her main focus includes UAV Networking ๐, Wireless Sensor Networks ๐ฐ๏ธ, Network Systems ๐ง, and Optimization Algorithms ๐. She also works with Machine Learning ๐ง , Deep Learning ๐งฌ, Image Processing ๐ผ๏ธ, and AR/VR applications in AI ๐ฅฝ. Her work emphasizes solving real-world challenges like obstacle detection, fire/smoke recognition ๐ฅ๐จ, and human activity classification ๐ถโโ๏ธ๐จ. She publishes in SCIE and SCOPUS-indexed journals ๐, contributing to the academic community with innovations in intelligent systems and computer vision ๐๏ธโ๐จ๏ธ.
๐น Awards & Honorsย
Shakila Rahman has received prestigious academic honors ๐
, including the Brain Korea Scholarship (BK21) ๐ฐ during her M.Sc. at the University of Ulsan ๐ฐ๐ท. She was also awarded the fully funded AF1 Scholarship worth approx. $21,000 USD ๐๐ต. Her research was supported by the National Research Foundation of Korea (NRF) ๐ฐ๐ท for multiple publications ๐. These recognitions highlight her academic excellence ๐, research potential ๐ฌ, and commitment to global scientific collaboration ๐. Her accolades stand as a testament to her dedication and passion in the field of Artificial Intelligence and networked systems ๐๐ก.
Publication Top Notes:
๐ง Bilingual Sign Language Recognition: A YOLOv11-Based Model for Bangla and English Alphabets
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Journal: Journal of Imaging
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Year: 2025
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Citations: 0
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Summary: This study presents a YOLOv11-based model designed for real-time recognition of Bangla and English sign language alphabets. The model aims to bridge communication gaps for the hearing-impaired by accurately detecting and classifying sign language gestures.
๐๏ธ Towards Safer Cities: AI-Powered Infrastructure Fault Detection Based on YOLOv11
๐งด A Hybrid CNN Framework DLI-Net for Acne Detection with XAI
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Journal: Journal of Imaging
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Year: 2025
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Citations: 0
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Summary: The study proposes DLI-Net, a hybrid CNN framework combining DeepLabV3 for segmentation and InceptionV3 for classification, to detect and classify acne lesions. Achieving a 97% accuracy rate, the model incorporates Grad-CAM for explainable AI, enhancing transparency in dermatological diagnostics.
๐ A Deep Q-Learning Based UAV Detouring Algorithm in a Constrained Wireless Sensor Network Environment
๐ก๏ธ A Deep Learning Model for YOLOv9-based Human Abnormal Activity Detection: Violence and Non-Violence Classification
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Journal: Iranian Journal of Electrical and Electronic Engineering
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Year: 2024
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Citations: 0
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Summary: The research develops a YOLOv9-based deep learning model to classify human activities as violent or non-violent. Aimed at enhancing surveillance systems, the model contributes to improved public safety through accurate activity detection.
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Conclusion
Shakila Rahman is a highly suitable candidate for the Best Researcher Award due to her blend of academic excellence, innovative research, international exposure, and impactful contributions to AI and networking fields. Her work not only advances fundamental knowledge but also targets practical challenges, demonstrating a research trajectory with strong future promise. Recognizing her with this award would celebrate her achievements and encourage continued excellence in emerging technology domains. ๐๐ค๐ก