Aiman Syahmi Mohd Haryzal

A passionate Computer Science graduate specializing in Artificial Intelligence. Experienced in developing AI systems, with skills in coding, machine learning, and data analysis. Eager to apply expertise to solve real-world challenges in a dynamic tech environment.

Bronze Medal in Virtual Innovation Competition 2024

(International)

Awarded for developing a Face Recognition System for i-MARS Application Access Under Dynamic Lighting Conditions.

Recognized for innovation in creating a secure, adaptable access solution for Intelligent Modernized Armament Rack Systems, with application in varied lighting environments.

• Face Recognition for i-MARS (Intelligent Modernized Armaments Rack System) Application
  Access Under Dynamic Lighting Conditions
  Developed and implemented a robust face recognition module to ensure secure, seamless
  access to i-MARS under varying lighting conditions.
  Utilized advanced image processing techniques and machine learning algorithms to enhance
  recognition accuracy and reliability.
  Optimized the system to adapt to fluctuating light levels, improving usability in real-world
  settings.
• Vehicle Detection for KAWBRA (Unmanned Ground Vehicle)
  Employed computer vision and deep learning models to accurately detect and classify
  vehicles, even in challenging terrains and environments.
  Streamlined real-time data processing to enable rapid response and decision-making in
  dynamic situations.

• Ts. Aizul Fazli Bin Suhaimi, aizulfazli.suhaimi@stride.gov.my, +603-8732 4400 (cont 4511)

• Assoc Prof. Ts. Dr. Suzaimah Binti Ramli, National Defence University of Malaysia, suzaimah@upnm.edu.my, 019-3569265

• Ts. Aizul Fazli Bin Suhaimi, aizulfazli.suhaimi@stride.gov.my, +603-8732 4400 (cont 4511)

• Assoc Prof. Ts. Dr. Suzaimah Binti Ramli, National Defence University of Malaysia, suzaimah@upnm.edu.my, 019-3569265

• Face Recognition for i-MARS: Developed a secure face recognition module using advanced image processing and machine learning, optimized for varying lighting conditions to ensure reliable access in real-world scenarios.
• Vehicle Detection for KAWBRA: Created a vehicle detection system using computer vision and deep learning, enabling accurate classification and real-time response for autonomous operations in challenging environments.