Nuramin Fitri Aminuddin

I pride myself on my ability to work collaboratively with others and my dedication to ensuring the success of each project. With a blend of technical expertise and good problem-solving skills, I constantly seek new challenges and opportunities to grow my skills and achieve the best possible results to help companies, businesses, and organizations succeed.

Graduate Research Assistant (Research Grant vot H916)           Universiti Tun Hussein Onn Malaysia, Johor

Designed and implemented an Artificial Intelligence-powered vision tool for automatic plant disease identification, resulting in holistic recommendations on disease management for clients and optimized accuracy. Achievements include:

• Conducting on-site investigations into plant diseases to identify effective treatment and prevention methods, resulting in a success rate of above 90% in recommended disease management for clients.

• Leveraging cloud-based platforms to efficiently transfer and process data collected from sensor devices, reducing data processing time by 45%.

• Developing and implementing Artificial Intelligence-driven techniques to enhance data processing and analysis capabilities, resulting in an 18% improvement in efficiency.

• Validating AI-driven techniques to ensure accuracy and effectiveness, resulting in a 98% accuracy rate in disease prediction.

Graduate Research Assistant (Research Grant vot H499)          Universiti Tun Hussein Onn Malaysia, Johor

Contributed to a research project focused on the development of a leak sensor for accurate monitoring of leakage detection in steel pipelines. Achievements include:

• Investigating signals from 25 leaking and non-leaking pipelines based on acoustic and ultrasonic principles, leading to the identification of 12 key signal characteristics.

• Characterizing signals from 70+ leaking and non-leaking pipeline conditions through the development of 15 prototypal detections and signal filtering algorithms, resulting in a 30% improvement in signal-to-noise ratio.

• Inventing a leak sensor through signals characterization with pipeline tomography adaptation in a dynamic fluid environment, resulting in a 40% reduction in the time required to identify pipeline leaks.

• Implementing wireless sensor networks for 5 sensor devices to enable the transfer of recorded and processed data to a remote station, resulting in real-time monitoring capabilities.

Master of Electrical Engineering with Honours, major in Computing

Universiti Tun Hussein Onn Malaysia, Johor.

Bachelor of Electronic Engineering with Honours, major in Computing

Universiti Tun Hussein Onn Malaysia, Johor.