Ana Antoniette C. Illahi
Papers
1
Total Citations
7
H-Index
1
About
Ana Antoniette C. Illahi is a researcher at the forefront of applying machine learning to critical safety and security challenges, with a particular focus on bomb detection technologies. Her most cited work, "BombNose: A Multiple Bomb-Related Gas Prediction Model Using Machine Learning with Electronic Nose Sensor Substitution Technique" (2022, 7 citations), addresses the pressing need for energy-efficient and compact bomb removal robots. Illahi’s key contribution lies in developing a machine learning model that uses an electronic nose sensor substitution technique, enabling a reduced sensor array to accurately predict multiple bomb-related gases. This innovation overcomes the traditional limitation of requiring large, power-intensive sensor arrays, making robotic bomb disposal more practical and safer. Her work directly impacts public safety by enhancing the detection capabilities of autonomous systems in high-risk environments. Illahi’s research bridges artificial intelligence, sensor technology, and security, offering a scalable solution for real-world threat mitigation. With her focus on practical, life-saving applications, she stands out as an emerging voice in the intersection of machine learning and defense technology, demonstrating how intelligent systems can protect lives while optimizing resource use.
Research Focus
Key Achievements
Top Papers
- 1