Article Per Year (5 Year)
p-Index From 2020 - 2025
0.408
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering
Azmi, Intan Nabina
Unknown Affiliation
Computer Science & IT Electrical & Electronics Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Optimizing drone-assisted victim localization and identification in mass-disaster management: a study on feasible flying patterns and technical specifications Azmi, Intan Nabina; Kassim, Murizah; Mohd Yussoff, Yusnani; Md Tahir, Nooritawati
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i4.pp4097-4109

Abstract

The prompt emphasizes the importance of identifying victims in a disaster area within 48 hours and highlights the potential benefits of using drones in search and rescue missions. However, the use of drones is limited by factors such as battery life, processing speed, and communication range. To address these limitations, the paper presents a detailed research study on the most effective flying pattern for drones during search and rescue missions. The study utilized energy consumption and coverage area as performance metrics and collected precise images that could be analyzed by the forensic team. The research was conducted using OMNET++ and fieldwork at Pulau Sebang, Melaka, in collaboration with search and rescue agencies in Malaysia. The results suggest that the square flying pattern is the most effective, as it provides the highest coverage area with reasonable energy utilization. Both simulation and fieldwork results showed coverage of 100% and 97.96%, respectively, for this pattern. Additionally, the paper provides technical specifications for rescue teams to use when deploying drones during search and rescue missions.
Optimal efficiency on nuclear reactor secondary cooling process using machine learning model Hajar, Ibnu; Kassim, Murizah; Minhat, Mohd Sabri; Azmi, Intan Nabina
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp6287-6299

Abstract

This review delves into the quest for optimal efficiency in the secondary cooling process of nuclear reactor water plant coolant systems. Modeling secondary cooling nuclear processes is hardly performed. Thus, Neural networks with traditional statistical methodologies are integrated to innovate a hybrid model to revolutionize nuclear reactor cooling systems' performance, reliability, and safety. A total of 63 indexed papers were reviewed in the nuclear field that analyzed critical research gaps, including the need for uncertainty modeling and resilience against external hazards. Insights into sensor technologies, data analytics, and real-time monitoring underscore the importance of continuous optimization and predictive maintenance were reviewed. A descriptive analysis for a month of sampling data was presented for the parameters of temperature for TT003 and TT004 and pressure for PT002 and PT003 of the secondary process. The confidence level of 95.0% is identified for the temperature and pressure parameters. The lowest standard error was recognized at 0.00032 and 0.01691, respectively. The review culminates with a forward-looking perspective, recognizing the pivotal role of hybrid machine learning models in shaping the future of secondary cooling processes for nuclear reactor water coolant plants to improve the efficiency and sustainability of nuclear reactor systems.
Co-Authors Ibnu Hajar Kassim, Murizah Minhat, Mohd Sabri Mohd Yussoff, Yusnani Nooritawati Md Tahir