Article Per Year (5 Year)
p-Index From 2020 - 2025
0.444
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering Indonesian Journal of Electrical Engineering and Computer Science
Rana Jassim Mohammed
University of Diyala
Computer Science & IT Electrical & Electronics Engineering

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

Found 2 Documents
Search

 1 
Comparative study between metaheuristic algorithms for internet of things wireless nodes localization Rana Jassim Mohammed; Enas Abbas Abed; Mostafa Mahmoud El-gayar
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 1: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i1.pp660-668

Abstract

Wireless networks are currently used in a wide range of healthcare, military, or environmental applications. Wireless networks contain many nodes and sensors that have many limitations, including limited power, limited processing, and narrow range. Therefore, determining the coordinates of the location of a node of the unknown location at a low cost and a limited treatment is one of the most important challenges facing this field. There are many meta-heuristic algorithms that help in identifying unknown nodes for some known nodes. In this manuscript, hybrid metaheuristic optimization algorithms such as grey wolf optimization and salp swarm algorithm are used to solve localization problem of internet of things (IoT) sensors. Several experiments are conducted on every meta-heuristic optimization algorithm to compare them with the proposed method. The proposed algorithm achieved high accuracy with low error rate (0.001) and low power consumption.
Intelligent multimodal identification system based on local feature fusion between iris and finger vein Enas Abbas Abed; Rana Jassim Mohammed; Dhafer Taha Shihab
Indonesian Journal of Electrical Engineering and Computer Science Vol 21, No 1: January 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v21.i1.pp224-232

Abstract

Biometric identification systems, which use physical features to check a person's identity, ensure much higher security than password and number systems. Biometric features such as the face or a fingerprint can be stored on a microchip in a credit card, for example. A single modal biometric identification system fails to extract enough features for identification. Another disadvantage of using only one feature is not always readable. In this article, a smart multimodal biometric verification model for identifying and verifying a person's identity is recommended based on artificial intelligence methods. The proposed model is identified the iris and finger vein unique patterns each individual to overcome many challenges such as identity fraud, poor image quality, noise, and instability of the surrounding environment. Several experiments were performed on a dataset containing 50 people by using many matching methods. The results of the proposed model were provided a higher accuracy of 98%, with FAR and FRR of 0.0015% and 0.025%, respectively.
Co-Authors Dhafer Taha Shihab Enas Abbas Abed Mostafa Mahmoud El-gayar