Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal Indonesian Journal of Electrical Engineering and Computer Science
Alrabea, Adnan
Unknown Affiliation

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

Found 2 Documents
Search

 1 
Efficient and secure data transmission: cryptography techniques using ECC Alhaj, Abdullah Ahmad; Alrabea, Adnan; Jawabreh, Omar
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 1: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i1.pp486-492

Abstract

Data transmission has become a crucial aspect of our daily lives in the current digital age. However, this transmission comes with the risk of security breaches, which can result in data theft and unauthorized access. This issue can be addressed by using cryptographic techniques such as elliptic curve cryptography (ECC). In comparison to other cryptosystems, ECC is a potent cryptographic tool that provides high levels of security with comparatively reduced key sizes. This paper discusses the use of ECC in efficient and secure data transmission. It provides a comprehensive overview of ECC, including its mathematical background and how it can be applied to encryption and decryption processes. The paper also presents a comparison of ECC with other cryptographic techniques and highlights its advantages, including its resistance to attacks and efficiency in resource-constrained environments. Finally, the paper discusses the implementation of ECC in real-world scenarios and its potential to revolutionize secure data transmission.
The role of artificial intelligence in advancing the performance of information retrieval Alrabea, Adnan; Ahmad Alhaj, Abdullah; Senthil Kumar, A. V.
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 3: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i3.pp1478-1485

Abstract

The motivation behind applying artificial intelligence (AI) in information retrieval (IR) is that the current methodologies include algorithms designed by researchers, leaving space for the applicability of genetic AI algorithms in IR. While different algorithms designed by developers rely on the originality or performance of the algorithm, precise results are achieved through integrating AI algorithms with traditional algorithms. The proposed methodology introduces document structure weighting with optimized performance. It is enabled by employing genetic algorithm and genetic programming for learning optimal weights in ranking document components. The Croft probabilistic ranking, vector space inner product models, and the BM25 standard were compared with each other after AI integration. Genetic algorithm and genetic programming were applied in the stemming and thesaurus forming processes of these models. Inducing genetic algorithm and genetic programming into the specified models increased the mean average precision of the Croft model and the vector space method by approximately 5% while there were no observable result improvements in BM25. It was found that applying genetic algorithm and genetic programming in learning synonyms and stemming rules, respectively, increased the overall performance of IR models, emphasizing the need for AI in IR.
Co-Authors A. V. Senthil Kumar Ahmad Alhaj, Abdullah Alhaj, Abdullah Ahmad Jawabreh, Omar