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INDONESIA
Indonesian Journal of Electrical Engineering and Computer Science
Published by Institute of Advanced Engineering and Science
ISSN : 25024752     EISSN : 25024760     DOI : -
Core Subject :
Arjuna Subject : -
Articles 65 Documents
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Search results for , issue "Vol 39, No 1: July 2025" : 65 Documents clear
Evolution of the optical add/drop multiplexer in dense wavelength division multiplexing optical networks Mkhwanazi, Mnotho P.; Mpofu, Khumbulani; Malele, Vusumuzi
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 1: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i1.pp247-257

Abstract

Mobile network operators are facing ever-increasing traffic demands because of the numerous data-hungry applications used by subscribers nowadays. As a result, technologies that support high bandwidth and network availability have become essential. One such technology is dense wavelength division multiplexing (DWDM). This study investigated the evolution of an optical add/drop multiplexer (OADM), which is one of the key components of DWDM technology. The goal of this research was to investigate how the evolution of an OADM has contributed to network survivability and bandwidth enhancement in DWDM optical networks. A thorough search of the literature on an OADM was undertaken using data sources like Google Scholar, Elsevier, ResearchGate, ScienceDirect, Springer, and DWDM vendor manuals. The study found that in order to address present and future DWDM optical network demands, a reconfigurable optical add/drop multiplexer (ROADM) deployed over flexgrid spectrum is essential. The most advanced iteration of a ROADM supports colorless, directionless, contentionless, and flex-grid functionalities, resulting in the most robust, flexible, and future-proof DWDM optical network. The study further found that flex-grid technology supports uplinks with high line rates and has superior spectral efficiency.
Classification model for infectious lung diseases using convolutional neural networks on web and mobile applications Okokpujie, Kennedy; Agamah, Alvin K.; Orimogunje, Abidemi; Adaora, Ijeh Princess; Omolara, Olusanya Olamide; Daramola, Samuel Adebayo; Awomoyi, Morayo Emitha
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 1: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i1.pp410-424

Abstract

Accurate lung disease diagnosis in infected patients is critical for effective treatment. Tuberculosis, COVID-19, pneumonia, and lung opacity are infectious lung diseases with visually similar chest X-ray presentations. Human expertise can be susceptible to errors due to fatigue or emotional factors. This research proposes a real-time deep learning-based classification system for lung diseases. Three models of convolutional neural networks (CNNs) were deployed to classify lung illnesses from chest X-ray images: MobileNetV3, ResNet-50, and InceptionV3. To evaluate the effect of high interclass similarity, the models were evaluated in 3-class (Tuberculosis, COVID-19, normal), 4-class (lung opacity, tuberculosis, COVID-19, normal), and 5-class (tuberculosis, lung opacity, pneumonia, COVID-19, normal) modes. The best classification accuracy was attained by retraining MobileNetV3, which obtained 94% and 93.5% for 5-class and 4-class, respectively. InceptionV3 had the lowest accuracy (90%, 89%, 93% for 5-, 4-, and 3-class), while ResNet-50 performed best for the 3-class setting. These findings suggest MobileNetV3's potential for accurate lung disease diagnosis from chest X-rays despite the interclass similarity, supporting the adoption of computer-aided detection systems for lung disease classification.
A novel (????, ????) multi-secret image sharing scheme harnessing RNA cryptography and 1-D group cellular automata Abdul, Yasmin; Ramasamy, Venkatesan; Kukaram, Gaverchand
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 1: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i1.pp700-709

Abstract

In the modern landscape, securing digital media is crucial, as digital images are increasingly disseminated through unsecured channels. Therefore, image encryption is widely employed, transforming visual data into an unreadable format to enhance image security and prevent unauthorized access. This paper proposes an efficient (????, ????) multi-secret image sharing (MSIS) scheme that leverages ribonucleic acid (RNA) cryptography and one-dimensional (1-D) group cellular automata (GCA) rules. The (????, ????) MSIS scheme encrypts ???? images into ???? distinct shares, necessitating all ???? shares for decryption to accurately reconstruct the original ???? images. Initially, a key image is generated using RNA cryptography, harnessing the extensive sequence variability and inherent complexity of RNA. This secret key is then used to encrypt ???? images in the primary phase. In the secondary phase, pixel values are transformed through multiple processes, with randomness achieved by executing a key function derived from GCA, known for its reversible properties, computational efficiency, and robustness against cryptographic attacks. The proposed model, implemented in Python, is validated through experimental results, demonstrating its effectiveness in resisting a broad spectrum of attacks, including statistical, entropy, differential, and pixel parity analyses. These findings affirm the model's durability, security, and resilience, underscoring its superior performance compared to existing models.
Seeking best performance: a comparative evaluation of machine learning models in the prediction of hepatitis C Cabanillas-Carbonell, Michael; Zapata-Paulini, Joselyn
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 1: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i1.pp374-386

Abstract

Hepatitis C is a disease that affects millions of people worldwide. It is spread through contact with contaminated blood through injections, transfusions, or other means. It is estimated that with early detection patients have a higher rate of recovery. The objective of this study is to perform a comparative evaluation of different models focused on the prediction of hepatitis C, to determine which of the models offers better performance in accuracy, precision, and sensitivity. The models used were logistic regression (LR), random forest (RF), K-nearest neighbors (KNN), decision tree (DT), and gradient boosting (GB), aimed at hepatitis C prediction. The training of the models was carried out using a dataset composed of 615 records, which incorporate 14 attributes. The structure of the article is divided into six sections, including introduction, review of related articles, methodology, results, discussion, and conclusions. The performance of the models was evaluated through metrics such as accuracy, sensitivity, F1 count, and, mainly, precision. The results obtained place the DT model as the most efficient predictor, reaching a precision, accuracy, sensitivity, and F1-score of 95%.
Unraveling the relationships among essential oil compounds in Aquilaria species using GC-MS and GC-FID techniques Syafiqah Noramli, Nur Athirah; Ahmad Sabri, Noor Aida Syakira; Roslan, Muhammad Ikhsan; Ismail, Nurlaila; Yusoff, Zakiah Mohd; Taib, Mohd Nasir
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 1: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i1.pp167-177

Abstract

Agarwood, a prized non-timber resource from the Aquilaria genus, is highly valued for its aromatic and medicinal properties, playing a significant role in the healthcare, fragrance, and pharmaceutical industries. This research analyzes essential oils from four Aquilaria species-A. beccariana, A. malaccensis, A. crassna, and A. subintegra-using gas chromatography-mass spectrometry (GC-MS) and gas chromatography-flame ionization detection (GC-FID). The primary objective is to optimize classification efficiency by reducing computational time and reducing multicollinearity through feature selection. Pearson correlation analysis revealed strong relationships among six chemical compounds-β-selinene (A), dihydro-β-agarofuran (B), δguaiene (C), 10-epi-γ-eudesmol (D), γ-eudesmol (E), and pentadecanoic acid (F). Through feature selection, the three most significant compoundsdihydro-β-agarofuran (B), γ-eudesmol (D), and 10-epi-γ-eudesmol (E)-were identified, achieving a remarkable 90.02% reduction in computational time (from 0.0403 to 0.0040 seconds). These findings highlight the effectiveness of structured feature selection in refining essential oil profiling and enhancing species classification accuracy. Future research directions include exploring machine learning-based feature selection techniques to further streamline feature reduction processes and expand the scope of essential oil authentication. This study contributes to advancing the scientific understanding and practical utilization of agarwood essential oils, paving the way for more efficient and reliable analytical frameworks.

Page 7 of 7 | Total Record : 65

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