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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 9,138 Documents
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Lung cancer detection using hybrid integration of autoencoder feature extraction and ML techniques Lakshmanarao, Annemneedi; Gopal, Nirmal; Vullam, Nagagopiraju; Sridhar, Mandapati; Kanth, Modalavalasa Krishna; Rayudu, Uma Maheswari
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 1: January 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i1.pp416-424

Abstract

Lung cancer posed a significant global health challenge, necessitating innovative approaches for early detection and accurate diagnosis. In this paper, CT scan images for lung cancer with three classes namely benign, malignant, and normal are collected from Kaggle. We initially applied conventional machine learning (ML) algorithms including support vector machine (SVM), random forests (RF), decision trees (DT), logistic regression (LR), naive bayes (NB), and k-nearest neighbor for lung cancer detection. The results with these conventional algorithms are recorded. Later, we proposed a novel hybrid model that integrated diverse machine learning algorithms to further enhance accuracy. Our approach combined the power of autoencoders for feature extraction. Using Autoencoder technique, features from images are extracted and a new feature vector is created. Later, the same conventional ML classifiers applied and achieved enhanced performance. The hybrid model demonstrated remarkable performance in identifying lung cancer cases when compared to individual classifiers. Through extensive experimentation, we showcased the efficacy of our integrated framework, achieving high accuracy, precision, recall and F1-score metrics across multiple classifiers. This hybrid approach represented a significant advancement in lung cancer detection, offering a versatile and robust solution for early diagnosis and personalized treatment strategies in clinical settings.
Particle swarm optimization for beamforming design in a cognitive radio Atzemourt, Mossaab; Chihab, Younes; Bencharef, Omar; Hachkar, Zakaria
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 1: January 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i1.pp154-163

Abstract

Beamforming is essential for improving transmission in wireless sensor networks (WSNs), particularly in cognitive radio networks (CRNs) with several secondary users (SU) equipped with transmitting antennas. Optimizing beamforming while minimizing interference with primary users (PU) is of great interest. This study proposes an improved particle swarm optimization (PSO) algorithm to enhance beamforming performance. This approach aims to maximize the power of the beam directed to the SU receiver while controlling interference in the PU protection region. The results show that this algorithm constantly improves beam focus and signal-to-noise ratio to effectively optimize beamforming. Firstly, beam focusing becomes narrower as the number of antenna elements increases, generating optimal transmission conditions. Secondly, the algorithm achieves a considerable improvement in signal-to-noise ratio as the number of antenna elements increases. Furthermore, optimization performance improves as the number of antenna elements increases, as shown by the best fitness values. The simulations also illustrate the performance of the proposed method.
High-gain circularly polarized metasurface antenna for NR257 band millimeter-wave 5G communication Haandi Lakshman, Praveen; Thatti, Yerriswamy; Kumar Tharehalli Rajanna, Puneeth; Mudukavvanavar, Shambulinga
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 2: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i2.pp867-877

Abstract

A metasurface inspired circularly polarized (CP) compact patch antenna with high gain for fifth-generation communication systems is designed and implemented in this article. The proposed structure features a corner truncated patch antenna and a metasurface of 3×3 double sided identical circular metallic patches. The attribute that puts this design distinct is that it minimizes the impact of scattering and edge diffraction at millimeter wave frequencies. The metasurface above a patch with an air gap is designed using the similar substrate material with the same thickness, resulting in a simplified antenna design with high gain and low cost. The antenna’s overall dimension is , with a peak gain of 11.5 dBic and a 3-dB axial ratio bandwidth of 28.45 - 28.88 GHz. The simulated and experimental results show that the metasurface-inspired antenna has better impedance matching and radiation efficiency between 28.23 - 30.01 GHz. Additionally, the experimental results of the proposed antenna exhibits stable right-hand circular polarization in the desired frequency range and a flat gain response with a little variation. The proposed antenna design could be well suited for millimeter-wave communication systems, in scenarios requiring robust long-range performance and high data throughput.
Novel method for multi-user collaborative spectral decision in decentralized cognitive radio networks Hernández, Cesar; Giral, Diego; Salgado, Camila
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 2: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i2.pp888-902

Abstract

Cognitive radio networks positively impact the performance of wireless communications and have proven to be an excellent alternative for efficient and effective use of the radio spectrum. However, few proposals collaboratively work on decision-making in decentralized cognitive radio networks. The present work refers to a novel method and device that reduces the rate of channel changes during secondary user communications in decentralized cognitive radio networks through a collaborative spectral decision between several secondary users while allowing multiple secondary users access to the network. This proposal consists of a multi-user unit that regulates the access of multiple secondary users (SUs) to the spectrum, a priority unit that guarantees timely access to the SUs according to their level of importance, and a prediction unit that forecasts the arrival time of the primary user (PU). This multichannel unit regulates the assignment of multiple spectral opportunities to the SU according to the type of application it is using and a unit of deep learning that determines which spectral opportunity(s) are most suitable for each SU and spectral allocation. The results obtained allow us to satisfactorily validate the proposal developed and corroborate the importance of collaborative work in decision-making to select spectral opportunities.
Blockchain and smart contracts based system for criminal record management Jlil, Manal; Jouti, Kaoutar; Loqman, Chakir
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 1: January 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i1.pp365-379

Abstract

Reducing crime rate in a country is the most important concern of developing robust systems to automate the criminal record-obtaining process. Generally, the criminal record is managed manually, which makes the information collection from other criminal records very difficult. Therefore, investigations that could be carried out using criminal records to understand the purpose of crime and countering it are outdated. However, the integrity, security, and traceability of data exchange, especially for the judicial sector are the most frequent issues faced by information systems of public organizations. In this paper, we present a study of using blockchain technology and smart contracts to design a new architecture for a decentralized system to manage criminal record storage. This proposed architecture automates the process of getting a criminal record by moving past the techniques employed in developing traditional systems of data management such as centralized systems. In this study, blockchain technology is used to ensure data security, integrity, and traceability as well as ensure timely access to criminal records, and smart contracts are used to allow traceability and authenticity. This architecture will significantly reduce the impact of corruption in law enforcement by eliminating fraud cases, which will revolutionize E-governance in the Moroccan country.
Bit-rate aware effective inter-layer motion prediction using multi-loop encoding structure Siddaramappa, Sandeep Gowdra; Mamatha, Gowdra Shivanandappa
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 1: January 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i1.pp569-579

Abstract

Recently, there has been a notable increase in the use of video content on the internet, leading for the creation of improved codecs like versatile-video-coding (VVC) and high-efficiency video-coding (HEVC). It is important to note that these video coding techniques continue to demonstrate quality degradation and the presence of noise throughout the decoded frames. A number of deep-learning (DL) algorithm-based network structures have been developed by experts to tackle this problem; nevertheless, because many of these solutions use in-loop filtration, extra bits must be sent among the encoding and decoding layers. Moreover, because they used fewer reference frames, they were unable to extract significant features by taking advantage from the temporal connection between frames. Hence, this paper introduces inter-layer motion prediction aware multi-loop video coding (ILMPA-MLVC) techniques. The ILMPA-MLVC first designs an multi-loop adaptive encoder (MLAE) architecture to enhance inter-layer motion prediction and optimization process; second, this work designs multi-loop probabilistic-bitrate aware compression (MLPBAC) model to attain improved bitrate efficiency with minimal overhead; the training of ILMPA-MLVC is done through novel distortion loss function using UVG dataset; the result shows the proposed ILMPA-MLVC attain improved peak-singal-to-noise-ratio (PSNR) and structural similarity (SSIM) performance in comparison with existing video coding techniques.
Integration of message queue and drop policy in spray and hop distance protocol for DTNs in smart city scenario Agussalim, Agussalim; Tsuru, Masato; Susrama Mas Diyasa, I Gede; Rahmat, Basuki
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 2: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i2.pp839-848

Abstract

Implementing delay tolerant networks (DTNs) in smart cities for developing countries is promising. DTNs offer a low-cost network communication solution without expensive infrastructure, such as 3G/4G or LPWA networks provided by commercial operators. This paper considers sensor data collection in the Surabaya smart city scenario, and the use of DTNs is examined instead of the expensive infrastructure. However, a customized and optimized protocol is required since no existing DTN routing protocol perfectly matches the scenario. This paper proposes integrating a hop count-based message queue (HCMQ) and a message time-to-live (TTL)-based drop policy (MTDP) into the spray and hop distance protocol (SNHD), an enhanced version of the spray and wait (SNW) protocol. The Surabaya smart city scenario was simulated on the one simulator with a wide range of message generation rates at each sensor node. The proposed integration significantly improves the total size of delivered messages, especially when the message generation rate is high, i.e., in congested situations, compared to other routing protocols in this scenario. It also exhibits an average latency lower than other routing protocols. Overall, this integration enhances the DTNs protocol’s performance in a low-cost alternative data collection in the Surabaya smart city scenario.
Designing fuzzy membership functions using genetic algorithm with a new encoding method Hamed, Ali; Hireche, Slimane; Bekri, Abdelkader; Cheriet, Ahmed
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 2: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i2.pp781-788

Abstract

This article presents a new method for designing fuzzy membership functions using the genetic algorithm (GA) without the use of constraints. Conventional approaches to designing these functions often involve manual tuning or optimization techniques with limitations. However, this article introduces a constraint-free approach, as the GA requires all constraints to be met for a chromosome; if even one condition is not satisfied, the chromosome is discarded, regardless of its ideal values for other variables. Consequently, a high number of constraints, especially in the studied case, increases the likelihood of chromosome rejection, leading to a time-consuming design process and suboptimal results.
Enhancing hyperspectral image object classification through robust feature extraction and spatial-spectral fusion using deep learning Kochari, Vijaylaxmi; Sannakki, Sanjeev S.; Rajpurohit, Vijay S.; Huddar, Mahesh G.
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 1: January 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i1.pp279-287

Abstract

Hyperspectral imaging (HSI) has gained significant attention in recent years due to its broad applications across agriculture, environmental monitoring, urban planning, infrastructure management, and defense and security for object detection and classification. Despite its potential, current methodologies face challenges such as insufficient feature extraction, noise interference, and inadequate spatial-spectral fusion, limiting classification accuracy and robustness. This study reviews advancements in HSI object detection and classification methodologies, emphasizing the role of machine-learning (ML) and deep-learning (DL) techniques. Hence, this work proposes a novel framework to address these challenges, prioritizing robust feature extraction, effective spatial-spectral fusion, and comprehensive noise removal mechanisms. By integrating DL techniques and training with HSI noisy data, this framework aims to enhance classification accuracy and robustness. The findings suggest that the proposed approach significantly improves the reliability and performance of HSI-based object classification systems. This research provides a pathway for future development in the domain, promising to elevate the effectiveness of HSI applications in real-world scenarios.
Optimization signal writing with machine learning assisted control Sapapporn, Chaweng; Seangsri, Soontaree; Khaengkarn, Sorada; Srisertpol, Jiraphon
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 1: January 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i1.pp90-100

Abstract

The high-precision signal writing machine, experiencing a 0.1% failure rate due to discrete fourier transform (DFT) of position error signal (PES) exceeding control limits, can be improved with an appropriate controller gain. This paper combines machine learning (ML) classification and controller optimization to determine the suitable gain for the hard disk drive (HDD) signal writing process. The result from machine classification has a high potential for position error improvement, distinguishing them from those with obvious degradation. The identified machine classes with high potential for signal write quality improvement undergo controller optimization using a genetic algorithm (GA). The objective function considers gain crossover frequency, phase margin, and PES DFT at low frequencies. Experimental results demonstrate that the new controller gain enhances signal write quality of class 0 and class 3 by 14.68% and 17.18%, respectively, leading to a reduced failure rate down to 0.05%.

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