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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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Mutual coupling effects in 2×2 antenna array for ground penetrating radar on multilayered soil Antonio Becerra-Pérez, Marco; Guerra-Huaranga, Tanith; Elisia Armas-Alvarado, Maria; Clemente-Arenas, Mark; Esther Rubio-Noriega, Ruth

Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i2.pp812-823

Abstract

Caral stands as the Americas’ oldest city, boasting a heritage spanning 5,000 years. Over time, various natural forces have woven a complex geological stratum. To gain a deeper understanding of the Caral civilization, non-intrusive exploration methodologies like ground penetrating radar (GPR) are beginning to be used. This method safeguards the integrity of ancient subterranean remains. A GPR system is in development, tailored to the [200-500] MHz range, employing a 2×2 antenna array with dual polarization. These features enhance resolution without compromising penetration depth. However, using multiple antennas within complex, multi-layered environments introduce impedance band constraints and exacerbates antenna coupling issues. This study assesses the coupling of two antenna candidates: the Vivaldi with defected ground structures (DGS) and the log periodic dipole array (LPDA). The scattering parameters show that the LPDA antenna performed better considering measured and simulated data. Cross-polarization exhibited a broader bandwidth in the LPDA antenna, evident in both simulated and measured data. Additionally, a comprehensive comparison of GPR simulations for each antenna type within an 11-level multilayer medium, with different electromagnetic properties, further highlights LPDA. This antenna boasts a 209 MHz bandwidth and a coupling better than -23 dB for the cross-polarization configuration, firmly showing its best performance.
Biomedical signal compression using deep learning based multi-task compressed sensing Shruthi Khadri; Naveen K Bhoganna; Madam Aravind Kumar
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 1: January 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i1.pp63-70

Abstract

Real-time transmission of biomedical signals is immensely challenging and requires cloud and internet of things (IoT) infrastructure. Security is also an important factor; however, to accomplish this, a reconstruction method is developed in which the entire signal is supplied as an input, the primary portion is considered here, and the signal is further encoded and transmitted to the destination. Electrocardiogram (ECG) compression for the lightweight wireless network is quite challenging for long-term healthcare monitoring. Compressed sensing (CS) involves efficient encoding mechanisms for error rate estimation for reconstruction and energy consumption for wireless transmission of data. We propose a multi-task compressed sensing (MT-CS) reconstruction mechanism in this study for ECG compression of data is most chosen for a wireless network system that has various sensors embedded in it. This model further extracts the essential adaptive features for correlation existing in the ECG signals. The performance of the proposed MT-CS reconstruction mechanism is evaluated on the multiparameter intelligent monitoring in intensive care (MIMIC-II) dataset, which ensures its robustness and generalization. The results obtained upon simulation ensure that the proposed MT-CS based reconstruction approach ensures excellent reconstruction signal with fewer measurements in comparison with the existing state-of-art CS techniques.
Patient-patient interactions visualization for drug side effects in patients’ reviews Zaher Salah; Esraa Elsoud; Kamal Salah; Waleed T. Al-Sit; Manal Maaya'a; Ahmad Al Khawaldeh
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i3.pp2007-2020

Abstract

This paper describes the patient-patient interactions (PPIs) graph extraction framework from patient’s review transcripts. The concept is to visualise patients as nodes and interactions representing links. Links are made based on review text similarity. Nodes are categorized as positive or negative according to the patient’s attitude toward a given drug. Attitudes are then utilized to categorize the links as supporting or opposing the use of a certain drug. If both patients share the same attitude: negative (severe side effect) or positive (moderate side effect), the relationship is considered supportive; if not, the link is considered opposed. Resulting graph represent a drug as a dispute between two factions arguing on related drug. The framework is explained and evaluated using a dataset included 3,763 patients’ reviews linked to 255 different drugs, -predictive-value (0.37). We argue that, this is caused by derogatory jargon that is an expected feature of patient’s review. The true-negative-recognition-rate is 0.70 and true-positive-recognition-rate is 0.54. Total-average-accuracy, which is independent of class priors, is 0.66. Results show that, it is possible to use text proximity measures and sentiment analysis to capture PPIs structure.
A smart emergency response system based on deep learning and Kalman filter: the case of COVID-19 Hounaida Frikha; Ferdaous Kamoun-Abid; Amel Meddeb-Makhoulf; Faouzi Zarai
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 1: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i1.pp630-640

Abstract

During an epidemic, the transportation of patients to emergency departments and the monitoring of their physiological parameters pose significant challenges in this critical scenario. Swift and efficient diagnosis has the potential to rescue the lives of these patients. The objective is accomplished through the utilization of deep learning to categorize information into emergencies, prioritizing its dispatch. In this article, we present a sophisticated emergency system that employs deep learning to swiftly transmit vital information from emergency patients to the hospital that can provide the highest quality healthcare for these individuals. The fusion method integrates data obtained and refined from patients' electronic medical records with data acquired by the wireless medical sensor network during the transportation phase. Subsequently, the process of choosing the parameters is employed as inputs to the learning model. The data gathered and educational outcomes, such as emergency notifications, are transmitted through Wi-Fi and 5G devices in our sophisticated system. The proposed contribution achieves a 98% accuracy with a runtime of 1.53 seconds. This discovery demonstrates the efficacy of our system, particularly in the context of epidemic situations such as COVID-19.
SEM and TEM images’ dehazing using multiscale progressive feature fusion techniques Chellapilla V. K. N. S. N. Moorthy; Mukesh Kumar Tripathi; Suvarna Joshi; Ashwini Shinde; Tejaswini Kishor Zope; Vaibhavi Umesh Avachat
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 3: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i3.pp2007-2014

Abstract

We present a highly effective algorithm for image dehazing that leverages the valuable information within the hazy image to guide the haze removal process. Our proposed algorithm begins by employing a neural network that has been trained to establish a mapping between hazy images and their corresponding clear versions. This network learns to identify the shared structural elements and patterns between hazy and clear images through the training process. To enhance the utilization of guidance information from the generated reference image, we introduce a progressive feature fusion module that combines the features extracted from the hazy image and the reference image. Our proposed algorithm is an effective solution for image dehazing, as it capitalizes on the guidance information in the hazy appearance. By combining the strengths of deep learning, progressive feature fusion, and end-to-end training, we achieve impressive results in restoring clear images from hazy counterparts. The practical applicability of our algorithm is further validated by its success on benchmark data sets and real-world SEM and TEM images.
Deep neural networks approach with transfer learning to detect fake accounts social media on Twitter Arif Ridho Lubis; Santi Prayudani; Muhammad Luthfi Hamzah; Yuyun Yusnida Lase; Muharman Lubis; Al-Khowarizmi Al-Khowarizmi; Gabriel Ardi Hutagalung
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 1: January 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i1.pp269-277

Abstract

The massive use of social media makes people take actions that have a negative impact on cyberspace, such as creating fake accounts that aim to commit crimes such as spam and fraud to spread false information. Fake accounts are difficult to detect in the traditional way because fake accounts always use photos, names, and unreal information, there are several criteria that can identify a fake account such as no information, few followers, and minimal activity. In the traditional model, it is difficult to detect fake accounts on many Twitters social media accounts, so the application of the deep learning model with the convolutional neural network (CNN) algorithm and the application of deep learning can help detect fake accounts. This study will use data on Twitter social media so that this research produces good accuracy for the scenarios described at the methodology stage. This research produces an accuracy of 86% for the deep learning model with the CNN algorithm, and with the traditional model, it produces an accuracy of 51% while the use of transfer learning produces an accuracy of 93.9%.
The development of low-cost spin coater with wireless IoT control for thin film deposition Ahmad Muhajer Abdul Aziz; Muhammad Idzdihar Idris; Zul Atfyi Fauzan Mohammed Napiah; Muhammad Noorazlan Shah Zainudin; Marzaini Rashid; Luke Bradley
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i3.pp1519-1529

Abstract

A low-cost spin coater with a wireless remote system that can deposit thin films of uniform thickness and quality at a significantly lower cost than traditional methods. The system consists of three main parts, a motorized spindle, a spin-coating head, and a control system connected to the network. The mechanical design on the mechanical part, spin coater system design with ESP32, and implementation of wireless control through visual basic. The network-enabled control system allows for real-time monitoring and adjustment of the deposition process, which can improve efficiency and reproducibility. This low-cost spin coating system represents a promising solution for organizations seeking to access thin film deposition technology at a fraction of the cost of traditional systems. By integrating wireless IoT control into low-cost spin coaters, the impact of this technology on coating uniformity will provide valuable insights for future advancements in this field.
An ontology-based knowledge representation using OWL for Indonesian local regulations Tri Astoto Kurniawan; Rachmad Safa’at; Nurudin Santoso; Muhammad Farabi Ismail
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 2: May 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i2.pp983-990

Abstract

Inconsistencies in legislation can significantly hinder the effectiveness and efficiency of the central and local government administrations. The Indonesian government requires standard harmonization of each piece of legislation to prevent such problems. However, this process is often manual and requires the involvement of multiple experts with varying backgrounds. This leads to high resource expenses regarding human resources, cost, and time. To address this, a software system should be developed to detect potential disharmony among legislation. However, the system requires a well-constructed legislation conceptual model represented in an appropriate modeling language. This research aims to develop the Indonesian local regulation ontology in web ontology language (OWL), where no such ontology exists. The ontology was created using the Ontology Development 101 methodology and evaluated using competency questions and expert judgment approaches. The resulting ontology becomes a basis for developing an automatic recommendation system to detect potentially inconsistent legislation in future works.
Enhancing Moroccan legal cases analysis through ontology-based information extraction Kaoutar Belhoucine; Nadia Zame; Mohammed Mourchid
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 2: May 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i2.pp1081-1091

Abstract

The efficient organization of diverse disorder cases within a unified memory necessitates an adaptable representation. This study introduces an ontology-based approach for extracting facts from Moroccan legal cases. Leveraging ontological frameworks, a comprehensive case architecture is established, enabling advanced information extraction. Utilizing rules, patterns, and knowledge modeling harmonizes cases and identifies pervasive legal concepts. Statistical techniques unveil latent entities within complex legal textual discourse. Empirical validation demonstrates proficiency, extracting up to 25 regular entities. The rule-based mechanism achieves an F1-score of 99.5%, highlighting precision, while the statistical extractor achieves an 88.3% F1-score, revealing concealed entities. This work presents an innovative ontology-based paradigm for legal information extraction, contributing to advanced knowledge management in the legal domain.
An ensemble deep learning model for automatic classification of cotton leaves diseases Hirenkumar Kukadiya; Nidhi Arora; Divyakant Meva; Shilpa Srivastava
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 3: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i3.pp1942-1949

Abstract

Cotton plant (Gossypium herbaceum), is one of the significant fiber crop grown worldwide. However, the crop is quite prone to leaves diseases, for which deep learning (DL) techniques can be utilized for early disease prediction and prevent stakeholders from losing the harvest. The objective of this paper is to develop a novel ensemble based deep convolutional neural network (DCNN) model developed on two base pretrained models named: VGG16 and InceptionV3 for early detection of cotton leaves diseases. The proposed ensemble model trained on cotton leaves dataset reports higher training and testing prediction accuracies as compared to the base pretrained models. Given that, deep learning architectures have hyper-parameters, this paper presents exhaustive experimental evaluations on ensemble model to tune hyper-parameters named learning rate, optimizer and no of epochs. The suggested hyper-parameter settings can be directly utilized while employing the ensemble model for cotton plant leaves disease detection and prediction. With suggested hyper-parameters settings of learning rate 0.0001, 20 epochs and stochastic gradient descent (SGD) optimizer, ensemble model reported training and testing accuracies of 98% and 95% respectively, which was higher than the training and testing accuracies of VGG16 and InceptionV3 pretrained DCNN models.

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