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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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Brain computer interfaces in computer science and engineering areas: a systematic study Jozsef Katona; Attila Kovari; Tibor Guzsvinecz; Judit Szűcs; Robert Demeter; Veronika Szücs
Indonesian Journal of Electrical Engineering and Computer Science Vol 32, No 3: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v32.i3.pp1755-1765

Abstract

Brain-computer interfaces (BCI) are a channel that implements direct communication between the brain and some external unit. Developments of BCIs can provide new application opportunities in a large number of fields of use. In the development of BCI devices, the development of technology and digital technology represented a big change, as it provided the necessary computing power to implement and run the continuously developing signal processing algorithms that ensure processing and evaluation. The aim of this paper is to provide an overview of BCI research results which were published in the engineering field. In the present study, articles that had a greater impact, where the annual average number of citations is greater than 30, in the BCI field were reviewed and processed in a systematic way, in order to make individual research more comparable. The systematic processing was focused on the aims of application, used device/ dataset, applied data process and achieved best accuracy. This systematic study summarizes the most effective methods used in the BCI processing and highlights the future trends. The results showed an accuracy of 85% thanks to increasingly reliable, accurate and cost-effective signal detection and processing devices, as well as algorithms.
Plant pathology identification using local-global feature level based on transformer Manh-Hung Ha; Duc-Chinh Nguyen; Manh-Tuan Do; Dinh-Thai Kim; Xuan-Hai Le; Ngoc-Thanh Pham
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.pp1582-1592

Abstract

Deep learning plays a crucial role in addressing the challenge of plant disease identification in the field of agriculture. Detecting diseases in plants requires extensive effort, along with a comprehensive understanding of various plant diseases and increased processing time. Balancing both speed and accuracy in predicting leaf diseases in plants can significantly improve crop production and reduce environmental damage. In this paper, we examined deseases on popular plants in agriculture. We proposed a novel model to predict crop pathology on a feature space of global-local based on transformer aggregation. Paticular, we use refined feature of different layer to correlate semantics from high-level feature and low-level feature. Besides, to capture the extended temporal scale across the entire image, we employ a transformer to discern long-range dependencies among frames. Subsequently, the enhanced features incorporating these dependencies are inputted into a classifier for preliminary crop pathology prediction. The plant village dataset and VietNam strawberry disease (VNStr) dataset were utilized for training and disease classification in the experiments. Extensive experiments show that the proposed method outperforms by 99.18% and 94.05% accuracy in plant village and VNStr, respectivly. The model after being judged was applied on Android devices and therefore is easy to use.
Coherence-based sufficient condition for support recovery using block generalized orthogonal matching pursuit Aravindan Madhavan; Yamuna Govindarajan
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.pp152-161

Abstract

Challenge is to find the support vectors of the unknown block sparse vector with compressed measurements in an underdetermined system where the number of unknowns is more than that of measurements. To recover unknown block sparse vector, restricted isometry property (RIP) is a sufficient condition need to be satisfied. Finding the restricted isometric constant is a non-polynomial hard problem for large values of n. In this paper coherence-based recovery guarantee has been proposed to recover the support vectors using block generalized orthogonal matching pursuit (BGOMP). It is proved that BGOMP can able to recover the support vectors with lesser number of iteration than block orthogonal matching pursuit (BOMP) by selecting multiple block support elements per iteration. Simulation results show detection performance of BGOMP is better than BOMP, block subspace pursuit (BSP) and block compressive sampling matching pursuit (BCoSaMP) for different block sparsity and block length. In most of the cases for different block sparsity and block length computation time for BGOMP is lesser than BCoSaMP, BSP and BOMP due to the multiple selection of elements in each iteration.
Semi-decentralized Lyapunov-based formation control of multiple omnidirectional mobile robots Agung, Hendi Wicaksono; Jordan, Fransisco
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 2: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i2.pp823-833

Abstract

This paper introduces an advanced formation control algorithm based on a Lyapunov approach for coordinating multiple omnidirectional mobile robots in collaborative object transport tasks. The semi-decentralized strategy ensures that the robots maintain a predefined geometric formation, crucial for stability during material transportation, and dynamically adapt to avoid collisions using onboard sensors. Experimental with a physical robot simulator demonstrates successful maintenance of line and triangle formations achieving an average side length maintenance of 1.00 meters with minimal deviation. Quantitative analysis across 30 experimental runs reveals consistent performance, with a maximum side length fluctuation of only 2 centimeters, validating the effectiveness of maintaining formation within a multi-robot system (MRS) framework. The Lyapunov-based approach proves to be an efficient method for cooperative object transport, achieving consistent performance with minimal deviation.
Alzheimer image registration using hybrid random forest and deep regression network algorithm Siddabathuni, Ramakoteswararao; Palanivel, Sivagurunathan; Lakshmi Narasimha Murthy, Godavarthi

Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i2.pp824-831

Abstract

Image registration involves superimposing images (two or more) of similar background obtained at various periods of time, at different angles, and/or with various detectors. Geometrical alignment of two scans, reference image as well as capture image. The current dissimilarity between images is because of distinct image conditions. Image registration is difficult step in image analysis works on change detection, image fusion as well as multi-channel images recovery to obtain concluded data from integration of different sources. In this analysis image registration using hybrid random forest (RF) and deep regression network algorithm for magnetic resonance imaging (MRI) applications is implemented. The Alzheimer’s disease neuroimaging initiative (ADNI) database provided by the dataset utilised in this implementation. From results it can observe that compared with individual random of forest, Hybrid RF and deep regression network algorithm improves the accuracy, precision and F1-score in effective way.
A novel two-tier feature selection model for Alzheimer’s disease prediction Sonam V. Maju; Gnana Prakasi Oliver Sirya Pushpam
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.pp227-235

Abstract

The interdisciplinary research studies of artificial intelligence in health sector is bringing drastic life saving changes in the healthcare domain. One such aspect is the early disease prediction using machine learning and regression algorithms. The purpose of this research is to improve the prediction accuracy of Alzheimer ’s disease by analysing the correlation of unexplored Alzheimer causing diseases. The work proposes Chi square-lasso ridge linear (Chi-LRL) model, a new two-tier feature ranking model which recognizes the significance of including diabetes, blood pressure and body mass index as potential Alzhiemer predictive parameters. The newly added predictive parameters of Alzheimer’s disease were statistically verified along with the conventional prediction parameters using chi-square method (Chi) as Tier 1 and an embedded model of lasso, ridge and linear (LRL) Regression for feature ranking as Tier 2. The performance of the proposed Chi-LRL model with selected features were then analysed using machine learning algorithms for performance analysis. The result shows a noticeable performance by selecting eleven significant features and a 4.5% increase in the prediction accuracy of Alzheirmer disease.
Predicting child alimony under Islamic shariah law using hybrid fuzzy inference system Nur Aqilah Khadijah Rosili; Rohayanti Hassan; Noor Hidayah Binti Zakaria; Farid Zamani Che Rose; Shahreen Kasim; Tole Sutikno
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.pp1367-1375

Abstract

This paper proposes a novel approach to predicting child alimony under Islamic Shariah law using a hybrid fuzzy inference system, integrating Mamdani and Takagi-Sugeno-Kang (TSK) fuzzy systems. Machine learning algorithms have become valuable tools for legal decision-making, but judicial process delays can lead to adverse effects. Our model aims to expedite decision-making and minimize legal fees by accurately determining the proper amount of alimony for children after divorce. We collected data from 94 alimony cases and evaluated the model’s performance using accuracy, precision, recall, and F1 score metrics. The hybrid fuzzy system achieved promising results with 88% accuracy, 84% precision, 89% recall, and an 86% F1 score. Notably, the model reduced bias and standardization in decision-making, promoting fairness. However, the study suggests potential areas for improvement and emphasizes trans-parent judgment processes and coordination among judges in assessing alimony costs based on sufficiency and ma’ruf criteria. This research significantly contributes to machine learning applications in the judicial domain. It provides a valuable decision-making tool for judges and lawyers to enhance the judicial process’s efficiency and ensure children’s welfare in divorce cases under Islamic Shariah law. Further research can enhance the model’s effectiveness and reliability, opening avenues for continued exploration in this field.
Effects of hammer configurations on pearl millet grinding system with a hammer mill: theory and experiment Moustapha Diop; Mouhamadou Thiam; Abdoulaye Kebe; Ibrahima Gueye
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.pp658-665

Abstract

In grinding processes using hammer mills, the configuration, number and speed of hammers are some of the main factors that can affect system performance. This paper aims to investigate the effects of hammer configurations in terms of specific energy consumption (SEC), grinding mass efficiency, and productivity. These effects were studied theoretically on the basis of classical grinding laws and experimentally with four different hammer configurations. From theoretical studies, a decreasing power model of SEC versus hammer configurations was developed, which was then validated with a determination coefficient of 0.99 in experiments using a 2 HP-DC hammer mill. The good agreement between theoretical and experimental results confirms that the specific energy consumption and the productivity are directly dependent on hammer configurations, but the effects are not significant for grinding mass efficiency.
An autonomous robotic arm for efficient rock collection in uncharted territories Deshmukh, Sanjay; Thakker, Bhaumik Hitesh; Gupte, Vedangi Nilesh; Kapadia, Taher Kutbuddin
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 2: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i2.pp779-786

Abstract

The autonomous rock collector using robotic arm for exploration of unknown territories (ARCAxUT) is introduced as an innovative solution for the efficient retrieval of rock samples in unexplored space regions. Traditional, human-reliant methods are costly and hazardous, prompting the development of ARCAxUT. Equipped with a smart robotic arm, an RGB-D camera, and NUC computer, the system autonomously detects and estimates the mass of various rock samples. Validated in simulated and real-world environments, the algorithm ensures precise gripper control, achieving an impressive 95.4% accuracy in rock size estimation. This breakthrough offers transformative capabilities for space missions, revolutionizing celestial body sample collection and advancing broader societal implications in space exploration technologies.
Enhancing security mechanisms for robot-fog computing networks Abdlehak Sakhi; Salah-Eddine Mansour; Abderrahim Sekkaki
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.pp1660-1666

Abstract

The evolution from conventional Internet usage to the internet of things (IoT) is reshaping communication norms significantly. Cloud computing, while prevalent, faces challenges like limited capacity, high latency, and network failures, especially when handling connected objects, leading to the emergence of fog computing as a more suitable approach for IoT. However, establishing secure connections among heterogeneous IoT entities is complex due to resource disparities and the unsuitability of existing security protocols for resource-constrained devices. This article explores fog computing's architecture, drawing comparisons with cloud computing while emphasizing its significance within the realm of IoT. Moreover, it delves into the practical application of fog computing within the context of the robot teacher project. Subsequently, our exploration introduces an advanced mutual authentication protocol, centered around hashed message authentication code (HMAC), aimed at enhancing the security infrastructure between the robot and the fog computing server.

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