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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 35, No 1: July 2024" : 65 Documents clear
CNN and Adaboost fusion model for multiface recognition based automated verification system of students attendance Hussain Hassan, Nashaat M.; Moussa, Mahmoud A.; M. Mahmoud, Mohamed Hassan
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 1: July 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i1.pp133-139

Abstract

In recent times, companies and institutions globally are increasingly adopting automated systems for recording employee attendance due to the inefficiency and error-prone nature of traditional methods. Face recognition is the fastest, most natural, and most accurate way to identify someone, despite its difficulty. Remote deployment and control of the technology using internet of things (IoT) protocols provides real-time attendance data worldwide. We use the Haar-cascade algorithm to detect and extract features and the adaptive boost algorithm confused with convolutional neural network (CNN) algorithm to recognize the face in our proposed smart attendance system. Per frame, the proposed system recognizes multiple faces. Face recognition in 18 conditions was designed into the proposed system to ensure its versatility. The system's graphical user interface (GUI) was made for average users. This work is more important because IoT technology records student attendance and sends data to authorities. We use Raspberry Pi 4 and camera module for our suggested system. Python and OpenCV libraries tested the multiple face image recognition proposal in 18 situations under four conditions. Single-face image recognition was compared to other methods. In most cases, the proposed method was 100% accurate and outperformed related methods.
Development of virtual tour reality using 360-degree panoramic images and Leaflet JavaScript Amali, Lanto Ningrayati; Katili, Muhammad Rifai; Sugeha, Alif Perdana
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 1: July 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i1.pp655-664

Abstract

This paper describes virtual reality (VR) development using a 360-degree panoramic and Leaflet JavaScript (Leaflet JS) to introduce campus buildings in real-time. The campus building of Universitas Negeri Gorontalo (UNG) in Bone Bolango Regency was chosen as a case study. It allows users to navigate and listen to background sound and narration, open the site map interactively, and read brief information about each location. Each panorama contains hotspots that allow users to explore further. All images are combined using a photo-stitching technique to produce a panoramic image. The research method used is the multimedia development life cycle (MDLC), which consists of six stages: concept, design, material collection, assembly, testing, and distribution. Based on the system usability scale (SUS) test, the virtual tour reality website application received feedback from users regarding its usability, satisfaction, and effectiveness, and it is interesting to use this application. The results show that the website application can visualize the campus building environment with various layers of information and can create a very realistic and detailed representation of the campus environment.
Cloud-based machine learning algorithms for anomalies detection Amarnath, Raveendra N; Gurulakshmanan, Gurumoorthi
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 1: July 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i1.pp156-164

Abstract

Gradient boosting machines harnesses the inherent capabilities of decision trees and meticulously corrects their errors in a sequential fashion, culminating in remarkably precise predictions. Word2Vec, a prominent word embedding technique, occupies a pivotal role in natural language processing (NLP) tasks. Its proficiency lies in capturing intricate semantic relationships among words, thereby facilitating applications such as sentiment analysis, document classification, and machine translation to discern subtle nuances present in textual data. Bayesian networks introduce probabilistic modeling capabilities, predominantly in contexts marked by uncertainty. Their versatile applications encompass risk assessment, fault diagnosis, and recommendation systems. Gated recurrent units (GRU), a variant of recurrent neural networks, emerges as a formidable asset in modeling sequential data. Both training and testing are crucial to the success of an intrusion detection system (IDS). During the training phase, several models are created, each of which can recognize typical from anomalous patterns within a given dataset. To acquire passwords and credit card details, "phishing" usually entails impersonating a trusted company. Predictions of student performance on academic tasks are improved by hyper parameter optimization of the gradient boosting regression tree using the grid search approach.
Hybrid RIS-assisted interference mitigation for heterogeneous networks Soumana Hamadou, Abdel Nasser; wa Maina, Ciira; Soidridine, Moussa Moindze
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 1: July 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i1.pp175-190

Abstract

Reconfigurable intelligent surfaces (RIS) have evolved as a low-cost and energy- efficient option to increase wireless communication capacity. In this research, we suggest using hybrid RIS (H-RIS) to reduce interference in heterogeneous networks (HetNet). In contrast to traditional passive RIS, a hybrid RIS is suggested, which is fitted with a few active elements to not only reflect but also amplify incident signals for a significant performance increase. By jointly optimising the passive and active coefficients of the H-RIS, we aim to maximise the rate of the small cell user (SUE). We presented an effective alternating optimisation (AO)-based phase shift matrix coefficients (AO-PMC) technique to tackle this problem by iteratively optimising these variables because the optimisation problem is not convex. The simulation results demonstrate that, in comparison to the passive RIS-assisted HetNet scheme and the scheme without RIS, the suggested scheme, with just 8% of active elements, can enable HetNet to gain superior spectral efficiency (SE) and energy efficiency (EE). The outcomes also demonstrate that, in the majority of the cases taken into account, H-RIS can outperform the active RIS-assisted HetNet scheme.
Deep learning for economic transformation: a parametric review Tariq, Usman; Ahmed, Irfan; Khan, Muhammad Attique; Bashir, Ali Kashif
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 1: July 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i1.pp520-541

Abstract

Deep learning (DL) is increasingly recognized for its effectiveness in analyzing and forecasting complex economic systems, particularly in the context of Pakistan's evolving economy. This paper investigates DL's transformative role in managing and interpreting increasing volumes of intricate economic data, leading to more nuanced insights. DL models show a marked improvement in predictive accuracy and depth over traditional methods across various economic domains and policymaking scenarios. Applications include demand forecasting, risk evaluation, market trend analysis, and resource allocation optimization. These processes utilize extensive datasets and advanced algorithms to identify patterns that traditional methods cannot detect. Nonetheless, DL's broader application in economic research faces challenges like limited data availability, complexity of economic interactions, interpretability of model outputs, and significant computational power requirements. The paper outlines strategies to overcome these barriers, such as enhancing model interpretability, employing federated learning for better data privacy, and integrating behavioral and social economic theories. It concludes by stressing the importance of targeted research and ethical considerations in maximizing DL's impact on economic insights and innovation, particularly in Pakistan and globally.
Sampled-data observer design for sensorless control of wind energy conversion system with PMSG Zaggaf, Mohammed Hicham; Mansouri, Adil; El Magri, Abdelmounime; Watil, Aziz; Lajouad, Rachid; Bahatti, Lhoussain
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 1: July 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i1.pp52-61

Abstract

This paper presents a nonlinear observer for a variable-speed wind energy conversion system (WECS) utilizing a permanent magnet synchronous generator (PMSG). The study addresses the design of high-gain sampled-data observers based on the nonlinear WECS model, supported by formal convergence analysis. An essential aspect of this observer design is the incorporation of a time-varying gain, significantly enhancing system performance. Convergence of estimation errors is demonstrated using the input-to-state stability method. Simulation of the proposed observer is conducted using the MATLAB-Simulink tool. The obtained results are presented and analyzed to showcase the overall effectiveness of the proposed system.
Smart solar maintenance: IoT-enabled automated cleaning for enhanced photovoltaic efficiency Ramalingam, Puviarasi; Kathirvel, Jayashree; Adaikalam, Arul Doss; Somasundaram, Deepa; Sreenivasan, Pushpa
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 1: July 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i1.pp14-19

Abstract

This innovative project aims to increase the effectiveness and user experience of solar panel systems by introducing a state-of-the-art dust and speck removal system. Leveraging cutting-edge technology, the system demonstrates a remarkable 32% increase in power output compared to dirty solar panels. The approach is characterized by its reliance on the universe as the system controller, reducing the need for manual intervention and minimizing the workforce required for panel cleaning. The proposed timed system utilizes water and wipers, facilitated by internet of things (IoT) technology, microcontrollers, and sensor modules for efficient and automated operation. An Android application provides user control and notifications about ongoing processes. The system’s adaptability for various settings is emphasized, offering a portable solution. The smart IoT based automatic solar panel cleaning ensures reliable performance, underscoring the project’s commitment to improve scalability, cost-efficiency, performance, integrity, and consistency.
ADKNN fostered BIST with Namib Beetle optimization algorithm espoused BISR for SoC-based devices Alnatheer, Suleman; Ahmed, Mohammed Altaf
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 1: July 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i1.pp90-101

Abstract

Redundancy analysis is a widely used method in fault-tolerant memory systems, and it is essential for large-size memories. In current security operations centers (SoCs), memory occupies most of the chip space. To correct these memories using a conventional external equipment test approach is more difficult. To overcome this issue, memory creators utilize redundancy mechanism for substituting the columns and rows along with a spare one to increase output of the memories. In this study, a built-in-self-test (BIST) to test memories and built-in-self-repair (BISR) mechanism to repair the faulty cells for any recent SoC devices is proposed. The BIST, based on adaptive activation functions with a deep Kronecker neural network (ADKNN), not only detects the defect but also determines the kind of defect. The BISR block uses the Namib Beetle optimization algorithm (NBOA) to fix the mistakes in the memory under test (MUT). The study attempts to determine how the characteristics of SoC-based devices change in the real world and then contributes to the suggested controller blocks. Performance metrics such as slice register, region, delay, maximum operating frequency, power consumption, minimum clock period, and access time evaluate performance. Comparing the proposed ADKNN-NBOA-BIST-BISR scheme to existing BIST, BISR, and BISD-based methods reveals its significant performance.
An efficient controller-based architecture for AES algorithm using FPGA Nadaf, Reshma; Bhairannawar, Satish S.
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 1: July 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i1.pp397-404

Abstract

The importance of crucial current technical advancements, particularly those centered on the cryptography process such as Cryptographic advanced encryption standard (AES) hardware architectures are gaining momentum with respect to improving the speed and area optimizations. In this paper, we have proposed a novel architecture to implement AES on a reconfigurable hardware i.e., field programmable gate arrays (FPGA). The controller in AES algorithm is responsible to generate the signals to perform operations to generate the 128 bits ciphertext. The proposed controller uses multiplexer and synchronous register-based approach to obtain area and speed efficient on the FPGA hardware. The entire architecture of AES with proposed controller is implemented on Virtex 5, Virtex 6, and Virtex 7series using XilinxISE 14.7 and tested for critical path delay, frequency, slices, efficiency and throughput. It is observed that all the parameters are improved compared to existing architectures achieving the throughput of 32.29, 40.01, and 43.01 Gbps respectively. The key benefit of this approach is the high level of parallelism it displays in a quick and efficient manner.
Integration of statistical methods and neural networks for temperature regulation parameter optimization Kaddar, Leila Benaissa; Khelifa, Said; Zareb, Mohamed El Mehdi
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 1: July 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i1.pp124-132

Abstract

Temperature control plays a crucial role in various industrial processes, ensuring optimal performance and product quality. The conventional approach to optimizing temperature controller parameters involves manual tuning, which can be time-consuming, labor-intensive, and often lacks precision. This paper introduces an innovative methodology for optimizing the parameters of a temperature controller by integrating statistical methods in the preparation of the experimental plan utilized by neural networks. The integration of statistical techniques in designing the experimental framework enhances the efficiency of data collection, providing a robust foundation for subsequent analysis. The neural network leverages this well-structured dataset to model and optimize the temperature controller parameters, resulting in improved precision and performance. The synergistic integration of statistical methods and neural networks not only streamlines the optimization process but also enhances the reliability of the temperature control system. The effectiveness of the proposed approach is demonstrated through case studies on the Procon level/flow and temperature 38-003 process. The results show significant improvements in temperature control performance, with reduced process variability and faster response times.

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