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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 34, No 3: June 2024" : 65 Documents clear
A device to device driven approach towards optimizing energy efficiency for 6G networks Sonia Aneesh; Alam N. Shaikh
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.pp1682-1689

Abstract

Our study aims to develop more energy-efficient mobile communication systems through the exploration of the 6th generation (6G) technology that is expected to be implemented in 2033. We focus on the impact of device-to-device (D2D) communication on power efficiency, which is a crucial need in this domain. To achieve this, we conducted a pioneering experiment using an in-house testbed and K-means clustering to classify locations as D2D enabled or disabled. Our findings show that there is a dynamic clustering mechanism that enables certain nodes to sustain D2D functionality around temporary base stations, resulting in a remarkable 5% improvement in network lifetime per second. This research not only enhances our understanding of 6G networks but also provides a practical methodology for optimizing energy consumption, which holds significant implications for society in advancing sustainable and efficient communication.
Blockchain-based e-voting system in a university Adil Marouan; Morad Badrani; Nabil Kannouf; Abderrahim Zannou; Abdelaziz Chetouani
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.pp1915-1923

Abstract

The blockchain-based electronic voting (e-voting) system, offers universities a safe, easy-to-use platform that enhances accuracy and integrity. Despite that, it is challenging to integrate the blockchain-based e-voting system with current platforms and private data. Managing latency is another requirement during the blockchain transactions (votes/elections). In this work, we suggested a novel system that uses smart contracts on the consortium blockchain to address these constraints. The voters and electors in a university can vote and elect respecting the rules established in smart contracts. The miners validate transactions using proof of work (PoW) and proof of stake (PoS). Data integrity and voter validity are ensured via the SHA-256 hash algorithm and the ECDSA signature. The implementation results demonstrate that the suggested method works better than the state-of-the-art. exceeds the state-of-the-art in terms of gas cost and execution time.
Multi-microgrids system’s resilience enhancement Samira Chalah; Hadjira Belaidi
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.pp1399-1409

Abstract

Nowadays, electricity consumption is increasing rapidly which leads to conventional power systems exhaustion. Therefore, micro-grids (MGs) implantation can enhance the resilience of power systems by implication of new resources, such as renewable energy sources (solar panel and wind power systems), electric vehicles (EV), and energy storage systems (ESS). This paper proposes a new strategy for optimal power consumption inside one microgrid; then, the approach will be extended to optimize the power consumption to enhance the resilience in the case of multi-MGs systems. The system controller of each microgrid has been implemented using ESP32 microcontroller and Raspberry IP4. The proposed approach intends to enhance the resilience of the system to react to any contingency in the system such as loss of power linkage between MG and the network in case of any natural disaster, especially in the rural area. Two controllers are implemented; the first one ensures MG autonomy by the efficient use of its own sources. The second one handles the system resilience cases by demanding/delivering power from/into neighbor microgrids. Hence, this work enhances the system resilience with an optimal cost. Thus, the MG can offer ancillary services for the neighboring MGs.
Comparing hybrid models for recognising objects in thermal images at nighttime Maheswari Bandi; Reeja Sundaran Rajakumari
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.pp1823-1831

Abstract

This research aims to revolutionize urban object recognition by developing cloud-based Python programs using intelligent algorithms. Unlike current models that focus on colour enhancement in nighttime thermal images, this work addresses the critical challenge of accurate object detection in urban landscapes. The proposed method incorporates a binary generative adversarial network (GAN) generator that can switch bidirectionally between daytime colour (DC) and nighttime infrared (NTIR) images. memory-based visual image memory (MVAM), system extracts important descriptive information from urban landscape images, reducing problems related to small sample sizes. This discussion presents a comprehensive improvement and evaluation of a deep learning image classification pipeline using Google Colab, demonstrating advanced image processing. Using TensorFlow, Keres and scikit image libraries combined with advanced algorithms such as DenseNet121 and MobileNetV2 presents a clear approach. We created a Bidirectional GAN + MVAM for object recognition in this work. Our method performed well, with an accuracy of 81.43%, precision of 51.16, recall of 50.11, and F-score of 46.37. The systematic presentation of the code presents a careful strategy to ensure optimal performance, stability, and efficiency of deep learning and image processing tasks.
Game-based augmented reality learning of Sarawak history in enhancing cultural heritage preservation Clive Lai Yi Cheng; Goh Eg Su; Johanna Binti Ahmad; Tole Sutikno
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.pp1718-1729

Abstract

The augmented reality (AR) technology had been proliferating for years. However, the implementation of AR technology still has room to be explored, especially in the form of cultural heritage preservation. The aim of this study is to enhance AR technology in game-based cultural heritage and history preservation in Sarawak, as well as supplement the gamified experiences in learning James Brooke’s history. Three research objectives are proposed: to design an AR game prototype for the history of James Brooke; to develop an AR game prototype with a collaborative learning element; and to evaluate an AR game prototype for enhancing cultural heritage preservation. This study proposes a game-based prototype that contains AR markers to assign each with different game features. Furthermore, collaborative learning theory is enhanced through AR experiences with multiplayer support. The game-based prototype is evaluated by a group of participants through prototype measuring and testing. The participants feel mediocre about the challenge and knowledge factors of the prototype. Overall, this study highlights the enhancement of cultural heritage preservation through AR game-based experiences intensively learned from James Brooke’s history in Sarawak. These implementations have an apparent promising contribution to make in protecting the available cultural heritage in Sarawak and extensively to the country’s cultural heritage preservation.
A new deep learning model with interface for fine needle aspiration cytology image-based breast cancer detection Manjula Kalita; Lipi B. Mahanta; Anup Kumar Das; Mananjay Nath
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.pp1739-1752

Abstract

Cytological evaluation through microscopic image analysis of fine needle aspiration cytology (FNAC) is pivotal in the initial screening of breast cancer. The sensitivity of FNAC as a screening tool relies on both image quality and the pathologist’s expertise. To enhance diagnostic accuracy and alleviate the pathologist’s workload, a computer-aided diagnosis (CAD) system was developed. A comparative study was conducted, assessing twelve candidate pre-trained models. Utilizing a locally gathered FNAC image dataset, three superior models-MobileNet-V2, DenseNet-121, and Inception-V3-were selected based on their training, validation, and testing accuracies. Further, these models underwent evaluation in four transfer learning scenarios to enhance testing accuracy. While the outcomes were promising, they left room for improvement, motivating us to create a novel deep convolutional neural network (CNN). The newly proposed model exhibited robust performance with testing accuracy at 85%. Our research concludes that the most lightweight, high-accuracy model is the one we propose. We’ve integrated it into our user-friendly Android App, “Breast Cancer Detection System,” in TensorFlow Lite format, with cloud database support, showcasing its effectiveness. Implementing an artificial intelligent (AI)-based diagnosis system with a user-friendly interface holds the potential to enhance early breast cancer detection using FNAC.
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.
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.
Uncovering botnets in IoT sensor networks: a hybrid self-organizing maps approach Mwaffaq Abu AlHija; Hamza Jehad Alqudah; Hiba Dar-Othman
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.pp1840-1857

Abstract

The integration of the internet of things (IoT) has revolutionized diverse industries, introducing interconnected devices and IoT sensor networks for improved data acquisition. However, this connectivity exposes IoT ecosystems to emerging threats, with botnets posing significant risks to security. This research aims to develop an innovative solution for detecting botnets in IoT sensor networks. Leveraging insights from existing research, the study focuses on designing a hybrid self-organization map (SOM) Approach that integrates lightweight deep learning (DL) techniques. The objective is to enhance detection accuracy by exploring various DL architectures. Proposed methodology aims to balance computational efficiency for resource-constrained IoT devices while improving the discriminatory power of the detection system. The study advancing IoT cybersecurity and addresses critical challenges in botnet detection within IoT sensor networks. The testing of the artificial neural networks (ANN) classifier involves three models, each represented based on parameters related to the construction of the training models. The most effective ANN achieves 86%, works on anomaly intrusion detection systems (AIDS).
Deep learning based COVID and Pneumonia detection using chest X-ray Praveen Kumar; Mira Rakhimzhanova; Seema Rawat; Alibek Orynbek; Vikas Kamra
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.pp1944-1952

Abstract

Since the outbreak, the novel coronavirus (COVID-19) has infected more than 180 million people and has taken a toll of 3.91 million lives globally as of June 2021. This virus causes symptoms like fever, cold, and fatigue, and can develop into Pneumonia which can be detected using chest X-rays (CXRs). Therefore, early detection of COVID-19 can help get early medical attention. However, a sudden rise in the number of cases in many countries caused by COVID waves increases the burden on their testing facilities. As a result, they sometimes fail to perform enough testing to contain the spread. This work proposes a deep learning model to detect COVID-19 and Pneumonia based on CXRs. The dataset for our COVID model contains a total of 3,400 CXRs images of COVID-19 patients and 3,400 normal CXRs. The dataset for our Pneumonia model contains 1,300 CXR images of Pneumonia patients and 1,300 normal CXRs. We use convolutional neural network provided by TensorFlow to build our model, which gave 94.17% and 93.55% accuracy for COVID model and Pneumonia model, respectively. Finally, we deployed our model on the web and added a web tracker, which gives us the cases, deaths, and recoveries state-wise and nationwide.

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