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International Journal of Electrical and Computer Engineering
Published by Institute of Advanced Engineering and Science
ISSN : 20888708     EISSN : 27222578     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
International Journal of Electrical and Computer Engineering (IJECE, ISSN: 2088-8708, a SCOPUS indexed Journal, SNIP: 1.001; SJR: 0.296; CiteScore: 0.99; SJR & CiteScore Q2 on both of the Electrical & Electronics Engineering, and Computer Science) is the official publication of the Institute of Advanced Engineering and Science (IAES). The journal is open to submission from scholars and experts in the wide areas of electrical, electronics, instrumentation, control, telecommunication and computer engineering from the global world.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 111 Documents
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Search results for , issue "Vol 14, No 6: December 2024" : 111 Documents clear
Comparison of algorithms for the detection of marine vessels with machine vision Rodríguez-Gonzales, José; Niquin-Jaimes, Junior; Paiva-Peredo, Ernesto
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp6332-6338

Abstract

The detection of marine vessels for revenue control has many tracking deficiencies, which has resulted in losses of logistical resources, time, and money. However, digital cameras are not fully exploited since they capture images to recognize the vessels and give immediate notice to the control center. The analyzed images go through an incredibly detailed process, which, thanks to neural training, allows us to recognize vessels without false positives. To do this, we must understand the behavior of object detection; we must know critical issues such as neural training, image digitization, types of filters, and machine learning, among others. We present results by comparing two development environments with their corresponding algorithms, making the recognition of ships immediately under neural training. In conclusion, it is analyzed based on 100 images to measure the boat detection capability between both algorithms, the response time, and the effectiveness of an image obtained by a digital camera. The result obtained by YOLOv7 was 100% effective under the application of processing techniques based on neural networks in convolutional neural network (CNN) regions compared to MATLAB, which applies processing metrics based on morphological images, obtaining low results.
Secure data transmission in power systems using blockchain technology Srivatsa, Anand; Thammaiah, Ananthapadmanabha; Kumar MV, Likith; D, Rajeshwari; AP, Suma
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp6170-6181

Abstract

Recent advances in intelligent systems have significantly improved power management, load distribution, and resource management capabilities, far beyond past constraints. Despite these gains, the development of internet-connected technology has brought various vulnerabilities, leading to negative results. The integration of intelligent technology has unintentionally offered chances for hackers to enter networks and modify data sent to central systems for analysis. One of the most serious risks is the false data injection attack (FDIA), which may drastically impair analytical outcomes. Previous research has shown that standard approaches for recovering data affected by FDIA are unreliable and inefficient. This paper investigates the use of the proof of stake (PoS) consensus method in this framework improves data integrity and makes it easier to identify illegal changes. Participating nodes may reject or change block transactions, ensuring the ledger's correctness. Our results show that the PoS consensus method is exceptionally successful in creating and adding transactions to the blockchain. Furthermore, the PoS mechanism's simplicity in block formation enhances both time and energy efficiency, resulting in considerable benefits in operational performance.
A novel secured open standard framework for internet of things applications integrating elliptic curve cryptography and fog computing Ravindra, Krishnapura Srinivasa; Rao, Malode Vishwanatha Panduranga
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp7224-7235

Abstract

The internet of things (IoT) has revolutionized various fields by enabling seamless connectivity and data exchange among numerous devices. However, this interconnectivity introduces significant security challenges, particularly in ensuring data confidentiality, integrity, and authenticity. This study proposes a novel secure open standard framework for IoT applications, addressing these challenges through the integration of elliptic curve cryptography (ECC) and fog computing. The framework consists of three core components: secure device registration, data encryption within the fog gateway, and a robust mechanism for detecting man-in-the-middle (MITM) attacks. The unique aspect of the proposed method lies in its comprehensive approach to IoT security. Utilizing ECC, the framework ensures secure communication among resource constrained IoT devices, balancing encryption strength and efficiency. The integration of fog computing reduces latency and enhances processing efficiency by offloading intensive tasks from IoT devices to the fog layer. The MITM attack detection mechanism continuously monitors cryptographic keys and communication patterns, providing an additional layer of security against advanced cyber threats. The system was implemented and evaluated using the NS-3.26 network simulator and Python for data visualization. The experimental setup included 100 IoT devices, 25 users, a fog gateway, a datacenter, and a cloud server. Results demonstrate the framework's scalability and efficiency, with consistent throughput increases and balanced power consumption across varying IoT device numbers.
About one lightweight encryption algorithm ensuring the security of data transmission and communication between internet of things devices Atanov, Sabyrzhan; Seitkulov, Yerzhan; Moldamurat, Khuralay; Yergaliyeva, Banu; Kyzyrkanov, Abzal; Seitbattalov, Zhexen
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp6846-6860

Abstract

In this paper, a new encryption algorithm Twine-Mersenne was developed based on the Twine algorithm with the addition of a random number generator for the dynamic generation of S-boxes. Dynamic generation of random numbers based on the Mersenne Twister helps to increase the cryptographic strength of the proposed algorithm. The algorithm we propose solves the issues of optimizing the costs of computing and energy resources of internet of things (IoT) devices, using a combination of lightweight cryptographic principles and fuzzy logic, and also provides reliable security and intelligent authentication of the mobile application user. The paper also considers the practical implementation of the proposed algorithm based on Arduino ESP32, a device with limited computing resources. In addition to this, fuzzy logic has found its practical application in the field of intelligent user authentication in developed mobile applications based on Arduino Studio for mobile cellular applications. As a result, the proposed lightweight encryption algorithm has proven itself to be an effective tool in ensuring the security of data transmission and communication between IoT devices.
A comparative analysis of constant impedance and constant power loads in a distribution network Ramya, Ramya; Joseph, Rex
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp6111-6121

Abstract

Most conventional power systems adopt radial distribution network wherein multiple loads are connected across the distribution transformer. As the number of loads increases, it results in poor voltage profile at the distant receiving end reducing power delivery. This issue worsens with the large-scale influx of electric vehicles and power converter-fed loads, which draw constant power irrespective of supply voltage. Such loads exhibit negative incremental resistance behavior and also have a dynamic response which affects the network in a manner different from constant impedance loads. This paper compares the effects of constant power and constant impedance loads by modeling adjustable converter dynamics for constant power loads. It analyzes line currents, load voltages and power transmitted in a four-load radial test system with optional distributed sources. Results show poorer voltage profile and the effect of power converter dynamics in constant power loads compared to conventional loads. Adding distributed sources improves voltage profile considerably, and transmission losses are reduced. Steady state analysis is then extended to an IEEE 31-bus 23 kV distribution test system with similar results. Transmission losses are computed along different branches, and the influence of loads and sources are analyzed. The outcomes of the analysis can be used in arrival of loss allocation in a system where peer to peer energy sharing is envisaged.
Control and monitoring of 1 phase generator automatic voltage regulator internet of things Hasibuan, Arnawan; Akbar, Farhan; Meliala, Selamat; Rosdiana, Rosdiana; Putri, Raihan; Nrartha, I Made Ari
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp7158-7168

Abstract

This research focuses on the importance of maintaining generator output voltage stabilization using automatic voltage regulator (AVR), especially in dealing with load impacts and environmental changes. Through the implementation of internet of things (IoT), this system can be controlled and monitored remotely, enabling real-time monitoring without physical presence at the generator location. The main objective of this research is to increase energy efficiency by optimizing generator operation. System development methods include design, prototyping and testing stages. Test results show that the automatic voltage regulator is effective in maintaining a stable output voltage of around 220 V, even though the current varies according to the existing load. The power produced ranges from 23 to 670 W, with a power factor between 0.7 and 1. Despite a slight voltage drop to 217 V, the power factor increases to 0.93. The system uses NodeMCU to send data to Blynk and Google Spreadsheets servers, as well as servo motors and PZEM-004T sensors for control and monitoring. Overall, this research shows that the internet of things-based automatic voltage regulator system is effective in maintaining stability and increasing generator operational efficiency, with the ability to manage voltage, current, power, and power factor efficiently.
Optimal control of the dynamics of nonlinear oscillating systems using synergetic principles of self-organization Xakimovich, Siddikov Isomiddin; Maxamadjanovna, Umurzakova Dilnoza
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp6271-6278

Abstract

This paper analyses the evolution of nonlinear oscillation control methods and presents an innovative approach known as analytical design of aggregated oscillation controllers (ADACO). This method is based on the principles of synergetic control theory and focuses on the integration of self-organization and control processes to synthesize energy-efficient control laws for nonlinear oscillating systems. The authors elaborate on the theoretical foundations of ADACO, which extends the previous analytical design of aggregated controllers (ADAC) method by incorporating energy invariants and integrals of motion into the synthesis of control laws. This approach demonstrates significant advantages over traditional methods, offering a versatile framework for the design of energy-efficient control systems for a wide range of nonlinear oscillating systems in various fields such as aerospace, robotics, vibromechanical systems, and objects with chaotic dynamics. The aim of the paper is to establish a unified approach to the control of nonlinear oscillations, solving both the problems of generation of stable oscillations and suppression of unwanted perturbations. The application of synergetic control principles in the framework of ADACO opens prospects for further development of nonlinear control theory.
A novel scheme for unified streamlined traffic management in 5G backhaul network Thippanna, Deepa; Jayappa, Praveen
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp6981-6991

Abstract

The issues associated with the 5G backhaul network act as the underlying reason for concern. Existing literature towards the 5G backhaul network offers various ranges of sophisticated schemes that are yet to possess open-end issues. Therefore, the proposed study introduces a novel scheme for effective 5G backhaul network traffic management. The scheme hypothesizes that if an efficient gateway node is selected, it can better communicate between the macro-base station and the core network. The study contributes to developing a sophisticated system design to identify the blockage region, a macro-base station, and a small base station. These attributes incorporate the capability of a gateway node to identify the bottleneck condition during peak traffic situations in the 5G backhaul network. The study outcome shows better communication performance in contrast to the existing system. The study outcome shows the proposed scheme to offer 47% increased throughput, 80% reduced latency, and 55% reduced algorithmic processing time in contrast to existing schemes.
Revolutionizing brain tumor diagnoses: a ResNet18 and focal loss approach to magnetic resonance imaging-based classification in neuro-oncology Kempanna, Shashi Raj; Rangappa, Aswatha Anoor; Maheshappa, Shruthi; Kumar Siddaraju, Druva; Gowda, Kumar Puttaswamy; Ramachandragowda, Santhosh Kumar; Tagare, Trupti Shripad
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp6551-6559

Abstract

Brain tumor diagnosis remains a critical challenge in neuro- oncology, where accurate and timely identification of malignancies can significantly impact patient outcomes. This research explores the integration of deep learning techniques, specifically leveraging the ResNet18 architecture coupled with focal loss, to enhance the classification accuracy of magnetic resonance imaging (MRI)-based brain tumor diagnoses. ResNet18, known for its powerful feature extraction capabilities, was employed to analyze MRI scans, while focal loss was utilized to address class imbalance issues prevalent in medical datasets. The model was trained on a comprehensive dataset, achieving an accuracy of 95.54%. These results demonstrate the potential of this approach in providing robust and precise diagnostic support in clinical settings, potentially revolutionizing the current methodologies in brain tumor detection and classification. The integration of advanced neural networks with specialized loss functions presents a significant advancement in the field, paving the way for more reliable and automated neuro-oncological diagnostics.
Enhancing El Niño-Southern oscillation prediction using an attention-based sequence-to-sequence architecture Setiawan, Karli Eka; Fredyan, Renaldy; Alam, Islam Nur
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp7057-7066

Abstract

The ability to accurately predict the EI Nino-Southern oscillation (ENSO) is essential for seasonal climate forecasting. Monitoring the Pacific Ocean's surface temperature has many benefits for human life, including a better understanding of climate and weather, the ability to predict summer and winter, the ability to manage natural resources, serving as a reference for maritime transportation and navigation needs, serving as a reference for climate change monitoring needs, and even serving as a renewable energy source by utilizing high sea surface temperatures. This study introduces a deep learning (DL) model with AttentionSeq2Luong model as our proposed model to the ENSO research community. The present study showcases the capability of our proposed model to effectively forecast the forthcoming monthly average Nino index compared to the baseline seq2seq architecture model. For the dataset, this study utilized monthly observations of Nino 12, Nino 3, Nino 34, and Nino 4 between January 1870 and August 2022. The brief result of our experiment was that applying Luong Attention in the seq2seq model reduced the RMSE error by around 0.03494, 0.04635, 0.03853, and 0.03892 for forecasting Nino 12, Nino 3, Nino 34, and Nino 4, respectively.

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