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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 4: August 2024" : 111 Documents clear
Automated gas-controlled cooker system design and implementation Amuta, Elizabeth; Orovwode, Hope; Airoboman, Abel Ehimen; Mene, Joseph Anirejuoritse; Sobola, Gabriel Oluwatobi; Matthew, Simeon; Onyema, Daniel
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i4.pp3905-3912

Abstract

Cooking from ancient times has evolved from using open fires to wood, gas cookers, using liquefied petroleum gas (LPG). This has also come with various adverse effects ranging from gas leakages to burnt food due to absent-mindedness, thereby creating a significant disaster that could lead to loss of life and property damage. The study aimed to reduce the rate of liquefied petroleum gas related accidents in domestic usage and improve the safety of domestic gas users. An automated method to enforce safety was proposed to avoid unwanted cooking gas flow consequences, especially in homes. The paper presents a control system using an Arduino Uno with a control design interfaced with a utensil sensor, solenoid valve, and a timer circuit to allow gas flow to commence and ignite a flame automatically. The automatic ignition apparatus, which has a high-voltage electric circuit, begins to function once the utensil detector comes in contact with silverware. The system is designed to function in different modes to ensure safety and prevent gas flow. The prototype serves as a means of curbing gas wastage and increasing the safety of people who use LPG as a source of fuel for cooking.
Design and optimization of high electron mobility transistor with high-k dielectric material integration Sreenivasa Rao, Devireddy; Sirisha, Malluri; Srinivas Murthy, Deepthi Tumkur; Krishne Gowda, Nayana Dunthur; Balaji, Bukya; Kiran Kumar, Padakanti
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i4.pp3855-3862

Abstract

We have developed and simulated a high electron mobility transistor (HEMT) operating in the 5 nm regime. This HEMT uses hafnium oxide (HfO2), a high-k dielectric material, to create an undoped region (UR) beneath the gate. While the gate and undoped regions share equal thickness, the channel length differs. This innovative undoped under the gate dielectric HEMT design mitigates the maximum electric field (V) within the channel area, leading to a significant increase in drain current. The utilization of a high-k dielectric in the HEMT structure results in a saturated Ion current that is 60% higher compared to conventional structures. Specifically, we use an AlGaN/GaN/SiC-based HEMT with an intrinsic section below the gate, using HfO2 as the high-k dielectric substantial, for applications requiring high power and high-frequency power amplifiers. Compare this advanced HEMT design to conventional HEMTs and you will see improved conductivity, a greater drain current (Id), a 54% increase in transconductance (Gm), and a lower on-resistance (Ron). Additionally, advancements in the electric field in the Y direction are seen. This HEMT structure exhibits superior performance compared to alternative materials analyzed. The integration of AlGaN/GaN materials in HEMTs opens up extensive opportunities in the realms of radio frequency very large-scale integration (VLSI) and power electronics.
Multi-temporal assessment of wind, solar, and hydropower resources for off-grid microgrid Odetoye, Oyinlolu Ayomidotun; Kehinde Olulope, Paul; Olabisi Olanrewaju, Matthew; Olusola Alimi, Adeleke
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i4.pp3755-3767

Abstract

For a proposed multi-source all-renewable microgrid in Nigeria’s Middle-belt region, this paper presents a multi-temporal approach to the investigation of the uncertainty in the potential of renewable energy resources. The wind, solar, and hydropower resources for a proposed multi-source all-renewable off-grid community microgrid are considered using an array of probabilistic techniques. The peculiar variances in the location’s climate throughout the year make the more common method of annual models of renewable resources unsuitable for power system planning. Consequently, a more granular model of its renewable resources over time is needed. Therefore, for the chosen location, for each renewable resource, a composite multitemporal maximum-likelihood estimation-based (MLE) probabilistic model for characterization is developed. A total of 39 probabilistic models are developed. Up to 40% improvement in the accuracy of the statistical measures for renewable resource uncertainty was observed. Multi-temporal approach provides more accurate information for power system planning over time than the conventional approach of single aggregate models, especially for hydropower, which is strongly affected by the relatively sporadic occurrence of rainfall. The study shows that solar energy is promising, hydropower potential is seasonal and complementary, and wind potential is low at the location considered in this study.
The use of genetic algorithm and particle swarm optimization on tiered feature selection method in machine learning-based coronary heart disease diagnosis system Wiharto, Wiharto; Mufidah, Yasmin; Salamah, Umi; Suryani, Esti; Setyawan, Sigit
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i4.pp4563-4576

Abstract

Coronary heart disease (CHD) is a leading global cause of death. Early detection is the right step to reduce mortality rates and treatment costs. Early detection can be developed using machine learning by utilizing patient medical record datasets. Unfortunately, this dataset has excessive features which can reduce machine learning performance. For this reason, it is necessary to reduce the number of redundant features and irrelevant data to improve machine learning performance. Therefore, this research proposes a tiered of feature selection model with genetic algorithm (GA) and particle swarm optimization (PSO) to improve the performance of the diagnosis model. The feature selection model is evaluated using parameters derived from the confusion matrix and using the CatBoost machine learning algorithm. Model testing uses z-Alizadeh Sani, Cleveland, Statlog, and Hungarian datasets. The best results for this model were obtained on the z-Alizadeh Sani dataset with 6 selected features from 54 features and the resulting performance for accuracy parameters was 99.32%, specificity 98.57%, sensitivity 100.00%, area under the curve (AUC) 99.28%, and F1-Score 99.37%. The proposed feature selection model is able to provide machine learning performance in the very good category. The diagnostic model proposed is of excellent standard.
An efficient object detection by autonomous vehicle using deep learning Kolukula, Nitalaksheswara Rao; Kalapala, Rajendra Prasad; Ivaturi, Sundara Siva Rao; Tammineni, Ravi Kumar; Annavarapu, Mahalakshmi; Pyla, Uma
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i4.pp4287-4295

Abstract

The automation industries have been developing since the first demonstration in the period 1980 to 2000 it is mainly used on automated driving vehicle. Now a day’s automotive companies, technology companies, government bodies, research institutions and academia, investors and venture capitalists are interested in autonomous vehicles. In this work, object detection on road is proposed, which uses deep learning (DL) algorithms. You only look once (YOLO V3, V4, V5). In this system object detection on the road data set is taken as input and the objects are mainly on-road vehicles, traffic signals, cars, trucks and buses. These inputs are given to the models to predict and detect the objects. The Performance of the proposed system is compared with performance of deep learning algorithms convolution neural network (CNN). The proposed system accuracy greater than 76.5% to 93.3%, mean average precision (Map) and frame per second (FPS) are 0.895 and 43.95%.
Dissolved gas analysis comparison of electrically stressed methyl ester and mineral oil Rajab, Abdul; Andre, Hanalde; Pawawoi, Andi; Baharuddin, Baharuddin; Gumilang, Harry
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i4.pp3628-3639

Abstract

Methyl ester is considered one of the alternative substitutes to mineral oil as an insulating liquid. This study investigates the dissolved gas analysis (DGA) of the methyl ester derived from palm oil, under low energy discharge faults. The aims are to understand the gas composition and evaluate the applicability of the well-established fault interpretation methods for mineral oil to the methyl ester. Experimental procedures were conducted based on the International Electrotechnical Commission (IEC) standards. It involved simulating electrical breakdowns in laboratory conditions as per IEC-156 standard and analyzing gas samples using gas chromatography based on IEC-567. Results show that methyl ester oils produce similar types of gases as mineral oils but at higher concentrations. The interpretation of DGA results using fault identification methods such as Duval Triangle, Duval Pentagon, and IEC ratio indicates an overestimation of fault severity in methyl ester oils, and categorizing the faults as high energy discharge. However, the key gas method correctly identifies the discharge in both methyl ester and mineral oils. These findings suggest the need for adjustments in existing DGA methods to account for the higher gas concentrations in methyl ester oils, for effective condition monitoring and maintenance of transformers if it was filled with methyl ester oil.
Advancing cryptographic security: a novel hybrid AES-RSA model with byte-level tokenization Durge, Renuka Shone; Deshmukh, Vaishali M.
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i4.pp4306-4314

Abstract

As cyberattacks are getting more complex and sophisticated, stringent, multi-layered security measures are required. Existing approaches often rely on tokenization or encryption algorithms, both of which have drawbacks. Previous attempts to ensure data security have primarily focused on tokenization techniques or complex encryption algorithms. While these methods work well on their own, they have proven vulnerable to sophisticated cyberattacks. This research presents new ways to improve data security in digital storage and communication systems. We solve data security issues by proposing a multi-level encryption strategy that combines double encryption technology along with tokenization. The first step in the procedure is a byte-level byte-pair encoding (BPE) tokenizer, which tokenizes the input data and adds a layer of protection to make it unreadable. After tokenization, data is encrypted using Rivest–Shamir–Adleman (RSA) to create a strong initial level of security. To further enhance security, data encrypted with RSA has an additional layer of encryption applied using the advanced encryption standard (AES) method. This article describes how this approach is implemented in practice and shows how it is effective in protecting data at a higher level than single-layer encryption or tokenization systems.
Assessing smart sustainable library practices in higher education: development and validation of instrument Yunus, Norhazura; Ismail, Mohd Nasir
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i4.pp4394-4406

Abstract

A smart sustainable library is a new form of a library that blends sustainability and smart libraries with an emphasis on ethics. This study focuses on the need for thorough tools to assess the evolving concept of a smart sustainable library, especially within Malaysian higher education. This study emphasizes the need for a comprehensive tool that combines smart library, sustainability practices, and ethical values in libraries. Developed and conducted a pilot study to validate a new instrument designed to assess these intertwined aspects thoroughly. By distributing a survey to 30 librarians from different academic institutions in Malaysia, we used statistical measures such as Cronbach's alpha, omega, and corrected item-total correlation to assess the validity and reliability of the instrument. The results showed a high level of reliability with Cronbach's alpha at 0.929 and Omega at 0.918, suggesting that the instrument has strong internal consistency and could be effective for wider use. Our research indicates that the newly developed instrument effectively captures the complex nature of smart sustainable libraries, demonstrating its potential for future research and practical use in the field. This research significantly contributes to the library science field by offering a validated tool to evaluate smart sustainable library development.
Personalized diabetes diagnosis using machine learning and electronic health records S., Gowthami; Reddy, R. Venkata Siva; Ahmed, Mohammed Riyaz
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i4.pp4791-4801

Abstract

Diabetes mellitus (DM) poses a significant health challenge globally, necessitating accurate and timely diagnosis for effective management. Conventional diagnostic methods often struggle to address the multifaceted nature of diabetes and the requisite lifestyle adjustments. In this study, we propose a data-driven approach utilizing machine learning techniques to enhance diabetes diagnosis. By leveraging extensive patient attributes and medical records, machine learning algorithms can uncover intricate patterns and correlations. Our methodology, validated on the PIMA India dataset, demonstrates promising results. The random forest model achieved the highest accuracy of 87%, followed closely by gradient boost at 90%. Notably, XGBoost and CATBoost models attained a peak accuracy of 90.9%. These findings underscore the potential of machine learning in transforming diabetes diagnosis. Beyond improving diagnostic accuracy, our approach aims to guide individuals towards healthier lifestyles. Intelligent systems driven by machine learning hold promise for revolutionizing diabetes management, ultimately leading to better patient outcomes and more effective health care delivery.
Advanced particle swarm optimization for efficient and fast global maximum power point tracking under partial shading conditions El Moujahid, Yassine; El Harfaoui, Nadia; Hadjoudja, Abdelkader; Benlafkih, Abdessamad
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i4.pp3570-3579

Abstract

Partial shading (PS) is a common issue in photovoltaic systems (PVs), and it can significantly reduce the system's output power. This paper presents the advanced particle swarm optimization (APSO) algorithm. APSO is designed to alleviate the challenges posed by PS in PVs in from where of effectiveness and stability speed so that it works to achieve and maintain the global maximum power point (GMPP) under PS conditions. It leverages persistent variables to store and track system states and iterations; it also includes checks to ensure that the duty cycle remains within specified bounds facilitating more effective optimization. Additionally, APSO optimizes solar panel duty cycles and velocities to converge toward an optimal solution to improve overall power generation efficiency and settling time. The results evaluation involves testing the performance of photovoltaic panels under three different shading scenarios and comparative analysis against recent Heuristic-optimization-based GMPP techniques, this study and comparative analyses demonstrate APSO's effectiveness and superiority in terms of high efficiency that reaches 99.85% and fast settling time of GMPP at less than 0.01 second across all test cases. APSO presents a promising solution for maximizing PV power output in the presence of partial shading.

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