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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 6,301 Documents
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Advancements in ammonia gas detection: a comparative study of sensor technologies Hadi, Amran Abdul; Shaipuzaman, Nurulain Nadhirah; Aspar, Mohd Amir Shahlan Mohd; Salim, Mohd Rashidi; Manap, Hadi
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i5.pp5107-5116

Abstract

Ammonia gas is a colorless gas that is known for its pungent odor. It is commonly used in various industries, such as agriculture, refrigeration, and chemical manufacturing. This paper provides a comprehensive overview of various technologies employed in ammonia gas sensors. The objective is to compare and identify the optimum method to detect ammonia gas. The review encompasses catalytic gas sensors, metal oxide gas sensors, polymer conductivity gas sensors, optical gas sensors, and indirect gas sensors, detailing their respective operational principles. Additionally, the advantages and disadvantages of each technology for ammonia gas detection are outlined. All these technologies have been used for many applications and some of them have been commercialized. Some sensor characteristics suggestions are also stated in order to develop an improved optical ammonia sensor for industrial applications.
Enhancing feature selection with a novel hybrid approach incorporating genetic algorithms and swarm intelligence techniques Benghazouani, Salsabila; Nouh, Said; Zakrani, Abdelali; Haloum, Ihsane; Jebbar, Mostafa
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i1.pp944-959

Abstract

Computing advances in data storage are leading to rapid growth in large-scale datasets. Using all features increases temporal/spatial complexity and negatively influences performance. Feature selection is a fundamental stage in data preprocessing, removing redundant and irrelevant features to minimize the number of features and enhance the performance of classification accuracy. Numerous optimization algorithms were employed to handle feature selection (FS) problems, and they outperform conventional FS techniques. However, there is no metaheuristic FS method that outperforms other optimization algorithms in many datasets. This motivated our study to incorporate the advantages of various optimization techniques to obtain a powerful technique that outperforms other methods in many datasets from different domains. In this article, a novel combined method GASI is developed using swarm intelligence (SI) based feature selection techniques and genetic algorithms (GA) that uses a multi-objective fitness function to seek the optimal subset of features. To assess the performance of the proposed approach, seven datasets have been collected from the UCI repository and exploited to test the newly established feature selection technique. The experimental results demonstrate that the suggested method GASI outperforms many powerful SI-based feature selection techniques studied. GASI obtains a better average fitness value and improves classification performance.
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.
Electrical signal interference minimization using appropriate core material for 3D integrate circuit at high frequency applications Kumar, Malagonda Siva; Mohanraj, Jayavelu
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i3.pp2500-2507

Abstract

As demand for smaller, quicker, and more powerful devices rises, Moore's law is strictly followed. The industry has worked hard to make little devices that boost productivity. The goal is to optimize device density. Scientists are reducing connection delays to improve circuit performance. This helped them understand three-dimensional integrated circuit (3D IC) concepts, which stack active devices and create vertical connections to diminish latency and lower interconnects. Electrical involvement is a big worry with 3D integrates circuits. Researchers have developed and tested through silicon via (TSV) and substrates to decrease electrical wave involvement. This study illustrates a novel noise coupling reduction method using several electrical involvement models. A 22% drop in electrical involvement from wave-carrying to victim TSVs introduces this new paradigm and improves system performance even at higher THz frequencies.
Detection and classification of pneumonia using the Orange3 data mining tool Altayeb, Muneera; Arabiat, Areen; Al-Ghraibah, Amani
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.pp6894-6903

Abstract

A chest X-ray can convey a lot about a patient's condition. However, it requires a specialized and skilled doctor to determine the type of lung disease with high accuracy. Here comes the role of deep learning techniques (DL) and artificial intelligence (AI) in accelerating the process of detecting lung diseases and classifying them with high precision, which saves time and effort for the patient and the doctor alike. This work presents a proposed model for a machine learning (ML) and AI system to analyze chest X-ray images and categorize them into four cases normal, viral pneumonia, bacterial pneumonia, and coronavirus disease 2019 (COVID-19). The system relies on extracting Mel frequency cepstral coefficient (MFCC) features from a dataset consisting of 4,800 chest X-ray images, and then these features are used to train four basic classifiers based on the data mining tool Orange3, which are adaptive boosting (AdaBoost), decision trees (DTs), gradient boosting (GB), and random forest (RF). The model was tested and evaluated, where the AdaBoost classifier excelled with an accuracy of 100%, followed by RF with an accuracy of 99.5%. Finally, GB and DTs came with a classification accuracy of 98.5%, and 97.2%, respectively.
Application of deep learning methods for automated analysis of retinal structures in ophthalmology Kassymova, Akmaral; Konyrkhanova, Assem; Issembayeva, Aida; Saimanova, Zagira; Saltayev, Alisher; Ongarbayeva, Maral; Issakova, Gulnur
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i2.pp1987-1995

Abstract

This article examines a current area of research in the field of ophthalmology the use of deep learning methods for automated analysis of retinal structures. This work explores the use of deep learning methods such as EfficientNet and DenseNet for the automated analysis of retinal structures in ophthalmology. EfficientNet, originally proposed to balance between accuracy and computational efficiency, and DenseNet, based on dense connections between layers, are considered as tools for identifying and classifying retina features. Automated analysis includes identifying pathologies, assessing the degree of their development and, possibly, diagnosing various eye diseases. Experiments are performed on a dataset containing a variety of images of retinal structures. Results are evaluated using metrics of accuracy, sensitivity, and specificity. It is expected that the proposed deep learning methods can significantly improve the automated analysis of retinal images, which is important for the diagnosis and monitoring of eye diseases. As a result, the article highlights the significance and promise of using deep learning methods in ophthalmology for automated analysis of retinal structures. These methods help improve the early diagnosis, treatment and monitoring of eye diseases, which can ultimately lead to improved healthcare quality and improved patient lives.
Using modified Chebyshev functions for approximation in 5G technologies Yerzhan, Assel; Nakisbekova, Balausa; Manbetova, Zhanat; Boykachev, Pavel; Imankul, Manat; Dzhanuzakova, Raushan; Shedreyeva, Indira; Karnakova, Gaini
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.pp6508-6518

Abstract

This research addresses the critical challenge of broadband matching in radio engineering, focusing on enhancing phase-frequency response (PFC) linearity across wide frequency bands. A novel approach, utilizing modified Chebyshev functions, demonstrates significant potential in reducing phase distortions within 5G technology applications. Unlike traditional Chebyshev functions, this method incorporates strategically placed transmission zeros— complex conjugate pairs on the s-variable complex plane—without increasing the filter circuit's order. This innovation results in a low-order filter circuit characterized by uniform phase response and group delay characteristics (GDT), offering an effective solution for matching circuit design with less phase-frequency distortion and improved group delay uniformity across diverse load conditions. The modified Chebyshev approximation outperforms its classical counterpart in both phase linearity and selectivity within the 1 to 1.2 cutoff frequency range. This enhancement is crucial for the development of low-frequency filters, with broader implications for creating high-frequency, band-pass, and band-stop filters via known frequency transformations. Empirical results validate the proposed method's reliability and effectiveness, marking a significant advancement in the field of radio engineering by addressing broadband matching challenges with increased efficiency and simplified design implementations.
Emoji’s sentiment score estimation using convolutional neural network with multi-scale emoji images Kulkongkoon, Theerawee; Cooharojananone, Nagul; Lipikorn, Rajalida
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i1.pp698-710

Abstract

Emojis are any small images, symbols, or icons that are used in social media. Several well-known emojis have been ranked and sentiment scores have been assigned to them. These ranked emojis can be used for sentiment analysis; however, many new released emojis have not been ranked and have no sentiment score yet. This paper proposes a new method to estimate the sentiment score of any unranked emotion emoji from its image by classifying it into the class of the most similar ranked emoji and then estimating the sentiment score using the score of the most similar emoji. The accuracy of sentiment score estimation is improved by using multi-scale images. The ranked emoji image data set consisted of 613 classes with 161 emoji images from three different platforms in each class. The images were cropped to produce multi-scale images. The classification and estimation were performed by using convolutional neural network (CNN) with multi-scale emoji images and the proposed voting algorithm called the majority voting with probability (MVP). The proposed method was evaluated on two datasets: ranked emoji images and unranked emoji images. The accuracies of sentiment score estimation for the ranked and unranked emoji test images are 98% and 51%, respectively.
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.
Next-gen security in IIoT: integrating intrusion detection systems with machine learning for industry 4.0 resilience Idouglid, Lahcen; Tkatek, Said; Elfayq, Khalid; Guezzaz, Azidine
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i3.pp3512-3521

Abstract

In the dynamic landscape of Industry 4.0, characterized by the integration of smart technologies and the industrial internet of things (IIoT), ensuring robust security measures is imperative. This paper explores advanced security solutions tailored for the IIoT, focusing on the integration of intrusion detection systems (IDS) with advanced machine learning (ML) and deep learning (DL) techniques. In this paper, we present a novel intrusion detection model to fortify to fortify Industry 4.0 systems against evolving cyber threats by leveraging ML an DL algorithms for dynamic adaptation. To evaluate the performances and effectiveness of our proposed model, we use the improved Coburg intrusion detection data sets (CIDDS) and BoT-IoT datasets, showcasing notable performance attributes with an exceptional 99.99% accuracy, high recall, and precision scores. The model demonstrates computational efficiency, with rapid learning and detection phases. This research contributes to advancing next-gen security solutions for Industry 4.0, offering a promising approach to tackle contemporary cyber.

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