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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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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%.
Feature selection techniques and classification algorithms for student performance classification: a review Alias, Muhamad Aqif Hadi; Hambali, Najidah; Abdul Aziz, Mohd Azri; Taib, Mohd Nasir; Jailani, Rozita
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.pp3230-3243

Abstract

The process of categorizing students’ performance based on input data, encompassing demographic information and final exam results, is recognized as student performance classification. Educational data mining has gained traction in assessing students’ performance. However, this study entails the need to analyze the diverse attributes of students’ information within an educational institution by using data mining techniques. This study thoroughly examines both previous and current methodologies presented by researchers, addressing two main aspects: data preprocessing and classification algorithms applied in student performance classification. Data preprocessing specifically delves into the exploration of feature selection techniques, encompassing three types of feature selection and search methods. These techniques aim to identify the most significant features, eliminate unnecessary ones, and reduce data dimensionality. In addition, classification algorithms play a crucial role in categorizing or predicting student performance. Models such as k-nearest neighbors (KNN), decision tree (DT), artificial neural networks (ANN), and linear models (LR) were scrutinized based on their performance in prior research. Ultimately, this study highlights the potential for further exploration of feature selection techniques like information gain, Chi-square, and sequential selection, particularly when applied to new datasets such as students’ online learning activities, utilizing a variety of classification algorithms.
Deep learning-based attention models for sarcasm detection in text Chandrasekaran, Ganesh; Chowdary, Mandalapu Kalpana; Babu, Jyothi Chinna; Kiran, Ajmeera; Kumar, Kotthuru Anil; Kadry, Seifedine
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.pp6786-6796

Abstract

Finding sarcastic statements has recently drawn a lot of curiosity in social media, mainly because sarcastic tweets may include favorable phrases that fill in unattractive or undesirable attributes. As the internet becomes increasingly ingrained in our daily lives, many multimedia information is being produced online. Much of the information recorded mostly on the internet is textual data. It is crucial to comprehend people's sentiments. However, sarcastic content will hinder the effectiveness of sentiment analysis systems. Correctly identifying sarcasm and correctly predicting people's motives are extremely important. Sarcasm is particularly hard to recognize, both by humans and by machines. We employ the deep bi-directional long-short memory (Bi-LSTM) and a hybrid architecture of the convolution neural network+Bi-LSTM (CNN+Bi-LSTM) with attention networks for identifying sarcastic remarks in a corpus. Using the SarcasmV2 dataset, we test the efficacy of deep learning methods BiLSTM, and CNN+BiLSTM with attention network) for the task of identifying text sarcasm. The suggested approach incorporating deep networks is consistent with various recent and advanced techniques for sarcasm detection. With attention processes, the improved CNN+Bi-LSTM model achieved an accuracy rate of 91.76%, which is a notable increase over earlier research.
Development and evaluation of a 2oo3 safety controller in FPGA using fault tree analysis and Markov models Nadir, Fatima Ezzahra; Bsiss, Mohammed; Amami, Benaissa
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.pp1496-1507

Abstract

The Safety integrity level (SIL) is a measure of the reliability and availability of a safety instrumented system. SIL determination involves qualitative and quantitative analysis based on international standards such as IEC 61508 and IEC 61511. Several techniques can be used to analyze safety instrumented systems, including reliability block diagrams, fault tree analysis, and Markov models. The aim of this paper is to design and evaluate a pressure control system for a compressed nitrogen tank using a PID controller implemented in a field programmable gate array with 2 out of 3 architecture. This architecture ensures the safety of measurements and command of the system through a voting arrangement. The availability of the system is determined by the redundancy and the one hardware failure tolerance. The quantitative analysis is performed by calculating the probability of failure on demand per hour using Markov models or a relevant probabilistic approach based on fault tree analysis. The Markov model method gives the probability of failure of the system in different states during the system life cycle. The fault tree analysis method determines the probability of failure of the system using its equivalent failure rate. Furthermore, this paper compares the SIL result obtained by each model.
Developing decision-making serious games using Ren’Py visual novel engine Hafizalshah, Muhammad Hariz; Ghazali, Aimi Shazwani; Sidek, Shahrul Na’im
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.pp5458-5467

Abstract

Serious games are effective tools defined as games designed with a focus on explicit utility rather than the generally construed notion of games purely as a source of entertainment. Decision-making games are a type of serious game that can be developed with the intent of studying behavior, educating, appraising or other similar applications that benefit through the information collected from the decision-making process. Digital versions of serious games are gaining prominence due to a higher level of interactivity and complexity, especially in Human-Agent Interaction (HAI) applications. The development of digital serious games generally extends beyond software developers, typically involving individuals from diverse backgrounds who may not possess the necessary programming skills required for the development process. The paper proposed the use of Ren’Py, an open-source visual novel game engine as a platform to develop decision-making games. The study examined the Ren'Py game engine’s potential through an assessment of the development process for the production of a decision-making serious game. Findings showed that Ren’Py satisfies the need for a relatively easy-to-develop platform for decision-making-based serious games due to its built-in systems that conform to currently applied serious decision-making game design principles.
A study of feature extraction for Arabic calligraphy characters recognition Zoizou, Abdelhay; Errebiai, Chaimae; Zarghili, Arsalane; Chaker, Ilham
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.pp870-877

Abstract

Optical character recognition (OCR) is one of the widely used pattern recognition systems. However, the research on ancient Arabic writing recognition has suffered from a lack of interest for decades, despite the availability of thousands of historical documents. One of the reasons for this lack of interest is the absence of a standard dataset, which is fundamental for building and evaluating an OCR system. In 2022, we published a database of ancient Arabic words as the only public dataset of characters written in Al-Mojawhar Moroccan calligraphy. Therefore, such a database needs to be studied and evaluated. In this paper, we explored the proposed database and investigated the recognition of Al-Mojawhar Arabic characters. We studied feature extraction by using the most popular descriptors used in Arabic OCR. The studied descriptors were associated with different machine learning classifiers to build recognition models and verify their performance. In order to compare the learned and handcrafted features on the proposed dataset, we proposed a deep convolutional neural network for character recognition. Regarding the complexity of the character shapes, the results obtained were very promising, especially by using the convolutional neural network model, which gave the highest accuracy score.
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.
k-dStHash tree for indexing big spatio-temporal datasets Hooda, Meenakshi; Gill, Sumeet
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.pp2937-2944

Abstract

Today’s era is witness of tremendous ever growing spatial, temporal and spatiotemporal data. The huge spatio-temporal data immensely pushes the need for design and development of novel methods tailored for indexing spatio-temporal data. In this research paper, we propose the design of a novel spatio-temporal data indexing method, named as k-dStHash. We have proposed the algorithm k-dStHashInsertion for inserting spatio-temporal objects and an algorithm k-dStHashSrchPlaceTime has been used to search for the objects at given location and time. It is able to handle datasets with duplicate keys which has been ignored in many research works. Though the algorithm k-dStHashInsertion takes 1.3-1.5 times longer time to insert data in k-dStHash data structure as it needs to find a specific location to organize data efficiently, but when it comes to search for required records it is even more than 90 times faster when analyzed in comparison to brute force method. It is generalized enough to organize any kind of k-dimensional data and time-based data also including object finding, fleet management, clustering, leader identification, nearest neighbor, human/animal tracking, path finding and many more.
Dipterocarpaceae trunk texture classification using two-stage convolutional neural network-based transfer learning model Wati, Masna; Puspitasari, Novianti; Hairah, Ummul; Widians, Joan Angelina; Tjikoa, Ade Fiqri
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.pp6874-6882

Abstract

The importance of plant identification has been recognized by academia and industry. There have been several attempts to utilize leaves and flowers for identification. However, the trunk can also be helpful, especially for tall trees. In Borneo, the Dipterocarpaceae family are the main constituents of the tropical rainforest ecosystem. This research focuses on the classification of the dipterocarp family, which can reach a height of between 70 and 85 m. Leveraging convolutional neural network (CNN) models, this research proposes a two-stage transfer learning strategy. In the first stage, the pre-trained CNN models are refined by only modifying the classification layer while keeping the feature layer frozen. The second stage involves selecting and freezing several convolutional layers to adapt the model to classify dipterocarp stems. The dataset consists of 857 images of different dipterocarp species. Experiments show that the VGG16 model with a two-stage transfer learning strategy achieves a high accuracy of 98.246%. This study aims to accurately identify species, benefiting conservation and ecological studies by enabling fast and reliable tree species classification based on stem texture images.
A proposal and simulation analysis for a novel architecture of gate-all-around polycrystalline silicon nanowire field effect transistor El-amiri, Asseya; Demami, Fouad
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.pp1390-1397

Abstract

A proposal for a novel gate-all-around (GAA) polycrystalline silicon nanowire (poly-SiNW) field effect transistor (FET) is presented and discussed in this paper. The device architecture is based on the realization of poly-SiNW in a V-shaped cavity obtained by tetra methyl ammonium hydroxide (TMAH) etch of monocrystalline silicon (100). The device’s behavior is simulated using Silvaco commercial software, including the density of states (DOS) model described by the double exponential distribution of acceptor trap density within the gap. The electric field, potential, and free electron concentration are analyzed in different nanowire regions to investigate the device's performance. The results show good performance despite the high density of deep states in poly-SiNW. This can be explained by the strong electric field caused by the corner effect in the nanowire, which favors the ionization of the acceptor traps and increases the free electron concentration.

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