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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 64 Documents
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Search results for , issue "Vol 36, No 2: November 2024" : 64 Documents clear
Performance of dyslexia dataset for machine learning algorithms Jincy, J.; Hency Jose, P. Subha
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 2: November 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i2.pp994-1001

Abstract

Learning disability is a condition usual amongst most populace due to poor phonological capability in humans making them impaired. One such neurological disorder is developmental dyslexia, a lack of reading and writing skills leading to difficulty in school education. The essential causes of developmental dyslexia are the consumption of more drug treatments during pregnancy, the over-the-counter purchase of medicines for minor ailments without the recommendation of physicians, and uncared-for head accidents during early life. The occurrence of this trouble is acute in India. Attempts were made by many to detect dyslexic children to reduce the intensity of this hassle. In this proposed effort, machine learning is used to locate significant styles characterizing people using EEG samples. A dataset is used for examination of developmental dyslexia, and classification is done using K nearest neighbor (KNN), decision tree, linear discriminant analysis (LDA), and support vector machine (SVM) to evaluate the performance. This piece of research work is done on MATLAB to provide results on simulation with classification accuracy of 90.76% for SVM, sensitivity of 89% for SVM, and LDA with 91.89% specificity for SVM providing optimum yield.
Neurons to heartbeats: spiking neural networks for electrocardiogram pattern recognition Mohamad Noor, Nor Amalia Dayana Binti; Chiew, Wong Yan; Noh, Zarina Mohd; Sarban Singh, Ranjit Singh
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 2: November 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i2.pp863-871

Abstract

The electrocardiogram (ECG) is one of the most significant methods of diagnostics for determining heart rhythm disorders. For this study, raw ECG signals from the Physio Bank database are subjected to an important preprocessing step that uses empirical mode decomposition (EMD) on signal denoising and distortion elimination. Establishing functioning spiking neural networks (SNN) involves figuring out the neuron’s state through its activity level, challenging due to its resemblance to the human brain’s data processing, yet appealing due to factors like improved unsupervised learning methods, with ten parameters chosen for the learning algorithm of SNN. A comprehensive set of 15 different time-domain features and 10 Cepstral domain features is precisely extracted to train the SNN classifier. An extensive study is conducted to analyse the learning parameters that affect SNN performance, significantly influencing result accuracy. Through a two-classification process, the differentiation between normal and abnormal ECG patterns can be achieved in this study. A maximum testing accuracy of 91.6667% and a maximum training accuracy of 99.1667% have been attained by the process. These results demonstrate the competency of the system in distinguishing between distinct ECG classes, particularly in identifying normal and abnormal cardiac rhythms through ECG classification.
Artificial intelligence in accounting and auditing: bibliometric analysis in Scopus 2020-2023 Chávez-Díaz, Jorge Miguel; Aquiño-Perales, Laura; De-Velazco-Borda, Jorge Luis; Villagómez-Chinchay, Juan Alberto; Flores-Sotelo, Willian Sebastian
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 2: November 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i2.pp1319-1328

Abstract

El propósito del estudio fue presentar los resultados de un análisis bibliométrico y revisión de literatura sobre la producción científica relacionada con la inteligencia artificial aplicada a la contabilidad y auditoría, contenida en la base de datos Scopus entre 2020 y 2023. Para la primera parte, se realizó un análisis descriptivo y cuantitativo. Se utilizó análisis bibliométrico con búsqueda de palabras clave en la base de datos Scopus. Para la segunda parte se siguió un enfoque subjetivo basado en un análisis cualitativo a partir de la interpretación del autor. Ambos enfoques fueron considerados por su complementariedad. Se identificaron las principales características cuantitativas de las revistas, autores, artículos, estructura conceptual y estructura social. Asimismo, las implicaciones éticas de la Inteligencia Artificial aplicada a la contabilidad y auditoría, y la forma en que impacta en la contabilidad, la auditoría fiscal, la estrategia financiera y la toma de decisiones que contribuyen a la creación de valor para su organización. Cambie la aversión a la IA por la adaptabilidad y la comprensión de que se extrajeron sus auditorías y contabilidad forense. El trabajo del contable-auditor estará cada vez más informatizado. Deberían centrarse más en el análisis, hay que transformar el perfil profesional de forma sinérgica con la inteligencia artificial. El estudio pretende servir como base teórica para futuras investigaciones.
Cost-effective circularly polarized MIMO antenna for Wi-Fi applications Thommandru, Raju; Saravanakumar, Rengarasu
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 2: November 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i2.pp785-792

Abstract

Antenna is a backbone of communication system, and with the advent of technology, numerous innovations have been made to advance antenna development. An antenna, functioning as a smart device, transmits and receives signals while also working as a transducer. Wireless communication requires a useful device for transmitting and receiving electromagnetic waves. Wireless fidelity (Wi-Fi) is a specific type of wireless communication technology used to transmit data over the internet network. The bandwidth and signal coverage of Wi-Fi have significant limitations. Therefore, an antenna is crucial for improving signal reception to address this issue. This article presents the designing and developing of a cost-effective circularly polarized (CP) 2×2 multiple input multiple output (MIMO) antenna customized for Wi-Fi applications. The application of a notched circular patch antenna serves to achieve circular polarization. The radius of the circular patch is 0.26 λ, and the proposed MIMO antenna effectively showcases CP, characterized by an axial ratio (AR) of 1 dB at 5 GHz and an impressive bandwidth spanning 0.2 GHz (4.9-5.1 GHz). Additionally, the antenna is designed to achieve a high-isolation 2×2 MIMO setup, ensuring antenna isolation surpassing 20 dB. By utilizing the flame retardant (FR4) substrate, presented MIMO antenna strikes a balance between cost-effectiveness and operational efficiency for its intended application, and directional radiation patterns are well-aligned within the desired frequency range.
Research on the using of ZnO nanostructures to increase the white light-emitting diodes optics effectiveness Le, Phan Xuan; Cong, Pham Hong
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 2: November 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i2.pp793-799

Abstract

In conventional white light-emitting diodes (WLEDs), the combination of blue-LED chips with a yellow-phosphor type is the commonly employed method of production. However, this approach often results in low angular correlated color temperature (CCT) homogeneity. To address this issue, this research proposes the incorporation of ZnO nanostructures into WLED packages to enhance color homogeneity. The impacts of varying concentrations of ZnO nanoparticles on the morphologies, scattered energy, and CCT deviations in WLED packages are studied utilizing the Mie-scattering theory and MATLAB measurement techniques to analyze the scattering effects of ZnO nanoparticles. The scattering analysis reveals that the presence of ZnO nanoparticles significantly increases the scattered strength of WLEDs, especially with larger particles’ radii, due to their strong scattering influence. Then, 1 µm is the selected size of the ZnO used in further tests. With different ZnO concentrations (2-50 wt.%) in the phosphor layer, the CCT deviation holds an inverse proportion to the luminous efficiency. Particularly, higher concentrations of ZnO nanoparticles reduce the CCT deviation, leading to improved color homogeneity, but a decline in lumen efficiency. The findings provide the basis of ZnO scattering performance, which can be utilized to explore potential ways for enhancing WLED’s color uniformity and overall performance.
Forecasting research influence: a recurrent neural network approach to citation prediction Jamal, Naser; Alauthman, Mohammad; Malhis, Muhannad; Ishtaiwi, Abdelraouf M.
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 2: November 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i2.pp1070-1082

Abstract

As the volume of scientific publications continues to proliferate, effective evaluation tools to determine the impact and quality of research articles are increasingly necessary. Citations serve as a widely utilized metric for gauging scientific impact. However, accurately prognosticating the long-term citation impact of nascent published research presents a formidable challenge due to the intricacy and unpredictability innate to the scientific ecosystem. Sophisticated machine learning methodologies, particularly recurrent neural networks (RNNs), have recently demonstrated promising potential in addressing this task. This research proposes an RNN architecture leveraging encoder-decoder sequence modeling capabilities to ingest historical chronicles and predict succeeding evolution via latent temporal dynamics learning. Comparative analysis between the RNN approach and baselines, including random forest, support vector regression, and multi-layer perceptron, demonstrate superior performance on unseen test data and rigorous k-fold cross-validation. On a corpus from Petra University, the RNN methodology attained the lowest errors (root mean squared error (RMSE) 1.84) and highest accuracy (0.91), area under the curve (AUC) (0.96), and F1-score (0.92). Statistical tests further verify significant improvements. The findings validate our deep learning solution's efficacy, robustness, and real-world viability for long-term scientific impact quantification to aid stakeholders in research evaluation. The findings intimate that RNN-based predictive modeling constitutes a potent technology for citation-driven scientific impact quantification.
Exploring the potential of DistilBERT architecture for automatic essay scoring task Ikiss, Soumia; Daoudi, Najima; Abourezq, Manar; Bellafkih, Mostafa
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 2: November 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i2.pp1234-1241

Abstract

Automatic assessment of writing essays, or the process of using computers to evaluate and assign grades to written text, is very needed in the education system as an alternative to reduce human burden and time consumption, especially for large-scale tests. This task has received more attention in the last few years, being one of the major uses for natural language processing (NLP). Traditional automatic scoring systems typically rely on handcrafted features, whereas recent studies have used deep neural networks. Since the advent of transformers, pre-trained language models have performed well in many downstream tasks. We utilize the Kaggle benchmarking automated student assessment prize dataset to fine-tune the pre-trained DistilBERT in three different scenarios, and we compare results with the existing neural network-based approaches to achieve improved performance in the automatic essay scoring task. We utilize quadratic weighted Kappa (QWK) as the main metric to evaluate the performance of our proposed method. Results show that fine-tuning DistilBERT gives good results, especially with the scenario of training all parameters, which achieve 0.90 of QWK and outperform neural network models.
Speech enhancement by using novel multiband spectral subtraction method along with a reduction of the cross spectral component Jakati, Jagadish S.; Koti, Ramesh B.; Matad, Sidramayya; Jadhav, Jagannath; Mule, Shrishail Basvant; Bedakihale, Sanmati; Mathad, Vireshkumar G.; Bandekar, Amar R.
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 2: November 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i2.pp933-941

Abstract

It is essential to enhance the speech signal's clarity and quality in order to maintain the message's content. By boosting the noisy voice signal, the speech signal quality can be raised. Two techniques are presented in this study to significantly minimize the additive background noise. In order to minimize non-stationary additive noise concerning the speech signal, the first approach employs modified multiband spectral subtraction. With this technique, spectral subtraction is carried out based on the signal to noise ratio (SNR) values in various noisy speech frames. When the noisy signal and noise signal are somewhat correlated, a second method is used to minimize the cross spectral components. These techniques are used to get over the drawbacks of the fundamental spectrum subtraction method. To improve the noisy speech signal, both techniques are combined.
Phishing website detection using novel integration of BERT and XLNet with deep learning sequential models Rao, Kongara Srinivasa; Valluru, Dinesh; Patnala, Satishkumar; Devareddi, Ravi Babu; Rama Krishna, Tummalapalli Siva; Sravani, Andavarapu
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 2: November 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i2.pp1273-1283

Abstract

Phishing websites pose a significant threat to online security, necessitating robust detection mechanisms to safeguard users' sensitive information. This study explores the efficacy of various deep learning architectures for phishing website detection. Initially, traditional sequential models, including recurrent neural networks (RNN), long short-term memory (LSTM), and gated recurrent unit (GRU), achieve accuracies of 95%, 96%, and 96.5%, respectively, on a curated dataset. Building upon these results, hybrid architectures that combine the strengths of traditional sequential models with state-of-the-art language representation models, bidirectional encoder representations from transformers (BERT) and XLNet, are investigated. Combinations such as RNN with BERT, BERT with LSTM, BERT with GRU, RNN with XLNet, XLNet with LSTM, and XLNet with GRU are evaluated. Through experimentation, accuracies of 94.5%, 96.5%, 96.1%, 95.7%, 97.4%, and 97%, respectively, are achieved, demonstrating the effectiveness of hybrid deep learning architectures in enhancing phishing detection performance. These findings contribute to advancing the state-of-the-art in cybersecurity practices and underscore the importance of leveraging diverse model types to combat online threats effectively.
Characterization of UF-18 cacao pods using Arduino-based load compressor testing machine Dayaday, Maricel Gamolo; Alucilja, Renel M.; T. Cuarteros, Ritchell Joy; Lavarias, Jeffrey A.
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 2: November 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i2.pp741-748

Abstract

Bean damage is one of the primary concerns in the pod-breaking process. Studies for pod-breaking machines are ongoing to ensure that the products made from these machines are of good quality. The objective of the study is to determine the physical and mechanical characteristics of the UF-18 pod. The Arduino-based load compressor testing machine was designed and developed to characterize the UF-18 pod. It was found that the average geometric mean diameter, surface area, and sphericity index of 115.37 mm, 41,899.48 mm², and 0.6372, respectively, and with a variation of ±27.17, ±14538133.04, and ±0.00038 respectively. Furthermore, the cacao pod samples had an average dimension of 181.29 mm, 94.26 mm, 90.01 mm, and 17.44 mm measured for the length, equatorial diameter, intermediate diameter and external thickness, respectively. Different pod sizes and thicknesses require various forces ranging from 36.94 to 92.42 kg (362.38 N to 906.64 N) and time ranging from 6-11 seconds to be able to break the pods. Determining the physical and mechanical properties of cacao pods enables fabricators to design efficient machines, which lessens the force to break and the damage to the beans, thus producing quality beans.

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