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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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Improvement of Philips MOS model 9 radio frequency performance with circuit level parasitic compensation Gadige, Aswini Kumar; Paremesha, Paremesha
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.pp4977-4986

Abstract

The two circuit-level parasitic compensation techniques for the Philips metal oxide semiconductor (MOS) model 9 metal oxide semiconductor field effect transistor (MOSFET) at high frequencies (in the GHz range) are presented in this paper. The first method involves connecting the series resonant LC circuit in parallel to the drain and grounded source/bulk of the MM9; the second method involves connecting two of these MM9s in parallel to increase the drain current at higher frequencies along with parasitic compensation. Using these compensatory techniques, it is possible to reduce the impact of drain-source parasitic capacitance on MOS model 9 by preventing the short circuit of MOSFET terminals at high frequencies. After adjustment, improvements were seen in a number of metrics, including output impedance, S-parameters, output power and stability. Finally, using a 10 dBm source power, these parasitic compensation techniques are applied to a single and two stage basic class-E power amplifier and simulated at 1.7 and 1.1 GHz, respectively. Improvements are noted in multiple performance parameters, including power Gain (16.5 dB), drain Efficiency (83%), power added efficiency (85.82%), output power (26 dBm), good Stability (K=2.23, B>0), and S-parameters (S11=-9.22 dB, S12=-39.78 dB, S21=16.38 dB, and S22=1.41 dB) in two-stage cascade power amplifier.
Memory built-in self-repair and correction for improving yield: a review Sontakke, Vijay; Atchina, Delsikreo
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.pp140-156

Abstract

Nanometer memories are highly prone to defects due to dense structure, necessitating memory built-in self-repair as a must-have feature to improve yield. Today’s system-on-chips contain memories occupying an area as high as 90% of the chip area. Shrinking technology uses stricter design rules for memories, making them more prone to manufacturing defects. Further, using 3D-stacked memories makes the system vulnerable to newer defects such as those coming from through-silicon-vias (TSV) and micro bumps. The increased memory size is also resulting in an increase in soft errors during system operation. Multiple memory repair techniques based on redundancy and correction codes have been presented to recover from such defects and prevent system failures. This paper reviews recently published memory repair methodologies, including various built-in self-repair (BISR) architectures, repair analysis algorithms, in-system repair, and soft repair handling using error correcting codes (ECC). It provides a classification of these techniques based on method and usage. Finally, it reviews evaluation methods used to determine the effectiveness of the repair algorithms. The paper aims to present a survey of these methodologies and prepare a platform for developing repair methods for upcoming-generation memories.
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.
Real-time phishing detection using deep learning methods by extensions Minh Linh, Dam; Hung, Ha Duy; Minh Chau, Han; Sy Vu, Quang; Tran, Thanh-Nam
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.pp3021-3035

Abstract

Phishing is an attack method that relies on a user’s insufficient vigilance and understanding of the internet. For example, an attacker creates an online transaction website and tricks users into logging into the fake website to steal their personal information, such as credit card numbers, email addresses, phone numbers, and physical addresses. This paper proposes implementing an extension to prevent phishing for internet users. In particular, this study develops a smart warning feature for the proposed extension using deep learning models. The proposed extension installed in the web browser protects users by checking for, warning about, and preventing untrusted connections. This study evaluated and compared the performance of machine learning models using a malicious uniform resource locator (URL) dataset containing 651,191 data samples. The results of the investigation confirm that the proposed extension using a convolutional neural network (CNN) achieved a high accuracy of 98.4%.
Aspect-based sentiment analysis: natural language understanding for implicit review Suhariyanto, Suhariyanto; Sarno, Riyanarto; Fatichah, Chastine; Abdullah, Rachmad
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.pp6711-6722

Abstract

The different types of implicit reviews should be well understood so that the developed extraction technique can solve all problems in implicit reviews and produce precise terms of aspects and opinions. We propose an aspect-based sentiment analysis (ABSA) method with natural language understanding for implicit reviews based on sentence and word structure. We built a text extraction method using a machine learning algorithm rule with a deep understanding of different types of sentences and words. Furthermore, the aspect category of each review is determined by measuring the word similarity between the aspect terms contained in each review and aspect keywords extracted from Wikipedia. Bidirectional encoder representations from transformers (BERT) embedding and semantic similarity are used to measure the word similarity value. Moreover, the proposed ABSA method uses BERT, a hybrid lexicon, and manual weighting of opinion terms. The purpose of the hybrid lexicon and the manual weighting of opinion terms is to update the existing lexicon and solve the problem of weighting words and phrases of opinion terms. The evaluation results were very good, with average F1-scores of 93.84% for aspect categorization and 92.42% for ABSA.
Reinforcement learning-empowered resource allocation with multi-head attention mechanism in V2X networks Khan, Irshad; Haladappa, Manjula Sunkadakatte
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.pp5691-5700

Abstract

Intelligent transport systems (ITS) offer safe and autonomous service in vehicular applications. The vehicle to everything (V2X) network aids in performing communication between any vehicle to other entities such as networks, pedestrians or other objects. However, the allocation of power in the V2X network is still seen as a challenging task in recent resource allocation approaches. So, multi-head attention mechanism with reinforcement learning (MHAMRL) is utilized in resource allocation. This work considers real traffic scenes in highway traffic model and wireless transmission model. Specifically, in the mode 4 cellular V2X, every individual vehicle is considered as a resource which does not rely on the base station for resource allocation. Vehicle users are classified into V2I or V2V links based on the varied service requirements of V2X. The combination of multi-head attention mechanism sequences the signal with minimal noises which diminishes the energy consumption and improves channel gain. In the velocity range of 20-25 m/s, the proposed approach achieves a sum rate of 53 Mb/s, surpassing the 50 Mb/s achieved by the existing multi-agent deep reinforcement learning-based attention mechanism (AMARL) algorithm.
Experimental analysis of intrusion detection systems using machine learning algorithms and artificial neural networks Abdulkareem, Ademola; Somefun, Tobiloba Emmanuel; Mutalub, Adesina Lambe; Adeyinka, Adewale
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.pp983-992

Abstract

Since the invention of the internet for military and academic research purposes, it has evolved to meet the demands of the increasing number of users on the network, who have their scope beyond military and academics. As the scope of the network expanded maintaining its security became a matter of increasing importance. With various users and interconnections of more diversified networks, the internet needs to be maintained as securely as possible for the transmission of sensitive information to be one hundred per cent safe; several anomalies may intrude on private networks. Several research works have been released around network security and this research seeks to add to the already existing body of knowledge by expounding on these attacks, proffering efficient measures to detect network intrusions, and introducing an ensemble classifier: a combination of 3 different machine learning algorithms. An ensemble classifier is used for detecting remote to local (R2L) attacks, which showed the lowest level of accuracy when the network dataset is tested using single machine learning models but the ensemble classifier gives an overall efficiency of 99.8%.
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.
Heat stroke prediction: a perspective from the internet of things and machine learning approach Ke Yin, Lim; Yogarayan, Sumendra; Abdul Razak, Siti Fatimah; Ali Bukar, Umar; Sayeed, Md. Shohel
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.pp3427-3433

Abstract

With the increasing occurrence of heat-related illnesses due to rising temperatures worldwide, there is a need for effective detection and prediction systems to mitigate the risks. Heat stroke, a life-threatening condition occurs when the body’s temperature exceeds 104 degrees Fahrenheit (40 degrees Celsius). It can happen due to prolonged exposure to temperatures. When the body struggles to cool itself down adequately. The internet of things (IoT) and machine learning (ML) are two advancing technologies that have the potential to revolutionize industries and enhance our lives in numerous ways. Currently, monitoring devices are primarily used to diagnose when individuals suffering from heatstroke are at the location. This paper delves into the exploration of utilizing the IoT and ML algorithms to predict heat strokes. It reviews existing studies in this field, focusing on how IoT has been deployed and the application of machine learning techniques. The research aims to define the integration of IoT devices and ML algorithms that has a great potential to detect and predict heat-related illnesses such as heat stroke at an early stage.
Aspect-based sentiment-analysis using topic modelling and machine-learning Dhanal, Radhika Jinendra; Ghorpade, Vijay Ram
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.pp6689-6698

Abstract

This study addresses the critical need for an accurate aspect-based sentiment-analysis (ABSA) model to understand sentiments effectively. The existing ABSA models often face challenges in accurately extracting aspects and determining sentiment polarity from textual data. Therefore, we propose a novel approach leveraging latent-Dirichlet-allocation (LDA) for aspect extraction and transformer-based bidirectional-encoder-representations from transformers (TF-BERT) for sentiment-polarity evaluation. The experiments were carried out on SemEval 2014 laptop and restaurant datasets. Also, a multi-domain dataset was generated by combining SemEval 2014, Amazon, and hospital reviews. The results demonstrate the superiority of the LDA-TF-BERT model, achieving 82.19% accuracy and 79.52% Macro-F1 score for the laptop task and 86.26% accuracy of 87.26% and 81.27% for Macro-F1 score for the restaurant task. This showcases the model's robustness and effectiveness in accurately analyzing textual data and extracting meaningful insights. The novelty of our work lies in combining LDA and TF-BERT, providing a comprehensive and accurate ABSA solution for various industries, thereby contributing significantly to the advancement of sentiment analysis techniques.

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