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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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Comparison of algorithms for the detection of marine vessels with machine vision Rodríguez-Gonzales, José; Niquin-Jaimes, Junior; Paiva-Peredo, Ernesto
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.pp6332-6338

Abstract

The detection of marine vessels for revenue control has many tracking deficiencies, which has resulted in losses of logistical resources, time, and money. However, digital cameras are not fully exploited since they capture images to recognize the vessels and give immediate notice to the control center. The analyzed images go through an incredibly detailed process, which, thanks to neural training, allows us to recognize vessels without false positives. To do this, we must understand the behavior of object detection; we must know critical issues such as neural training, image digitization, types of filters, and machine learning, among others. We present results by comparing two development environments with their corresponding algorithms, making the recognition of ships immediately under neural training. In conclusion, it is analyzed based on 100 images to measure the boat detection capability between both algorithms, the response time, and the effectiveness of an image obtained by a digital camera. The result obtained by YOLOv7 was 100% effective under the application of processing techniques based on neural networks in convolutional neural network (CNN) regions compared to MATLAB, which applies processing metrics based on morphological images, obtaining low results.
Cloud service ranking with an integration of k-means algorithm and decision-making trail and evaluation laboratory approach Goyal, Pooja; Singh Deora, Sukhvinder
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.pp1816-1824

Abstract

The present research focuses on ranking cloud services by using the k-means algorithm with multi-criteria decision-making (MCDM) approaches that are the prime factor in the decision-making process and have been used to choose cloud services. The tools offered by MCDM can solve almost any decision-making problem. When faced with a selection challenge in the cloud environment, the trusted party would need to weigh the client’s choice against a predetermined list of criteria. There is a wide range of approaches to evaluating the quality of cloud services. The deep learning model has been considered a branch of artificial intelligence that assesses datasets to perform training and testing and makes decisions accordingly. This paper presents a concise overview of MCDM approaches and discusses some of the most commonly used MCDM methods. Also, a model based on deep learning with the k-means algorithm based decision-making trial and evaluation laboratory (kDE-MATEL) and analytic network process (ANP) is proposed as k-means algorithm based decision-making trial and evaluation laboratory with analytic network process (kD-ANP) for selecting cloud services. The proposed model uses the k-means algorithm and gives different levels of priority and weight to a set of criteria. A traditional model is also compared with a proposed model to reflect the efficiency of the proposed approach.
Demographic information combined with collaborative filtering for an efficient recommendation system Nabil, Sana; Chkouri, Mohamed Yassin; Bouhdidi, Jaber El
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.pp5916-5925

Abstract

The recommendation system is a filtering system. It filters a collection of things based on the historical behavior of a user, it also tries to make predictions based on user preferences and make recommendations that interest customers. While incredibly useful, they can face various challenges affecting their performance and utility. Some common problems are, for example, when the number of users and items grows, the computational complexity of generating recommendations increases, which can increase the accuracy and precision of recommendations. So, for this purpose and to improve recommendation system results, we propose a recommendation system combining the demographic approach with collaborative filtering, our approach is based on users’ demographic information such as gender, age, zip code, occupation, and historical ratings of the users. We cluster the users based on their demographic data using the k-means algorithm and then apply collaborative filtering to the specific user cluster for recommendations. The proposed approach improves the results of the collaborative filtering recommendation system in terms of precision and recommends diverse items to users.
Robust automotive radar interference mitigation using multiplicative-adaptive filtering and Hilbert transform Asmaur Rohman, Budiman Putra; Suryadi Satyawan, Arief; Kurniawan, Dayat; Indrawijaya, Ratna; Bin Ali Wael, Chaeriah; Armi, Nasrullah
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.pp326-336

Abstract

Radar is one of the sensors that have significant attention to be implemented in an autonomous vehicle since its robustness under many possible environmental conditions such as fog, rain, and poor light. However, the implementation risks interference because of transmitting and/or receiving radar signals from/to other vehicles. This interference will increase the floor noise that can mask the target signal. This paper proposes multiplicative-adaptive filtering and Hilbert transform to mitigate the interference effect and maintain the target signal detectability. The method exploited the trade-off between the step-size and sidelobe effect on the least mean square-based adaptive filtering to improve the target detection accuracy, especially in the long-range case. The numerical analysis on the millimeter-wave frequency modulated continuous wave radar with multiple interferers concluded that the proposed method could maintain and enhance the target signal even if the target range is relatively far from the victim radar.
A novel configuration of a microstrip metamaterial reconfigurable bandstop filter Aghanim, Amina; Oulhaj, Otman; Zbitou, Jamal; Oukaira, Aziz; Lakhssassi, Ahmed; Lasri, Rafik
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.pp4128-4137

Abstract

This paper presents the design, simulation, and test measurements of a microstrip bandstop filter operating at 1.5 GHz, incorporating six split ring resonator (SRR) unit cells. The substrate employed is an FR-4 with a thickness of 1.6 mm and tangent losses of 0.025. In the initial phase, the design is conceptualized, simulated using computer simulation technology (CST) studio and advanced design system (ADS) Agilent simulators, and validated through test measurements. Building upon this foundation, the filter is transformed into a reconfigurable variant by integrating four SMV2019 varactor diodes. These varactors are modeled to ensure the reconfigurability of the bandwidth. The integration of varactors introduces dynamic tuning capabilities to the considered bandstop filter.
Medium access control protocol based on time division multiple access scheme for wireless body area network Haszerila Wan Hassan, Wan; Mohd Ali, Darmawaty; Mohd Sultan, Juwita; Kassim, Murizah
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.pp2762-2770

Abstract

In recent years, the demand for wireless body area network (WBAN) technology has increased, driven by advancements in medical and healthcare applications. WBAN consists of small, low-power, and heterogeneous sensor devices attached inside or outside the body for continuous health monitoring. Medium access control (MAC) is pivotal in addressing WBAN challenges by ensuring reliability and energy efficiency under a dynamic environment caused by body movement. Therefore, to tackle these challenges, this paper presents a MAC protocol based on time division multiple access (TDMA) to enhance the WBAN performance. The proposed TDMA-MAC protocol employs a one-periodic scheduled-based access method to provide reliable data transmission while satisfying the WBAN requirements. The proposed protocol is compared to the IEEE 802.15.6 MAC, enhanced packet scheduling algorithm MAC (EPSA-MAC), and concurrent MAC (C-MAC) protocols based on the performance metrics of packet delivery ratio (PDR), network throughput, energy consumption, and average delay. The simulation results show that the TDMA-MAC protocol outperforms its competitors as it could achieve up to 98% PDR, 30% enhanced throughput, 30% energy optimization, and 20% improvement in average delay.
Secure data transmission in power systems using blockchain technology Srivatsa, Anand; Thammaiah, Ananthapadmanabha; Kumar MV, Likith; D, Rajeshwari; AP, Suma
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.pp6170-6181

Abstract

Recent advances in intelligent systems have significantly improved power management, load distribution, and resource management capabilities, far beyond past constraints. Despite these gains, the development of internet-connected technology has brought various vulnerabilities, leading to negative results. The integration of intelligent technology has unintentionally offered chances for hackers to enter networks and modify data sent to central systems for analysis. One of the most serious risks is the false data injection attack (FDIA), which may drastically impair analytical outcomes. Previous research has shown that standard approaches for recovering data affected by FDIA are unreliable and inefficient. This paper investigates the use of the proof of stake (PoS) consensus method in this framework improves data integrity and makes it easier to identify illegal changes. Participating nodes may reject or change block transactions, ensuring the ledger's correctness. Our results show that the PoS consensus method is exceptionally successful in creating and adding transactions to the blockchain. Furthermore, the PoS mechanism's simplicity in block formation enhances both time and energy efficiency, resulting in considerable benefits in operational performance.
Efficient wireless power transfer for a moving electric vehicle by digital control of frequency Yamaguchi, Kazuya; Okamura, Ryusei; Yian Kiat, Adrian Wee; Iida, Kenichi
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.pp1308-1313

Abstract

Recently, demand for electric vehicles has been increasing as a countermeasure against global warming, but they currently face many problems compared to gasoline-powered vehicles. For example, charging takes time, and there are few places where electric vehicles can be charged. If AC power supplies that can transfer energy to electric vehicles wirelessly exist under the lanes where electric vehicles drive, the cruising range will be increased. In this study, assuming wireless power transfer to a moving electric vehicle, an experiment was conducted to light up a light-emitting diode (LED) on a moving electric model car. To improve the efficiency of transfer, the optimal frequency for the position of the electric model car was calculated, and the value was fed back to the power supply to adjust the frequency in real time.
Deep learning model for elevating internet of things intrusion detection Dash, Nitu; Chakravarty, Sujata; Rath, Amiya Kumar
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.pp5874-5883

Abstract

The internet of things (IoT) greatly impacts daily life by enabling efficient data exchange between objects and servers. However, cyber-attacks pose a serious threat to IoT devices. Intrusion detection systems (IDS) are vital for safeguarding networks, and machine learning methods are increasingly used to enhance security. Continuous improvement in accuracy and performance is crucial for effective IoT security. Deep learning not only outshines traditional machine learning methods but also holds untapped potential in fortifying IDS systems. This paper introduces an innovative deep learning framework tailored for anomaly detection within IoT networks, leveraging bidirectional long short-term memory (BiLSTM) and gated recurrent unit (GRU) architectures. The hyper parameters of the proposed model are optimized using the JAYA optimization technique. These models are validated using IoT-23 and MQTTset datasets. Several performance metrics including accuracy, precision, recall, f-score, true negative rate (TNR), false positive rate (FPR), and false negative rate (FNR), have been selected to assess the effectiveness of the suggested model. The empirical results are scrutinized and juxtaposed with prevailing approaches in the realm of intrusion detection for IoT. Notably, the proposed method emerges as showcasing superior accuracy when contrasted with existing methods.
Sensing complicated meanings from unstructured data: a novel hybrid approach Shastri, Shankarayya; Teligi Math, Veeragangadhara Swamy; Siddalingappa, Patil Nagaraja
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.pp711-720

Abstract

The majority of data on computers nowadays is in the form of unstructured data and unstructured text. The inherent ambiguity of natural language makes it incredibly difficult but also highly profitable to find hidden information or comprehend complex semantics in unstructured text. In this paper, we present the combination of natural language processing (NLP) and convolution neural network (CNN) hybrid architecture called automated analysis of unstructured text using machine learning (AAUT-ML) for the detection of complex semantics from unstructured data that enables different users to make understand formal semantic knowledge to be extracted from an unstructured text corpus. The AAUT-ML has been evaluated using three datasets data mining (DM), operating system (OS), and data base (DB), and compared with the existing models, i.e., YAKE, term frequency-inverse document frequency (TF-IDF) and text-R. The results show better outcomes in terms of precision, recall, and macro-averaged F1-score. This work presents a novel method for identifying complex semantics using unstructured data.

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