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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 113 Documents
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Search results for , issue "Vol 13, No 3: June 2023" : 113 Documents clear
Lifetime enhanced energy efficient wireless sensor networks using renewable energy Trupti Shripad Tagare; Rajashree Narendra
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i3.pp3088-3098

Abstract

In this paper, we consider a remote environment with randomly deployed sensor nodes, with an initial energy of E0 (J) and a solar panel. A hierarchical clustering technique is implemented. At each round, the normal nodes send the sensed data to the nearest cluster head (CH) which is chosen on the probability value. Data after aggregation at CHs is sent to the base station (BS). CH requires more energy than normal nodes. Here, we energize only CHs if their energy is less than 5% of its initial value with the use of solar energy. We evaluate parameters like energy consumption, the lifetime of the network, and data packets sent to CH and BS. The obtained results are compared with existing techniques. The proposed protocol provides better energy efficiency and network lifetime. The results show increased stability with delayed death of the first node. The network lifetime of the proposed protocol is compared to the multi-level hybrid energy efficient distributed (MLHEED) technique and low-energy adaptive clustering hierarchy (LEACH) variants. Network lifetime is enhanced by 13.35%. Energy consumption is reduced with respect to MLHEED-4, 5, and 6 by 7.15%, 12.10%, and 14.975% respectively. The no. of packets transferred to the BS is greater than the MLHEED protocol by 39.03%.
New hybrid ensemble method for anomaly detection in data science Amina Mohamed Elmahalwy; Hayam M. Mousa; Khalid M. Amin
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i3.pp3498-3508

Abstract

Anomaly detection is a significant research area in data science. Anomaly detection is used to find unusual points or uncommon events in data streams. It is gaining popularity not only in the business world but also in different of other fields, such as cyber security, fraud detection for financial systems, and healthcare. Detecting anomalies could be useful to find new knowledge in the data. This study aims to build an effective model to protect the data from these anomalies. We propose a new hyper ensemble machine learning method that combines the predictions from two methodologies the outcomes of isolation forest-k-means and random forest using a voting majority. Several available datasets, including KDD Cup-99, Credit Card, Wisconsin Prognosis Breast Cancer (WPBC), Forest Cover, and Pima, were used to evaluate the proposed method. The experimental results exhibit that our proposed model gives the highest realization in terms of receiver operating characteristic performance, accuracy, precision, and recall. Our approach is more efficient in detecting anomalies than other approaches. The highest accuracy rate achieved is 99.9%, compared to accuracy without a voting method, which achieves 97%.
Deep learning in phishing mitigation: a uniform resource locator-based predictive model Hamzah Salah; Hiba Zuhair
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i3.pp3227-3243

Abstract

To mitigate the evolution of phish websites, various phishing prediction8 schemes are being optimized eventually. However, the optimized methods produce gratuitous performance overhead due to the limited exploration of advanced phishing cues. Thus, a phishing uniform resource locator-based predictive model is enhanced by this work to defeat this deficiency using deep learning algorithms. This model’s architecture encompasses pre-processing of the effective feature space that is made up of 60 mutual uniform resource locator (URL) phishing features, and a dual deep learning-based model of convolution neural network with bi-directional long short-term memory (CNN-BiLSTM). The proposed predictive model is trained and tested on a dataset of 14,000 phish URLs and 28,074 legitimate URLs. Experimentally, the performance outputs are remarked with a 0.01% false positive rate (FPR) and 99.27% testing accuracy.
Hybrid NarrowBand-internet of things protocol for real time data optimization Denny Kurniawan; Muhammad Ashar; Harits Ar Rosyid
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i3.pp2827-2836

Abstract

The level of dependence on data communication in the modern era is increasing exponentially. The internet of things (IoT) plays a very important role in the advancement of the industrial revolution 4.0 that utilizes data communication systems. IoT deployments require data communication protocols, such as hypertext transfer protocol (HTTP), and message queuing telemetry transport (MQTT) as well as network communication protocols (wireless) to meet the network needs of devices with limited resources. Optimization of data communication in IoT is needed to maintain the quality of sending and receiving data in real time. This research proposes a hybrid NarrowBand-IoT (NB-IoT) protocol designed using NarrowBand communication network technology with optimization of data communication using MQTT and HTTP protocols. In this research, the hybrid NB-IoT protocol has the best packet loss value of 0.010% against the HTTP NB-IoT protocol which has a value of 0.017%, and the MQTT NB-IoT protocol of 0.024%. The hybrid NB-IoT protocol has a latency value of 8.7 seconds compared to the HTTP NB-IoT protocol which has a latency of 10.9 seconds. Meanwhile, the throughput value of the hybrid NB-IoT protocol is 158906.1 byte/s and is better than the MQTT NB-IoT protocol which is only 158898.6 bytes/s.
Sentiment analysis in SemEval: a review of sentiment identification approaches Bousselham El Haddaoui; Raddouane Chiheb; Rdouan Faizi; Abdellatif El Afia
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i3.pp3322-3338

Abstract

ocial media platforms are becoming the foundations of social interactions including messaging and opinion expression. In this regard, sentiment analysis techniques focus on providing solutions to ensure the retrieval and analysis of generated data including sentiments, emotions, and discussed topics. International competitions such as the International Workshop on Semantic Evaluation (SemEval) have attracted many researchers and practitioners with a special research interest in building sentiment analysis systems. In our work, we study top-ranking systems for each SemEval edition during the 2013-2021 period, a total of 658 teams participated in these editions with increasing interest over years. We analyze the proposed systems marking the evolution of research trends with a focus on the main components of sentiment analysis systems including data acquisition, preprocessing, and classification. Our study shows an active use of preprocessing techniques, an evolution of features engineering and word representation from lexicon-based approaches to word embeddings, and the dominance of neural networks and transformers over the classification phasefostering the use of ready-to-use models. Moreover, we provide researchers with insights based on experimented systems which will allow rapid prototyping of new systems and help practitioners build for future SemEval editions.
Health Electroencephalogram epileptic classification based on Hilbert probability similarity Abdulkareem A. Al-Hamzawi; Dhiah Al-Shammary; Alaa Hussein Hammadi
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i3.pp3339-3347

Abstract

This paper has proposed a new classification method based on Hilbert probability similarity to detect epileptic seizures from electroencephalogram (EEG) signals. Hilbert similarity probability-based measure is exploited to measure the similarity between signals. The proposed system consisted of models based on Hilbert probability similarity (HPS) to predict the state for the specific EEG signal. Particle swarm optimization (PSO) has been employed for feature selection and extraction. Furthermore, the used dataset in this study is Bonn University's publicly available EEG dataset. Several metrics are calculated to assess the performance of the suggested systems such as accuracy, precision, recall, and F1-score. The experimental results show that the suggested model is an effective tool for classifying EEG signals, with an accuracy of up to 100% for two-class status.
Stability analysis and speed control of brushless DC motor based on self-ameliorate soft switching control methods Nagaraj Rao; Shantharama Rai Chelladka
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i3.pp2459-2470

Abstract

In recent years, electric vehicles are the large-scale spread of the transportation field has led to the emergence of brushless direct current (DC) motors (BLDCM), which are mostly utilized in electrical vehicle systems. The speed control of a BLDCM is a subsystem, consisting of torque, flux hysteresis comparators, and appropriate switching logic of an inverter. Due to the sudden load torque variation and improper switching pulse, the speed of the BLDCM is not maintained properly. In recent research, the BLDC current control method gives a better way to control the speed of the motor. Also, the rotor position information should be the need for feedback control of the power electronic converters to varying the appropriate pulse width modulation (PWM) of the inverter. The proposed optimization work controls the switching device to manage the power supply BLDCM. In this proposed self-ameliorate soft switching (SASS) system is a simple and effective way for BLDC motor current control technology, a proposed control strategy is intended to stabilize the speed of the BLDCM at different load torque conditions. The proposed SASS system method is analyzing hall-based sensor values continuously. The suggested model is simulated using the MATLAB Simulink tool, and the results reveal that the maximum steady-state error value achieved is 4.2, as well as a speedy recovery of the BLDCM's speed.
Design and fabrication of a moving robotic glove system Vo Thu Ha; Nguyen Thi Thanh; Vo Thanh Ha
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i3.pp2704-2710

Abstract

This paper presents the research, design, and manufacture of a robotic hand to control movement with a glove. The moving glove-controlled robotic hand is based on two main parts: the hand mechanism and the control circuit. The control glove unit includes an Arduino nRF24l01 microcontroller module and five flex sensors for five fingers. These sensors are used to collect data about the curvature of each finger. Then those data will be received by the Arduino microcontroller and sent by the nRF24l01 module. The hand's microcontroller will process that information and control five servo motors so that the five fingers of the robotic hand are moved. The result of this research is to produce a robotic hand that accurately simulates the curvature of a user's finger and mimics the motion of a glove well. Moreover, the robot hand can grip objects of different sizes (from 0.1 to 1 kg) and shapes, from which this robot helps users easily manipulate objects.
Ultra-wideband CMOS power amplifier for wireless body area network applications: a review Nagham Gamal El-Feky; Dina Mohamed Ellaithy; Mostafa Hassan Fedawy
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i3.pp2618-2631

Abstract

A survey on ultra-wideband complementary metal-oxide semiconductor (CMOS) power amplifiers for wireless body area network (WBAN) applications is presented in this paper. Formidable growth in the CMOS integrated circuits technology enhances the development in biomedical manufacture. WBAN is a promising mechanism that collects essential data from wearable sensors connected to the network and transmitted it wirelessly to a central patient monitoring station. The ultra-wideband (UWB) technology exploits the frequency band from 3.1 to 10.6 GHz and provides no interference to other communication systems, low power consumption, low-radiated power, and high data rate. These features permit it to be compatible with medical applications. The demand target is to have one transceiver integrated circuit (IC) for WBAN applications, consequently, UWB is utilized to decrease the hardware complexity. The power amplifier (PA) is the common electronic device that employing in the UWB transmitter to boost the input power to the desired output power and then feed it to the antenna of the transmitter. The advance in the design and implementation of ultra-wideband CMOS power amplifiers enhances the performance of the UWB-transceivers for WBAN applications. A review of recently published CMOS PA designs is reported in this paper with comparison tables listing wideband power amplifiers' performance.
K-means variations analysis for translation of English Tafseer Al-Quran text Mohammed A. Ahmed; Hanif Baharin; Puteri Nor Ellyza Nohuddin
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i3.pp3255-3265

Abstract

Text mining is a powerful modern technique used to obtain interesting information from huge datasets. Text clustering is used to distinguish between documents that have the same themes or topics. The absence of the datasets ground truth enforces the use of clustering (unsupervised learning) rather than others, such as classification (supervised learning). The “no free lunch” (NFL) theorem supposed that no algorithm outperformed the other in a variety of conditions (several datasets). This study aims to analyze the k-means cluster algorithm variations (three algorithms (k-means, mini-batch k-means, and k-medoids) at the clustering process stage. Six datasets were used/analyzed in chapter Al-Baqarah English translation (text) of 286 verses at the preprocessing stage. Moreover, feature selection used the term frequency–inverse document frequency (TF-IDF) to get the weighting term. At the final stage, five internal cluster validations metrics were implemented silhouette coefficient (SC), Calinski-Harabasz index (CHI), C-index (CI), Dunn’s indices (DI) and Davies Bouldin index (DBI) and regarding execution time (ET). The experiments proved that k-medoids outperformed the other two algorithms in terms of ET only. In contrast, no algorithm is superior to the other in terms of the clustering process for the six datasets, which confirms the NFL theorem assumption.

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