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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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Unobtrusive hand gesture recognition using ultra-wide band radar and deep learning Korti, Djazila Souhila; Slimane, Zohra
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i6.pp6872-6881

Abstract

Hand function after stroke injuries is not regained rapidly and requires physical rehabilitation for at least 6 months. Due to the heavy burden on the healthcare system, assisted rehabilitation is prescribed for a limited time, whereas so-called home rehabilitation is offered. It is therefore essential to develop robust solutions that facilitate monitoring while preserving the privacy of patients in a home-based setting. To meet these expectations, an unobtrusive solution based on radar sensing and deep learning is proposed. The multi-input multi-output convolutional eXtra trees (MIMO-CxT) is a new deep hybrid model used for hand gesture recognition (HGR) with impulse-radio ultra-wide band (IR-UWB) radars. It consists of a lightweight architecture based on a multi-input convolutional neural network (CNN) used in a hybrid configuration with extremely randomized trees (ETs). The model takes data from multiple sensors as input and processes them separately. The outputs of the CNN branches are concatenated before the prediction is made by the ETs. Moreover, the model uses depthwise separable convolution layers, which reduce computational cost and learning time while maintaining high performance. The model is evaluated on a publicly available dataset of gestures collected by three IR-UWB radars and achieved an average accuracy of 98.86%.
Low-cost colorimetric setup for concentration measurement of manganese ions based on optical absorbance Maidan Dali, Mohd Rumaizan; Ali, Nurul Hidayah; Mohamad Robi, Fatin Izyani; Osman, Mohamed Syazwan; Abd Rahman, Mohamad Faizal
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i6.pp6195-6203

Abstract

This study presents a cost-effective setup for measuring the concentration of Mn2+ ions using colorimetry. The current method involves a calibration curve created with expensive and large commercial laboratory-based instruments, limiting its use in financially constrained situations. To address this issue, the study proposes a low-cost setup consisting of a light-emitting diode and photodiode that utilizes colorimetric and absorbance effects for Mn2+ concentration measurement. Mn2+ colorimetric samples were prepared using the 1-(2-pyridylazo)-2-naphthol (PAN) method with concentrations ranging from 0.2 to 1.0 mg/L. The samples were tested using the proposed setup, followed by a spectrophotometry test to determine the optimal configuration for the setup. The validity of the setup was confirmed by measuring the voltage and calculating the optical absorbance, which exhibited a good correlation with the concentration, consistent with the initial expectation. The correlation coefficient for voltage and absorbance against Mn2+ concentration was found to be 0.9976 and 0.9987, respectively, indicating good linearity and suitability as a calibration curve for Mn2+ detection and measurement. Consequently, the study’s objectives were successfully achieved, and the proposed setup is considered a viable platform for more complex applications, such as real-time monitoring activities.
Survey analysis for optimization algorithms applied to electroencephalogram Hakem, Ekram; Al-Shammary, Dhiah; Mahdi, Ahmed M.
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i6.pp6891-6903

Abstract

This paper presents a survey for optimization approaches that analyze and classify Electroencephalogram (EEG) signals. The automatic analysis of EEG presents a significant challenge due to the high-dimensional data volume. Optimization algorithms seek to achieve better accuracy by selecting practical features and reducing unwanted features. Forty-seven reputable research papers are provided in this work, emphasizing the developed and executed techniques divided into seven groups based on the applied optimization algorithm particle swarm optimization (PSO), ant colony optimization (ACO), artificial bee colony (ABC), grey wolf optimizer (GWO), Bat, Firefly, and other optimizer approaches). The main measures to analyze this paper are accuracy, precision, recall, and F1-score assessment. Several datasets have been utilized in the included papers like EEG Bonn University, CHB-MIT, electrocardiography (ECG) dataset, and other datasets. The results have proven that the PSO and GWO algorithms have achieved the highest accuracy rate of around 99% compared with other techniques.
Quality of service adaptive modulation and coding scheme for IEEE 802.11ac Anuar, Aliya Syahira Mohd; Muhamad, Wan Norsyafizan W.; Ali, Darmawaty Mohd; Yusof, Azita Laily
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i6.pp6443-6451

Abstract

Nowadays, the rising demand for digital communication technologies has contributed to the increase in the volume of traffic. This continuous trend of internet traffic has led to the deterioration of the quality of service (QoS) with reduced throughput and increased latency. This also is due to the proliferation of new broadband applications which require low latency and high throughput such as virtual reality and real-time gaming. Therefore, considering the aforementioned challenge in QoS of wireless networks, a link adaptation method is suggested in this study, in order to enhance the performance of the QoS in IEEE 802.11ac amendment wireless local-area network (WLAN). The proposed technique adaptively changes the transmission data rate by increasing or decreasing the modulation and coding scheme (MCS) level according to the traffic conditions. With the use of an OMNeT++ computer-aided design (CAD)-based simulation model, the effectiveness of the suggested approach is examined. Simulated findings were compared with the link adaptation approach of the default condition. The results of the simulation demonstrate that the proposed technique significantly increases throughput (36.48%) and decreases latency in comparison to the default situation. These findings demonstrate the technique's potential to improve WLAN QoS efficiency, notably in regard to throughput and latency.
Energy efficient data transmission using multiobjective improved remora optimization algorithm for wireless sensor network with mobile sink Jemla Naik, Anil Kumar; Parameswarappa, Manjunatha; Ramachandra, Mohan Naik
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i6.pp6476-6488

Abstract

A wireless sensor network (WSN) is a collection of nodes fitted with small sensors and transceiver elements. Energy consumption, data loss, and transmission delays are the major drawback of creating mobile sinks. For instance, battery life and data latency might result in node isolation, which breaks the link between nodes in the network. These issues have been avoided by means of mobile data sinks, which move between nodes with connection issues. Therefore, energy aware multiobjective improved remora optimization algorithm and multiobjective ant colony optimization (EA-MIROA-MACO) is proposed in this research to improve the WSN’s energy efficiency by eliminating node isolation issue. MIRO is utilized to pick the optimal cluster heads (CHs), while multiobjective ant colony optimization (MACO) is employed to find the path through the CHs. The EA-MIROA-MACO aims to optimize energy consumption in nodes and enhance data transmission within a WSN. The analysis of EA-MIROA-MACO’s performance is conducted by considering the number of alive along with dead nodes, average residual energy, and network lifespan. The EA-MIROA-MACO is compared with traditional approaches such as mobile sink and fuzzy based relay node routing (MSFBRR) protocol as well as hybrid neural network (HNN). The EA-MIROA-MACO demonstrates a higher number of alive nodes, specifically 192, over the MSFBRR and HNN for 2,000 rounds.
Prototype design of a mobile app oriented to adults with obesity Andrade-Arenas, Laberiano; Molina-Velarde, Pedro; Yactayo-Arias, Cesar
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i6.pp6745-6753

Abstract

Obesity in adults is a worldwide problem, which is why different countries, through their health-related agencies, implement policies to fight this disease. One of the tools is the use of a mobile application that controls obesity. In this sense, the prototype was designed taking into account different items such as physical activities, body mass index, calorie intake, and food options, among others. The objective of the research is to design a mobile app that allows us to control of obesity in adults. The methodology used is design thinking which allows us to use a systematic approach to reach the objective. An interview was conducted to identify the needs of the user and obtain information regarding their essential needs. In addition, a survey was carried out, the outcome shows satisfaction with a 58% acceptance rate. The beneficiaries of this research are adults who suffer from obesity and healthcare centers. Likewise, research has a positive impact since it focuses on solving problems directly related to health issues.
Selection of crop varieties and yield prediction based on phenotype applying deep learning Shanmugam, Iniyan; Rethnaraj, Jebakumar; Mani, Gayathri
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i6.pp6806-6816

Abstract

In India, agriculture plays an important role in the nation’s gross domestic product (GDP) and is also a part of civilization. Countries’ economies are also influenced by the amount of crop production. All business trading involves farming as a major factor. In order to increase crop production, different technological advancements are developed to acquire the information required for crop production. The proposed work is mainly focused on suitable crop selection across districts in Tamil Nadu, considering phenotype factors such as soil type, climatic factors, cropping season, and crop region. The key objective is to predict the suitable crop for the farmers based on their locations, soil types, and environmental factors. This results in less financial loss and a shorter crop production timeframe. Combined feature selection (CFS)-based machine regression helps increase crop production rates. A brief comparative analysis was also made between various machine learning (ML) regression algorithms, which majorly contributed to the process of crop selection considering phenotype factors. Stacked long short-term memory (LSTM) classifiers outperformed other decision tree (DT), k-nearest neighbor (KNN), and logistic regression (LR) with a prediction accuracy of 93% with the lowest classification accuracy metrics. The proposed method can help us select the perfect crop for maximum yield.
Spiking ink drop spread clustering algorithm and its memristor crossbar conceptual hardware design Paeen Afrakoti, Iman Esmaili; Nazerian, Vahdat; Sutikno, Tole
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i6.pp7125-7136

Abstract

In this study, a novel neuro-fuzzy clustering algorithm is proposed based on spiking neural network and ink drop spread (IDS) concepts. The proposed structure is a one-layer artificial neural network with leaky integrate and fire (LIF) neurons. The structure implements the IDS algorithm as a fuzzy concept. Each training data will result in firing the corresponding input neuron and its neighboring neurons. A synchronous time coding algorithm is used to manage input and output neurons firing time. For an input data, one or several output neurons of the network will fire; confidence degree of the network to outputs is defined as the relative delay of the firing times with respect to the synchronous pulse. A memristor crossbar-based hardware is utilized for hardware implementation of the proposed algorithm. The simulation result corroborates that the proposed algorithm can be used as a neuro-fuzzy clustering and vector quantization algorithm.
IoT-based smart irrigation management system using real-time data Hafian, Asmae; Benbrahim, Mohammed; Kabbaj, Mohammed Nabil
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i6.pp7078-7088

Abstract

An adequate water supply is essential for the growth and development of crops. When rainfall is insufficient, irrigation is necessary to meet crop water needs. It is a crucial and strategic aspect of economic and social development. To combat climate change, there is a need to adopt irrigation management techniques that increase and stabilize agricultural production while saving water, using intelligent agricultural water technologies. Internet of things (IoT) based technologies can achieve optimal use of water resources. This article introduces a smart realtime irrigation management system based on the internet of things. It provides optimal management of irrigation decisions using real-time weather and soil moisture data, as well as data from precipitation forecasts. The proposed algorithm is developed in real-time based on the IoT, enabling us to guide irrigation and control the amount of water in agricultural applications. The system uses real-time data analysis of climate, soil, and crop data to provide flexible planning of the irrigation system’s use. A case study from the Fez-Meknes region in Morocco is presented to demonstrate the proposed system’s effectiveness
Secrecy performance analysis on spatial modeling of wireless communications with unmanned aerial vehicle and ground devices Van, Cuu Ho; Nguyen, Hong-Nhu; Le, Si-Phu; Voznak, Miroslav
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i6.pp6410-6418

Abstract

In this paper, the secrecy performance of the spatial modeling for ground devices with randomly placed eavesdroppers when an unmanned aerial vehicle (UAV) acted as two hops decode and forward (DF) was investigated. We characterize the secrecy outage probability (SOP) and intercept probability (IP) expressions. Our capacity performance analysis is based on the Rayleigh fading distributions. After analytical results by Monte Carlo simulation, and the Gauss-Chebyshev parameter was selected to yield a close approximation, the results demonstrate the SOP with the average signal-to-noise ratio (SNR) between UAV and ground users among the eavesdroppers and the IP relationship with the ability to intercept the information of the ground users successfully.

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