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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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Bitcoin volatility forecasting: a comparative analysis of conventional econometric models with deep learning models Tripathy, Nrusingha; Mishra, Debahuti; Hota, Sarbeswara; Mishra, Sashikala; Das, Gobinda Chandra; Dalai, Sasanka Sekhar; Nayak, Subrat Kumar
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i1.pp614-623

Abstract

The behavior of the Bitcoin market is dynamic and erratic, impacted by a range of elements including news developments and investor mood. One well-known aspect of bitcoin is its extreme volatility. This study uses both conventional econometric techniques and deep learning algorithms to anticipate the volatility of Bitcoin returns. The research is based on historical Bitcoin price data spanning October 2014 to February 2022, which was obtained using the Yahoo Finance API. In this work, we contrast the efficacy of generalized autoregressive conditional heteroskedasticity (GARCH) and threshold ARCH (TARCH) models with long short-term memory (LSTM), bidirectional LSTM (Bi-LSTM), and multivariate Bi-LSTM models. Model effectiveness is evaluated by means of root mean squared error (RMSE) and root mean squared percentage error (RMSPE) scores. The multivariate Bi-LSTM model emerges as mostly effective, achieving an RMSE score of 0.0425 and an RMSPE score of 0.1106. This comparative scrutiny contributes to understanding the dynamics of Bitcoin volatility prediction, offering insights that can inform investment strategies and risk management practices in this quickly changing environment of finance.
Cloud computing environment based hierarchical anomaly intrusion detection system using artificial neural network Vamsikrishna, Mangalapalli; Latha, Garapati Swarna; Babu, Gajjala Venkata Ramesh; Giridhar, Koppisetti; Alluri, Lakshmeelavanya; Somasekhar, Giddaluru; Sagar, Bhimunipadu Jestadi Job Karuna; Dondapati, Naresh
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i1.pp1209-1217

Abstract

Nowadays, computer technology is essential to everyday life, including banking, education, entertainment, and communication. Network security is essential in the digital era, and detecting intrusion threats is the most difficult problem. As a result, the network is monitored for unusual activity using this hierarchical anomaly intrusion detection system, and when these actions are detected, an alert is generated. This hierarchical anomaly intrusion detection system, which uses artificial neural network (ANN) and is implemented on a cloud computing environment, analyzes data even in the high levels of traffic and protects computer networks and data from malicious activity. As a result, this system shows better detection, accuracy, and precision rates.
Deep learning for skin melanoma classification using dermoscopic images in different color spaces Manikandan, Sankarakutti Palanichamy; Narani, Sandeep Reddy; Karthikeyan, Sakthivel; Mohankumar, Nagarajan
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i1.pp319-327

Abstract

Skin cancer begins in the skin cells. The damage to the skin cells can cause genetic mutations that lead to uncontrolled growth and the formation of tumors. It is estimated that millions of people are diagnosed with skin cancer of different kinds each year. The earlier a skin cancer is diagnosed, the better the patient's prognosis and the lower their chance of complications. In this work, an efficient deep learning classification (EDLCS) to classify dermoscopic images is developed. The importance of color in the diagnosis of skin melanoma has caused color analysis to attract considerable attention from researchers of image-based skin melanoma analysis. Three different color spaces such as red-green-blue (RGB), hue-saturation-lightness (HIS) and LAB are investigated in this study. The obtained dermoscopic images are in RGB color space. The RGB dermoscopic images are first converted into HSV and LAB spaces to investigate the HSV and LAB color spaces for melanoma classification. Then, the color space converted image is fed to the proposed EDLCS to evaluate their performances. Results show that the proposed EDLCS provides 99.58% accuracy while using the LAB color model to classify preprocessed images while other models provide 99.17%.
Analytic hierarchy process geographic information system based model for sustainable construction and demolition waste landfill site selection Soussi, Mohamed Ayet Allah Bilel; Madsia, Nermine El; Zaki, Chamseddine; Ramadan, Alaaeddine; Saker, Louai; Ibrahim, Moustafa
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i1.pp803-816

Abstract

Properly managing waste generated by buildings and public works is a significant challenge in Tunisia, particularly in the city of Bizerte. The inadequate disposal of such waste can cause substantial harm to human life, property, and the environment. This paper proposes an multi-criteria decision making (MCDM) that utilizes the analytic hierarchy process (AHP) decision support tool to identify suitable landfill sites for construction and demolition waste (CDW) in Bizerte. The AHP method is widely used in MCDM applications. The approach involves classifying different scenarios based on various exclusion and appreciation criteria to determine the optimal locations for future landfills. Furthermore, the paper develops a conceptual approach for identifying better sites for the disposal of CDW, resulting in a comprehensive database capable of storing, accessing, and extracting information at both conceptual and operational levels. The proposed model considers spatial, technical, and environmental criteria in the selection of a suitable landfill site. The proposed methodology offers an effective and practical solution for properly managing CDW waste in Bizerte, Tunisia, and can be applied to other regions facing similar challenges.
Hybrid optimization algorithm for analysis of influence propagation in social network Bhayyar, Akshata Sandeep; Purushotham, Kiran
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i1.pp624-634

Abstract

Influence maximization(IM) is defined as the problem of identifying a node subset in a social network which increases the spread of influence. IM plays a crucial role in social networks by catalyzing the dissemination of influence, resulting in an augmented count of influenced nodes following the propagation process. The existing researches mainly concentrated on increasing the spread of influence, but did not consider the running time of the network. In this manuscript, the salp swarm algorithm (SSA) and bi-adaptive strategy particle swarm optimization (BiAS-PSO) algorithms are integrated and named as SS-BiAS-PSO algorithm to increase the spread of influence based on the IM problem to minimize the running time of the network. The datasets utilized for the research are Ego-Facebook, Epinions, Gowalla, and HepTh, while linear threshold (LT) is utilized as a diffusion method. Then, the proposed SS-BiAS-PSO algorithm is deployed for the analysis of influence propagation. The proposed algorithm reaches a high influence spread of 645, 680, 715, and 750 with less running times respectively for 10, 20, 30, and 40 seed set sizes in Ego-Facebook. The proposed algorithm proves more effective than the existing techniques like traditional SSA and particle swarm optimization (PSO).
Automation of 5G network slicing security using intent-based networking Islam, Md. Zahirul; Galib, Syed Md.; Kabir, Md. Humaun
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i1.pp401-413

Abstract

Network slicing is a fundamental technological advancement that facilitates the provision of novel services and solutions within the realm of 5G and the forthcoming 6G communications. Numerous challenges emerge when implementing network slicing on a large-scale commercial level since it necessitates comprehensive control and automation of the entire network. Cyberattacks, such as distributed denial of service (DDoS) and address resolution protocol (ARP) spoofing, can significantly disrupt the performance and accessibility of slices inside a multi-tenant virtualized networking infrastructure due to the shared utilization of physical resources. This article employs intent-based networking (IBN) to identify and address diverse threats through automated methods. A conceptual framework is presented in which the IBN manager is integrated into the network-slicing architecture to facilitate the implementation of automated security controls. The proposed work is assessed using an experimental test bed. The study's findings indicate that the network slice's performance exhibits improvement when successful detection and mitigation measures are implemented. This improvement is observed in various metrics: availability, packet loss, response time, central processing unit (CPU) and memory utilization.
Performance of 5G and Wi-Fi 6 coexistence: spectrum sharing based on optimized duty cycle Zaid, Asmaa Helmy; Zaki, Fayez Wanis; Nafea, Hala Bahy-Eldeen
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i1.pp386-400

Abstract

Smart mobile device usage is increasing rapidly; hence, cellular operators face the challenge of spectrum resource shortage. To address this issue, researchers have explored several approaches to achieving a highly efficient utilization of wireless communication network resources. One promising solution lies in the fair coexistence of 5G/Wi-Fi 6 in the unlicensed 5 GHz band. This research investigates a duty cycle mechanism to perform fair spectrum sharing between these two wireless technologies, intending to optimize performance metrics such as throughput, capacity, bit error rate (BER), and latency. The results of this study demonstrate a significant improvement in system performance when employing the proposed coexistence method compared to using 5G alone in a single cell. Specifically, a 40% increase in throughput and a 14% improvement in capacity are reported. Moreover, for a single cell using Wi-Fi 6 only, the BER was reduced by 19%, and the latency was less than one millisecond. Additionally, the duty cycle mechanism reported here is used to prioritize call services, with the blocking probability for voice-over internet protocol (VoIP) and video stream calls being improved. Furthermore, the adaptive bandwidth reservation reduced the blocking probability of video calls from 21.8% to 0.9% compared to the fixed method; no VoIP calls were blocked.
Comparison of machine learning algorithms to identify and prevent low back injury Paulino, Christian Ovalle; Correa, Jorge Huamani
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i1.pp894-907

Abstract

With the advancement of technology, remote work and virtual classes have become increasingly common, leading to prolonged periods in front of computers and, consequently, to discomfort and even lower back pain. This study compares machine learning algorithms to identify and prevent low back pain, a common health problem. A predictive model for early diagnosis and prevention of these injuries was developed using datasets from open data repositories. Six machine learning models were used to train the data. Results showed that logistic regression was the most effective model, with performance curves of 70%, 90%, and 99%. Performance metrics indicated 86% accuracy, 85% recall, and 86% F1-score. Accuracy of 70%, recall of 71%, and F1-score of 63% reflect the robust ability of the model to address the problem. In addition, an intuitive interface was implemented using Gradio Software to improve data visualization.
Perspective on the applications of terahertz imaging in skin cancer diagnosis Owida, Hamza Abu; Al-Nabulsi, Jamal I.; Al-Ayyad, Muhammad; Turab, Nidal; Alshdaifat, Nawaf
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i1.pp1242-1250

Abstract

Applications of terahertz (THz) imaging technologies have advanced significantly in the disciplines of biology, medical diagnostics, and non- destructive testing in the past several decades. Significant progress has been made in THz biomedical imaging, allowing for the label-free diagnosis of malignant tumors. Terahertz frequencies, which lie between those of the microwave and infrared, are highly sensitive to water concentration and are significantly muted by water. Terahertz radiation does not cause ionization of biological tissues because of its low photon energy. Recently, terahertz spectra, including spectroscopic investigations of cancer, have been reported at an increasing rate due to the growing interest in their biological applications sparked by these unique features. To improve cancer diagnosis with terahertz imaging, an appropriate differentiation technique is required to increased blood supply and localized rise in tissue water content that commonly accompany the presence of malignancy. Terahertz imaging has been found to benefit from structural alterations in afflicted tissues. This study provides an overview of terahertz technology and briefly discusses the use of terahertz imaging techniques in the detection of skin cancer. Research into the promise and perils of terahertz imaging will also be discussed.
A novel global maximum power point tracking based on flamingo search algorithm for photovoltaic systems Draoui, Abdelghani; Saidi, Ahmed; Allaoua, Boumediene; Bourezg, Abdrabbi
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i1.pp1015-1026

Abstract

Due to the high dependency of photovoltaic (PV) solar cell’s output on solar irradiance and, the ambient temperature. Maximum power point tracking (MPPT) algorithms are used extensively to operate the system at its full potentials. Moreover, being installed in outdoor spaces, PV modules are inevitably subjected to partial shading conditions, where different parts of the system are receiving different amounts of solar irradiance. In case of occurrence of partial shading conditions on a PV module that is equipped with bypass diodes, the power-voltage (P-V) curve will have multiple peaks. This multi-peak curve requires using an advanced algorithm which track the global maximum power point (GMPP) instead of being deceived and trapped in a local maximum power point. In this paper, the flamingo search algorithm (FSA) is adapted for GMPP tracking for a PV system under partial shading conditions. The FSA algorithm fetch for the GMPP by reading the PV panel power and setting accordingly the duty cycle of the buck converter. To investigate model validity, simulation is performed using the MATLAB/Simulink platform and results demonstrate good tracking performance and fast response that prove the robustness of the system against rapid variations in solar irradiance levels.

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