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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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Hybrid deep learning for estimation of state-of-health in lithium-ion batteries Cahyani, Denis Eka; Gumilar, Langlang; Afandi, Arif Nur; Wibawa, Aji Prasetya; Junoh, Ahmad Kadri
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.pp995-1006

Abstract

Lithium-ion (li-ion) batteries have a high energy density and a long cycle life. Lithium-ion batteries have a finite lifespan, and their energy storage capacity diminishes with use. In order to properly plan battery maintenance, the state of health (SoH) of lithium-ion batteries is crucial. This study aims to combine two deep learning techniques (hybrid deep learning), namely convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM), for SoH estimation in li-ion batteries. This study contrasts hybrid deep learning methods to single deep learning models so that the most suitable model for accurately measuring the SoH in lithium-ion batteries can be determined. In comparison to other methodologies, CNN-BiLSTM yields the best results. The CNN-BiLSTM algorithm yields RMSE, mean square error (MSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) in the following order: 0.00916, 0.000084, 0.0048, and 0.00603. This indicates that CNN-BiLSTM, as a hybrid deep learning model, is able to calculate the approximate capacity of the lithium-ion battery more accurately than other methods.
Enhancing internet of things security against structured query language injection and brute force attacks through federated learning Adamova, Aigul; Zhukabayeva, Tamara; Mukanova, Zhanna; Oralbekova, Zhanar
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.pp1187-1199

Abstract

The internet of things (IoT) encompasses various devices for monitoring, data collection, tracking people and assets, and interacting with other gadgets without human intervention. Implementing a system for predicting the development and assessing the criticality of detected attacks is essential for ensuring security in IoT interactions. This work analyses existing methods for detecting attacks, including machine learning, deep learning, and ensemble methods, and explores the federated learning (FL) method. The aim is to study FL to enhance security, develop a methodology for predicting the development of attacks, and assess their criticality in real-time. FL enables devices and the aggregation server to jointly train a common global model while keeping the original data locally on each client. We demonstrate the performance of the proposed methodology against structured query language (SQL) injection and brute force attacks using the CICIOT2023 dataset. We used accuracy and F1 score metrics to evaluate the effectiveness of our proposed methodology. As a result, the accuracy in predicting SQL injection reached 100%, and for brute force attacks, it reached 98.25%. The high rates of experimental results clearly show that the proposed FL-based attack prediction methodology can be used to ensure security in IoT interactions.
Secured and cloud-based electronic health records by homomorphic encryption algorithm Annapurna, Bala; Geetha, Gaddam; Madhiraju, Priyanka; Kalaiselvi, Subbarayan; Sushith, Mishmala; Ramadevi, Rathinasabapathy; Pandey, Pramod
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.pp1152-1161

Abstract

This uses homomorphic encryption in cloud-based platforms to improve electronic health records (EHR) security and accessibility. Protecting sensitive medical data while enabling data processing and analysis is the main goal. The study examines how homomorphic encryption protects EHR data privacy and integrity. Its main purpose is to reduce risks of unauthorized access and data breaches to build trust between healthcare professionals and patients in digital healthcare. The research uses homomorphic encryption to safeguard cloud EHR storage and transmission. Results will highlight the algorithm's influence on data security and computing efficiency, revealing its potential use in healthcare to protect patient privacy and meet regulatory requirements. Results from dataset of patient health metrics show in the 1st instance sample data for 5 instances with ages between 57 to 88, blood pressure (BP) values from 33 to 85, glucose values from 5 to 99, and heart rate values from 24 to 88. In another study of 5 patients, cholesterol levels ranged from 10 to 80 mg/dL, body mass index (BMI) from 10 to 96 kg/m², smoking status from 14 to 79, and medication adherence from 6 to 78%.
Enhancing automatic license plate recognition in Indian scenarios Samaga, Abhinav; Lobo, Allen Joel; Nasreen, Azra; Pattar, Ramakanth Kumar; Trivedi, Neeta; Raj, Peehu; Sreelakshmi, Koratagere
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.pp365-373

Abstract

Automatic license plate recognition (ALPR) technology has gained widespread use in many countries, including India. With the explosion in the number of vehicles plying over the roads in the past few years, automating the process of documenting vehicle license plates for use by law enforcement agencies and traffic management authorities has great significance. There have been various advancements in the object detection, object tracking, and optical character recognition domain but integrated pipelines for ALPR in Indian scenarios are a rare occurrence. This paper proposes an architecture that can track vehicles across multiple frames, detect number plates and perform optical character recognition (OCR) on them. A dataset consisting of Indian vehicles for the detection of oblique license plates is collected and a framework to increase the accuracy of OCR using the data across multiple frames is proposed. The proposed system can record license plate readings of vehicles averaging 527.99 and 2157.09 ms per frame using graphics processing unit (GPU) and central processing unit (CPU) respectively.
Timed concurrent system modeling and verification of home care plan Taryana, Acep; Adzkiya, Dieky; Mufid, Muhammad Syifa'ul; Mukhlash, Imam
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.pp870-882

Abstract

A home care plan (HCP) can be integrated with an electronic medical records (EMR) system, serving as an example of a real-time system with concurrent processes. To ensure effective operation, HCPs must be free of software bugs. In this paper, we explore the modeling and verification of HCPs from the perspective of scheduling data operationalization. Specifically, we investigate how patients can obtain home services while preventing scheduling conflicts in the context of limited resources. Our goal is to develop and verify robust models for this purpose. We employ formalism to construct and validate the model, following these steps: i) develop requirements and specifications; ii) create a model with concurrent processes using timed automata; and iii) verify the model using UPPAAL tools. Our study focuses on HCP implementation at a regional general hospital in Banyumas District, Central Java, Indonesia. The results include models and specifications based on timed automata and timed computation tree logic (TCTL). We successfully verified a concurrent model that utilizes synchronized counter variables and a sender-receiver approach to analyze collision constraints arising from the synchronization of patient and resource plans.
A new 13N-complexity memory built-in self-test algorithm to balance static random access memory static fault coverage and test time Jidin, Aiman Zakwan; Hussin, Razaidi; Mispan, Mohd Syafiq; Fook, Lee Weng
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.pp163-173

Abstract

As memories dominate the system-on-chip (SoC), their quality significantly impacts the chip manufacturing yield. There is a growing need to reduce the chip production time and cost, which mainly depends on the testing phase. Hence, a memory built-in self-test (MBIST) utilizing a low-complexity, high-fault-coverage test algorithm is essential for efficient and thorough memory testing. The March AZ1 algorithm, with 13N complexity, was created earlier to balance the test length and fault coverage. However, poor positioning of a write operation in its test sequence caused the reduction of the transition coupling fault (CFtr) detection. This paper presents the creation of the March AZ algorithm, modified from the March AZ1 algorithm, to increase CFtr coverage while preserving the same complexity. It was accomplished by analyzing the fault coverage offered by the March AZ1 algorithm and then reorganizing its test sequence to address the limitation in detecting CFtr. The newly produced March AZ1 algorithm was successfully implemented in an MBIST controller. The simulation tests validated its functionality and demonstrated that the CFtr coverage was enhanced from 62.5% to 75%, achieving an overall fault coverage of 83.3%. Therefore, with 13N complexity, it offers the best fault coverage among all the existing test algorithms with a complexity below 18N.
Challenges and opportunities to location independent human activity recognition utilizing Wi-Fi sensing Abuhoureyah, Fahd; Wong, Yan Chiew; Al-Taweel, Malik Hasan; Abdullah, Nihad Ibrahim
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.pp921-939

Abstract

Wireless sensing has emerged as a dynamic field with diverse applications across smart cities, healthcare, the internet of things (IoT), and virtual reality gaming. This burgeoning area capitalizes on the capacity to detect locations, activities, gestures, and vital signs by assessing their impact on ambient wireless signals. This review critically examines the prevailing challenges within wireless sensing and predicts future research trajectories. Even with the potential for nuanced signal processing facilitated by Wi-Fi propagation, its efficacy is impeded by noise interference in confined areas during transmission and reception. Consequently, this work aims to augment signal processing performance accuracy by delving into the most promising techniques and underexplored methods utilizing channel state information (CSI). Furthermore, the work offers a view into the potential of human activity recognition predicated on CSI properties. The study focusses on exploring location-independent sensing technique based on CSI, discussing relevant considerations and contemporary approaches used in Wi-Fi sensing tasks. The optimal practices in analysis are based on model design, data collection, and result interpretation. The discussions analysis investigates in detail the representative applications and outlines the major considerations of developing human activity recognition human activity recognition (HAR) based on Wi-Fi by analyzing the current critical issues of CSI-based behavior recognition methods and pointing out possible future research directions.
Context-aware self-powered intelligent soil monitoring system for precise agriculture Kee, Keh-Kim; Rashidi, Ramli; Kee, Owen Kwong-Hong; Han, Andrew Ballang; Patrick, Isaiah Zunduvan; Bawen, Loreena Michelle
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.pp1123-1131

Abstract

The agricultural sector is transforming with advanced technologies such as internet of things (IoT), cloud computing, and machine learning, for increased productivity and sustainability. However, fixed sensor deployments struggle to capture the dynamic and heterogeneous soil properties with irregularities in farming operations, and negatively impacting crop performance and resource utilization. This paper presents a novel context-aware, self-powered intelligent soil monitoring system (ISMS) applied in precision agriculture. By integrating advanced sensors, energy harvesting, real-time data analytics, and context-aware decision support, ISMS provides real-time context insights into soil, energy, and weather conditions. The informed decisions are enabled and tailored to their specific agricultural environment. The system utilizes a multi-parameter soil sensor, photovoltaic (PV) panel, and intelligent context-aware analytics for a sustainable, cost-effective solution powered by solar energy and OpenWeather application program interface (API) for weather data. Field tests over two months demonstrated the system's effectiveness, together with continuous operation without grid power. This research highlights ISMS's potential in enhancing soil nutrient management and decision-making and offering significant economic and environmental benefits for modern agriculture, especially in remote areas.
Measuring anxiety level on phobia using electrodermal activity, electrocardiogram and respiratory signals Ain, Khusnul; Rahma, Osmalina Nur; Purwanti, Endah; Varyan, Richa; Ittaqilah, Sayyidul Istighfar; Arfensia, Danny Sanjaya; Sosialita, Tiara Dyah; Qulub, Fitriyatul; Chai, Rifai
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.pp337-348

Abstract

People with spider phobia experience excessive anxiety reactions when exposed to spiders that will interfere with daily life. Diagnosing and measuring anxiety levels in patients with spider phobia is a complex challenge. Conventional diagnosis requires psychological evaluations and clinical interviews that take time and often result in a high degree of subjectivity. Therefore, there is a need for a more objective and efficient approach to measuring anxiety levels in patients. This study performs anxiety level classification based on electrodermal activity, electrocardiogram (ECG) and respiratory signals using the dataset of Arachnophobia subjects. Each raw data is preprocessed using 24 types of features. Feature performance is processed using the recursive feature elimination method. Data processing was performed in 3 anxiety levels (high, medium, low) and two anxiety levels (high, low) with the support vector machine method and hold-out validation method (7:3). The performance of the model is evaluated by showing the accuracy, precision, recall and F1 score values. The polynomial kernel can perform optimal classification and obtain 100% accuracy in 2 classes and three classes with 100% precision, recall, and F1 score values. This result shows excellent potential in measuring anxiety levels that correlate with mental health issues.
Determination of biomass energy potential based on regional characteristics using adaptive clustering method Alvianingsih, Ginas; Hashim, Haslenda; Jamian, Jasrul Jamani; Senen, Adri
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.pp46-55

Abstract

Determining the energy potential of biomass is the first step in selecting the most suitable and efficient energy conversion technology based on regional characteristics. The approach to estimating and determining biomass potential generally uses geospatial technology related to collecting and processing data about mapping an area. Unfortunately, this method is inadequate for simulating the interaction between variables, nor can it provide accurate predictions for the biomass supply chain. As a result, the results obtained from this method tend to be biased and macro, particularly in regions experiencing rapid land-use development. In this paper, the author has developed a clustering methodology with a fuzzy c-means (FCM) algorithm to determine biomass energy potential based on regional characteristics to produce data clusters with high accuracy. Grouping the characteristics of clustering-based areas involves grouping physical or abstract objects into classes or similar objects.

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