cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Tole Sutikno
Contact Email
ijece@iaesjournal.com
Phone
-
Journal Mail Official
ijece@iaesjournal.com
Editorial Address
-
Location
Kota yogyakarta,
Daerah istimewa yogyakarta
INDONESIA
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
 607   608   609   610   611 
Advancement in driver drowsiness and alcohol detection system using internet of things and machine learning Sivaprakasam, Avenaish; Yogarayan, Sumendra; Mogan, Jashila Nair; Razak, Siti Fatimah Abdul; Abdullah, Mohd. Fikri Azli; Azman, Afizan; Raman, Kavilan
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp3477-3493

Abstract

Globally traffic accidents are influenced by factors such as drowsiness and alcohol consumption. Consequently, there has been a considerable focus on the development of detection systems as part of ongoing efforts to mitigate these risks. This review paper aims to offer a comprehensive analysis of various drowsiness and alcohol detection methods. The paper particularly emphasizes drowsiness and alcohol detection methods, including those centered on sensor-based approaches, physiological-based techniques, and visual analysis of the eye and mouth state. The aim is to evaluate their method, effectiveness and highlight recent advancements within this domain. Additionally, this review paper evaluates the research gaps of these detection methods, considering factors such as precision, sensitivity, specificity, and adaptability to different environmental conditions.
MOERID: enhancing open educational resource discoverability through an artificial intelligence-powered chatbot recommender system Rima, Sandoussi; Meriem, Hnida; Najima, Daoudi; Rachida, Ajhoun
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp3107-3117

Abstract

Open educational resources (OER) are valuable assets in learning and teaching. They ensure cost-effectiveness and customizability, and contribute to global collaboration in the education realm. Hence, education stakeholders face a critical challenge in locating suitable OERs that meet their needs and pedagogical objectives. To cater to this issue, researchers propose diverse digital solutions, one being recommender systems (RS). While a wide array of the suggested tools focused on learners only, this study introduces MOERID, an AI-powered OER Recommender Chatbot aimed at the teaching community. It aspires to facilitate resource discoverability, allowing instructors to save time and energy to concentrate on other pedagogical duties. MOERID engages NLP and recommendation filtering techniques to locate and deliver relevant OERs to instructors. The study describes the implementation of MOERID, highlights its efficacy and provides actionable recommendations for future research by outlining the research gaps.
A hybrid adaptive neuro-fuzzy inference system and reptile search algorithm model for wind power forecasting Al-Widyan, Mohamad I.; Abualigah, Laith; Jaradat, Ghaith M.; Alsmadi, Mutasem Khalil
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp2857-2873

Abstract

Estimating the number of wind ranches generated in the upcoming minutes, hours, or days is the focus of wind power forecasting. Deep learning has garnered a lot of interest in wind control estimation because of how well they perform classification, grouping, and recurrence. The adaptive neuro-fuzzy inference system was successfully applied in wind power forecasting. However, its performance relies on optimal selection of hyperparameters. This study introduces a novel predictive model by incorporating the reptile search algorithm with adaptive neuro-fuzzy inference system (ANFIS) for short-term wind power forecasting. It employs reptile search algorithm (RSA), known for adjustable parameters, disentangled search, and consistent outcomes, to optimize ANFIS’s hyperparameters. Additionally, via exploitation during training, RSA performs a selection of best features in the dataset that contributes to the classification accuracy of ANFIS. This aims to enhance precision of the anticipated yield. Employing authentic wind power data from Jordan is undertaken to evaluate efficiency. The performance is compared with alternative techniques, including artificial neural networks, random forests, and support vector machines. Findings showed that ANFIS-RSA performs competitively for the well-known Chinese benchmark dataset (99.9% accuracy; 0.99 R2; 10.54 MAE; 11.62 RMSE) and is more robustly accurate than others over the Jordanian dataset (0.84.6% accuracy; 0.96 R2; 0.098 MAE; 0.203 RMSE).
Structure of quaternion-type algebras and a post-quantum signature algorithm Duong, May Thu; Moldovyan, Alexander Andreevich; Moldovya, Dmitriy Nikolaevich; Nguyen, Minh Hieu; Do, Bac Thi
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp2965-2976

Abstract

Algebraic digital signature algorithms with a commutative hidden group, which are based on the computational difficulty of solving large systems of power equa- tions, are promising candidates for post-quantum cryptoschemes, especially in securing applications like the internet of things (IoT) and other information tech- nologies. Associative finite non-commutative algebras are used as an algebraic support of the said algorithms. Among such algebras, finite quaternion-type al- gebras have been identified as strong candidates for providing algebraic support. This paper investigates the decomposition of these algebras into commutative subrings and explores their multiplicative groups, which can serve as poten- tial hidden groups. The analysis reveals the existence of three distinct types of subrings, with derived formulas for the number of subrings and the orders of their multiplicative groups. These findings align with previous studies on four- dimensional algebras defined by sparse basis vector multiplication tables. Using the finite quaternion-type algebras as algebraic support, a novel post-quantum signature algorithm characterized in using two mutually non-commutative hid- den groups has been developed.
Name privacy on named data networking: a survey and future research Shah, Mohammad Shahrul Mohd; Leau, Yu-Beng; Anbar, Mohammed; Zhao, Liang
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp3039-3053

Abstract

Information-centric networking (ICN) has gained significant interest in recent years, attracting both academic and industry, it represents a paradigm shift and moving away from the host-based IP networks that dominate today landscape. As ICN technology matures and advances towards real-world deployment, the importance of addressing security and privacy concerns has grown exponentially. The ICN paradigm is deliberately designed to encompass numerous security and privacy features, including but not limited to provenance and privacy. These features, which are often lacking in the host-centric paradigm, inherently form a core aspect of ICN. Nevertheless, due to its relatively recent emergence, the ICN paradigm also presents a range of unresolved privacy challenges. This paper offers a comprehensive survey of the existing literature on privacy primarily focuses on major domains name privacy. We delve into the fundamental principles of existing research and evaluate the limitations of proposed methodologies. In name privacy, we also explore strategies to preserve name privacy. We have identified future research directions and highlighted ongoing challenges in the pursuit of enhancing ICN privacy.
Butterfly optimization-based ensemble learning strategy for advanced intrusion detection in internet of things networks Choukhairi, Mouad; Tahiri, Sara; Choukhairi, Ouail; Fakhri, Youssef; Amnai, Mohamed
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp3494-3505

Abstract

The massive growth in internet of things (IoT) devices has led to enhanced functionalities through their interconnections with other devices, smart infrastructures, and networks. However, increased connectivity also increases the risk of cyberattacks. To protect IoT systems from these threats, intrusion detection systems (IDS) employing machine learning (ML) techniques have been developed to identify cybersecurity threats. This paper introduces a novel ensemble IDS framework called butterfly optimization-based ensemble learning (BOEL). This framework integrates the butterfly optimization algorithm (BOA) with ensemble learning techniques to improve IDS detection performance in IoT networks. BOEL is designed to accurately detect various types of attacks in IoT networks by dynamically optimizing the weights of base learners, which are the four sophisticated ML gradient-boosting algorithms (GBM, CatBoost, XGBoost, and LightGBM) for each attack category, and identifying the best weight combination for ensemble models. Experiments conducted on two public IoT security datasets, CICIDS2017 and Bot-IoT, demonstrate the robustness of the proposed BOEL in intrusion detection across diverse IoT environments, achieving 99.795% accuracy on CICIDS2017 and 99.966% accuracy on Bot-IoT. These results outline the successful application of diverse learning approaches and highlight the framework’s potential to enhance IDS in addressing IoT cyber threats.
Enhancing cyberbullying detection with advanced text preprocessing and machine learning Dhumale, Rakesh Bapu; Dass, Ajay Kumar; Umbrajkaar, Amit; Mane, Pradeep
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp3139-3148

Abstract

The use of social media and the internet has been increasing dramatically in recent years. Cyber-bullying is the term used to describe the misuse of social media by some people who make threatening comments. This has a devastating influence on people's lives, especially those of children and teenagers, and can lead to feelings of depression and suicidal thoughts. The methodology proposed in this paper includes four steps for identifying cyberbullying: preprocessing, feature extraction, classification, and evaluation. The first step is to create a labeled, varied dataset. Word2Vec and term frequency-inverse document frequency are used in feature extraction to transform text into high-dimensional vectors. Word2Vec creates word embeddings using the skip-gram and continuous bag-of-words models, while term frequency-inverse document frequency assesses the text's term relevancy. Support vector machine classifiers are used in the model, and their effectiveness is compared to that of other techniques like logistic regression and naïve Bayes. The classifiers support vector machine, naïve Bayes, and logistic regression were assessed. The maximum accuracy was 95% for the support vector classifier with skip-gram and 93% for continuous bag-of-words. For sentiment categories, F1-scores, recall, and precision were computed. The average precision and recall were 0.77 and 0.79, respectively.
Particle swarm optimization tuned controllers for capacitor voltage balancing and harmonic suppression in modular multilevel converters Outazkrit, Mbarek; aamri, Faicel El; Jaoide, Essaid; Radouane, Abdelhadi; Mouhsen, Azeddine
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp2616-2630

Abstract

The modular multilevel converter (MMC) has become a highly attractive power converter topology for various applications due to its modularity and scalability. However, it faces significant challenges, such as capacitor voltage balancing and circulating current, which can lead to instability and high-power losses. While the sorting algorithm is commonly used to balance capacitor voltages, this paper uses an individual balancing control method as an alternative. Additionally, a proportional resonant controller is employed to suppress the second and fourth harmonics in the circulating current. This paper presents a method for tuning the parameters of both the circulating current controller and the individual balancing control using the particle swarm optimization (PSO) algorithm, which represents the main contribution of this work. The MMC system, connected to a grid with a low number of submodules, is modeled and evaluated using the PLECS and MATLAB/Simulink environments. The results demonstrate the effectiveness of the proposed PSO-based tuning method in improving the performance and stability of the MMC.
Challenges of load balancing algorithms in cloud computing utilizing data mining tools Halima, Anouar Ben; Benaboud, Hafssa
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp3449-3457

Abstract

In the cloud computing environment, load balancing plays an important role in the efficient operation of cloud computing, where a multitude of resources serve diverse workloads and fluctuating demands. In the rapidly evolving cloud computing, efficient resource management, and optimization are critical for maximizing performance, scalability, and cost-effectiveness. Load balancing algorithms aim to distribute workloads across cloud resources to ensure optimal utilization and maintain high availability of services. This paper presents a comparative study of load balancing algorithms in cloud computing using data mining tools. It underscores the complexity of selecting algorithms for effective load balancing in scenarios with diverse criteria, emphasizing its critical importance for future research and practical implementations. The experimental results are presented, evaluating the performance of different load balancing algorithms using data-mining tools. The outcomes highlight the substantial difficulties when building a model with unacceptable errors to cover users’ needs while selecting the desired load balancing method.
Marginalized particle filtering for reliable land vehicle navigation in global navigation satellite system-denied environments Lahrech, Abdelkabir; Soulhi, Aziz
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp2735-2747

Abstract

Accurate localization in land vehicle navigation systems is highly dependent on the global navigation satellite system (GNSS). However, GNSS signal outages are common in urban areas due to obstacles such as tall buildings and tunnels. To mitigate these issues, digital road maps and dead reckoning sensors, like odometers, are often integrated to provide continuous vehicle localization. This paper presents a robust estimation method to solve the fusion problem of GNSS, odometer, and digital road map measurements in the presence of GNSS out- ages. The proposed solution utilizes a marginalized particle filter (MPF), which combines the robustness of particle filtering with the efficiency of a Kalman filter to handle the linear and non-linear parts of the state and/or measurement equations, respectively. When GNSS signals are unavailable, the MPF fuses all available pseudo-range data with odometric and map information to enhance vehicle positioning. The effectiveness of the proposed method is demonstrated using real-world data in an urban transportation scenario, highlighting signifi- cant performance improvements and real-time application potential.

Page 609 of 631 | Total Record : 6301

 607   608   609   610   611 

Filter by Year

2011 2026


Reset
Filter By Issues
All Issue Vol 16, No 1: February 2026 Vol 15, No 6: December 2025 Vol 15, No 5: October 2025 Vol 15, No 4: August 2025 Vol 15, No 3: June 2025 Vol 15, No 2: April 2025 Vol 15, No 1: February 2025 Vol 14, No 6: December 2024 Vol 14, No 5: October 2024 Vol 14, No 4: August 2024 Vol 14, No 3: June 2024 Vol 14, No 2: April 2024 Vol 14, No 1: February 2024 Vol 13, No 6: December 2023 Vol 13, No 5: October 2023 Vol 13, No 4: August 2023 Vol 13, No 3: June 2023 Vol 13, No 2: April 2023 Vol 13, No 1: February 2023 Vol 12, No 6: December 2022 Vol 12, No 5: October 2022 Vol 12, No 4: August 2022 Vol 12, No 3: June 2022 Vol 12, No 2: April 2022 Vol 12, No 1: February 2022 Vol 11, No 6: December 2021 Vol 11, No 5: October 2021 Vol 11, No 4: August 2021 Vol 11, No 3: June 2021 Vol 11, No 2: April 2021 Vol 11, No 1: February 2021 Vol 10, No 6: December 2020 Vol 10, No 5: October 2020 Vol 10, No 4: August 2020 Vol 10, No 3: June 2020 Vol 10, No 2: April 2020 Vol 10, No 1: February 2020 Vol 9, No 6: December 2019 Vol 9, No 5: October 2019 Vol 9, No 4: August 2019 Vol 9, No 3: June 2019 Vol 9, No 2: April 2019 Vol 9, No 1: February 2019 Vol 8, No 6: December 2018 Vol 8, No 5: October 2018 Vol 8, No 4: August 2018 Vol 8, No 3: June 2018 Vol 8, No 2: April 2018 Vol 8, No 1: February 2018 Vol 7, No 6: December 2017 Vol 7, No 5: October 2017 Vol 7, No 4: August 2017 Vol 7, No 3: June 2017 Vol 7, No 2: April 2017 Vol 7, No 1: February 2017 Vol 6, No 6: December 2016 Vol 6, No 5: October 2016 Vol 6, No 4: August 2016 Vol 6, No 3: June 2016 Vol 6, No 2: April 2016 Vol 6, No 1: February 2016 Vol 5, No 6: December 2015 Vol 5, No 5: October 2015 Vol 5, No 4: August 2015 Vol 5, No 3: June 2015 Vol 5, No 2: April 2015 Vol 5, No 1: February 2015 Vol 4, No 6: December 2014 Vol 4, No 5: October 2014 Vol 4, No 4: August 2014 Vol 4, No 3: June 2014 Vol 4, No 2: April 2014 Vol 4, No 1: February 2014 Vol 3, No 6: December 2013 Vol 3, No 5: October 2013 Vol 3, No 4: August 2013 Vol 3, No 3: June 2013 Vol 3, No 2: April 2013 Vol 3, No 1: February 2013 Vol 2, No 6: December 2012 Vol 2, No 5: October 2012 Vol 2, No 4: August 2012 Vol 2, No 3: June 2012 Vol 2, No 2: April 2012 Vol 2, No 1: February 2012 Vol 1, No 2: December 2011 Vol 1, No 1: September 2011 More Issue