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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 111 Documents
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Search results for , issue "Vol 14, No 2: April 2024" : 111 Documents clear
Analysis of driving style using self-organizing maps to analyze driver behavior Shichkina, Yulia; Fatkieva, Roza; Kopylov, Maxim
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i2.pp2212-2225

Abstract

Modern life is strongly associated with the use of cars, but the increase in acceleration speeds and their maneuverability leads to a dangerous driving style for some drivers. In these conditions, the development of a method that allows you to track the behavior of the driver is relevant. The article provides an overview of existing methods and models for assessing the functioning of motor vehicles and driver behavior. Based on this, a combined algorithm for recognizing driving style is proposed. To do this, a set of input data was formed, including 20 descriptive features: About the environment, the driver's behavior and the characteristics of the functioning of the car, collected using OBD II. The generated data set is sent to the Kohonen network, where clustering is performed according to driving style and degree of danger. Getting the driving characteristics into a particular cluster allows you to switch to the private indicators of an individual driver and considering individual driving characteristics. The application of the method allows you to identify potentially dangerous driving styles that can prevent accidents.
Seizure stage detection of epileptic seizure using convolutional neural networks Krori Dutta, Kusumika; Manohar, Premila; Krishnappa, Indira
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i2.pp2226-2233

Abstract

According to the World Health Organization (WHO), seventy million individuals worldwide suffer from epilepsy, a neurological disorder. While electroencephalography (EEG) is crucial for diagnosing epilepsy and monitoring the brain activity of epilepsy patients, it requires a specialist to examine all EEG recordings to find epileptic behavior. This procedure needs an experienced doctor, and a precise epilepsy diagnosis is crucial for appropriate treatment. To identify epileptic seizures, this study employed a convolutional neural network (CNN) based on raw scalp EEG signals to discriminate between preictal, ictal, postictal, and interictal segments. The possibility of these characteristics is explored by examining how well time-domain signals work in the detection of epileptic signals using intracranial Freiburg Hospital (FH), scalp Children's Hospital Boston-Massachusetts Institute of Technology (CHB-MIT) databases, and Temple University Hospital (TUH) EEG. To test the viability of this approach, two types of experiments were carried out. Firstly, binary class classification (preictal, ictal, postictal each versus interictal) and four-class classification (interictal versus preictal versus ictal versus postictal). The average accuracy for stage detection using CHB-MIT database was 84.4%, while the Freiburg database's time-domain signals had an accuracy of 79.7% and the highest accuracy of 94.02% for classification in the TUH EEG database when comparing interictal stage to preictal stage.
Testing nanometer memories: a review of architectures, applications, and challenges Sontakke, Vijay; Atchina, Delsikreo
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i2.pp1406-1423

Abstract

Newer defects in memories arising from shrinking manufacturing technologies demand improved memory testing methodologies. The percentage of memories on chips continues to rise. With shrinking technologies (10 nm up to 1.8 nm), the structure of memories is becoming denser. Due to the dense structure and significant portion of a chip, the nanometer memories are highly susceptible to defects. High-frequency specifications, the complexity of internal connections, and the process variation due to newer manufacturing technology further increased the probability of the physical failure of memories to a great extent. Memories need to be defect-free for the chip to operate successfully. Therefore, testing embedded memories has become crucial and is taking significant test costs. Researchers have proposed multiple approaches considering these factors to test the nanometer memories. They include using new fault models, march algorithms, memory built-in self-test (MBIST) architectures, and validation strategies. This paper surveys the methodologies presented in recent times. It discusses the core principles used in them, along with benefits. Finally, it discusses key opens in each and offers the scope for future research.
A trust based secure access control using authentication mechanism for interoperability in internet of things Narayanappa, Shashikala; Narayanareddy Anitha, Tulavanur; Mishra, Priti; Raichur Patil Herakal, Renuka; Kolur, Jayasudha
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i2.pp2262-2273

Abstract

The internet of things (IoT) is a revolutionary innovation in many aspects of our society including interactions, financial activity, and global security such as the military and battlefield internet. Due to the limited energy and processing capacity of network devices, security, energy consumption, compatibility, and device heterogeneity are the long-term IoT problems. As a result, energy and security are critical for data transmission across edge and IoT networks. Existing IoT interoperability techniques need more computation time, have unreliable authentication mechanisms that break easily, lose data easily, and have low confidentiality. In this paper, a key agreement protocol-based authentication mechanism for IoT devices is offered as a solution to this issue. This system makes use of information exchange, which must be secured to prevent access by unauthorized users. Using a compact contiki/cooja simulator, the performance and design of the suggested framework are validated. The simulation findings are evaluated based on detection of malicious nodes after 60 minutes of simulation. The suggested trust method, which is based on privacy access control, reduced packet loss ratio to 0.32%, consumed 0.39% power, and had the greatest average residual energy of 0.99 mJoules at 10 nodes.
A novel dynamic enterprise architecture model: leveraging MAPE-K loop and case-based reasoning for context awareness Ettahiri, Imane; Doumi, Karim
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i2.pp1875-1889

Abstract

Nowadays, enterprises are required to take the uncertainty of the environment as decisive factor of success. For this reason, Enterprises should be prepared up-stream to react dynamically to the turbulent context. Considering that enterprise architecture is a tool drawing a blueprint that gives a holistic view of the enterprise, this blueprint should be able to represent this awareness to context and implements the techniques and mechanisms to react in a dynamic manner depending on the triggers of change. In this paper, the proposed model stipulates a “context-awareness” that monitors the internal and external context, and then adapt its reaction in alignment with the prefixed goals. The operationalization of our conception is realized through the monitor-analyze-plan-execute-knowledge (MAPE-K) loop, the case-based reasoning and machine learning techniques organized and orchestrated through a global algorithm of 6 main functions to monitor, compare, analyze, plan, execute and enrich the knowledge base. The results are verified in the light of a case study that demonstrates the applicability of our proposed model.
Optimized parameter extraction techniques for enhanced performance evaluation of organic solar cells Uvais, Mohd; Ansari, Asif Jamil; Asim, Mohammed; Manzar, Mohammad Saood
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i2.pp1263-1273

Abstract

The global energy landscape is in the midst of a transformative shift, compelled by the urgent need to reduce our reliance on fossil fuels and embrace eco-friendly alternatives. Organic photovoltaics (OPVs) have emerged as a promising alternative, offering the distinct advantage of performing well in low-light conditions, including indoor environments. Extensive research and development efforts are dedicated to realizing the full potential of OPVs as adaptable, cost-effective, and environmentally friendly solar energy solutions. This paper conducts a thorough examination of the intricate characterization of organic solar cells, with a specific emphasis on crucial parameters like power conversion efficiency, open-circuit voltage, and fill factor. The study utilizes a single diode model to simulate these cells' behavior, employing a meticulous process for parameter extraction. This method leverages Origin software and Python programming, incorporating open-source packages to ensure robust validation. This systematic and rigorous approach significantly enhances our comprehension of OPVs and plays a substantial role in optimizing their performance. In essence, this research represents a significant step forward in advancing sustainable energy technologies, laying a foundation for a greener and more environmentally conscious future.
Phase delay through slot-line beam switching microstrip patch array antenna design for sub-6 GHz 5G band applications Das, Debprosad; Hossain, Md. Farhad; Hossain, Md. Azad; Rahman, Muhammad Asad; Hossain, Md. Motahar; Hossam-E-Haider, Md.
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i2.pp1625-1633

Abstract

Two, four, eight, and sixteen-element patch array antennas for beam switching are presented in this study. For a 1×2 array, an aperture-coupled feeding mechanism is used to feed patches while a slot line on the ground plane provides the phase delay between antenna elements. The 1×2 array is used to create the 2×2, 4×2, and 8×2 arrays, and an equal power divider provides the signal for each. For applications in the 5G sub-6 GHz frequency spectrum, the antennas are modeled. With -37.14 dB, -17.85 dB, -21.51 dB, and -26.03 dB return loss for two, four, eight, and sixteen-element array antennas respectively the simulation demonstrates that the antennas are properly matched at the resonant frequency. The antennas can switch its radiated beam to ±24o, ±24o, ±28o, and ±26o with gains of 8.97 dBi, 11.19 dBi, 13.23 dBi, and 16.24 dBi, respectively at the resonance frequency. The directivity of the proposed antenna is found to be 9.17 dBi, 11.20 dBi, 13.40 dBi, and 16.45 dBi respectively. The antennas are constructed with two 0.8 mm-thick Teflon substrate layers. The ground plane between the two substrate layers contains the aperture and the slot line that generates the phase delay.
Predictive modeling for breast cancer based on machine learning algorithms and features selection methods Al Tawil, Arar; Almazaydeh, Laiali; Alqudah, Bilal; Zaid Abualkishik, Abedallah; A. Alwan, Ali
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i2.pp1937-1947

Abstract

Breast cancer is one of the leading causes of death among women worldwide. However, early prediction of breast cancer plays a crucial role. Therefore, strong needs exist for automatic accurate early prediction of breast cancer. In this paper, machine learning (ML) classifiers combined with features selection methods are used to build an intelligent tool for breast cancer prediction. The Wisconsin diagnostic breast cancer (WDBC) dataset is used to train and test the model. Classification algorithms, including support vector machine (SVM), light gradient boosting machine (LightGBM), random forest (RF), logistic regression (LR), k-nearest neighbors (k-NN), and naïve Bayes, were employed. Performance measures for each of them were obtained, namely: accuracy, precision, recall, F-score, Kappa, Matthews correlation coefficient (MCC), and time. The results indicate that without feature selection, LightGBM achieves the highest accuracy at 95%. With minimum redundancy maximum relevance (mRMR) feature selection (15 features), LightGBM outperforms other classifiers, achieving an accuracy of 98%. For Pearson correlation coefficient feature selection (15 features), LightGBM also excels with a 95% accuracy rate. Lasso feature selection (5 features) produces varied results across classifiers, with logistic regression achieving the highest accuracy at 96%. These findings underscore the importance of feature selection in refining model performance and in improving detection for breast cancer.
A simplified classification computational model of opinion mining using deep learning Dembala, Rajeshwari; Thammaiah, Ananthapadmanabha
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i2.pp2043-2054

Abstract

Opinion and attempts to develop an automated system to determine people's viewpoints towards various units such as events, topics, products, services, organizations, individuals, and issues. Opinion analysis from the natural text can be regarded as a text and sequence classification problem which poses high feature space due to the involvement of dynamic information that needs to be addressed precisely. This paper introduces effective modelling of human opinion analysis from social media data subjected to complex and dynamic content. Firstly, a customized preprocessing operation based on natural language processing mechanisms as an effective data treatment process towards building quality-aware input data. On the other hand, a suitable deep learning technique, bidirectional long short term-memory (Bi-LSTM), is implemented for the opinion classification, followed by a data modelling process where truncating and padding is performed manually to achieve better data generalization in the training phase. The design and development of the model are carried on the MATLAB tool. The performance analysis has shown that the proposed system offers a significant advantage in terms of classification accuracy and less training time due to a reduction in the feature space by the data treatment operation.
Situational judgment test measures administrator computational thinking with factor analysis Indrawati, Cicilia Dyah Sulistyaningrum; Permansah, Sigit; Ninghardjanti, Patni; Subarno, Anton; Winarno, Winarno; Rusmana, Dede
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i2.pp2088-2099

Abstract

Computational thinking skills (CTS) play a crucial role across diverse domains, involving a thinking process that allows the execution of solutions by information processing agents. Measuring the level of CTS becomes essential to ensure that administrators effectively leverage technology. However, finding suitable instruments to measure and justify CTS levels in administration can be challenging. The selection of situational judgement test (SJT) is supported by its validity and reliability in assessing attributes, including professionalism. The instrument’s development includes face validity, discriminant validity (using Pearson correlation and Cronbach’s alpha), and exploratory factor analysis (EFA). The study involved 111 undergraduate administration students from various Indonesian universities, and data were collected in 2023. Following a discriminant validity analysis, ten items were eliminated, while 23 met the criteria with p0.185. Subsequently, EFA yielded 16 items forming seven components, covering algorithmic thinking, problem-solving, technology literacy, problem abstraction, problem reformulation, data management in administration technology, and administrative data presentation with loading factor variations (0.421-0.868). The final instrument, consisting of 16 valid items and seven components, effectively evaluates the level of administrator computational thinking skills (ACTS) among administration students.

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