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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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Enhancing internet of things attack detection using principal component analysis and kernel principal component analysis with cosine distance and sigmoid kernel Elkhadir, Zyad; Achkari Begdouri, Mohammed
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.pp1099-1108

Abstract

The widespread adoption of internet of things (IoT) devices has brought about unprecedented levels of connectivity and convenience. However, it has also introduced significant challenges, particularly in the areas of security and privacy. This study addresses the critical issue of intrusion detection within IoT environments, with a specific focus on analyzing the Iot-23 dataset. Our methodology involves employing principal component analysis (PCA) and kernel PCA for dimensionality reduction. Subsequently, we utilize the k-nearest neighbors (KNN) algorithm for classification purposes. To optimize the performance of the KNN algorithm, we experiment with various feature scaling techniques, such as StandardScaler, MinMaxScaler, and RobustScaler, utilizing different distance metrics. In our analysis, we discovered that employing the cosine distance metric in combination with KNN resulted in superior intrusion detection performance when utilizing PCA. Additionally, when utilizing kernel PCA, we evaluated multiple kernel functions and determined that the radial basis function and sigmoid kernel yielded the most favorable results.
Narrative review of the literature: application of mechanical self powered sensors for continuous surveillance of heart functions Owida, Hamza Abu; Al-Nabulsi, Jamal I.; Turab, Nidal; Al-Ayyad, Muhammad; Al Hawamdeh, Nour; 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.pp243-251

Abstract

Cardiovascular disease consistently occupies a prominent position among the leading global causes of mortality. Continuous and real-time monitoring of cardiovascular signs over an extended duration is necessary to identify irregularities and prompt timely intervention. Due to this reason, researchers have invested heavily in developing adaptive sensors that may be worn or implanted and continuously monitor numerous vital physiological characteristics. Mechanical sensors represent a category of devices capable of precisely capturing the temporal variations in pressure within the heart and arteries. Mechanical sensors possess inherent advantages such as exceptional precision and a wide range of adaptability. This article examines four distinct mechanical sensor technologies that rely on capacitive, piezoresistive, piezoelectric, and triboelectric principles. These technologies show great potential as novel approaches for monitoring the cardiovascular system. The subsequent section provides a comprehensive analysis of the biomechanical components of the cardiovascular system, accompanied by an in-depth examination of the methods employed to monitor these intricate systems. These systems measure blood and endocardial pressure, pulse wave, and heart rhythm. Finally, we discuss the potential benefits of continuing health monitoring in vascular disease treatment and the challenges of integrating it into clinical settings.
Implementing cloud computing in drug discovery and telemedicine for quantitative structure-activity relationship analysis Ramapraba, Palayanoor Seethapathy; Babu, Bellam Ravindra; Paul, Nallathampi Rajamani Rejin; Sharmila, Varadan; Babu, Venkatachalam Ramesh; Ramya, Raman; Murugan, Subbiah
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.pp1132-1141

Abstract

This work aims to use cutting-edge machine learning methods to improve quantitative structure-activity relationship (QSAR) analysis, which is used in drug development and telemedicine. The major goal is to examine the performance of several predictive modeling approaches, including random forest, deep learning-based QSAR models, and support vector machines (SVM). It investigates the potential of feature selection techniques developed in chemoinformatics for enhancing model accuracy. The innovative aspect is using cloud computing resources to strengthen computational skills, allowing for managing massive amounts of chemical information. This strategy produces accurate and generalizable QSAR models. By using the cloud's scalability and constant availability, remote healthcare apps have a workable answer. The goal is to show how these methods may improve telemedicine and the drug development process. Utilizing cloud computing equips researchers with a flexible set of tools for precise and timely QSAR analysis, speeding up the discovery of bioactive chemicals for therapeutic use. This new method fits well with the dynamic nature of pharmaceutical study and has the potential to transform the way drugs are developed and delivered to patients via telemedicine.
An innovative and efficient approach for searching and selecting web services operations Rekkal, Sara; Rekkal, Kahina
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.pp827-835

Abstract

The marketing of web services on the internet continues to increase, resulting in an increasing number of web services and, therefore, operations offering equivalent functionalities. As a consequence, finding an appropriate web service (operation) for a particular task has become a difficult challenge, taking a lot of time and leading to an insufficient selection of relevant services. This work aims to propose a new approach facilitating the search and localization of relevant web services (operations) in an acceptable time while ensuring the totality of the response. This approach is divided into three crucial phases. The first step involves collecting web services from various universal description, discovery, and integration (UDDI) registries and different domains and forming specialized sub-registries. The second phase involves the extraction of operations from various services, followed by a similarity study whose goal is the formation of clusters of similar operations. The third phase processes user requests by identifying the desired features. A list of operations is then provided to the client, including the non-functional properties, from which they select the one that best meets their needs and begin to invoke it.
Analysis of big data from New York taxi trip 2023: revenue prediction using ordinary least squares solution and limited-memory Broyden-Fletcher-Goldfarb-Shanno algorithms Rhouas, Sara; El Hami, Norelislam
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.pp711-718

Abstract

This study explores the prediction of taxi trip fares using two linear regression methods: normal equations (ordinary least squares solution (OLS)) and limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS). Utilizing a dataset of New York City yellow taxi trips from 2023, the analysis involves data cleaning, feature engineering, and model training. The data consists of over 12 million records, managed, and processed that involves configuring the Spark driver and executor memory to efficiently process the Parquet-format data stored on hadoop distributed file system (HDFS). Key features influencing fare amount, such as passenger count, trip distance, fare amount, and tip amount, were analyzed for correlation. Models were trained on an 80-20 train-test split, and their performance was evaluated using root-mean-square error (RMSE) and mean squared error (MSE). Results show that both methods provide comparable accuracy, with slight differences in coefficients and training time. Additionally, vendor performance metrics, including total trips, average trip distance, fare amount, and tip amount, were analyzed to reveal trends and inform strategic decisions for fleet management. This comprehensive analysis demonstrates the efficacy of linear regression techniques in predicting taxi fares and offers valuable insights for optimizing taxi operations.
Quadratic multivariate linear regressive distributed proximity feature engineering for cybercrime detection in digital fund transactions with big data Paulraj, Arul Jeyanthi; Thalaimalai, Balaji
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.pp689-699

Abstract

Digital fund transactions involve the electronic transfer of funds between parties through digital channels such as online banking platforms, mobile applications, and electronic payment systems. However, the rapid advancement of digital transactions has also directed cybercriminals to exploit vulnerabilities, engaging in money laundering and other illegal activities, resulting in substantial financial losses. The improve accuracy of cybercriminal detection by lesser time consumption, a novel technique called quadratic multivariate linear regressive distributed proximity feature engineering (QMLRDPFE) is developed. The proposed QMLRDPFE technique comprises two primary steps namely data preprocessing and feature engineering. Analyzed results prove that the QMLRDPFE technique outperforms existing methods in attaining superior accuracy and precision. Furthermore, QMLRDPFE method shows effective in reducing time utilization and space complexity for fraudulent transaction detection compared to existing approaches. Results to provide effective in reducing time utilization and space complexity for fraudulent transaction detection than the conventional methods.
From concept to application: building and testing a low-cost light detection and ranging system for small mobile robots using time-of-flight sensors García, Andrés; Díaz, Mauricio; Martínez, Fredy
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.pp292-302

Abstract

Advancements in light detection and ranging (LiDAR) technology have significantly improved robotics and automated navigation. However, the high cost of traditional LiDAR sensors restricts their use in small-scale robotic projects. This paper details the development of a low-cost LiDAR prototype for small mobile robots, using time-of-flight (ToF) sensors as a cost-effective alternative. Integrated with an ESP32 microcontroller for real-time data processing and Wi-Fi connectivity, the prototype facilitates accurate distance measurement and environmental mapping, crucial for autonomous navigation. Our approach included hardware design and assembly, followed by programming the ToF sensors and ESP32 for data collection and actuation. Experiments validated the accuracy of the ToF sensors under static, dynamic, and varied lighting conditions. Results show that our low-cost system achieves accuracy and reliability comparable to more expensive options, with an average mapping error within acceptable limits for practical use. This work offers a blueprint for affordable LiDAR systems, expanding access to technology for research and education, and demonstrating the viability of ToF sensors in economical robotic navigation and mapping solutions.
Berkeley wavelet transform and improved YOLOv7-based classification technique for brain tumor severity prediction Bahadure, Nilesh Bhaskarrao; Routray, Sidheswar; Patni, Jagdish Chandra; Raju, Nagrajan; Patil, Prasenjeet Damodar
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.pp958-969

Abstract

Abnormality in brain tissues is a life-threatening illness in humans Un-bias to gender and age if it is unrecognized and untreated within time, will lead to severe complications and extreme conditions. The brain tumor is mainly influenced by a variety of unpredicted and unavoidable reasons. Its evaluation, spread pattern, and identification involves complex assignment. Its early grading and the proper classification ensure effective treatment. The proposed work attempts to extract and classify the tumor region using an automatic classification system for magnetic resonance imaging (MRI) brain tumors. A deep learning convolutional neural network-based architecture YOLO is employed to classify and detect the tumor from brain MR images. The proposed method resulted in superior segmentation, and classification performance in terms of subjective visualization and objective metrics as compared to state of art approaches. The proposed YOLO-based method collectively achieved 98.89% classification accuracy on the BRAINIX and Kaggle datasets.
Multi-objective optimized task scheduling in cognitive internet of vehicles: towards energy-efficiency Divyashree, M.; Rangaraju, H. G.; Revanna, C. R.
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.pp1229-1241

Abstract

The rise of intelligent and connected vehicles has led to new vehicular applications, but vehicle computing capabilities remain limited. Mobile edge computing (MEC) can mitigate this by offloading computation tasks to the network's edge. However, limited computational capacities in vehicles lead to increased latency and energy consumption. To address this, roadside units (RSUs) with cloud servers, known as edge computing devices (ECDs), can be expanded to provide energy-efficient scheduling for task computation. A new energy-efficient scheduling method called multi-objective optimization energy computation (MOEC) is proposed, based on multi-objective particle swarm optimization (MOPSO) to reduce ECDs' energy usage and execution time. Simulation results using MATLAB show that MOEC can balance the trade-off between energy usage and execution time, leading to more efficient offloading.
Adaptive control techniques for improving anti-lock braking system performance in diverse friction scenarios Abdullah, Mohammed Fadhl; Qasem, Gehad Ali Abdulrahman; Ramadhan, Mazen Farid; Lim, Heng Siong; Lee, Chin Poo; Alsakkaf, Nasr Alsakkaf
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.pp260-279

Abstract

Anti-lock braking systems (ABS) enhance vehicle safety by preventing wheel lock-up, but their effectiveness depends on tire-road friction. Traditional braking systems struggle to maintain effective performance due to the risk of wheel lock-up on varying road surfaces, affecting vehicle stability and control. This study presents a novel method to improve ABS efficiency across varying friction conditions. The proposed approach employs a feedback control mechanism to dynamically adjust the braking force of each wheel based on the prevailing friction coefficient. Specifically, we incorporate a P-controller in the input signal and two additional P-controllers as output and input parameters for friction. By manipulating the proportional control values, key parameters such as wheel speed, stopping distance, and slip rate can be effectively managed. Notably, our investigation reveals intriguing interactions between the proportional controls, highlighting the complexity of ABS optimization. The method was evaluated through simulations across various friction conditions, comparing it to conventional ABS in terms of brake performance, stability, and stopping distances. The results indicate that the proposed method significantly enhances ABS performance across varying friction coefficients; however, additional research is warranted to address stopping distance and time issues, particularly in snowy and icy conditions.

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