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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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Human motion classification by micro-doppler radar using intelligent algorithms Ballen, Andres Felipe Arias; Cuesta, Edith Paola Estupiñan; Quintero, Juan Carlos Martinez
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.pp455-466

Abstract

This article introduces a technique for detecting four human movements using micro-doppler radar and intelligent algorithms. Micro-doppler radar exhibits the capability to detect and measure object movements with intricate detail, even capturing complex or non-rigid motions, while accurately identifying direction, velocity, and motion patterns. The application of intelligent algorithms enhances detection efficiency and reduces false alarms by discerning subtle movement patterns, thereby facilitating more accurate detection and a deeper understanding of observed object dynamics. A continuous wave radar setup was implemented utilizing a spectrum analyzer and radio frequency (RF) generator capturing signals in a spectrogram centered at 2,395 MHz. Six models were assessed for image classification: VGG-16, VGG-19, MobileNet, MobileNet V2, Xception, and Inception V3. A dataset comprising 500 images depicting four movements-running, walking, arm raising, and jumping-was curated. Our findings reveal that the most optimal architecture in terms of training time, accuracy, and loss is VGG-16, achieving an accuracy of 96%. Furthermore, precision values of 96%, 100%, and 98% were obtained for the movements of walking, running, and arm raising, respectively. Notably, VGG-16 exhibited a training loss of 4.191E-04, attributed to the utilization of the Adam optimizer with a learning rate of 0.001 over 15 epochs and a batch size of 32.
An integrated smart water management system for efficient water conservation Rajanbabu, Jeya; Venkatakrishnan, Giri Rajanbabu; Rengaraj, Ramasubbu; Rajalakshmi, Mohandoss; Jayaprakash, Neythra
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.pp635-644

Abstract

Water is a fundamental resource that sustains life, supports ecosystems, and plays a crucial role in various natural processes on earth. Water wastage is a major problem in the world, with a variety of causes including leaks in infrastructure and inefficient usage methods. A typical cause of water wastage is overflow from reservoirs or tanks as a result of poor maintenance or monitoring. This paper proposes a novel water resource management using internet of things (WARM-IoT) system to monitor and regulate the water level remotely by integrating IoT technology with demand side management (DSM) strategies, real-time monitoring of water levels has been achieved. The approach utilizes an ultrasonic sensor and Raspberry Pi for data acquisition and processing, fuzzy logic for decision-making, and an Android app for remote monitoring and control. The WARM-IoT assesses the system's performance, showcasing its efficacy in managing water levels and lowering electricity expenses. By analyzing consumption costs under different activation timings, significant potential for cost savings is observed, with a notable reduction of up to 6% in electricity expenses. Overall, the proposed WARM-IoT offers a comprehensive solution to water wastage and inefficient electricity usage in water management systems.
Hybrid long short-term memory and decision tree model for optimizing patient volume predictions in emergency departments Abatal, Ahmed; Mzili, Mourad; Benlalia, Zakaria; Khallouki, Hajar; Mzili, Toufik; Billah, Mohammed El Kaim; Abualigah, Laith
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.pp669-676

Abstract

In this study, we address critical operational inefficiencies in emergency departments (EDs) by developing a hybrid predictive model that integrates long short-term memory (LSTM) networks with decision trees (DT). This model significantly enhances the prediction of patient volumes, a key factor in reducing wait times, optimizing resource allocation, and improving overall service quality in hospitals. By accurately forecasting the number of incoming patients, our model facilitates the efficient distribution of both human and material resources, tailored specifically to anticipated demand. Furthermore, this predictive accuracy ensures that EDs can maintain high service standards even during peak times, ultimately leading to better patient outcomes and more effective use of healthcare facilities. This paper demonstrates how advanced data analytics can be leveraged to solve some of the most pressing challenges faced by emergency medical services today.
Negative-sequence current filter based on inductance coils Kletsel, Mark; Mashrapov, Bauyrzhan; Mashrapova, Rizagul; Kislov, Alexandr
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.pp24-35

Abstract

The construction of new relay protection systems without the use of current transformers is a fundamental problem of electro energetics, which has not yet been solved. This works suggests a negative-sequence current filter which receives information from inductance coils (ICs) mounted at a safe distance in the magnetic field of phase currents. This filter does not require current transformers, thus saving high-quality copper, steel, and expensive high-voltage insulation in amount unprecedented for relay protection (a 6 to 110 kV current transformer has 19 to 480 kg in weight). A circuit (including functional diagnostics) and a technique for selecting the parameters of filter components and the points where ICs should be fixed are presented; a structure for IC fastening is described. Computer simulation and experiment were used for data collection. The data show that i) the filter conversion coefficient m= 1.6, and imbalance increases by 7% at the network frequency f= 48–52 Hz; ii) protections based on this filter should have a time delay; iii) the filter is not inferior to well-known well-tested filters with current transformers; and iv) it is functional, but can only be used for single-standing electrical installations.
Enhancing sentiment analysis through deep layer integration with long short-term memory networks Dubey, Parul; Dubey, Pushkar; Gehani, Hitesh
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.pp949-957

Abstract

This involves studying one of the most important parts of natural language processing (NLP): sentiment, or whether a thing that makes a sentence is neutral, positive, or negative. This paper presents an enhanced long short-term memory (LSTM) network for the sentiment analysis task using an additional deep layer to capture sublevel patterns from the word input. So, the process that we followed in our approach is that we cleaned the data, preprocessed it, built the model, trained the model, and finally tested it. The novelty here lies in the additional layer in the architecture of LSTM model, which improves the model performance. We added a deep layer with the intention of improving accuracy and generalizing the model. The results of the experiment are analyzed using recall, F1-score, and accuracy, which in turn show that the deep-layered LSTM model gives us a better prediction. The LSTM model outperformed the baseline in terms of accuracy, recall, and f1-score. The deep layer's forecast accuracy increased dramatically once it was trained to capture intricate sequences. However, the improved model overfitted, necessitating additional regularization and hyperparameter adjustment. In this paper, we have discussed the advantages and disadvantages of using deep layers in LSTM networks and their application to developing models for deep learning with better-performing sentiment analysis.
Enhancing PETRONAS share price forecasts: a hybrid Holt integrated moving average Fozi, Nurin Qistina Mohamad; Hasan, Nurhasniza Idham Abu; Aziz, Azlan Abdul; Zahari, Siti Meriam; Ganggayah, Mogana Darshini
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.pp728-740

Abstract

Understanding the variations in PETRONAS share price over time is important for improving the forecast accuracy of PETRONAS share prices to provide stakeholders with reliable analyses for future market predictions. Therefore, the main objective of this study is to improve the accuracy of PETRONAS share price by utilizing a hybrid Holt method with the moving average (MA) from the Box-Jenkins model. Holt's method will address linear trends for non-stationary data, while MA will analyze residual aspects of the data. This combination transforms non-stationary data into stationary by removing noise and averaging out fluctuations. The secondary data used in this study consists of daily observation from bursa Malaysia, the official national stock exchange of Malaysia, covering the period from January 3, 2000, to October 2, 2023. The study encompasses both low and high share price scenarios. The models’ performance was compared using various error metrics across different training and testing splits. The findings highlight that the proposed hybrid [Holt–MA] model called Holt integrated moving average (HIMA) improves the accuracy of forecasting model with the smallest errors for both daily low and high share price. The HIMA model demonstrates significant potential, particularly in reducing residuals and improving prediction accuracy.
Road feature extraction from LANDSAT-8 operational land imager images using simplified U-Net model Reddy, Sama Lenin Kumar; Rao, Chandu Venkateswara; Kumar, Pullakura Rajesh
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.pp328-336

Abstract

Automatic road feature extraction from the remote sensing (RS) imagery has a significant role in various applications such as urban planning, transportation management, and environmental monitoring. In this paper, propose a method based on the U-Net model to extract the road features from the LANDSAT-8 operational land imager (OLI) images. This method aims to extract road features in OLI images that appear as curvilinear features and roads with widths greater than 25 meters, which are mostly covered within a single pixel of the OLI resolution of multi-spectral images. The U-Net architecture is well-known for its effectiveness in image segmentation tasks. However, to optimize the complexity in the U-Net model, simplified the architecture while retaining its key components and principles. The proposed model by decreasing the convolution layers and the parameters which are involved to optimize the model called as simplified U-Net model. To train this model, the label images were generated for LANDSAT-8 OLI images, by using the saturation based adaptive thresholding and morphology (SATM) method. This reduces the efforts to draw the labels in the vector format labels and convert to raster images. The model is able to effectively generate weights, which are able to extract the road features. This model weights applied on the OLI images which covers the urban and rural areas of India, producing the satisfactory results. The experimental results with the quantitative analysis presented in the paper.
Integration of web scraping, fine-tuning, and data enrichment in a continuous monitoring context via large language model operations Bodor, Anas; Hnida, Meriem; Daoudi, Najima
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.pp1027-1037

Abstract

This paper presents and discusses a framework that leverages large-scale language models (LLMs) for data enrichment and continuous monitoring emphasizing its essential role in optimizing the performance of deployed models. It introduces a comprehensive large language model operations (LLMOps) methodology based on continuous monitoring and continuous improvement of the data, the primary determinant of the model, in order to optimize the prediction of a given phenomenon. To this end, first we examine the use of real-time web scraping using tools such as Kafka and Spark Streaming for data acquisition and processing. In addition, we explore the integration of LLMOps for complete lifecycle management of machine learning models. Focusing on continuous monitoring and improvement, we highlight the importance of this approach for ensuring optimal performance of deployed models based on data and machine learning (ML) model monitoring. We also illustrate this methodology through a case study based on real data from several real estate listing sites, demonstrating how MLflow can be integrated into an LLMOps pipeline to guarantee complete development traceability, proactive detection of performance degradations and effective model lifecycle management.
An improved reptile search algorithm-based machine learning for sentiment analysis Sureja, Nitesh; Chaudhari, Nandini M.; Bhatt, Jalpa; Desai, Tushar; Parikh, Vruti; Panesar, Sonia; Sureja, Heli; Kharva, Jahnavi
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.pp755-766

Abstract

The rapid growth of mobile technologies has transformed social media, making it crucial for expressing emotions and thoughts. When making significant decisions, businesses and governments can benefit from understanding public opinion. This information makes sentiment analysis vital for understanding public sentiment polarity. This study develops a hyper tuned deep learning model with swarm intelligence and many approaches for sentiment analysis. convolutional neural network (CNN), bidirectional encoder representations from transformers (BERT), long short-term memory (LSTM), CNN-LSTM, BERT-LSTM, and BERT-CNN are the six deep learning models of the sentiment analysis using deep learning with reinforced learning based on reptile search algorithm (SA-DLRLRSA) model. The reptile search algorithm, an enhanced swarm intelligence algorithm (SIA), optimizes deep learning model hyper parameters. Word2Vec word embedding is used to convert textual input sequences to representative embedding spaces. Pre-trained Word2Vec embedding is also used to address issue of unbalanced datasets. Experimental results demonstrate that the SA-DLRLRSA model works best with accuracies of 93.1%, 94.7%, 96.8%, 96.3%, 97.2%, and 98.3% utilizing CNN, LSTM, BERT, CNN-LSTM, BERT-CNN, and BERT-LSTM.
Ant lion and ant colony optimization integrated ensemble machine learning model for effective cancer diagnosis Panda, Pinakshi; Bisoy, Sukant Kishoro; Panigrahi, Amrutanshu; Pati, Abhilash
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.pp604-613

Abstract

Statistics from reputable sources, including the World Health Organization (WHO), demonstrate that cancer is a leading cause of death globally, accounting for millions of deaths each year. When it comes to the early identification of cancer, machine learning (ML) is crucial. To analyze complex data and identify minute patterns that may indicate the presence of cancer, it employs robust computational approaches. Improving patient outcomes relies on early cancer detection since it paves the way for faster treatment and intervention, which might lead to better prognoses and higher survival rates. To choose features, this study intends to build an ML-based ensemble model utilizing ant colony optimization (ACO) and ant lion optimization (ALO). Next, ML classifiers are used as the initial predictions' basis learners. The last forecast is the result of combining two ensemble methods: voting and averaging classifiers. Four distinct cancer microarray datasets are used to assess the approach. With an accuracy of 99.08% on the Lung cancer dataset, the voting ensemble classifier outperforms the others, according to the empirical analysis.

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