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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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Design of an optimized energy-efficient routing protocol for reliable wireless body area networks Almutairi, Hissah; Alqahtani, Abdullah; S. Jabbar, Zinah; Fadhil Tawfeq, Jamal; Dheyaa Radhi, Ahmed; Soon JosephNg, Poh
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i4.pp4386-4393

Abstract

Energy limitation is one of the essential parameters in the design of a Wireless body area networks (WBANs) as it is important to improve the lifetime of the network. WBAN routing is an effective approach for establishing energy efficiency sets and assign time slots for the network. Many algorithms that deal with interference model treats the whole WBAN as a minimum interference unit and increase their lifetime cycle. In this research, we report an effective low-energy adaptive clustering hierarchy (LEACH) routing protocol using MATLAB simulation and related C++ simulation codes to enhance the overall performance of the network by improving the energy efficiency and network lifetime cycles. Furthermore, the study sheds light up on the comparison of the protocol and proposes a modified protocol for WBAN. Based on the results obtained from conducting different configurations in the proposed design, the base station should be situated near the network to insure high network performance.
Enhancing online learning: sentiment analysis and collaborative filtering from Twitter social network for personalized recommendations El maazouzi, Qamar; Retbi, Asmaa; Bennani, Samir
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i3.pp3266-3276

Abstract

Online learning presents a major challenge for learners, namely the diversification of courses and information overload. In response to this issue, recommender systems are widely used. Nowadays, social networks have become a global platform where individuals share a multitude of information. For instance, Twitter is a social network where users exchange messages and interact with various communities. These interactions on social networks have created a new dimension in the field of online learning. In this article, we propose a novel approach that combines sentiment analysis of learners’ reviews on social networks with collaborative filtering methods to provide more personalized and relevant course recommendations. To achieve this, we explored different models to analyze the sentiments of tweets related to online courses. Additionally, we used collaborative filtering based on k-nearest neighbors (KNN). Our results demonstrate that integrating sentiment analysis provides more relevant recommendations. This has also been shown based on the calculation of root mean square error (RMSE) compared to a traditional approach. In this study, we demonstrated that by leveraging this information from social networks like Twitter, online learning platforms can enhance the effectiveness of their course recommendations, tailoring them to each individual learner’s needs.
Impact of start-stop systems on motorcycle fuel savings in urban traffic Murga-Garcia, Kevin; Chacaltana-Silva, Rodrigo; Paiva-Peredo, Ernesto
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp6258-6264

Abstract

The start/stop (S/S) system implemented in motorcycles aims primarily at fuel savings. This study was conducted to assess the effectiveness of this system in conditions of heavy traffic and traffic lights in Lima, using a virtual channel identifier (VCI) and a technical schedule. The detailed analysis covered critical aspects of the S/S system, the description of the route taken, and its segmentation to understand the number of stops and mileage. Speed limits, schedules, and measurement equipment were established, including the MICODUS-ORBD2 device and the VCI-Hero. The study included tests conducted with and without the MICODUS-ORBD2 device, recording times, distances, and fuel consumption. Data were collected with the S/S activated and deactivated, concluding the system achieves a 10.1% fuel saving. This finding provides valuable insights into understanding the system's effectiveness in actual traffic conditions and emphasizes the importance of maintaining key vehicle components to optimize S/S performance.
Fuzzy logic method-based stress detector with blood pressure and body temperature parameters Fajrin, Hanifah Rahmi; Sasmeri, Sasmeri; Riski Prilia, Levina; Untara, Bambang; Ahdan Fawwaz Nurkholid, Muhammad
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.pp2156-2166

Abstract

In this study, using the fuzzy logic method, a stress detection tool was created with body temperature and blood pressure parameters as indicators to determine a person's stress level. This tool uses the LM35DZ sensor to detect body temperature, the MPX5100GP sensor to read blood pressure values, and Arduino Uno as a data processor from sensor readings which are then calculated using the fuzzy logic method as a stress level decision-maker. The resulting output measures blood pressure, body temperature, and the stress level experienced by a person, which will be displayed on the liquid crystal display. Based on the results of testing the body temperature parameter, the highest error generated was 1.17%, and for the blood pressure parameter, the highest error was 2.5% for systole and 0.93% for diastole. Furthermore, testing the stress level displayed on the tool is compared to the depression, anxiety, and stress scales 42 (DASS 42), a psychological stress measuring instrument. From the results of testing the tool with the questionnaire, the average conformity level is 74%.
Stochastic agent-based models optimization applied to the problem of rebalancing bike-share systems Soto, Daniel Anderson; Ceballos, Yony
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i5.pp5641-5651

Abstract

This paper presents an agent-based model employing a stochastic optimization search that attempts to find an optimal solution to the online bicycle rebalancing problem for general bike-sharing systems. The algorithm receives the initial and final global state configuration of the system. The main objective of the study is to find the minimum cost path from the initial to the final state. Each agent of the model has four behavioral options that search the optimal configuration; at each iteration, it selects one of these options based on random thresholds and shares the temporary solution found with neighboring agents to improve their search process. The algorithm presents a high exploratory behavior of the search space, which helps to find an approximation away from the local optimal configuration. Additionally, the exchanges between agents allow a consensus on the solutions found. The algorithm has been tested with two different generated configurations using as a basis a real dataset extracted from a functional bike-sharing system collected in 2019. The results show a positive evolution originating from the emerging effect of stochastic selection and interaction between agents.
Partitioning intensity inhomogeneity colour images via Saliency-based active contour Syukri Mazlin, Muhammad; Jumaat, Abdul Kadir; Embong, Rohana
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i1.pp337-346

Abstract

Partitioning or segmenting intensity inhomogeneity colour images is a challenging problem in computer vision and image shape analysis. Given an input image, the active contour model (ACM) which is formulated in variational framework is regularly used to partition objects in the image. A selective type of variational ACM approach is better than a global approach for segmenting specific target objects, which is useful for applications such as tumor segmentation or tissue classification in medical imaging. However, the existing selective ACMs yield unsatisfactory outcomes when performing the segmentation for colour (vector-valued) with intensity variations. Therefore, our new approach incorporates both local image fitting and saliency maps into a new variational selective ACM to tackle the problem. The euler-lagrange (EL) equations were presented to solve the proposed model. Thirty combinations of synthetic and medical images were tested. The visual observation and quantitative results show that the proposed model outshines the other existing models by average, with the accuracy of 2.23% more than the compared model and the Dice and Jaccard coefficients which were around 12.78% and 19.53% higher, respectively, than the compared model.
Prediction of vulnerability severity using vulnerability description with natural language processing and deep learning Ahmed Abdirahman, Abdullahi; Osman Hashi, Abdirahman; Romo Rodriguez, Octavio Ernesto; Abdirahman Elmi, Mohamed
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i4.pp4551-4562

Abstract

One of the most critical aspects of a software piece is its vulnerabilities. Regardless of the years of experience, type of project, or the size of the team, it is impossible to avoid introducing vulnerabilities while developing or maintaining software. This aspect becomes crucial when the software is deployed in production or released to the final users. At that point finding vulnerabilities becomes a race between the developers and malicious intruders, whoever finds it first can either exploit it or fix it. Acknowledging this situation and using the tools and standards that we have available in the field, such as common vulnerability exposures and common vulnerability scoring systems, and based on modern researches, in this study, we propose to have an approach different from the common practices of manual classification, using a 2-layer convolutional neuronal network (CNN) to automatize the classification of vulnerabilities, speeding up this process and enabling developers to have a faster response towards vulnerabilities, producing safer software. The experimental results obtained in this study suggest that pre-trained word embeddings contributed to an increase in accuracy of approximately 2% and the overall accuracy become 0.816%.
Probability distributions in Kerala’s rainfall: implications for hydro energy planning Baranitharan, Balakrishnan; Chandran, Karthik; Subramaniyan Mathan, Vaithilingam; Chowdhury, Subrata; Nguyen Thi, Thu; Tran, Duc-Tan
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i3.pp3372-3381

Abstract

Heavy rainfall has consistently acted as the primary catalyst for floods, resulting in numerous casualties and significant economic losses globally. Rainfall forecasting is accomplished by analysing existing rainfall data, which is then used to analyse the hydraulic system’s features. Gaining an understanding of rainfall requirements is a crucial challenge for every location, particularly in the case of India, given its diverse geographical area, population, and other influencing factors that impact various demands. This study evaluated the rainfall data for a span of 1990-2021 in six districts of Kerala State, India. To match the rainfall data from all districts, we utilized both Kaumarasamy-distribution and Dagum-distributions. Various Probabilistic tests, were employed to comparing these distributions. The results revealed that, in Kasargod, the Kumarasamy distribution demonstrates superior goodness-of-fit with the lowest Kolmogorov-Smirnov statistic (0.0597) and Anderson-darling statistic (2.271). However, in Wayanad, Malappuram, Palakkad, Idukki, and Trivandrum, the Dagum distribution consistently exhibits the most accurate fit, evident from its lowest Kolmogorov-Smirnov statistics (0.07447, 0.05435, 0.0556, 0.03636, 0.04291) and favourable Chi-Squared statistics (19.471, 8.4907, 19.239, 5.7318, 7.5297). These results emphasize the regional variation in precipitation data and the suitability of specific distribution models for accurate representation across differentlocations.
Proactive monitoring and predictive alerts for COVID-19 patient management using internet of things, artificial intelligence, and cloud Leila, Ennaceur; Othman, Soufiene Ben; Sakli, Hedi; Yahia, Mohamed
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp7266-7274

Abstract

The coronavirus disease 2019 (COVID-19) pandemic has sparked changes across various domains, encompassing health, commerce, education, and the economy. Given the widespread impact of COVID-19 across numerous nations, it has strained hospital resources, oxygen reserves, and healthcare personnel. Consequently, there exists an urgent necessity to exploit sophisticated technologies such as artificial intelligence and the internet of things (IoT) to monitor patients effectively. This scholarly article proposes a prototype that integrates IoT and artificial intelligence (IA) for the surveillance of COVID-19 patients within healthcare facilities. Wearable IoT devices, equipped with embedded sensors, autonomously collect vital information like oxygen levels and body temperature. Notably, oxygen saturation and heart rate serve as significant markers in COVID-19 cases. These metrics are discerned through the deep learning capabilities of the TensorFlow library. The prototype aims to augment the intelligence of IoT sensors to identify these crucial signs through a trained model. A meticulously labeled dataset comprising oxygen saturation and heart rate data is amassed. Deep neural networks are deployed to prognosticate the disease's progression. The utilization of these technologies harbors the potential for rapid advancements in healthcare, thereby mitigating risks to human life and fostering more proactive responses to health crises.
Segmentation techniques for Arabic handwritten: a review Abdalla Sheikh, Ahmed; Sanusi Azmi, Mohd; Abdel Karim Abuain, Waleed; Abd Aziz, Maslita
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.pp1834-1841

Abstract

Image segmentation refers to the process of partitioning a page into distinct sections. This technique aims to improve and transform the image's representation into a more coherent and user-friendly format. Its common application involves identifying objects and boundaries (such as lines and curves) within images. However, this paper focuses on discussing segmentation methods specifically tailored for Arabic handwritten content. Dealing with the segmentation of Arabic handwritten material poses a significant challenge due to the diverse handwriting styles and the interconnection between Arabic letters. The paper will also touch on the classification of segmentation algorithms originally designed for modern documents, illustrating their adaptation in document processing. Furthermore, the paper will address the difficulties associated with segmenting Arabic handwritten content, including variations in writing style, the connected nature of Arabic characters, the complexity of Arabic cursive writing and as well as the diacritics challenges. Lastly, a concise overview of previously widely used segmentation techniques in various research endeavors will be provided.

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