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30,411 Article Results

Challenges of load balancing algorithms in cloud computing utilizing data mining tools

10.11591/ijece.v15i3.pp3449-3457
Anouar Ben Halima , Hafssa Benaboud
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.
Volume: 15
Issue: 3
Page: 3449-3457
Publish at: 2025-06-01

Active online learning with remote sensing data in higher education

10.11591/ijere.v14i3.30096
Khuralay Moldamurat , Sabyrzhan Atanov , Karakat Nagymzhanova , Luigi La Spada , Dinara Kalmanova , Sapiya Tazhikenova , Syrym Zhanzhigitov , Altynbek Zhakupov , Assylkhan Yessilov , Makhabbat Bakyt
The increasing popularity of online learning has created a need for effective methods to enhance educational quality. This study addresses this need by developing and evaluating an active online learning model incorporating remote sensing data (RSD). The study included a pedagogical experiment with 181 students divided into control and experimental groups. The model included an interactive database, a web portal with tools for processing and visualizing RSD, and the implementation of active learning methods. Data were collected through testing, analysis of completed projects, and questionnaires. Quantitative and qualitative analysis methods were used to process the data. The pedagogical experiment showed that the model improved students’ average scores, increased the number of students with high levels of knowledge acquisition, and enhanced motivation. Thus, the use of RSD and active learning methods in online education is a promising approach to improve the quality of the educational process and foster students’ digital competence.
Volume: 14
Issue: 3
Page: 2346-2357
Publish at: 2025-06-01

Examining the research and academic writing needs of preservice elementary teachers: a mixed-methods study

10.11591/ijere.v14i3.29888
Bonjovi Hassan Hajan , Abubakar Jaddal Radjuni , Alhisan Utoh-asim Jemsy
In teacher education, research plays a central role in the preparation and professional development of preservice teachers. Preservice teachers’ knowledge of research and their academic writing skills serve as a pathway for successfully completing a research project. This sequential-explanatory mixed methods study was conducted to provide an in-depth understanding of the preservice elementary teachers’ needs on research and academic writing. A total of 80 preservice elementary teachers participated in the study. Data were collected online using a structured questionnaire and an interview guide. Drawing from both the quantitative and qualitative analyses, a multitude of the participants’ research and academic writing needs were uncovered. On the research component, the participants’ needs encompassed a wide range of areas, including knowledge of research methodology, access to quality data, expert support, among others. As for the academic writing, the participants’ needs varied from language use, structure and mechanics to the writing process. Based on the findings, the study outlines practical implications useful for teaching research writing within the context of teacher education.
Volume: 14
Issue: 3
Page: 2070-2078
Publish at: 2025-06-01

Object detection in printed circuit board quality control: comparing algorithms faster region-based convolutional neural networks and YOLOv8

10.11591/ijece.v15i3.pp2796-2808
Jaja Kustija , Diki Fahrizal , Muhamad Nasir , Andi Adriansyah , Muhammad Husni Muttaqin
Along with the development of electronic technology, the integration of numerous components on printed circuit board (PCB) boards has resulted in increasingly complex and intricate layouts. Small defects in traces can lead to failures in electronic functions, making the inspection of PCB surface layouts a critical process in quality control. Given the limitations of manual inspection, which struggles to detect such defects due to their size and complexity, there is a growing need for a PCB inspection system that utilizes automated optical inspection (AOI) based on deep learning detection. This research develops and compares two deep learning algorithms, faster region-based convolutional neural networks (R-CNN) and YOLOv8, to identify the most effective algorithm for detecting defects on PCB layouts. The findings of this study indicate that the YOLOv8 algorithm outperforms faster R-CNN, with the YOLOv8x variant emerging as the best model for defect detection. The YOLOv8x model achieved performance scores of 0.962 (mAP@50), 0.503 (mAP@50:95), 0.953 (Precision), 0.945 (Recall), and 0.949 (F1-score). These results provide a strong foundation for further research into the application of AOI for PCB defect detection and other quality control processes in manufacturing, using optimized deep learning models.
Volume: 15
Issue: 3
Page: 2796-2808
Publish at: 2025-06-01

Assessing Saudi learners’ engagement with social media in English classrooms

10.11591/ijere.v14i3.28509
Mohammad Jamshed , Iftikhar Alam , Abduh Almashy , Wahaj Unnisa Warda
The study assessed Saudi English as a foreign language (EFL) learners’ perspectives on utilizing social media for educational and instructional activities, their experience with learning and teaching on social media, and how frequently they utilize these platforms to improve their language skills. The researcher randomly selected 288 EFL students from male and female campuses of College of Science and College of Business Administration, Prince Sattam bin Abdulaziz University. Respondents answered a questionnaire containing modified items from earlier studies. The data collected was examined using quantitative methods. The findings of the study revealed that tech-savvy Saudi EFL learners held exceptionally positive attitudes toward social media utilization as an effective language learning tool. It was also revealed that both learners and instructors had a very positive experiences utilizing social media for educational purposes, commonly employing it for different academic and educational activities in EFL classes. The study has implications for students and instructors because social networking sites and social media may be tailored to meet the demands of tech-savvy Saudi learners when conventional instruction no longer conforms to the taste of modern learners.
Volume: 14
Issue: 3
Page: 2450-2460
Publish at: 2025-06-01

Fault diagnosis of electric motors using vibration signal analysis

10.11591/ijape.v14.i2.pp300-307
Mandeep Singh , Tejinder Singh Saggu , Arvind Dhingra
In industrial applications, especially in manufacturing environments, electric motors are employed practically everywhere. They are necessary for many different sectors, which can sometimes make it challenging to prevent malfunctions and keep them operating at their best. Numerous defects can affect how well they work, but bearing-related errors are the most frequent reasons for motor failures. This research uses temporal and frequency domain analysis of vibration signals to identify motor faults. A public domain database has been used for the investigation and analysis. The findings show that electric motor problems, including inner raceway, outer raceway, and rolling element fault, can be identified and diagnosed using the time and frequency domain features extracted from the vibration signals. The effectiveness of the proposed technique is shown by comparing it with both the time domain and frequency domain techniques. The accuracy of the time domain and frequency domain techniques is 85.4% and 91.6% respectively. However, the proposed hybrid technique has a far better accuracy of 95.8% as compared to the two techniques.
Volume: 14
Issue: 2
Page: 300-307
Publish at: 2025-06-01

Machine learning-based hybrid emotions recognition model using electroencephalogram signals

10.11591/ijece.v15i3.pp3180-3190
Tarun Kumar , Rajendra Kumar , Ram Chandra Singh
This paper uses Hindi video clips to propose an electroencephalogram (EEG) signal-based hybrid system for emotion identification. EEG signals cannot be altered, unlike other forms of expressiveness-like voice and facial emotion. The suggested approach uses a self-created dataset under the control environments. Accuracy is the main objective of the proposed model. This study used a self-created constructed using an 8-channel unicorn black hybrid EEG machine on 30 participants while they viewed Hindi movie video clips mimicking emotions: happy, fearful, sad, and neutral. The proposed model used a two-hybrid classifier support vector machine (SVM) and k-nearest neighbor (KNN), implemented using MATLAB R2017a. In the proposed implementation, the four emotion classification categories (happy, sad, fear, and neutral) observed an average accuracy of 60.832%. The results of the presented study were compared with two recent systems. It was found that the proposed system observed better accuracy for the category of NHP five classes and the category of HP Five Classes.
Volume: 15
Issue: 3
Page: 3180-3190
Publish at: 2025-06-01

Artificial intelligence for automatic moderation of textual content in online chats and social networks

10.11591/ijece.v15i3.pp3396-3409
Solomiia Liaskovska , Rex Bacarra , Yevhen Martyn , Volodymyr Baidych , Jamil Alsayaydeh
The article explores fundamental techniques for converting text into numerical data for machine learning algorithms. It meticulously examines various methods, including word vector representation via neural networks like Word2Vec, and explains the principles behind linear models such as logistic regression and support vector machines. Convolutional neural networks (CNN) and long short-term memory (LSTM) methods are also discussed, covering their components, mechanisms, and training processes. The research extends to developing and testing software for spam detection, hate speech identification, and recognizing offensive language. Using two datasets—one for labeled text messages and another for Twitter posts—the study analyzes data to address challenges like imbalanced data. A comparative analysis among linear models, deep neural networks, and single-layer models, using pre-trained bidirectional encoder representations from transformers (BERT) network, reveals promising results. The convolutional neural network stands out with a remarkable accuracy of 0.95. The study also adapts neural network architectures for hate speech and offensive language classification.
Volume: 15
Issue: 3
Page: 3396-3409
Publish at: 2025-06-01

Enhancing training performance for small models using data-centric approaches

10.11591/ijece.v15i3.pp2951-2964
Reda A. El-Khoribi , Eid Emary , Amr Essam Hassan
In this work, we propose a new system to improve the performance of classification models by applying data-centric principles. The system optimizes datasets by removing poor-quality samples and generating high-quality synthetic data. We tested the system on various classification models and datasets, measuring its performance with accuracy, precision, recall, and F1-score. The results showed significant improvements in classification performance, highlighting the effectiveness of this data-centric approach. While the scalability to large-scale datasets is still an open question, it offers great potential for future research. This approach could be valuable in critical areas like healthcare, finance, and autonomous systems, where high-quality data is crucial. Future work could explore advanced data augmentation, adapting the system for different data types like text and time-series, and extending it to semi-supervised and unsupervised learning. Our findings emphasize the importance of data quality in achieving better model performance, often overlooked in favor of model architecture. By advancing data-centric artificial intelligence (AI), this work offers a practical framework for researchers and practitioners to optimize datasets and improve machine learning systems.
Volume: 15
Issue: 3
Page: 2951-2964
Publish at: 2025-06-01

Analysis of geothermal technology development in the Colombian energy transition to 2050 using system dynamics

10.11591/ijece.v15i3.pp2523-2533
Diego Alberto Carreño , Isaac Dyner , Enrique Ángel Sanint , Andrés Julián Aristizábal
This research analyzes the current and future prospects of geothermal energy in Colombia using a system dynamics model. The study focuses on evaluating geothermal potential linked to hydrothermal systems, surface manifestations like geysers, and areas near volcanoes. The model, projecting up to 2050, offers a comprehensive assessment of national geothermal potential, considering technical, economic, regulatory, and social factors that influence its integration into the energy matrix. Key findings highlight the need for adjustments to the existing regulatory framework, which currently lacks sufficient incentives for geothermal project development. Additionally, the study underscores the importance of implementing stronger government policies and incentives to promote this renewable energy source. Proper social and environmental management, with active involvement of local communities, is also identified as crucial for project success. The system dynamics approach effectively models the complex interrelationships between variables shaping the future of geothermal energy in Colombia. The developed model serves as a novel tool for technological foresight in this strategic field, identifying obstacles and opportunities to unlock Colombia's significant geothermal potential and providing a systemic perspective on this critical issue for the national energy transition.
Volume: 15
Issue: 3
Page: 2523-2533
Publish at: 2025-06-01

Arabic offensive text classification using emojis: Including emoji data in Arabic natural language processing

10.11591/ijece.v15i3.pp3332-3345
Amal Albalawi , Wael M. S. Yafooz
In the digital social media ecosystem, controlling offensive language requires advanced algorithmic tools. This study examines the influence of including emojis translation in the text preprocessing stage of the classification of offensive Arabic text. A novel dataset of 10,000 Arabic tweets was developed, with rigorous annotations to classify content as offensive or non-offensive. The dataset was meticulously annotated and validated using Cohen's kappa (CK) and Krippendorff's Alpha (α) to ensure consistency and accuracy. Several experiments evaluated the dataset with the most common text classification models: seven machine learning (ML) classifiers and three deep learning (DL) models. Two experimental sets were conducted: one with emoji translation in preprocessing to enrich text input and another without emoji translation to directly assess the impact of emojis on classification accuracy. The findings indicate that emojis significantly affect text classification models, with advanced DL models showing higher sensitivity to contextual nuances conveyed by emojis compared to traditional ML classifiers. This research highlights the dual role of emojis, which are often linked to positive emotions and offensive contexts, adding complexity to digital communication. It contributes to the development of more accurate and context-sensitive natural language processing (NLP) tools.
Volume: 15
Issue: 3
Page: 3332-3345
Publish at: 2025-06-01

Routing mechanism ensuring congestion free communication in wireless sensor networks enabled by internet of things for applications in smart healthcare

10.11591/ijece.v15i3.pp2874-2887
Kasi Venkata kiran , T. Srinivasa Rao
Recently, the architecture of internet of things (IoT) has been applied towards gathering physical, biological, and dynamic signs of the patients within consumer-oriented electronic-health or health services. In these healthcare systems, various therapeutic sensors are placed on patients to monitor vital signs. However, the process of collecting data in IoT-enabled wireless sensor networks (WSNs) often faces congestion issues, resulting in packet loss, reduced reliability, and decreased throughput. To tackle this challenge, this proposed paper recommends a distributed congestion control algorithm tailored specifically representing IoT-enabled WSNs used in healthcare contexts. The suggested approach improves congestion by employing a priority-based data routing strategy and introduces the precedence queue- based scheduling method to improve reliability. Then the effectiveness of this congestion control process is analyzed statistically, and its performance is verified across extensive simulations and real-life experiments. This solution shows potential for applications like early warning systems for identifying peculiar heart rates, blood pressure, electromyography (EMG), and electrocardiogram (ECG) in hospital or home care settings, thus advancing the current diagnosis capabilities.
Volume: 15
Issue: 3
Page: 2874-2887
Publish at: 2025-06-01

TPACK-universal design for learning for Malaysian intellectual disability education: low-high tech integration

10.11591/ijere.v14i3.31801
Rosnani Saini , Zaimuariffudin Shukri Nordin , Mohd Hafizan Hashim , Mohamad Taha Abol
The integration of technological pedagogical content knowledge (TPACK) and universal design for learning (UDL) in special education remains challenging, particularly for intellectual disabilities (ID) learners in East Malaysia. This study investigated how special education teachers utilize technology tools and integrate UDL principles with TPACK frameworks to support ID learners, addressing the need for inclusive education aligned with sustainable development goal 4 (SDG 4). Using a qualitative approach, data were collected through semi-structured interviews, classroom observations, and document analysis from four purposively selected special education teachers in two East Malaysian secondary schools offering special education integration programs (SEIP). Thematic analysis revealed three key findings: i) low technology supports for accessibility and engagement; ii) high technology integration for personalization and empowerment; and iii) integrating of TPACK and UDL principles challenges due to limited understanding and resource constraints. The study concluded that while teachers show commitment to technology use, there is a pressing need for targeted professional development to enhance TPACK and UDL competencies. These findings align with SDG 4’s focus on quality education for all, emphasizing how enhanced teacher training and effective technology integration can significantly improve the quality and inclusivity of education for ID learners.
Volume: 14
Issue: 3
Page: 2099-2106
Publish at: 2025-06-01

Trend analysis of machine learning techniques for traffic control based on bibliometrics

10.11591/ijai.v14.i3.pp2402-2411
Hilda Luthfiyah , Eko Syamsuddin Hasrito , Tri Widodo , Sofwan Hidayat , Okghi Adam Qowiy
Machine learning in traffic control for intelligent transportation systems (ML-ITSTC) aims to enhance user coordination and safety within transportation networks, ultimately improving overall traffic system performance. ML-ITSTC is achieved by leveraging data to execute machine learning algorithms in intelligent transportation management and optimizing traffic flow to prevent or reduce congestion. This paper conducts bibliometric analysis to explain the research status, development trajectory, and challenges of ML-ITSTC, drawing insights from literature in the Scopus database literature covering 2013 to November 2023. The bibliometric analysis of ML-ITSTC includes: performance analysis, science mapping analysis, and citation analysis. The evaluation of ML algorithm trends over the 10-year span indicates that traffic prediction (TP), neural networks, and deep learning are frequently used keywords. Further, an examination of keywords used over the entire period and in 2023 (up to November) shows that reinforcement learning (RL) is the latest popular approach for traffic control in transportation. The results provide a comprehensive view of the opportunities and challenges in ML-ITSTC, covering data, models, and applications, offering researchers insights into the current and future directions of ML-ITSTC research.
Volume: 14
Issue: 3
Page: 2402-2411
Publish at: 2025-06-01

Generation of business process modeling notation diagrams from textual functional requirements in Indonesian

10.11591/ijece.v15i3.pp2938-2950
Sholiq Sholiq , Muhammad Ainul Yaqin , Apol Pribadi Subriadi , Bambang Setiawan
This study proposes a method for converting textual functional requirements in Indonesian to business process modeling notation (BPMN) diagrams. has not been found in previous studies. The use of BPMN diagrams to present software functional requirements has the advantage of being better in terms of presenting sequential activities than using use case diagrams. On the other hand, the requirements obtained from clients in the requirements collection session are more in the form of user stories, namely text in natural language. The method used in this study is to integrate natural language processing and a set of mapping rules and BPMN diagram generation rules. The proposed method is tested with 15 functional requirement cases from three applications, namely mini hospital software, employee cooperatives, and stores. Then, the results are compared with diagrams made by experts for the same cases. The test results show an accurate level of the proposed method of 94.4%.
Volume: 15
Issue: 3
Page: 2938-2950
Publish at: 2025-06-01
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