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29,939 Article Results

Investigation of microwave absorption performance of anti-radiation plastic cement brick

10.11591/ijece.v15i3.pp2900-2910
Linda Mohd Kasim , Hasnain Abdullah Idris , Mohd Nasir Taib , Norhayati Mohamad Noor , Azizah Ahmad , Noor Azila Ismail , Nazirah Mohamat Kasim , Nur Qaisarah Anuar
The increasing demand for effective anti-microwave radiation materials motivates the exploration of sustainable and eco-friendly alternatives. This research investigates the microwave absorption properties of various brick compositions, including commercial brick (CB) and solid bricks (SB1, SB2 and SB3) incorporating recycled materials, polyethylene terephthalate (PET) and palm oil fuel ash (POFA). The dimension of the developed brick is 200×100×60 mm (length×width×height). The absorption performance of the bricks was measured in 100 mm and 60 mm thickness across the frequency range of 1 to 12 GHz using the naval research laboratory (NRL) free space arch method. At 100 mm thickness, SB3 shows the highest absorption up to-32.2061 dB at 1.98 GHz. At 60 mm thickness, SB1 achieved the maximum absorption at -57.6511 dB at 2.505 GHz. SB2 shows consistent average absorption performance at 15.2064 dB at 100 mm thickness and -19.5 dB at 60 mm thickness respectively. The compressive strength of the brick was measured, and it was shown that SB2 exhibited the highest average compressive strength of 7.17 MPa. Considering the standard wall thickness and brick strength, SB2 shows the most effective performance due to its enhanced composition and consistent performance across frequencies.
Volume: 15
Issue: 3
Page: 2900-2910
Publish at: 2025-06-01

Enhancing artificial neural network performance for energy efficiency in laboratories through principal component analysis

10.11591/ijaas.v14.i2.pp310-321
Desmira Desmira , Norazhar Abu Bakar , Mustofa Abi Hamid , Muhammad Hakiki , Affero Ismail , Radinal Fadli
This study investigates energy efficiency challenges during laboratory activities. Inefficient energy use in the practicum phase remains a critical issue, prompting the exploration of innovative forecasting models. This research employs artificial neural network (ANN) models integrated with principal component analysis (PCA) to predict energy consumption and optimize usage. The findings reveal that PCA components, including eigenvalues, eigenvectors, and matrix covariance values, significantly influence the ANN model's performance in forecasting energy production. The ANN training achieved a high correlation coefficient (R=1) with a mean squared error (MSE) of 0.045931 after 200,000 epochs, demonstrating the model's robustness. While testing results showed a moderate correlation (R=0.46169), the models demonstrated potential for refinement and scalability. This integration of ANN and PCA models provides a reliable framework for accurately forecasting energy usage, offering an effective strategy to enhance energy efficiency in laboratory settings. By optimizing energy consumption, this approach has the potential to reduce operational costs and environmental impact. The strong performance metrics highlight the practical utility of these models in educational contexts, contributing to sustainable energy management and better resource allocation. Furthermore, the reduction in energy-related environmental impacts underscores the broader applicability of these models for fostering sustainable development in similar contexts.
Volume: 14
Issue: 2
Page: 310-321
Publish at: 2025-06-01

Research capability of Filipino teacher educators: insights from a criterion-referenced test

10.11591/ijere.v14i3.32849
Jay-cen T. Amanonce , Conchita M. Temporal , Rudolf T. Vecaldo , Jhoanna B. Calubaquib , Antonio I. Tamayao , Maribel F. Malana , Ria A. Tamayo , Marie Claudette M. Calanoga
The research capability of Filipino teacher educators has been found to be lacking, which limits their ability to contribute effectively to academic research. This study aims to assess their foundational knowledge in research, as understanding their capability is essential for improvement. A quantitative approach was employed, evaluating 100 teacher educators from a state university in Northern Philippines using the research capability test (RCT), a validated criterion-referenced tool. Results showed that teacher educators generally possess average research capability, with significant differences based on educational attainment, field of specialization, and research teaching experience. Those with doctoral degrees, specializations in natural sciences and mathematics, and experience teaching research demonstrate higher capability. These findings suggest that, while basic research knowledge exists, there is a critical need for focused professional development programs to address specific gaps. Strengthening research capability not only improves the teacher educators’ performance but also enhances the overall quality of research outputs in the Philippine education system, ensuring long-term academic growth and global competitiveness.
Volume: 14
Issue: 3
Page: 1706-1716
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

Brain tumor classification for optimizing performance using hybrid RNN classifier

10.11591/ijeecs.v38.i3.pp1905-1913
Boya Nethappa Gari Kalavathi , Umadevi Ramamoorthy
Tumor is the uncontrolled growth of cancer cells in any part of the human body. Brain tumoris the leading cause of cancer deaths worldwide among adults and childrens. Early detection of brain cancers is essential. To prevent more issues, early defect detection is essential. Healthcare physicians may discover and categorize brain tumors with the use of computational intelligence-focused tools. An essential task for diagnosing tumors and choosing the right type of therapy is classifying brain tumors. Brain tumor identification and segmentation using magnetic resonance imaging (MRI) scans is now recognized as one of the most significant and difficult research areas in the world of medical image processing. The field of medical imaging has gained greatly from the use of artificial intelligence (AI) in the form of machine learning (ML) and deep learning (DL). DL has shown significant presentation, especially in the areas of brain tumor classification and segmentation. In this work, brain tumor classification for optimizing performance using hybrid recurrent neural network (RNN) classifier is presented. Different types of brain tumors are classified using a mix of RNN and inception residual neural network (ResNet). This strategy will produce improved F1-score, precision, accuracy, and recall scores.
Volume: 38
Issue: 3
Page: 1905-1913
Publish at: 2025-06-01

ClearNet: auto-encoder based denoising model for endoscopy images

10.11591/ijeecs.v38.i3.pp1990-2000
Vikrant Shokeen , Sandeep Kumar , Vidhu Mathur , Amit Sharma , Indrajeet Gupta , Parita Jain
Gastrointestinal (GI) endoscopy images play a crucial role in the detection and diagnosis of diseases within the digestive tract. However, the development of effective computer vision models for automated analysis and denoising of endoscopy images faces challenges arising from the diverse nature of abnormalities and the presence of image artefacts. In this work, the utilization of an encoder-decoder network for denoising GI endoscopy images using the HyperKvasir dataset has been analyzed. This approach involves training a custom encoder-decoder model on this extensive multiclass endoscopy image dataset and assessing its performance across 23 prevalent classes of digestive tract issues. Here experiments showcase the model’s ability to learn robust visual representations from endoscopic data, enabling accurate disease prediction. The achieved promising results highlight the potential of encoder-decoder architectures as a foundational framework for computer-aided endoscopy analysis with a specific focus on denoising applications. Our model manages to increase the peak signal-tonoise ratio (PSNR) of original-noisy pair from 19.118954 to 69.892631 for original-reconstructed pair showcasing almost perfect reconstruction.
Volume: 38
Issue: 3
Page: 1990-2000
Publish at: 2025-06-01

Need analysis: development of a teaching module for enhancing higher-order thinking skills of primary school students

10.11591/ijere.v14i3.30335
Hamidah Mat , Toto Nusantara , Adi Atmoko , Yusuf Hanafi , Siti Salina Mustakim
This research identified a pressing need to create specialized teaching modules for electrical topics within the science curriculum that target students’ higher-order thinking skills (HOTS). Despite the recognized significance of HOTS in improving students’ educational achievements, science educators encounter obstacles when attempting to effectively teach these skills. To tackle this challenge, the study utilized a qualitative research methodology, conducting semi-structured interviews with six science teachers from diverse Malaysian schools. The primary objective was to pinpoint the necessity for developing instructional modules that enhance students’ HOTS in primary school science subjects. This study revealed four key themes arising from the needs assessment: the importance of HOTS knowledge, obstacles in teaching HOTS, effective teaching strategies, and the actual teaching of HOTS. This study underscores the critical need for enhanced professional development opportunities for teachers to effectively impart HOTS and stresses the importance of providing suitable teaching resources. By developing these tailored modules, students’ critical thinking and problem-solving skills can be nurtured, paving the way for their academic and professional success. Consequently, the study’s recommendations offer valuable insights for policymakers, educators, and researchers seeking to create impactful teaching modules that cater to students’ HOTS in primary school science subjects.
Volume: 14
Issue: 3
Page: 1643-1650
Publish at: 2025-06-01

Generator analysis and comparison of working fluids in the organic Rankine cycle for biomass power plants using Aspen Plus software

10.11591/ijape.v14.i2.pp467-478
Yulianta Siregar , Wahyu Franciscus Sihotang , Nur Nabila Mohamed
The organic Rankine cycle utilizes low-temperature heat (flue heat) in power plants to produce electrical power. Several factors, including the working fluid's temperature and pressure, influence the efficiency of an organic Rankine cycle. This research method includes calculations using the gasification method in calculating electrical energy in PLTBM and calculating the experimental results of a series of organic Rankine cycles by taking into account the temperature and pressure of the working fluid using Aspen Plus Software, which is analyzed using statistical methods. The results of research using the gasification method in PLTBM fuel produced power of 27,279.38 MW/year for coconut shells, 6,489.66 MW/year for rice husks, and 532.62 MW/year for corn cobs. For the organic Rankine cycle series, rice husk waste produces the largest power of 8,336.67 kW, for coconut shells of 569,723.95 kW. For corn cobs of 358,639.63 with an efficiency value of organic working fluid in R-22 of 25.37% and the R-32 organic working fluid of 11.92% at a temperature of 125 °C in coconut shell waste, it can be concluded that the temperature of the working fluid has more influence on the efficiency of the organic Rankine cycle than the pressure of the working fluid.
Volume: 14
Issue: 2
Page: 467-478
Publish at: 2025-06-01

Multilayer stacking for polycystic ovary syndrome diagnosis

10.11591/ijai.v14.i3.pp1968-1975
Kazi Abu Taher , Samia Ahmed , Jannatul Ferdous Esha , Md. Sazzadur Rahman , A. S. M. Sanwar Hosen
Polycystic ovary syndrome (PCOS) is a complicated hormonal condition that is experienced by women. Despite extensive research, the precise reason be hind PCOS remains unknown, and effective treatments are still lacking. Thus, early diagnosis and treatment have a significant positive impact on the health of women. Recently, there has been remarkable performance demonstrated by machine learning (ML)-based detection models for PCOS identification. They are fast and low cost compared to the traditional processes. In this work, a multi stacking PCOS detection model is proposed using K-fold cross validation. The model uses three different ML algorithms namely: na¨ıve Bayes (NB), ran dom forest (RF), and logistic regression (LR) as base classifiers and a neural network, multi-layer perception (MLP) as meta model. This approach utilizes two feature selection techniques and compares the performances on the stack ing methods. Among the two feature selection techniques, Pearson correlation approach performed better with average 98.79% accuracy, 99.17% sensitivity, 98.40% specificity, and 98.79% f1-score.
Volume: 14
Issue: 3
Page: 1968-1975
Publish at: 2025-06-01

Robust two-stage object detection using YOLOv5 for enhancing tomato leaf disease detection

10.11591/ijai.v14.i3.pp2246-2257
Endang Suryawati , Syifa Auliyah Hasanah , Raden Sandra Yuwana , Jimmy Abdel Kadar , Hilman Ferdinandus Pardede
Deep learning facilitates human activities across various sectors, including agriculture. Early disease detection in plants, such as tomato plant that are susceptible to diseases, is critical because it helps farmers reduce losses and control the disease spread more effectively. However, the ability of machine to recognize diseased leaf objects is also influenced by the quality of data. Data collected directly from the field typically yields lower accuracy due to challenges faced in machine interpretation. To address this challenge, we propose a two-stage detection architecture for identifying infected tomato plant classes, leveraging YOLOv5 to detect objects within the images obtained from the field. We use Inception-V3 for classifying objects into known classes. Additionally, we employ a combination of two dataset: PlantDocs which represent field data, and PlantVillage dataset which serves as a cleaner dataset. Our experimental results indicate that the use of YOLOv5 in handling data under actual field conditions can enhance model performance, although the accuracy value is moderate (62.50 %).
Volume: 14
Issue: 3
Page: 2246-2257
Publish at: 2025-06-01

Viral hepatitis morbidity and mortality data in major urban cities in the Philippines

10.11591/ijphs.v14i2.24577
Rael S. Manriquez , Mark Anthony J. Torres , Cesar G. Demayo
This study investigates the transmission, impact, and prevention of viral hepatitis A (HAV), hepatitis B (HBV), hepatitis C (HCV), hepatitis D (HDV), and hepatitis E (HEV) in the National Capital Region (NCR) and Region 7, Philippines, from 1960 to 2020. These infections significantly contribute to liver complications, including cirrhosis and hepatocellular carcinoma, affecting mental well-being and posing risks to pregnant women. Although hepatitis mortality is notable, complete treatment can mitigate the risk. Transmission occurs through various routes, such as blood products, body secretions, and perinatal routes. The study underscores the importance of understanding transmission and implementing screening and prevention measures. Vaccination, particularly for Hepatitis A and B, is crucial, reshaping disease epidemiology through universal infant immunization. Challenges like low vaccination coverage persist, especially among children and healthcare workers. Analyzing mortality data reveals a significant recent decrease attributed to government efforts and vaccination programs since 1995. Despite regional variations, mortality remains relatively low. The study recommends prioritizing and expanding vaccination programs, raising awareness, improving healthcare accessibility, and strengthening surveillance systems. Coupled with community engagement, these measures promise sustained success against viral hepatitis, reinforcing the observed trend in mortality reduction.
Volume: 14
Issue: 2
Page: 1015-1021
Publish at: 2025-06-01

Novel preemptive intelligent artificial intelligence-model for detecting inconsistency during software testing

10.11591/ijai.v14.i3.pp1781-1789
Sangeetha Govinda , B. G. Prasanthi , Agnes Nalini Vincent
The contribution of artificial intelligence (AI)-based modelling is highly significant in automating the software testing process; thereby enhancing the cost, resources, and productivity while performing testing. Review of existing AI-models towards software testing showcases yet an open-scope for further improvement as yet the conventional AI-model suffers from various challenges especially in perspective of test case generation. Therefore, the proposed scheme presents a novel preemptive intelligent computational framework that harnesses a unique ensembled AI-model for generating and executing highly precise and optimized test-cases resulting in an outcome of adversary or inconsistencies associated with test cases. The ensembled AI-model uses both unsupervised and supervised learning approaches on publicly available outlier dataset. The benchmarked outcome exhibits supervised learning-based AI-model to offer 21% of reduced error and 1.6% of reduced processing time in contrast to unsupervised scheme while performing software testing.
Volume: 14
Issue: 3
Page: 1781-1789
Publish at: 2025-06-01

Experiential learning using Google Classroom on students’ academic performance and motivation in language subject

10.11591/ijere.v14i3.29489
Loh Boon Ping , Norasykin Mohd Zaid , Nor Hasniza Ibrahim , Johari Surif , Megat Aman Zahiri Megat Zakaria , Hendro Permadi
This study investigates the effectiveness of experiential learning using Google Classroom on year 2 students’ academic performance and motivation in Malay language. This study also highlighted the elements in Google Classroom’s experiential learning that motivate students to achieve academic performance. The study conducted with 32 students at Chinese primary school in Johor Bahru; utilized online pre-tests, post-tests, and 5-point Likert scale online questionnaire to identify students’ motivation level. Results revealed significant improvements in students’ Malay language post-test scores, indicating the effectiveness of experiential learning using Google Classroom. Descriptive statistics showed a high level of student motivation, significantly motivated by the experiential learning treatment using Google Classroom, with the materials provided by the teacher being the most preferred by students and effective element in motivating them to achieve academic success. The study suggests that implementing experiential learning with Google Classroom positively influences academic performance in Malay language. Teachers, schools, and communities are suggested to review current learning methods and platforms; and strive to incorporate experiential learning through Google Classroom to enhance students’ academic performance in Malay language. Future studies are encouraged to provide more reliable data, particularly within the context of Chinese primary schools in Malaysia, to further enrich educational practices.
Volume: 14
Issue: 3
Page: 2304-2313
Publish at: 2025-06-01

Systematic review of teaching methods in language education: trends and innovation

10.11591/ijere.v14i3.31774
Nur Atikah Mohd Noor , Zamri Mahamod , Nurfaradilla Mohamad Nasri
This systematic literature review (SLR) examines the developing pedagogical methods in language instruction, emphasizing modern practices, technological advancements, and cultural diversity. The research seeks to fill significant gaps in the literature by examining effective pedagogical strategies that improve language acquisition results. In accordance with the preferred reporting items for systematic reviews and meta-analyses (PRISMA) methodology, a systematic search of the Scopus and Web of Science (WoS) databases was performed, focusing on publications published from 2022 to 2024. 30 primary studies were examined through topic synthesis and integrative analysis. The findings are categorized into three themes: i) contextualized and adaptive teaching methods in language education: balancing traditional approach and innovation; ii) impact of innovative teaching on language learning: technology and student engagement; and iii) cultural diversity’s impact on language education and engagement. The results underscore the imperative of modifying teaching strategies according to different situations and incorporating technology to enhance engagement and results. Furthermore, culturally sensitive techniques were demonstrated to improve inclusivity in multilingual classrooms. These insights are pertinent to academic and professional settings, indicating widespread significance for enhancing communication and cross-cultural skills. Future research should investigate the long-term effects of these initiatives and ensure equal access to educational resources and teacher training.
Volume: 14
Issue: 3
Page: 2031-2041
Publish at: 2025-06-01

Exploring the factors influencing innovative teaching practices in Moroccan primary schools: an exploratory study

10.11591/ijere.v14i3.32917
Karim Lkamel , Jalal Assermouh
In education, pedagogical innovation is crucial for improving student learning outcomes, but teachers’ adoption of innovative practices is influenced by various sociodemographic factors, which remain underexplored. This study aims to investigate how factors such as age, gender, education level, and prior training shape teachers’ engagement with innovative teaching methods. A quantitative analysis of 110 teachers from multiple schools was conducted, utilizing multiple correspondence analysis (MCA) to identify distinct teacher profiles based on their innovation practices. The findings revealed four key profiles: non-innovative teachers, who rely on traditional methods; active teachers, who adopt active learning strategies; untrained teachers, who work without formal training; and innovative teachers, who integrate information and communication technologies (ICT) and blended learning techniques. The study concludes that sociodemographic factors significantly impact the adoption of pedagogical innovation. To address this, targeted professional development and tailored policies are needed to support teachers in overcoming barriers and adopting innovative practices. By promoting a more inclusive and adaptive approach to teacher training, this research offers valuable insights to improve teaching effectiveness and ultimately enhance student engagement and learning outcomes.
Volume: 14
Issue: 3
Page: 1834-1843
Publish at: 2025-06-01
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