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29,061 Article Results

Tailoring collaborative learning with jigsaw and VARK: a case study in teaching physics with environmental protection

10.11591/ijere.v14i6.32823
Ngan Hai My Le , Anh Thi Kim Nguyen
Collaboration is a crucial 21st century skill, requiring learning environments that foster teamwork while leveraging students’ individual strengths. This study aimed to enhance collaboration using the jigsaw strategy, which was adapted to students’ learning styles based on the model: visual, aural, read/write, and kinesthetic (VARK). The study involved 27 tenth-grade students in Ho Chi Minh City and focused on the topic “physics with environmental protection.” Students were initially grouped by learning styles into expert groups and later reorganized into mixed jigsaw groups to collaboratively address tasks related to environmental issues. A quasi-experimental design was employed, utilizing pre- and post-test self-assessment surveys, video observations, and group discussions to assess collaborative performance. Quantitative data were analyzed using the Wilcoxon signed rank test, while qualitative data provided deeper insights. Results demonstrated a significant improvement in team support (p=0.030), suggesting that aligning learning tasks with students’ styles foster group cohesion. However, participation and contribution showed minimal improvement, with students preferring reading/writing styles facing challenges in adapting to group activities. While the integration of jigsaw and VARK proved effective in enhancing collaboration, the study underscores the need to develop strategies to accommodate diverse learning preferences. Future research should involve larger sample sizes and consider teachers’ perspectives to optimize the practical implementation of learning styles in collaborative learning environments.
Volume: 14
Issue: 6
Page: 4364-4374
Publish at: 2025-12-01

Interpersonal conflicts as a predictor of academic performance among secondary school students

10.11591/ijere.v14i6.33799
Abigael Cherono , Ciriaka Gitonga Muriithi , Elizabeth Atieno Obura
There is a global concern about promoting peaceful coexistence in school learning environments. Interpersonal conflicts in schools can lead to academic difficulties and violent interactions among the students. The academic performance among students in Embu East Sub-County has not been satisfactory and has become an issue of concern. The study conducted a quantitative correlational study to determine the relationship between interpersonal conflicts and students’ academic performance. A correlational research design was used, involving a sample of 357 form 2 students sampled through simple random sampling. Data were collected using the interpersonal conflict questionnaire and document analysis. The items in the questionnaire yielded a Cronbach’s alpha coefficient value of 0.759. Data were analyzed using inferential statistics, including Pearson’s correlation and simple linear regression analysis. The findings revealed a weak negative and statistically significant relationship between interpersonal conflicts and academic performance among the students (β=-0.152**, p=0.008). The study concludes that interpersonal conflict engagement among students leads to poor academic performance. Therefore, schools should prioritize integrating programs that build interpersonal and social skills among students to improve interpersonal relationships and academic performance.
Volume: 14
Issue: 6
Page: 4821-4831
Publish at: 2025-12-01

Optimization of a level shifter integrated with a gate driver using TSMC 130 nm CMOS technology

10.11591/ijece.v15i6.pp5223-5233
Hicham Guissi , Khadija Slaoui
Modern electronic systems increasingly operate across multiple voltage domains, necessitating robust and efficient level shifter (LS) circuits to ensure reliable inter-domain communication. In low-power digital applications, minimizing propagation delay and transition time is critical for achieving high-speed and energy-efficient operation. This work presents a high-performance level shifter optimized for integration within Li-ion battery charger systems. The proposed design achieves a substantial reduction in propagation delays from 0.15 to 0.09062 ns while preserving signal integrity. When integrated with a gate driver, the overall structure exhibits a propagation delay of 0.20468 ns and a transition time of 0.014 ns, marking a significant improvement from the previous 0.036 ns. Furthermore, the proposed circuit occupies only 0.00039 mm² of silicon area, representing a 92% reduction compared to prior implementations (0.05 mm²). The complete design was implemented using Taiwan semiconductor manufacturing company (TSMC) 130 nm complementary metal–oxide– semiconductor (CMOS) technology, with both schematic simulation and layout carried out in the Cadence Virtuoso design environment. These results underscore the potential of the proposed solution for compact and high-efficiency system-on-chip (SoC) battery management applications.
Volume: 15
Issue: 6
Page: 5223-5233
Publish at: 2025-12-01

Autism virtual reality education media integration in applied behavior analysis training

10.11591/ijere.v14i6.34093
Yunxia Nie , Poonsri Vate-U-Lan , Panneepa Sivapirunthep
This study examined the impact of autism virtual reality education (autism VR-Ed) media in enhancing college student teachers’ skills in applied behavior analysis (ABA) for children with autism spectrum disorder (ASD). A total of 40 student teachers from Guangxi College for Preschool Education, China, participated in a three-month experiment using a 9-task autism VR-Ed media. Pre- and post-tests showed significant improvement in ABA skills, with scores rising from 18.05 (SD=3.00) to 41.10 (SD=2.25), t(39)=-48.394, p<.05. All participants achieved over 60 points, confirming effective skill acquisition. A perception survey revealed positive attitudes toward the media, highlighting its ease of use, engagement, and relevance to future professional needs. These results demonstrated the potential of VR technology to bridge the gap between theory and practice in special education by offering immersive learning experiences. Autism VR-Ed media enhanced ABA training beyond traditional methods, supporting the professional growth of special education teachers. This study contributed to integrating VR technology into special education curricula and improving teacher training quality, thereby effectively supporting the rehabilitation of children with ASD. Future research should explore the long-term benefits of VR-based training and its broader applications and assess its impact on the learning outcomes of children with ASD.
Volume: 14
Issue: 6
Page: 4677-4688
Publish at: 2025-12-01

Integration of ultra-wideband elliptical antenna with frequency selective surfaces array for performance improvement in wireless communication

10.11591/ijece.v15i6.pp5515-5523
Saleh Omar , Chokri Baccouch , Rhaimi Belgacem Chibani
The integration of frequency selective surfaces (FSS) with antennas has gained significant attention due to its ability to enhance key radio frequency (RF) performance parameters such as gain, directivity, and bandwidth, making it highly beneficial for modern wireless communication systems. In this work, we propose and investigate an ultra-wideband (UWB) elliptical antenna operating within the 5.2 to 10 GHz frequency range. To further improve its performance, we integrate the antenna with a 13×13 FSS array. The impact of the FSS on the antenna’s characteristics is analyzed, showing a remarkable gain enhancement from 2.6 dBi (without FSS) to 10.05 dBi (with FSS). These results confirm the effectiveness of FSS integration in optimizing UWB antenna performance, making it a promising approach for advanced wireless communication applications.
Volume: 15
Issue: 6
Page: 5515-5523
Publish at: 2025-12-01

Improving network security using deep learning for intrusion detection

10.11591/ijece.v15i6.pp5570-5583
Mohammed Al-Shabi , Anmar Abuhamdah , Malek Alzaqebah
As cyber threats and network complexity grow, it is crucial to implement effective intrusion detection systems (IDS) to safeguard sensitive data and infrastructure. Traditional methods often struggle to identify sophisticated attacks, necessitating advanced approaches like machine learning (ML) and deep learning (DL). This study explores the application of ML and DL algorithms in IDS. Feature selection techniques, such as correlation and variance analysis, were employed to identify key factors contributing to accurate classification. Tools like WEKA and MATLAB supported data pre-processing and model development. Using the UNSW-NB15 and NSL-KDD datasets, the study highlights the superior performance of random forest (RF) and multi-layer perceptron (MLP) algorithms. RF ensemble decision trees and MLP multi-layered architecture enable accurate attack detection, demonstrating the potential of these advanced techniques for enhanced network security.
Volume: 15
Issue: 6
Page: 5570-5583
Publish at: 2025-12-01

Efficient design of approximate carry-based sum calculating full adders for error-tolerant applications

10.11591/ijict.v14i3.pp1189-1198
Badiganchela Shiva Kumar , Galiveeti Umamaheswara Reddy
Approximate computing is an innovative circuit design approach which can be applied in error-tolerant applications. This strategy introduces errors in computation to reduce an area and delay. The major power-consuming elements of full adder are XOR, AND, and OR operations. The sum computation in a conventional full adder is modified to produce an approximate sum which is calculated based on carry term. The major advantage of a proposed adder is the approximation error does not propagate to the next stages due to the error only in the sum term. The proposed adder was coded in verilog HDL and verified for different bit sizes. Results show that the proposed adder reduces hardware complexity with delay requirements.
Volume: 14
Issue: 3
Page: 1189-1198
Publish at: 2025-12-01

The impact of work concerns on teaching effectiveness: evidence from Chinese private universities

10.11591/ijere.v14i6.35367
Liang Mingyu , Mohd Khairuddin Abdullah , Connie Shin
Understanding how young teachers cope with work concerns is crucial for improving teaching quality in Chinese private higher education. This study investigates the relationship between different stages of such concerns and teacher effectiveness of young lecturers in private universities. These lecturers often face workload pressure andlack of career supports, which may influence their effectiveness and professional development. This research involved 416 full-time lecturers under the age of 40 from Shandong Province. The sample was determined using Krejcie and Morgan’s formula and selected through a multi-stage sampling method. Private universities were stratified into four categories, one university from each category was purposively selected, and participants were randomly sampled. Data were gatheredthrough a structured questionnaire adapted from the stages of concern (SoC) and the school teacher effectiveness questionnaire (STEQ). Pearson correlation, multiple regression, and structural equation modeling (SEM) were conducted for analysis. The results show that task concerns and impact concerns significantly influenced teacher effectiveness across instructional planning and strategies, assessment, and learning environment. In contrast, self-concerns showed weaker influence. These findings suggest that work concerns reflect not only stress but also deeper professional motivation, pointing to the need for more purposeful supports to increase teacher effectiveness and career growth.
Volume: 14
Issue: 6
Page: 4604-4613
Publish at: 2025-12-01

Classification of breast cancer using a precise deep learning model architecture

10.11591/ijict.v14i3.pp933-940
Mohammed Ghazal , Murtadha Al-Ghadhanfari , Fajer Fadhil
Breast cancer is an important topic in medical image analysis because it is a high-risk disease and the leading cause of death in women. Early detection of breast cancer improves treatment outcomes, which can be achieved by identifying it using mammography images. Computer-aided diagnostic systems detect and classify medical images of breast lesions, allowing radiologists to make accurate diagnoses. Deep learning algorithms improved the performance of these diagnoses systems. We utilized efficient deep learning approaches to propose a system that can detect breast cancer in mammograms. The proposed approach adopted relies on two main elements: improving image contrast to enhance marginal information and extracting discriminatory features sufficient to improve overall classification quality, these improvements achieved based on a new model from scratch to focus on enhancing the accuracy and reliability of breast cancer detection. The model trained on the digital database for screening mammography (DDSM) dataset and compared with different convolutional neural network (CNN) models, namely EfficientNetB1, EfficientNetB5, ResNet-50, and ResNet101. Moreover, to enhance the feature selection process, we have integrated adam optimizer in our methodology. In evaluation, the proposed method achieved 96.5% accuracy across the dataset. These results show the effectiveness of this method in identifying breast cancer through images.
Volume: 14
Issue: 3
Page: 933-940
Publish at: 2025-12-01

Review of NLP in EMR: abbreviation, diagnosis, and ICD classification

10.11591/ijict.v14i3.pp881-891
Nurul Anis Balqis Iqbal Basheer , Sharifalillah Nordin , Sazzli Shahlan Kasim , Azliza Mohd Ali , Nurzeatul Hamimah Abdul Hamid
This review explores state-of-the-art natural language processing (NLP) methods applied to electronic medical records (EMRs) for key tasks such as expanding medical abbreviations, automated diagnosis generation, international classification of diseases (ICD) classification, and explaining model outcomes. With the growing digitization of healthcare data, the complexity of EMR analysis presents a significant challenge for accurate and interpretable results. This paper evaluates various methodologies, highlighting their strengths, limitations, and potential for improving clinical decision-making. Special attention is given to abbreviation expansion as a crucial step for disambiguating terms in the clinical text, followed by an exploration of auto-diagnosis models and ICD code assignment techniques. Finally, interpretability methods like integrated gradients and attention-based approaches are reviewed to understand model predictions and their applicability in healthcare. This review aims to provide a comprehensive guide for researchers and practitioners interested in leveraging NLP for clinical text analysis.
Volume: 14
Issue: 3
Page: 881-891
Publish at: 2025-12-01

AI-based federated learning for heart disease prediction: a collaborative and privacy-preserving approach

10.11591/ijict.v14i3.pp751-759
Stuti Bhatt , Surender Reddy Salkuti , Seong-Cheol Kim
People with symptoms like diabetes, high BP, and high cholesterol are at an increased risk for heart disease and stroke as they get older. To mitigate this threat, predictive fashions leveraging machine learning (ML) and artificial intelligence (AI) have emerged as a precious gear; however, heart disease prediction is a complicated task, and diagnosis outcomes are hardly ever accurate. Currently, the existing ML tech says it is necessary to have data in certain centralized locations to detect heart disease, as data can be found centrally and is easily accessible. This review introduces federated learning (FL) to answer data privacy challenges in heart disease prediction. FL, a collaborative technique pioneered by Google, trains algorithms across independent sessions using local datasets. This paper investigates recent ML methods and databases for predicting cardiovascular disease (heart attack). Previous research explores algorithms like region-based convolutional neural network (RCNN), convolutional neural network (CNN), and federated logistic regressions (FLRs) for heart and other disease prediction. FL allows the training of a collaborative model while keeping patient info spread out among various sites, ensuring privacy and security. This paper explores the efficacy of FL, a collaborative technique, in enhancing the accuracy of cardiovascular disease (CVD) prediction models while preserving data privacy across distributed datasets.
Volume: 14
Issue: 3
Page: 751-759
Publish at: 2025-12-01

SGcoSim: a co-simulation framework to explore smart grid applications

10.11591/ijece.v15i6.pp5106-5118
Abdalkarim Awad , Abdallatif Abu-Issa , Peter Bazan , Reinhard German
Under the smart grid concept, new novel applications are emerging. These applications make use of information and communication technology (ICT) to help the electrical grid run more smoothly. This paper introduces SGcoSim, a co-simulation framework that integrates power system modeling and data communication to enhance smart grid applications. The framework utilizes OpenDSS for simulating power distribution components and OMNeT++ for communication modeling, enabling real-time peer-to-peer interactions via wireless sensor network (WSN) techniques. Virtual cord protocol (VCP) is deployed for efficient routing and data management within the field area network. SGcoSim’s functionality is demonstrated through two case studies: a phasor measurement unit (PMU)-based wide-area monitoring system and an integrated volt/VAR optimization with demand response (IVVO-DR) application. Results indicate significant reductions in energy consumption and power losses, highlighting the capabilities of SGcoSim.
Volume: 15
Issue: 6
Page: 5106-5118
Publish at: 2025-12-01

Impact of outlier detection techniques on time-series forecasting accuracy for multi-country energy demand prediction

10.11591/ijece.v15i6.pp5067-5079
Shreyas Karnick , Sanjay Lakshminarayanan , Madhu Palati , Prakash R
Accurate energy demand prediction is crucial for efficient grid management and resource optimization, particularly across multiple countries with varying consumption patterns. However, real-world energy demand data often contains outliers that can distort forecasting accuracy. This study evaluates the impact of five outlier detection techniques—Z-Score, density- based spatial clustering of applications with noise (DBSCAN), isolation forest (IF), local outlier factor (LOF), and one-class support vector machine (SVM)—on the performance of three time-series forecasting models: long short-term memory (LSTM) networks, convolutional neural network (CNN) Autoencoders, and LSTM with attention mechanisms. The models are tested using energy demand data from four European countries— Germany, France, Spain, and Italy—derived from real-time consumption records. A comparative analysis based on root mean squared error (RMSE) demonstrates that incorporating outlier detection significantly enhances model robustness, reducing forecasting errors caused by anomalous data. The findings emphasize the importance of selecting appropriate outlier detection strategies to improve the accuracy and reliability of energy demand forecasting. This research provides valuable insights into the trade-offs involved in outlier removal, with implications for policy and operational practices in energy management.
Volume: 15
Issue: 6
Page: 5067-5079
Publish at: 2025-12-01

Advanced control techniques for performance improvement of axial flux machines

10.11591/ijict.v14i3.pp1095-1107
Kalpana Anumala , Ramesh Babu Veligatla
The topological advancements in twin rotor axial flux induction motors (TRAxFIMs) have spurred the interest in performance optimization and control strategies for electric vehicle (EV) applications in particular. This paper investigates for the enhanced performance of multi-level inverters (MLIs) fed TRAxFIMs with different advanced control techniques. The performance evaluation is done under variable speed conditions at constant torque and vice versa. The TRAxFIMs offer unique advantages like high power density, high efficiency and most suitable for EV applications. The performance analysis of MLIs fed TRAxFIM has been carried out with proportional-integral (PI), fuzzy controllers, and artificial neural network (ANN) controllers. The PI controller provides a conventional control approach, while the fuzzy and ANN controllers serve as advanced control strategies. The integration of MLIs and advanced control techniques with TRAxFIMs aims to enhance dynamic response, stability and efficiency. The proposed control strategies are evaluated through extensive MATLAB simulations and the potential of MLIs fed TRAxFIMs is emphasized for EV applications.
Volume: 14
Issue: 3
Page: 1095-1107
Publish at: 2025-12-01

A telemedicine platform empowered by 5G mobile networks for Tunisian rural places

10.11591/ijece.v15i6.pp5433-5442
Ibrahim Monia , Dadi Mohamed Bechir , Rhaimi Belgacem Chibani
A telemedicine platform needed to be developed to address the various challenges faced by patients in rural areas, such as the lack of specialist doctors, the distance to healthcare and the time spent accessing it, which can present a risk to their lives, especially for those with chronic illnesses. For its realization, we used Laravel 11, a framework that offers powerful features for building modern, high-performance applications. To enable seamless real-time communication, we integrated Laravel reverb, a robust package supporting live interactions, updates, and notifications. The database uses MySQL 8 in combination with PHP 8.2, ensuring performance, scalability, and reliability. The strengths of our systems compared with existing Tunisian platforms are real-time interaction between patient and doctor thanks to 5G, ensuring the transfer of data and access at the same time, real- time communications such as video and audio calls, live notifications and instant messaging.
Volume: 15
Issue: 6
Page: 5433-5442
Publish at: 2025-12-01
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