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29,061 Article Results

Emotional intelligence in teaching: a key to performance and institutional climate in basic education

10.11591/ijere.v14i6.34924
Benjamin Maraza-Quispe , Victor Hugo Rosas-Iman , Giuliana Feliciano-Yucra , Atilio Cesar Martinez-Lopez , Elizabeth Katherine Ortiz-Corimaya , Walter Choquehuanca-Quispe , Frida Karina Coasaca-Hancco , Luis Elfer Nuñez-Saavedra
This study addresses the lack of understanding regarding the relationship between emotional intelligence (EI), teaching performance, and institutional climate (IC) in basic education. As a solution, the study proposes evaluating and strengthening teachers’ EI to enhance both their performance and the school environment. Using a quantitative, non-experimental, correlational design, the research analyzed a randomly selected sample of 145 teachers. Validated questionnaires measured dimensions such as self-awareness, self-regulation, motivation, empathy, and social skills, as well as teaching preparation and IC. The results reveal significant positive correlations between EI and IC (r=0.85) and between teaching performance and IC (r=0.78). This suggests that higher EI not only improves teaching effectiveness but also fosters a positive institutional environment. The study concludes that enhancing teachers’ EI can optimize both their performance and institutional dynamics, contributing to higher-quality education. The findings support the implementation of EI training programs as a key strategy to improve teaching performance and the school climate (SC).
Volume: 14
Issue: 6
Page: 5054-5066
Publish at: 2025-12-01

Optimizing radial basis function networks for harmful algal bloom prediction: a hybrid machine learning approach

10.11591/ijece.v15i6.pp5647-5654
Nik Nor Muhammad Saifudin Nik Mohd Kamal , Ahmad Anwar Zainuddin , Amir ‘Aatieff Amir Hussin , Ammar Haziq Annas , Normawaty Mohammad-Noor , Roziawati Mohd Razali
The deployment of artificial intelligence in environmental monitoring demands models balancing efficiency, interpretability, and computational cost. This study proposes a hybrid radial basis function network (RBFN) framework integrated with fuzzy c-means (FCM) clustering for predicting harmful algal blooms (HABs) using water quality parameters. Unlike conventional approaches, our model leverages localized activation functions to capture non-linear relationships while maintaining computational efficiency. Experimental results demonstrate that the RBFN-FCM hybrid achieved high accuracy (F1-score: 1.00) on test data and identified Chlorophyll-a as the strongest predictor (r = 0.94). However, real-world validation revealed critical limitations: the model failed to generalize datasets with incomplete features or distribution shifts, predicting zero HAB outbreaks in an unlabeled 11,701-record dataset. Comparative analysis with Random Forests confirmed the RBFN-FCM's advantages in training speed and interpretability but highlighted its sensitivity to input completeness. This work underscores the potential of RBFNs as lightweight, explainable tools for environmental forecasting while emphasizing the need for robustness against data variability. The framework offers a foundation for real-time decision support in ecological conservation, pending further refinement for field deployment.
Volume: 15
Issue: 6
Page: 5647-5654
Publish at: 2025-12-01

Factors influencing enrollment intention in private schools

10.11591/ijere.v14i6.35364
Lim Lee Ping , Ong Choon Hee , Tan Owee Kowang , Lim Kim Yew
The growth in private school student enrollment in Malaysia has prompted institutions to upgrade to stay competitive in the market. However, despite the increasing number of private schools, regrettably, only a few studies have focused on the factors that influence private school enrollment. This study examines the relationship between social influences (SI), school environment (SE), characteristics, parent-administration-teacher relationship (PAT), and private school enrolment intention in Malaysia. It uses a quantitative method and G*Power to determine the minimum sample size. Data was gathered from 135 respondents who have enrolled at least one child in private schools using questionnaire surveys. The statistical package for social science (SPSS) was used to analyze the data. The results showed that SI and school characteristics (SC) significantly and positively correlated with enrolment intention. The PAT was not significantly associated with enrolment intention. This study clearly shows that SI factors and SCs are crucial for enrolment intention in private schools. The management should develop and implement marketing strategies that effectively tackle current market challenges by focusing on SI and improving the SC. They can tailor the marketing strategy with electronic word-of-mouth (e-WOM) for SI and apply learning analytics for SC.
Volume: 14
Issue: 6
Page: 4509-4516
Publish at: 2025-12-01

Design and performance analysis of an NSFET-based biosensor for the early detection of dengue

10.11591/ijece.v15i6.pp5183-5192
Tulasi Radhika Patnala , Madhavi Tatineni
Healthcare industry is changing due to technological breakthroughs that spur creative methods for diagnosing and treating illnesses. This study examines the development of nanowire-based stacked field-effect transistor (NSFET) biosensors for the early detection of dengue virus. Dengue fever is severe threat to public health and a flavivirus spread by mosquitoes. About half of the global population is at risk due to an endemic illness in tropical and subtropical regions, which affects approximately 100 million individuals annually in 130 countries. The virus has four antigenically distinct serotypes, and there may be a fifth. These serotypes induce variety of clinical symptoms. This can include benign infections that go away on their own or extremely serious, potentially fatal consequences like organ failure, plasma leakage, and bleeding. While many techniques are now used to diagnose dengue fever in the laboratory, no single technique satisfies the optimum standards for speed, economy, sensitivity, specificity. To close this gap in dengue diagnosis, newer detection technologies are desperately needed. This ultrasensitive label-free electrical device can detect the dengue virus (DENV) early on and prevent severe additional harm to humans. To detect various DENV concentrations in human blood and demonstrate potential for eventual point-of-care (POC) detection, NSFET constructed and simulated in this work.
Volume: 15
Issue: 6
Page: 5183-5192
Publish at: 2025-12-01

Intrusion detection based on image transformations and data augmentation

10.11591/ijece.v15i6.pp5594-5603
Nada Ali Abood , Asghar A. Asgharian Sardroud
The increasing growth of users and communication networks in different platforms has led to the emergence of various types of network attacks. intrusion detection systems (IDS) are one of the important solutions to cope with these problems. An IDS determines whether incoming traffic is intrusive or normal. IDSs often achieve high efficiency with methods based on deep neural networks. However, one of the shortcomings of these methods is the lack of sufficient attention to the spatial features in the data. This research presents an intrusion detection method based on image transformations and data augmentation is presented. In the proposed method, the intrusion detection process is performed by transforming the traffic vector into an image using a convolutional neural network (CNN). Also, we use data augmentation and dimension reduction techniques to increase accuracy and reduce complexity in the proposed method. Simulation results on network security laboratory - knowledge discovery and data mining (NSL-KDD) show that the proposed IDS can classify intrusion traffic with an accuracy of 97.58%.
Volume: 15
Issue: 6
Page: 5594-5603
Publish at: 2025-12-01

Improving time-domain winner-take-all circuit for neuromorphic computing systems

10.11591/ijece.v15i6.pp5173-5182
Son Ngoc Truong , Tu Tien Ngo
With the rapid advancements of information processing systems, winner- take-all (WTA) circuits have emerged as essential components in a wide range of cognitive functions and decision-making applications. Neuromorphic computing systems, inspired by the biological brain, utilize WTA circuits as selective mechanisms that identify and retain the strongest signal while suppressing all others. In this study, we present an effective time-domain WTA circuit with optimized multiple-input NOT AND (NAND) gate and delay circuit for neuromorphic computing applications. The circuit is evaluated using sinusoidal current inputs with varying phase delays, which successfully demonstrating precise winner selection. When applied to neuromorphic image recognition task, the enhanced time-domain WTA achieves an improvement of 0.2% in precision while significantly reducing power consumption, yielding a low figure of merit (FoM) of 0.03 µW/MHz, compared to the previous study with FoM of 0.25 µW/MHz. The optimized WTA circuit is highly promising for large-scale neuromorphic applications.
Volume: 15
Issue: 6
Page: 5173-5182
Publish at: 2025-12-01

Development and evaluation of a re-sequenced intervention module in learning chemical bonding

10.11591/ijere.v14i6.31851
Baby Eve N. Asequia , Leemarc C. Alia , Kevin Client B. Matutes
In the typical high school chemistry curriculum, chemical bonding precedes the chemical reactions. In this study, the re-sequenced effect of learning chemical bonding when chemical reactions are introduced first among grade 9 learners was investigated. A learning module with re-sequenced intervention in chemical bonding was developed using analysis, design, development, implementation, evaluation (ADDIE) model and validated by eight science education professionals rated as very satisfactory. A quasi-experimental research design was utilized in the implementation phase with 129 respondents selected through cluster random sampling. Pre- and post-tests, formative and summative assessments, and evaluation surveys were administered. Evaluation results revealed that the scores from the re-sequenced intervention group displayed a slightly higher overall satisfaction percentage (99.06%) compared to the control group (94.74%). In addition, the experimental group achieved significantly higher competency levels (M=49.3, SD=19.4) compared to the control group (M=41.4, SD=15.3), with p=0.016 and d=0.37. Furthermore, students reported positive perceptions despite initial misconceptions. These findings highlight that re-sequencing topic order enhances chemistry learning outcomes and student engagement. Hence, the re-sequenced learning module became a valuable tool for enhancing understanding of chemical bonding, independent of baseline competency or attitudes toward the material.
Volume: 14
Issue: 6
Page: 4864-4873
Publish at: 2025-12-01

Prediction of peripheral arterial disease through non-invasive diagnostic approach

10.11591/ijece.v15i6.pp5782-5791
Sobhana Mummaneni , Lalitha Devi Katakam , Pali Ramya Sri , Mounika Lingamallu , Smitha Chowdary Ch , D.N.V.S.L.S Indira
Peripheral arterial disease (PAD) is a cardiovascular condition caused by arterial blockages and poor blood circulation, increasing the risk of severe complications such as stroke, heart attack, and limb ischemia. Early and accurate detection is essential to prevent disease progression and improve patient outcomes. This study introduces a non-invasive diagnostic method using laser doppler flowmetry (LDF), electrocardiography (ECG), and photoplethysmography (PPG) to assess vascular health. LDF measures microvascular blood flow, ECG evaluates heart rate variability, and PPG captures pulse waveform characteristics. Key physiological features such as blood flow variability, pulse transit time, and hemodynamic responses are extracted and analyzed using machine learning. Random forest and XGBoost models are employed and combined using ensemble learning to classify individuals into non-PAD, moderate PAD, and severe PAD categories. A comparative evaluation shows that the ensemble model delivers superior classification accuracy. This integrated system offers a fast, reliable screening tool that supports early PAD detection and intervention. By combining multimodal signal analysis with machine learning, the approach enhances diagnostic precision and provides a scalable solution for preventive cardiovascular care.
Volume: 15
Issue: 6
Page: 5782-5791
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

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

Performance analysis of D2D network in heterogeneous multitier interference scenarios

10.11591/ijict.v14i3.pp811-821
Dhilipkumar Santhakumar , Arunachalaperumal Chellaperumal , Jenifer Suriya Lazer Jessie , Jerlin Arulpragasam
The trade-off between boosting network throughput and minimizing interference is a critical issue in fifth generation (5G) networks. Diverting the data traffic around the network access point in device-to-device (D2D) communication is an important step in realizing the vision of 5G. Though the D2D network improves the network performance, they complicate the interference management process. Interference is an invisible physical phenomenon occurring in wireless communication which happens when multiple transmissions happen simultaneously over a general wireless medium. Enormous growth in usage of mobile phone and other wireless gadgets in recent years has paved the way for significant role in Interference analysis over multi-tier network. Interference could affect communication systems performance and it might even prevent systems functioning properly. 3G and 4G wireless devices coexisted with reverse compatibility in a coverage area. Also, after their widespread adoption, 5G devices have become prevalent across the globe and this reaffirms interference coexistence as a significant field of research. Multiple systems operating in a region will cause severe interference and ultimately reduce the quality of received signal. A simulation environment for cellular standards coexistence considering real-time parameters is created and experimented. Various research works earlier addresses the interference mitigation techniques in multi-tier networks but none of them present the analysis of scenarios and interfering signal power levels in the respective contexts. In this paper various scenarios with different network interference coexistence were studied, simulated, and analyzed quantitatively.
Volume: 14
Issue: 3
Page: 811-821
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

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

Digital control of plant development through sensors and microcontrollers in Kosova

10.11591/ijict.v14i3.pp1072-1084
Ragmi M. Mustafa , Kujtim R. Mustafa , Refik Ramadani
The plant monitoring system aims to develop an automated solution for optimizing plant growth. Using the Arduino Uno ATMEGA328P microcontroller module and various sensors, this system regulates environmental conditions to promote optimal plant development. It requires adequate software to operate effectively, enabling the microcontroller to monitor and regulate climatic conditions. The primary goal of this paper is to present a comprehensive system that continuously measures parameters such as light intensity, air humidity, and soil moisture in real time within a vegetable greenhouse or a plastic-covered plant environment. This scientific paper provides an in-depth description of the hardware components used, their electronic connections, and the implementation of program code written in C++. Based on the measured physical parameters, the plant monitoring system performs specific actions, such as watering the plants and regulating the ambient temperature. In conclusion, this system effectively supports healthy plant growth and enhances the quality and yield of plant products. The paper serves as a practical example for improving plant cultivation in the agricultural sector in the Republic of Kosova.
Volume: 14
Issue: 3
Page: 1072-1084
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
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