Articles

Access the latest knowledge in applied science, electrical engineering, computer science and information technology, education, and health.

Advanced Search
Filter Icon

Filters article

Years

FAQ Arrow
Single Year
Range Year
0
0

Source Title

FAQ Arrow

Authors

FAQ Arrow

31,456 Article Results

Education and smart technologies: towards a new pedagogical paradigm

10.11591/ijere.v14i1.30470
Amine Dehbi , Abdellah Bakhouyi , Al Mahdi Khaddar , Mohamed Talea
Smart education, a new field of technology related to education, has emerged as a unique response to current educational challenges. This is becoming increasingly important for academic progress and aligns with the transformative impact of technology. This study addresses the transformative impact of smart technologies on education, focusing on the integration of the internet of things, big data, and artificial intelligence. Through a bibliometric and content analysis based on Scopus and Web of Science databases, we identify the most active researchers, leading universities, and the countries that contribute most significantly to the field of smart education. The findings reveal a significant increase in related publications, highlighting the growing importance of these technologies in enhancing teaching and learning experiences. The study shows the advantages and challenges of adopting such technologies, providing insights into their practical applications and the future direction of educational innovations. Integrating smart technologies in education is crucial for improving quality of life and academic outcomes, necessitating further research and development to fully realize their potential. This research contributes to the understanding of technological impacts on education and supports the development of strategies for their effective implementation.
Volume: 14
Issue: 1
Page: 297-309
Publish at: 2025-02-01

Hydrophobicity signal analysis for robust SARS-CoV-2 classification

10.11591/ijeecs.v37.i2.pp1294-1305
Mohammad Jamhuri , Mohammad Isa Irawan , Imam Mukhlash , Ni Nyoman Tri Puspaningsih
Rapid and accurate classification of viral pathogens is critical for effective public health interventions. This study introduces a novel approach using convolutional neural networks (CNN) to classify SARS-CoV-2 and non-SARS-CoV-2 viruses via hydrophobicity signal derived from DNA sequences. Conventional machine learning methods grapple with the variability of viral genetic material, requiring fixed-length sequences and extensive preprocessing. The proposed method transforms genetic sequences into image-based representations, enabling CNNs to handle complexity and variability without these constraints. The dataset includes 8,143 DNA sequences from seven coronaviruses, translated into amino acid sequences and evaluated for hydrophobicity. Experimental results demonstrate that the CNN model achieves superior performance, with an accuracy of over 99.84% in the classification task. The model also performs well with extended sequence lengths, showcasing robustness and adaptability. Compared to previous studies, this method offers higher accuracy and computational efficiency, providing a reliable solution for rapid virus detection with potential applications in bioinformatics and clinical settings.
Volume: 37
Issue: 2
Page: 1294-1305
Publish at: 2025-02-01

Development and validation of doctoral student social support perception scale

10.11591/ijere.v14i1.30920
Xiaohan Yang , Kee Jiar Yeo , Shih-Hui Lee , Boon Yew Wong , Lina Handayani
The perception of social support is crucial for doctoral students' academic careers, yet there is a notable absence of scales specifically designed to measure the social support that doctoral students receive. Consequently, there is a clear need for an effective tool to assess the level and nature of support perceived by these students. The Doctoral Students Social Support Perception Scale (DSSPS) is a multidimensional instrument developed to evaluate social support received by doctoral students from supervisors, family, and peers/friends. This scale operates in two phases: the first phase uses exploratory factor analysis to identify three potential dimensions of perceived social support: resource provision, emotional inspiration, and appropriate attention. The second phase employs confirmatory factor analysis to demonstrate the scale's robust overall fit. The results also indicate high internal consistency as well as convergent and discriminant validity. These findings suggest that the DSSPS is both an effective and reliable measure to assess the extent and nature of social support perceived by doctoral students.
Volume: 14
Issue: 1
Page: 1-9
Publish at: 2025-02-01

State-of-the-art in teachers’ online pedagogical competencies in higher education from 2011 to 2022

10.11591/ijere.v14i1.28717
Thi-Thanh-Hai Pham , Hung-Hiep Pham , Dinh-Hai Luong
University teachers play an important role in teaching and learning activities in the online environment. However, the emerging remote education due to the COVID-19 pandemic has revealed the shortcomings of online teaching competencies of teachers. The bibliometric study investigated the knowledge base of online teacher pedagogical competencies in higher education by analyzing 131 Web of Science-indexed documents from 2011 to 2022. The findings revealed the sudden decline of the number announced in the two years 2021 and 2022. Spain, Russia, the United States, and Canada are emerging countries in the network of international collaboration. The research community is based on small research groups and has emerged in recent years. Sources focus on subjects such as education, e-learning, computer science, developmental and educational psychology, among which education sciences is the most prominent one in the last years. With nine identified themes, these of interest are online teacher roles, remote emergency teaching, online professional development, and online teaching in COVID-19. Emerging keywords highlighted potential topics in the foreseeable future such as competency frameworks, global education, and teaching professionalism.
Volume: 14
Issue: 1
Page: 659-670
Publish at: 2025-02-01

Enhanced global navigation satellite system signal processing using field programmable gate array and system-on-chip based software receivers

10.11591/ijece.v15i1.pp480-492
Chetna Devi Kh , Malode Vishwanatha Panduranga Rao
This paper presents a new approach to improving global navigation satellite system (GNSS) signal processing by using baseband processing techniques on field-programmable gate array (FPGA) platforms and a system-on-chip (SoC)-based GNSS software receiver. By leveraging the flexibility and computational power of FPGAs and the integration capabilities of SoC platforms, the method significantly enhances signal acquisition, tracking accuracy, and overall system performance. The integration of the ADFMCOMMS3-EBZ RF front end with the Zynq 7000 SoC board, along with high-speed parallel I/O and serial peripheral interface (SPI) for data management and configuration, enables efficient processing of high-speed signals. The study also explores wavelet transform techniques, such as the discrete wavelet transform (DWT), to improve filtering and noise reduction in GNSS signals. The results show that the proposed baseband processing algorithm for GNSS software-defined radio (SDR) reduces acquisition time and enhances tracking accuracy compared to traditional personal computer (PC)-based systems. Additionally, the SoC-based receiver is more energy-efficient and uses fewer resources. Comparative analysis shows that the proposed method provides more received samples, fewer dropped samples, and a lower data loss rate, confirming its effectiveness in boosting GNSS signal processing reliability and efficiency.
Volume: 15
Issue: 1
Page: 480-492
Publish at: 2025-02-01

Psychometric properties of emotional intelligent scale: the application for university students in Indonesia

10.11591/ijere.v14i1.28599
Amin Akbar , Zulakbal Abd Karim , Jaffry Zakaria , Suryanef Suryanef , Muh Khairul Wajedi Imami , Utami Syahdiah , Hilwa Alfiani Fitri
The current research aims to assess the psychometric properties of the emotional intelligence scale among university students in Indonesia. This research used a survey design. The current research participants were 288 university students in Palembang, West Nusa Tenggara, Kupang, Yogyakarta, and Jakarta, Indonesia. Two procedures were used to analyze the data: exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). The results showed that EFA with comprehensive techniques that compromise parallel analysis yielded a 5-factor solution: empathy with five items, handling relationships with five items, motivation for oneself with six items, self-awareness with seven items, and managing emotion with eight items. The five factors solution was confirmed through CFA with the value: Chi square=2.631, Tucker-Lewis’s index (TLI)=.804, comparative fit index (CFI)=.823, root mean square error of approximation (RMSEA)=.075, and standardized root mean residual (SRMR)=.743. The scale validation and reliability were tested through average variance extracted (AVE) with the value ranged from 0.271 to 0.594, construct reliability (CR) with the value ranged from 0.706 to 0.879, and Cronbach’s alpha with the value ranged from 0.706 to 0.879. Therefore, based on psychometric analysis, the scale is valid and reliable to be used in measuring emotional intelligence among university students in Indonesia.
Volume: 14
Issue: 1
Page: 157-170
Publish at: 2025-02-01

Cloud computing environment based hierarchical anomaly intrusion detection system using artificial neural network

10.11591/ijece.v15i1.pp1209-1217
Mangalapalli Vamsikrishna , Garapati Swarna Latha , Gajjala Venkata Ramesh Babu , Koppisetti Giridhar , Lakshmeelavanya Alluri , Giddaluru Somasekhar , Bhimunipadu Jestadi Job Karuna Sagar , Naresh Dondapati
Nowadays, computer technology is essential to everyday life, including banking, education, entertainment, and communication. Network security is essential in the digital era, and detecting intrusion threats is the most difficult problem. As a result, the network is monitored for unusual activity using this hierarchical anomaly intrusion detection system, and when these actions are detected, an alert is generated. This hierarchical anomaly intrusion detection system, which uses artificial neural network (ANN) and is implemented on a cloud computing environment, analyzes data even in the high levels of traffic and protects computer networks and data from malicious activity. As a result, this system shows better detection, accuracy, and precision rates.
Volume: 15
Issue: 1
Page: 1209-1217
Publish at: 2025-02-01

Educational management in a digital age: boss phubbing and teacher motivation

10.11591/ijere.v14i1.29046
Mukadder Erdem , Esra Kaya
The study aims to assess the level of phubbing behavior an antisocial common mobile phone using behavior of school principals and how it affects the motivation of the teachers working at Anatolian High Schools and Vocational High Schools. This relational screening modelled study was conducted in these schools in the 2nd term in 2022 to 2023 school year. For data collection purposes, Boss Phubbing Scale and Multidimensional Work Motivation Scale were used and 268 answers were collected from the whole population. The data was analyzed through descriptive statistics, exploratory factor analysis (EFA), Cronbach’s alpha reliability analysis, one-way analysis of variance (ANOVA), unpaired t test, Pearson correlation analysis and regression analysis. The data derived from these analyses indicates that while school principals phub teachers at a low level and their boss phubbing scores differentiate in accordance with the work experience of teachers; teachers’ motivation is high and there is a significant and positive relationship between boss phubbing of school principals and two factors of Multidimensional Work Motivation Scale, which were extrinsic motivation and amotivation. It may be suggested that school principals should alter their boss phubbing behavior or modify it to make the best of it.
Volume: 14
Issue: 1
Page: 50-60
Publish at: 2025-02-01

Robust adaptive integral sliding mode control of a half-bridge bidirectional DC-DC converter

10.11591/ijece.v15i1.pp114-128
Julius Derghe Cham , Francis Lénine Djanna Koffi , Alexandre Teplaira Boum , Ambe Harrison
A novel approach to improving the dynamic response of a half-bridge bidirectional DC-DC converter is presented in this paper, particularly in the face of disturbances from internal or external sources. These converters, which are integral to the operation of DC microgrids, are responsible for stepping up or stepping down voltage as required. To optimize the converter's performance under varying conditions, we propose an adaptive integral sliding mode controller (AISMC) enhanced by particle swarm optimization (PSO). The proposed controller leverages the strengths of both super-twisting sliding mode control (STSMC) and adaptive control, providing a robust and responsive solution to the challenges posed by the converter's nonlinear dynamics. The system's stability is rigorously ensured through the application of Lyapunov stability criteria, which underpin the enhanced performance of the controller. Simulations conducted in the MATLAB/Simulink environment demonstrate that the AISMC-PSO outperforms conventional control strategies, offering superior stability, robustness, and precision. The results clearly indicate that the proposed approach minimizes errors and enhances the overall efficiency and reliability of the bidirectional half-bridge DC-DC converter, making it a highly effective solution for DC microgrid applications.
Volume: 15
Issue: 1
Page: 114-128
Publish at: 2025-02-01

Accurate prediction of chronic diseases using deep learning algorithms

10.11591/ijai.v14.i1.pp570-583
Ronald S. Cordova , Rolou Lyn R. Maata , Malik Jawarneh , Marwan I. Alshar'e , Oliver C. Agustin
In this paper, the researchers studied the effects of different activation functions in hidden layers and how they impact the overfitting or underfitting of the model in the multiclass prediction of chronic diseases. This paper also evaluated the effects of varying the number of layers, and hyperparameters and its impact on the accuracy of the model and its generalization capabilities. It was found that exponential linear unit (ELU) does not have a significant advantage over rectified linear unit (ReLU) when used as an activation function in the hidden layer. Additionally, the performance of softmax function, when used in the output layer, is the same as a classic sigmoid output activation function. In terms of the ability of the model to generalize, the researchers achieved a classification accuracy of 100% when the trained model was used to predict unseen data. Through this research, the researchers should be able to assist medical professionals and practitioners in Oman in the validation and diagnosis of chronic diseases in clinics and hospitals.
Volume: 14
Issue: 1
Page: 570-583
Publish at: 2025-02-01

Management of action research in school: a bibliometric analysis

10.11591/ijere.v14i1.31086
Azlin Moktar , Bity Salwana Alias , Mohamad Sattar Rasul
This paper employs bibliometric analysis and visualization techniques, including VOS Viewer, to scrutinize trends in managing action research in schools. Utilizing a dataset of 337 articles sourced from Web of Science (WoS) until 2023, the study adheres to PRISMA guidelines to systematically examine authorship patterns, trends, and themes. The analysis reveals a surge in publication rates and a focus on collaborative problem-solving, inclusivity, and global perspectives, with notable contributions from journals like educational action research and authors (such as Wood). Diverse author collaborations and global representation underscore interconnected research themes. Additionally, four distinct clusters—knowledge and leadership, action research and engagement, caring and impact, and empowerment and participation—emerge, encapsulating action research management in schools. However, challenges such as limited discussion platforms, resource constraints, and contextual barriers persist, necessitating enhanced reflective practices and collaborative learning. While bibliometric analysis provides quantitative insights, the study advocates for complementing such approaches with qualitative methods to ensure a comprehensive understanding of action research management in schools, guiding future strategies in educational research.
Volume: 14
Issue: 1
Page: 341-355
Publish at: 2025-02-01

High body temperature detection solution through touchless machine for health monitoring

10.11591/ijai.v14.i1.pp166-171
Siddharth Swami , Lalit Mohan Joshi , Mohammed Ismail Iqbal , Meera Sharma , Amar Jeet Rawat , Sameer Dev Sharma , Rajesh Singh
The demand for reliable health monitoring systems has surged in today's health-conscious society. Body temperature monitoring is crucial for preserving health and preventing infectious disease outbreaks. In this study an Arduino uno hardware board with a touchless temperature sensor is proposed to detect elevated body temperature, indicating fever and early signs of illness. The system prioritizes real-time health surveillance, accessibility, and usability, blending seamlessly with normal life. Arduino's versatility allows the system to function covertly, uphold privacy and autonomy, and foster wellbeing. The goal is to highlight the system's ability to function covertly, uphold privacy and autonomy, and foster wellbeing. This technology exemplifies the synergy between personal wellness and contemporary technologies, offering a useful and adaptable fever detection solution for various contexts, including homes and public areas.
Volume: 14
Issue: 1
Page: 166-171
Publish at: 2025-02-01

Machine learning for the detection of soil pH, macronutrients, and micronutrients with crop and fertilizer recommendations

10.11591/ijai.v14.i1.pp439-446
John Joshua Montañez , Jeffrey Sarmiento
The study aims to determine the levels of soil parameters such as soil pH, macronutrients, and micronutrients. After determining said parameters, the system appropriately recommends crops and fertilizers suitable for the soil samples. For soil pH and macronutrient levels, i.e., nitrogen, phosphorus, and potassium, these parameters can be detected using the soil test kit. Meanwhile, for soil micronutrients, i.e., copper, iron, and zinc, there is a need for the development of appropriate assays for colorimetric processes that can be done for the appropriate determination of said micronutrients. Comparison of available machine learning such as support vector machine algorithm, naïve Bayes algorithms, and K-nearest neighbor algorithm is a must to determine the well-fit algorithm that is considered fast and has high predictive power in classification and regression. The outputs of the colorimetric and spectrometric processes are the inputs in the machine learning activities intended for crop and fertilizer recommendation.
Volume: 14
Issue: 1
Page: 439-446
Publish at: 2025-02-01

Factors of students’ non-lecture attendance and administrative strategies for its effective management in Nigerian universities

10.11591/ijere.v14i1.30309
Osakwe Grace Nwamaka , Okonta Vinella
The aim of the study was to investigate factors of students’ non lecture attendance and administrative strategies used with a view to ascertain the extent of its implementation in universities in South-South states in Nigeria. A population of 22 universities from which a sample of six was drawn and 350 respondents; 50 lecturers and 300 students formed the sample size. The study was guided by three research questions and adopted the descriptive survey research design. A questionnaire was the instrument used for the study. It was divided into four subsections, which is part A, B, C and D. Its face and content validity were determined through expert judgment while the reliability index was determined using Cronbach alpha. Descriptive statistics was used to analyze the data collected. The findings revealed that emotional problems (3.93), sickness (3.71), finance (3.48), lecturers teaching skills (3.21) were key factors of non-lecture attendance. The main administrative strategies used were 75% policy on lecture attendance (3.33), administering short and unannounced quiz (3.24), provision of comfortable classrooms (3.30); while the strategies were sometimes used. The recommendations include counseling students and a 100% implementation of any policy adopted by individual universities.
Volume: 14
Issue: 1
Page: 382-388
Publish at: 2025-02-01

Electroencephalogram denoising using discrete wavelet transform and adaptive noise cancellation based on information theory

10.11591/ijai.v14.i1.pp769-779
Hashem Abdolahniya , Ali Akbar Khazaei , Mahdi Azarnoosh , Seyed Ehsan Razavi
One of the most frequently used techniques for removing background noise from electroencephalogram (EEG) data is adaptive noise cancellation (ANC). Nonetheless, there exist two primary disadvantages associated with the adaptive noise reduction of EEG signals: the adaptive filter, which is supposed to be an approximation of contaminated noise, lacks the reference signal. The mean squared error (MSE) criterion is frequently employed to achieve this goal in adaptive filters. The MSE criterion, which only considers second-order errors, cannot be used since neither the EEG signal nor the EOG artifact are Gaussian. In this work, we employ an ANC system, deriving an estimate of EOG noise with a discrete wavelet transform (DWT) and input this signal into the reference of the ANC system. The entropy-based error metric is used to reduce the error signal instead of the MSE. Results from computer simulations demonstrate that the suggested system outperforms competing methods with respect to root-mean-square-error, signal-to-noise ratio, and coherence measurements.
Volume: 14
Issue: 1
Page: 769-779
Publish at: 2025-02-01
1 2 3
...
2097 2098
Show 367 of 2098

Discover Our Library

Embark on a journey through our expansive collection of articles and let curiosity lead your path to innovation.

Explore Now
Library 3D Ilustration