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31,456 Article Results

Factors affecting career orientation: indigenous ethnic minority students in Vietnam’s Central Highlands

10.11591/ijere.v14i6.34954
Phung Viet Hai , Tran Thi Huong Xuan , Phung Thi To Loan , Nguyen Thi Thanh Phuong
Career orientation competency (COC) plays a crucial role in preparing students for lifelong learning and labor market adaptability. However, existing research has largely overlooked how this competency develops among indigenous ethnic minority students in culturally diverse and educationally disadvantaged contexts such as Vietnam’s Central Highlands. Addressing this gap, the present study adopts the social cognitive career theory (SCCT) to examine how personal, contextual, and behavioral factors interact to shape COC in this population. SCCT serves not only as a conceptual lens but also informs the development of the research model and interpretation of findings. A quantitative approach was employed using cross-sectional survey data collected from 669 ethnic minority students. Analytical techniques included reliability analysis (Cronbach’s alpha), exploratory factor analysis (EFA) for construct validation, and multiple linear regression to test predictive relationships. Results revealed six key determinants of COC: self-awareness (SA), expectations for results (ER), personal goals (PG), community connection (CC), career exploration (CE), and cultural identity (CI). Notably, CI had the most significant effect (β=0.308), suggesting its central role in guiding career-related behaviors. These findings have important implications for both theory and practice. They extend SCCT by integrating culturally specific constructs relevant to marginalized communities and they highlight the need for context-responsive career guidance programs that recognize and leverage students’ cultural identities. The study contributes to the empirical foundation for inclusive education policy reforms targeting ethnic minority youth in Vietnam.
Volume: 14
Issue: 6
Page: 4711-4723
Publish at: 2025-12-01

Effect of a digital literacy through workshop training programs on older adults: a quasi-experimental study

10.11591/ijere.v14i6.32631
Ruthairat Siriwattanarat , Kittikhun Meethongjan , Ornuma Charoensuk , Donsak Tsailexthim , Weerawich Wongroj
In Bangkok, Thailand, workshop training programs aimed at enhancing digital literacy among older adults have been developed. The objective of this study was to assess whether a structured digital literacy workshop could improve mobile device proficiency among older adults in Bangkok. This program consisted of six workshops focusing on mobile devices such as smartphones or tablets. This quasi-experimental study investigates the impact of these workshops on digital device usage among urban community adults aged 60 and above. Digital literacy was assessed using the mobile device proficiency questionnaire (MDPQ-16) at three stages, they are baseline (pretest), immediately after program completion (post-intervention), and one month later (follow-up). A total of 84 older adults participated in these workshop training programs. Our findings indicate a significant increase in mobile device proficiency (P<.000). Participants showed substantial improvement in digital literacy at both the posttest and intervention assessments compared to pretest, with no significant difference observed between posttest and intervention scores. However, participants aged 70 and older with lower education levels demonstrated comparatively lower levels of digital literacy. These findings underscore the importance of implementing structured and accessible digital literacy training for older adults, particularly for those with lower educational attainment, to reduce digital disparities and promote inclusive access to essential digital services.
Volume: 14
Issue: 6
Page: 4389-4403
Publish at: 2025-12-01

Data transmission technologies for the development of a drilling rig control and diagnostic system

10.11591/ijece.v15i6.pp5506-5514
Irina Rastvorova , Sergei Trufanov
This article examines telecommunication technologies used in automatic control and diagnostics systems and discusses key aspects of using telecommunication solutions for monitoring and controlling the operation processes of the electrical complex of a drilling rig, including remote access, data transmission and real-time information analysis. It provides a comprehensive overview of such communication technologies as Bluetooth, Wi-Fi, ZigBee, global system for mobile communication (GSM), RS-232, RS-422, RS-485, universal serial bus (USB), Ethernet, narrowband internet of things (NB-IoT), long range wide area network (LoRaWAN), and power line communication (PLC). Technologies that will be most effective for use in control and diagnostics systems of a drilling rig complex are proposed. The possibility of using machine learning to process a large amount of data obtained during the drilling process to optimize the controlled drilling parameters is investigated.
Volume: 15
Issue: 6
Page: 5506-5514
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

FIND-ROUTE: Fourier series integrated deep learning model for energy efficient routing in Internet of Things-wireless sensor network

10.11591/ijra.v14i3.pp468-478
Shobanbabu Ramaswamy Jaganathan , Sathya Rajendran , Karthikeyan Ramamoorthy
The Internet of Things (IoT) relies on wireless sensor networks (WSNs) to transmit data across a wide range of applications. However, the commonly encountered primary challenges in IoT-enabled WSNs are high energy consumption during data transmission, which insists energy optimized routing to prolong the network lifetime. To address these challenges, a novel Fourier series integrated deep learning-based routing (FIND-ROUTE) framework has been proposed for energy-aware communication among IoT nodes in WSN. Initially, a hybrid clustering approach forms an adaptive cluster for efficient data aggregation with reduced energy consumption. After clustering, stable cluster heads (CHs) are elected by a Fourier series-based metaheuristic optimization algorithm for balancing the energy usage with extended network lifetime. Finally, an Intelligent neural network dynamically selects the optimal path and transmits the data efficiently with reduced latency for reliable communication in IoT-WSN. The FIND-ROUTE framework is simulated by using MATLAB, and it is validated by using the WSN-DS dataset. The proposed FIND-ROUTE framework is evaluated based on several parameters, including energy consumption, packet delivery ratio (PDR), network lifetime (NL), time complexity, throughput, number of alive nodes, packet loss ratio (PLR), and space complexity. In comparison, the proposed FIND-ROUTE framework achieves a PDR of 90%, whereas MLBDARP, LQEER, and NBSHO-DRNN achieve 70%, 60%, and 67% respectively.
Volume: 14
Issue: 3
Page: 468-478
Publish at: 2025-12-01

Predicting staple crop yields under climate variability using multiple regression techniques

10.11591/ijeecs.v40.i3.pp1531-1538
Richard D. Hortizuela , Thelma D. Palaoag
Global food systems rely on staple crops—rice, wheat, maize, potato, soybean, and sugarcane, which are vital in Asia, where production is high. However, climate change threatens crop yields, potentially increasing hunger and malnutrition. Yield variability due to climate factors like rainfall and temperature underscores the need for accurate crop yield predictions. This paper analyzed the relationships between staple crop yields, climate variables, and pesticide usage. It aimed to develop a predictive model for crop yields in Asia using multiple regression techniques in Google Colab. The model was evaluated using a hybrid set of metrics like mean absolute error (MAE), root mean squared error (RMSE), and R² score. Findings revealed that reliable yield predictions are achievable despite weak linear relationships among variables. The extreme gradient boosting (XGBoost) achieves the highest R² score of 0.958367, which indicates superior predictive performance for staple crop yield forecasting due to its lower overall error rates and greater consistency in performance. This highlights the effectiveness of ensemble methods like XGBoost in capturing complex crop yield patterns. Despite newer machine learning (ML) techniques, these models remain recommended for similar tasks due to their robust performance.
Volume: 40
Issue: 3
Page: 1531-1538
Publish at: 2025-12-01

Beat the cheat: tendencies of academic misconduct and teachers’ interventions

10.11591/ijere.v14i6.34757
Lauro Santiago Aspiras , Emma Daynos Aspiras
This research employed an explanatory sequential mixed-methods approach, integrating quantitative and qualitative techniques across two phases to explore the prevalence of academic misconduct (AM) and the interventions implemented by educators. Data were gathered from randomly selected students and purposively chosen teachers using structured and semi-structured questionnaires, based on specific criteria. The findings revealed significant occurrences of AM among students, including practices such as plagiarism, copying from websites, and sharing quiz answers via social media. Teachers also reported these behaviors, highlighting the multifaceted motivations behind students’ engagement in AM. In response to these challenges, educators implemented a range of interventions. These included confirmatory communication through phone calls and text messages, the introduction of gamified learning activities, constructive feedback on plagiarized work, and supportive outreach via private messages. In some cases, home visits were conducted to reinforce accountability. The study identified four key outcomes of these interventions: student compliance with academic integrity measures, acceptance of the consequences of misconduct, receptivity to feedback, and the phenomenon known as “Ningas Kugon” mentality, which describes inconsistent or transient changes in student behavior. Hence, this inquiry provides valuable insights into the dynamics of AM and the effectiveness of various educator interventions.
Volume: 14
Issue: 6
Page: 4329-4339
Publish at: 2025-12-01

Accelerated framework for image compression and reconstruction based on compressive sensing

10.11591/ijeecs.v40.i3.pp1241-1250
Tasneem M. Yousif , Mohamed M. Ahmed
Image compression is a crucial field driven by advancements in communication and imaging technologies. Its primary goal is to achieve low bit rates while maintaining high-quality image reconstruction. Compression is essential in digital image processing, multimedia applications, and medical imaging. Various algorithms exist for image compression and reconstruction, each differing in efficiency. Compressive sensing (CS) algorithms, commonly used for radar data reconstruction, require iterative computations that demand significant processing power and time, limiting real-time applications. To overcome these challenges, this study proposes a parallel-pipelined processing approach to enhance compression and reconstruction efficiency. The method accelerates processing speeds, increases data throughput, and optimizes performance by reducing data size. The proposed approach divides image data into multiple parallel processing branches, significantly reducing computational cycles. This results in faster execution and improved real-time applicability. MATLAB simulations and field-programmable gate array (FPGA) hardware implementations have been conducted to validate the system’s effectiveness. The results demonstrate that the parallel-pipelined method significantly enhances efficiency compared to traditional approaches, making it suitable for applications requiring high-speed image processing, such as satellite imaging and medical diagnostics.
Volume: 40
Issue: 3
Page: 1241-1250
Publish at: 2025-12-01

Cardio meta-stack: a meta-classifier ensemble for enhanced cardiovascular disease prognosis

10.11591/ijeecs.v40.i3.pp1630-1637
Swetha S. , Sneha Zolgikar , Manjula S. H. , Venugopal K. R.
Cardiovascular diseases (CVDs) remain a significant global health concern, necessitating effective preventive measures and early diagnosis to reduce mortality rates. Leveraging machine learning models to identify risk factors holds great promise, especially in cardiology. This study introduces a robust methodology for prognosing cardiac illnesses based on patient-specific factors. By integrating five publicly available datasets from the UCI Repository and employing Feature Importance techniques for optimal risk factor selection, the proposed approach enhances prediction accuracy. Furthermore, the inclusion of the density-based spatial clustering of applications with noise (DBSCAN) algorithm assists in noise detection and removal, thereby improving model precision. The proposed Cardio MetaStack model, coupled with a stacking classifier ensemble, achieved an accuracy of 94.91%, surpassing that of traditional algorithms such as XGBoost 90.45%, demonstrating its efficacy in heart disease prediction.
Volume: 40
Issue: 3
Page: 1630-1637
Publish at: 2025-12-01

Impact of VR technology in physics teaching on students’ knowledge: a study on body acceleration

10.11591/ijere.v14i6.34942
Bakitzhan Kurbanbekov , Sherzod Ramankulov , Makpal Nurizinova , Beksultan Asanbek
The integration of virtual reality (VR) technology into education represents a promising innovation, particularly in enhancing the effectiveness of physics teaching. Traditional physics instruction often lacks interactive and immersive elements, which can limit students’ understanding of complex physical phenomena. This study addresses the challenge of improving comprehension of body acceleration by incorporating VR-based laboratory simulations. A quasi-experimental design was implemented, involving 222 university students randomly assigned to control and experimental groups. The experimental group conducted virtual experiments using VR simulations developed with Blender and Unity software, while the control group engaged in traditional lab activities. Data were collected through pre- and post-tests and analyzed using independent t-tests and G*Power software to assess statistical significance. The results revealed a notable improvement in learning outcomes for students exposed to VR-enhanced instruction, demonstrating increased engagement, deeper conceptual understanding, and improved ability to connect theoretical knowledge with practical application. This study confirms that VR technology is a powerful tool for modernizing science, technology, engineering, and mathematics (STEM) education and holds significant potential for improving cognitive outcomes and student motivation in scientific learning environments.
Volume: 14
Issue: 6
Page: 5038-5053
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

Quality of services LoRaWAN satellite communication

10.11591/ijeecs.v40.i3.pp1401-1416
Iwan Purnama , Muhammad Taufik Dwi Putra , Samsinar Samsinar , Masyitah Aulia , Ibnu Shina , Yudi Yuliyus Maulana , Bendjamin Benny Louhenapessy , Johanis Dominggus Lekalette , Peberlin Parulian Sitompul , Timbul Manik , Lasinta Ari Nendra Wibawa , Puput Dani Prasetyo Adi , La Sinaini , Pratiwi Lestari , Yaya Sulaeman , Iwan Rohman Setiawan , Budi Nugraha , Emi Yati , Lilis Sadiyah , Irwan Jatmiko , Rian Sacipto , Ros Sariningrum
This research discusses research that focuses on the capabilities of satellitebased LoRa, for satellite positions orbiting in low earth orbit (LEO). The expectation of low power wide area network (LPWAN) satellite can find the quality of transmitting data using LoRaWAN which is part of LPWAN and can provide quality of service (QoS) with high-quality real-time sensor data, low latency, long-range, low-power, no attenuation signal, no problem with obstacles in terrestrial areas, and other benefits that can be widely optimized. This article uses a comprehensive analysis of mathematical calculations as well as precise and accurate simulations for the actual development of satellite-based LPWAN. The satellite-based IoT is unlimited in terms of distance, to provide good services to all IoT users in the world. The comparison with terrestrial measurements is analyzed in detail, especially the signal attenuation factor that causes a lot of signal loss and data is not well received. Several methods are used to help reduce collision data, such as adaptive data rate (ADR) which can reduce collisions by 30%.
Volume: 40
Issue: 3
Page: 1401-1416
Publish at: 2025-12-01

Exploring cookies vulnerabilities: awareness, privacy risks and exploitation

10.11591/ijece.v15i6.pp5792-5803
Nor Anisah Amir Hamzah , Anis Safiyyah Adnan , Norsaremah Salleh
This study investigates cookie vulnerabilities, focusing on awareness, privacy risks, and exploitation techniques. We used a mixed-method approach that combines insights from a survey study and a systematic mapping study of 27 papers from online databases to comprehensively address the research topic. The results show a moderate level of user awareness about cookie-related privacy risks, with significant concerns over user tracking and profiling, identified in 88% of the reviewed studies. Key risks include sensitive data exposure, privacy and consent issues, targeted advertising, ineffective mitigation measures, and cyberattacks. Tracking via cookies, and especially third-party cookies were found to pose the greatest risk to end-users. Their widespread use for cross-site tracking and extensive fingerprinting often occurred without users’ awareness or explicit consent. These insights suggest the need for stricter privacy laws, better practices on cookies, and improved user awareness to mitigate concerning risks.
Volume: 15
Issue: 6
Page: 5792-5803
Publish at: 2025-12-01

Investigating Japanese EFL learners’ communication apprehension and oral presentation strategies

10.11591/ijere.v14i6.34246
Akiko Kamata , Kamisah Ariffin , Roslina Abdul Aziz , Nadhratunnaim Abas , Badli Esham Ahmad , Hiroki Okada
This study examines communication apprehension (CA) and oral presentation strategies among Japanese English as a foreign language (EFL) learner to understand their impact on verbal communication challenges. Despite educational reforms prioritizing communicative competence, Japanese learners face persistent difficulties, particularly in formal contexts like public speaking. A descriptive quantitative design was employed, and a purposive sampling method was used to select a sample of 140 EFL learners from a private Japanese university. Data was collected using the oral presentation strategies inventory (OPSI) and the personal report of communication apprehension (PRCA). Quantitative analysis via SPSS 29 assessed learners’ apprehension levels and strategy use patterns. Findings revealed moderate CA levels, with interpersonal communication inducing the most anxiety and public speaking causing the least. Learners relied heavily on message reduction and alteration (MRA) strategies, simplifying expressions to manage anxiety, while non-verbal (NV) strategies were minimally utilized. Positive correlations between strategy use and CA highlight the potential of targeted strategies to mitigate apprehension. Practical implications include the need to integrate Japanese cultural values into pedagogy to manage CA through collaborative learning, peer assistance, and structured oral activities, while also balancing accuracy with fluency and utilizing technological tools to support language development.
Volume: 14
Issue: 6
Page: 4231-4243
Publish at: 2025-12-01

Optimization of photovoltaic pumping system using neuro fuzzy inference system ANFIS control technique

10.11591/ijeecs.v40.i3.pp1270-1284
Laoufi Abdelhaq , Chergui Moulay-Idriss , Soufyane Chekroun
In recent years, artificial intelligence has become increasingly used due to the development of microcontrollers. In this paper, we propose an intelligent technique that employs the adaptive neuro-fuzzy inference system (ANFIS). We use this approach to improve the conventional direct torque control (DTC), which relies on a PI controller for the induction machine, and to enhance the conventional MPPT control based on the Perturb and Observe algorithm. The overall goal is to improve the performance of the photovoltaic pumping system. In this work, we apply ANFIS control to maximum power point tracking (MPPT-ANFIS). Additionally, we simultaneously optimize the efficiency of the DTC by applying ANFIS control (DTC-ANFIS). We present the results by comparing the photovoltaic pumping system using ANFIS control with the conventional photovoltaic pumping system, using MATLAB/Simulink. The results show that ANFIS control significantly improves the photovoltaic system compared to the conventional control, offering excellent dynamic performance of the induction motor and better utilization of photovoltaic solar energy. However, the ANFIS has some drawbacks, such as high computational time consumption and challenges in implementing a database.
Volume: 40
Issue: 3
Page: 1270-1284
Publish at: 2025-12-01
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