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29,082 Article Results

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

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

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

Memoryless state-recovery cryptanalysis method for lightweight stream cipher – A5/1

10.11591/ijece.v15i6.pp5453-5465
Khedkar Aboli Audumbar , Uday Pandit Khot , Balaji G. Hogade
Cryptology refers to the discipline concerned with securing communication and data in transit by transforming it into an unintelligible form, thereby preventing interpretation by unauthorized entities. Cryptanalysis is the study and practice of analyzing cryptographic systems with the aim of uncovering their weaknesses, finding vulnerabilities and obtaining unauthorized access to encrypted data. A5/1 is a lightweight stream cipher used to protect GSM communications. There are two memoryless cryptanalysis techniques used for this cipher which are Golic’s Guess-and-determine attack and Zhang’s Near Collision attack. In this paper a new guessing technique called move guessing technique used to construct linear equation filter along with Golic’s guess and determine technique is studied. Two modifications in move guessing technique are proposed for recovery of internal states S0 and S1. Further, a novel algorithm is proposed to select the modification to get minimum time complexity for recovery of internal states S0 and S1. The proposed algorithm gives minimum time complexity of 229.3138 at t = 14 for recovery of S0 state and 243.246 for recovery of S1 at t = 22.
Volume: 15
Issue: 6
Page: 5453-5465
Publish at: 2025-12-01

Determining student progression rates using discrete-time Markov chain model

10.11591/ijere.v14i6.32049
Mark John T. Mangsat , Daniel Bezalel A. Garcia , Andhee M. Jacobe , Maricel A. Bongolan
This study aims to analyze and understand the student progression from the Bachelor of Science in Mathematics (BS Math) program. A discrete-time Markov chain (DTMC) model was used to analyze data from 211 students enrolled from 2011-2012 to 2022-2023. The results reveal that there are students who will be retained in their year level, shift to another degree program, or drop. Additionally, the highest risk of shifting or dropping out of the program happens during the first two semesters in college or for first year in college. A bottleneck effect during the second year and third year was identified. Furthermore, the results suggest that there will be an approximately 35.22% graduation rate after eight semesters or four years, implying a large portion of BS Math students will be retained or dropped from the program, or shifted to other degree programs. To avoid such, it is suggested that the Mathematics and Natural Sciences Department should conduct review sessions, bridging programs, and continuous promotion. Lastly, it is suggested to conduct thorough studies about the possible intrinsic and extrinsic factors affecting the student progression to formulate a more specific intervention that may help in reducing the shifting and dropping rate.
Volume: 14
Issue: 6
Page: 4478-4486
Publish at: 2025-12-01

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

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

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

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

Low-power and reduced delay in inverter and universal logic gates using Hvt-FinFET technology

10.11591/ijece.v15i6.pp5193-5204
Veerappa Chikkagoudar , G. Indumathi
The rapid scaling of conventional complementary metal–oxide– semiconductor (CMOS) metal–oxide–semiconductor field-effect transistors (MOSFETs) led to significantly increasing power dissipation, delay, and short channel effects (SCEs). Fin field-effect transistor (FinFET) technology is a better alternative to MOSFETs with superior electrostatic control, low power, and reduced leakage current. FinFETs have been chosen for their efficiency in overcoming these issues. This work focuses on the design of high-threshold voltage fin field-effect transistor (Hvt-FinFET) 18 nm technology-based inverter with optimized parameters and implementing universal gates NAND and NOR in Cadence Virtuoso tool. These three gates are basic building blocks for any complex digital system design. The results demonstrate significant improvement in power and reduced propagation delay in comparison with conventional CMOS technology. The Hvt-FinFET inverter obtained power dissipation and delay reduction of 13.63% and 33.33%, respectively. Power and delay optimization of 29.10% and 11.8% have been obtained in the NAND gate and 31.28% and 29.08% in the NOR gate when compared to conventional CMOS circuits. The results demonstrate significant improvements in power savings, reduced propagation delay, and superior energy efficiency, validating the effectiveness of Hvt-FinFET technology for next-generation very large scale integration (VLSI) applications.
Volume: 15
Issue: 6
Page: 5193-5204
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

Geometrical determination of the focal point of parabolic solar concentrators

10.11591/ijece.v15i6.pp5055-5066
Bekzod Maxmudov , Sherzod A. Korabayev , Nosir Yu. Sharibaev , Abror Abdulkhaev , Xulkarxon Mahmudova , Sh A. Mahsudov
Parabolic solar concentrators play a crucial role in harnessing solar energy by focusing sunlight onto a single focal point, enhancing efficiency in solar thermal applications. However, accurately determining the focal point remains a significant challenge, affecting energy efficiency, stability, and operational costs. This study presents a novel approach to determining the focal point of parabolic solar concentrators using two distinct geometric and mathematical methods. The first method applies standard parabolic equations to derive the focal point, while the second method introduces a geometric approach based on the properties of straight-line tangents and angular measurements. Experimental validation was conducted by comparing the proposed method against laser-based focal point determination. The results demonstrate that the proposed method enhances heat collection efficiency and stability, leading to improved energy output. The findings of this study contribute to optimizing solar concentrator designs, reducing energy losses, and promoting sustainable energy applications.
Volume: 15
Issue: 6
Page: 5055-5066
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
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