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29,061 Article Results

Students’ and teachers’ perceptions and experiences of using social media in learning foreign languages

10.11591/ijere.v14i6.34732
Le Thanh Nguyet Anh , Bui Thanh Tinh , Nguyen Van Canh
The benefits of social media applications have emerged as an advancement in teaching and learning foreign languages. However, it falls short of studies investigating the perspectives and experiences of educators and learners about social media usage in Vietnamese tertiary education contexts in rural areas. Therefore, this study examined the perceptions and experiences of teachers and students regarding the application of social media in foreign language learning at a university in the Mekong Delta, Southern Vietnam. The study was conducted with the participation of 199 students and 20 teachers. This study employed a mixed-methods approach, collecting data from questionnaires, and an in-depth interview. The findings showed that most teachers and students had a positive attitude towards applying this mode to teaching and learning foreign languages. However, they indicated some problems when utilizing those methods. Additionally, they suggested some measures for blending direct teaching and learning with social media based on their experience. The research results provided more insights into this field in the literature, especially in local settings.
Volume: 14
Issue: 6
Page: 5197-5208
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

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

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

Crowdsourcing in Kazakhstan’s higher education in the system of dual education as predictor of universal competencies

10.11591/ijere.v14i6.32200
Mukhtar Tolegen , Botagoz Baimukhambetova , Irina Rovnyakova , Natalya Radchenko , Svetlana Sakhariyeva , Perizate Anafia
The rapid transformation of professional competencies and the emergence of new professions every 3-5 years have accentuated the quest for effective means to facilitate the process of predicting future universal competencies among university graduates. An empirical study was conducted in three stages: organizational, investigative, and analytical. The crowdsourcing process algorithm comprised information gathering, idea generation, filtering, and voting. The findings suggest the feasibility of applying crowdsourced forecasting in the educational sector, where a clear trend towards alignment with real sectors of the economy and constantly changing market business environment conditions is evident. Calculations revealed that consensus decision-making was achieved regarding competencies such as 3D modeling and computer graphics, multilingualism, emotional intelligence, project management competencies, legal literacy, neural networks and big data, intercultural communication, digital competencies, export potential of the agricultural sector, logistics outsourcing, systems thinking, virtual reality competencies, artificial intelligence proficiency, analytics, and critical thinking, as confirmed by the analysis of variance. Forecasts indicated a predominance of subject-specific competencies associated with the growing volatility of the Kazakhstani labor market. The formulated profile of future universal competency development serves as an additional guideline in the development of educational programs (EPs) in professional training directions. Modified crowdsourcing design and methodology for measuring results can be utilized or adapted for addressing other challenges facing the higher education system that require feedback.
Volume: 14
Issue: 6
Page: 4614-4627
Publish at: 2025-12-01

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

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

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

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

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

Robotic product-based manipulation in simulated environment

10.11591/ijece.v15i6.pp5894-5903
Juan Camilo Guacheta-Alba , Anny Astrid Espitia-Cubillos , Robinson Jimenez-Moreno
Before deploying algorithms in industrial settings, it is essential to validate them in virtual environments to anticipate real-world performance, identify potential limitations, and guide necessary optimizations. This study presents the development and integration of artificial intelligence algorithms for detecting labels and container formats of cleaning products using computer vision, enabling robotic manipulation via a UR5 arm. Label identification is performed using the speeded-up robust features (SURF) algorithm, ensuring robustness to scale and orientation changes. For container recognition, multiple methods were explored: edge detection using Sobel and Canny filters, Hopfield networks trained on filtered images, 2D cross-correlation, and finally, a you only look once (YOLO) deep learning model. Among these, the custom-trained YOLO detector provided the highest accuracy. For robotic control, smooth joint trajectories were computed using polynomial interpolation, allowing the UR5 robot to execute pick-and-place operations. The entire process was validated in the CoppeliaSim simulation environment, where the robot successfully identified, classified, and manipulated products, demonstrating the feasibility of the proposed pipeline for future applications in semi-structured industrial contexts.
Volume: 15
Issue: 6
Page: 5894-5903
Publish at: 2025-12-01

Leveraging IoT with LoRa and AI for predictive healthcare analytics

10.11591/ijict.v14i3.pp1156-1162
Pillalamarri Lavanya , Selvakumar Venkatachalam , Immareddy Venkata Subba Reddy
Progress in mobile technology, the internet, cloud computing, digital platforms, and social media has substantially facilitated interpersonal connections following the COVID-19 pandemic. As individuals increasingly prioritise health, there is an escalating desire for novel methods to assess health and well-being. This study presents an internet of things (IoT)-based system for remote monitoring utilizing a long range (LoRa), a low-cost and LoRa wireless network for the early identification of health issues in home healthcare environments. The project has three primary components: transmitter, receiver, and alarm systems. The transmission segment captures data via sensors and transmits it to the reception segment, which then uploads it to the cloud. Additionally, machine learning (ML) methods, including convolutional neural networks (CNN), artificial neural networks (ANN), Naïve Bayes (NB), and long short-term memory (LSTM), were utilized on the acquired data to forecast heart rate, blood oxygen levels, body temperature patterns. The forecasting models are trained and evaluated using data from various health parameters from five diverse persons to ascertain the architecture that exhibits optimal performance in modeling and predicting dynamics of different medical parameters. The models' accuracy was assessed using mean absolute error (MAE) and root mean square error (RMSE) measures. Although the models performed similarly, the ANN model outperformed them in all conditions.
Volume: 14
Issue: 3
Page: 1156-1162
Publish at: 2025-12-01

Unit commitment problem solved with adaptive particle swarm optimization

10.11591/ijict.v14i3.pp783-790
Ramesh Babu Muthu , Venkatesh Kumar Chandrasekaran , Bharathraj Munusamy , Dashagireevan Sankaranarayanan
This article presents an innovative approach that solves the problem of generation scheduling by supplying all possible operating states for generating units for the given time schedule over the day. The scheduling variables are set up to code the load demand as an integer each day. The proposed adaptive particle swarm optimization (APSO) technique is used to solve the generation scheduling issue by a method of optimization considering production as well as transitory costs. The system and generator constraints are considered when solving the problem, which includes minimum and maximum uptime and downtime as well as the amount of energy produced by each producing unit (like capacity reserves). This paper describes the suggested algorithm that can be applied for unit commitment problems with wind and heat units. Test systems with 26 and 10 units are used to validate the suggested algorithm.
Volume: 14
Issue: 3
Page: 783-790
Publish at: 2025-12-01

Indonesian automated short-answer grading using transformers-based semantic similarity

10.11591/ijict.v14i3.pp1034-1043
Samuel Situmeang , Sarah Rosdiana Tambunan , Lidia Ginting , Wahyu Krisdangolyanti Simamora , Winda Sari ButarButar
Automatic short answer grading (ASAG) systems offer a promising solution for improving the efficiency of reading literacy assessments. While promising, current Indonesian artificial intelligence (AI) grading systems still have room for improvement, especially when dealing with different domains. This study explores the effectiveness of large language models, specifically bidirectional encoder representations from transformers (BERT) variants, in conjunction with traditional hand-engineered features, to improve ASAG accuracy. We conducted experiments using various BERT models, hand-engineered features, text pre-processing techniques, and dimensionality reduction. Our findings show that BERT models consistently outperform traditional methods like term frequency-inverse document frequency (TF-IDF). IndoBERTLite-Base-P2 achieved the highest quadratic weighted kappa (QWK) score among the BERT variants. Integrating handengineered features with BERT resulted in a substantial enhancement of the QWK score. Utilizing comprehensive text pre-processing is a critical factor in achieving optimal performance. In addition, dimensionality reduction should be carefully used because it potentially removes semantic information.
Volume: 14
Issue: 3
Page: 1034-1043
Publish at: 2025-12-01

The bootstrap procedure for selecting the number of principal components in PCA

10.11591/ijict.v14i3.pp1136-1145
Borislava Toleva
The initial step in determining the number of principal components for both classification and regression involves evaluating how much each component contributes to the total variance in the data. Based on this analysis, a subset of components that explains the highest percentage of variance is typically selected. However, multiple valid combinations may exist, and the final choice is often made manually by the researcher. This study introduces a novel yet straightforward algorithm for the automatic selection of the number of principal components. By integrating ANOVA and bootstrapping with principal component analysis (PCA), the proposed method enables automatic component selection in classification tasks. The algorithm is evaluated using three publicly available datasets and applied with both decision tree and support vector machine (SVM) classifiers. Results indicate that this automated procedure not only eliminates researcher bias in selecting components but also improves classification accuracy. Unlike traditional methods, it selects a single optimal combination of principal components without manual intervention, offering a new and efficient approach to PCAbased model development.
Volume: 14
Issue: 3
Page: 1136-1145
Publish at: 2025-12-01

Quantifying the severity of cyber attack patterns using complex networks

10.11591/ijict.v14i3.pp1179-1188
Ahmed Salih Hasan , Yasir F. Mohammed , Basim Mahmood
This work quantifies the severity and likelihood of cyberattacks using complex network modelling. A dataset from common attack pattern enumerations and classifications (CAPEC) is collected and formalized as nodes and edges aiming at creating a network model. In this model, each attack pattern is represented as a node, and an edge is created between two nodes when there is a relation between them. The dataset includes 559 attack patterns and 1921 relations among them. Network metrics are used to perform the analysis on the network level and node level. Moreover, a rank of the CAPECs based on a complex network perspective is generated. This rank is compared with the CAPEC ranking system and deeply discussed based on cybersecurity perspective. The findings show interesting facts about the likelihood and severity of attacks. It is found that the network perspective should be given attention by the CAPEC ranking system. Finally, the results of this work can be of high interest to security architects.
Volume: 14
Issue: 3
Page: 1179-1188
Publish at: 2025-12-01
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