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28,910 Article Results

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

Perceptions of audiovisual media in vocabulary acquisition among English learners: benefits and challenges

10.11591/ijere.v14i6.34852
Xuan Hong Nguyen Thi , Thanh Thai Nguyen
Learning vocabulary through English audiovisual materials has long been a popular method among students. With the advancement of digital technology, this approach has gained even more attraction, leading to a growing number of studies that have investigated its effectiveness. However, there is a notable scarcity of research addressing the challenges that students face; therefore, the current study aims to explore students’ perspectives on the challenges along with the benefits of using audiovisual media as tools for learning vocabulary. This study was done through a quantitative approach using a questionnaire that included both open-ended and closed-ended questions. With the participation of 132 senior English-major students at Thu Dau Mot University in Vietnam, the study collected 117 valid questionnaires that provided valid data for analysis. Through descriptive statistics, the results reveal the improvements in pronunciation and listening skills, enhanced understanding of slang and idiomatic expressions, and increased exposure to the natural use of the target language. However, the findings also reveal that this method poses challenges for students, including misunderstandings stemming from the use of formal or informal language and an over-reliance on audiovisual media. Therefore, the study emphasizes the need for structured guidance to foster language learning outcomes.
Volume: 14
Issue: 6
Page: 5209-5218
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

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

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

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

Fault diagnosis for inverter open circuit faults using DC-link signal and random forest-based technique

10.11591/ijpeds.v16.i4.pp2178-2185
Hoang-Giang Vu , Dang Toan Nguyen
Three-phase voltage source inverters based on insulated-gate bipolar transistors (IGBTs) are widely used in various industrial applications. Faults in IGBTs significantly affect the performance of the inverter and entire system. Robust and accurate fault detection are the key requirements of fault diagnosis methods. This paper explores a method for diagnosing power switch open circuit faults of a voltage source inverter based on machine learning algorithms. The diagnosis is performed in two steps, firstly the fault is detected by applying the Random Forest classifier algorithm with the DC-link signal. Next, the fault switch location is performed by additionally using the inverter output AC current signals. The diagnostic results based on simulation data show that the fault can be detected with maximum accuracy. Meanwhile, the accuracy in locating the fault switch is also significantly improved with the additional use of current signals measured at the DC-link. Potential application of electromagnetic field signal is also highlighted for the practical implementation of fault diagnosis.
Volume: 16
Issue: 4
Page: 2178-2185
Publish at: 2025-12-01

Adaptive ANFIS-based MPPT for PV-powered green ships with high gain SEPIC converter

10.11591/ijpeds.v16.i4.pp2768-2779
G. Jegadeeswari , Rohini Govindaraju , D. Balakumar , D. Lakshmi , S. Marisargunam , M. Batumalay , B. Kirubadurai
To align with global climate goals, the International Maritime Organization (IMO) has enforced strict measures to reduce greenhouse gas emissions from the shipping industry by promoting energy efficiency and cleaner propulsion methods. Ship engines remain major contributors to environmental pollution due to their dependence on fossil fuels and inefficient propulsion systems, highlighting the need for clean and sustainable alternatives. This study aims to design a renewable energy-based marine power system that effectively stores and utilizes solar energy, improving overall efficiency and reducing emissions for process innovation. A hybrid setup was developed using photovoltaic (PV) panels, batteries, and a bidirectional DC-DC converter to enable flexible power flow during both charging and discharging cycles. An adaptive neuro-fuzzy inference system (ANFIS)-based maximum power point tracking (MPPT) algorithm was employed alongside a SEPIC converter to enhance energy extraction from the PV system under dynamic conditions. The integrated system achieved a power extraction efficiency of 97.12%, confirming the effectiveness of the ANFIS-based MPPT strategy and showcasing the viability of intelligent renewable energy solutions in maritime applications.
Volume: 16
Issue: 4
Page: 2768-2779
Publish at: 2025-12-01

Fuzzy logic-based adaptive PLL switching strategy for voltage control in DVR assisted grid tied PV systems

10.11591/ijpeds.v16.i4.pp2353-2368
R. Srilakshmi , V. Chayapathy
This study aims to enhance power quality in grid-connected photovoltaic (PV) systems by introducing an intelligent fuzzy logic-based adaptive control strategy for dynamic PLL switching in a DVR-supported configuration. A 100-kW grid-tied PV system is modeled with a digital phase-locked loop (DPLL), a conventional synchronous reference frame PLL (CTPLL), and a dynamic voltage restorer (DVR). A Mamdani-type fuzzy inference system (FIS) performs real-time PLL selection based on phase-wise real-time fault monitoring. The system was tested under symmetrical and asymmetrical 20% sag and swell conditions, evaluating voltage stability at both PCC and load, total harmonic distortion (THD), recovery time, and synchronization accuracy. Results show that the proposed method reduces unnecessary DVR voltage injection from ~50 V to ~5-6 V under healthy conditions, maintains a near-unity power factor (< 0.95), and achieves up to 15% THD reduction in inverter current and PCC currents compared to DPLL-only operation. Recovery times improved by up to 25%, with stable synchronization maintained in all fault cases. The integration of adaptive PLL switching and targeted DVR activation offers a novel, hardware-efficient approach to harmonic suppression, voltage stabilization, and fault resilience in medium-scale PV systems.
Volume: 16
Issue: 4
Page: 2353-2368
Publish at: 2025-12-01

Assessment of the efficiency and performance of different PV system configurations under various fault conditions

10.11591/ijpeds.v16.i4.pp2744-2756
Raghad Adeeb Othman , Omar Sharaf Al-Deen Yehya Al-Yozbaky
Partial shadowing, bypass-diode issues, photovoltaic (PV) module deterioration, and wiring issues are examples of PV failures that have a substantial effect on power production and cause distinct peaks in a PV system's P-V curves. Various PV fault types have been used in the solar cell system in this work. Four types were used: open circuit, line to ground, cross-line to line, and intra-line to line. The impact of various PV system failure types on the system's performance was emphasized in this study. MATLAB is used to display the simulation results for the four approaches (series parallel (SP), total cross tied (TCT), honeycomb (HC), and bridge link (BL)) under various fault scenarios. The current-voltage (I-V) and power-voltage (P-V) curves are used to compare the results for each fault scenario. The open circuit fault between PV (7.8) in the first string and PV (18.19) in the fourth string resulted in a 40% decrease in the short-circuit current of the photovoltaic system compared to its normal value in the SP topology, while in the HC and BL topologies, the current value exceeded the allowable limit. This, in turn, had an impact on the (I-V) characteristics of this topology. The fault's impact was minimal and within the typical bounds of its (I-V) characteristics in the TCT topology.
Volume: 16
Issue: 4
Page: 2744-2756
Publish at: 2025-12-01

Smart wireless charging architecture for electric vehicles using resonant inductive coupling and low-component design

10.11591/ijape.v14.i4.pp859-869
Devarakonda Mahidhar , Burthi Loveswara Rao , K. V. Govardhan Rao , C. H. Rami Reddy
A wireless power transfer system designed for electro-vehicle recharge and low-power device charging is explained in this document through resonant inductive coupling technology. Once switched on the pulse generator and IRF540 MOSFETs from the IC CD4047 drive high-frequency signals through the transmitter coil. IR sensors function as operational safety tools by detecting valid receivers which activate a relay control system for transmitter power management and reduce unnecessary energy consumption. A full-wave rectifier along with the 7805-voltage regulator enables the receiver unit to deliver fully stable 5 V DC output. System status is displayed through a user interface equipped with an LCD and real-time billing information runs on ThingSpeak IoT platform for visualization. Tests show that the system reaches a maximum power transfer efficiency of 90% alongside successful relay operation lasting less than 150 ms. The system provides an inexpensive solution to build smart wireless charging infrastructure networks that remain energy-efficient and expandable through its built-in control and monitoring functions.
Volume: 14
Issue: 4
Page: 859-869
Publish at: 2025-12-01

Design and development of a modular magnetic wheeled robot for out-pipe inspection

10.11591/ijra.v14i3.pp331-344
Sugin Elankavi Rajendran , Kuppan Chetty Ramanathan , Harish Kumar Guasekaran , Arun Kumar Pinagapani , Dinakaran Devaraj , Ramya Mathanagopal
This paper presents the design of a modular mobile robot capable of climbing and inspecting vertical ferromagnetic pipes using magnetic wheels. Mobile robots used for climbing ferromagnetic surfaces employ magnetic tracks, wheels, and magnets attached to the robot’s body. When it comes to ferromagnetic pipes, magnetic wheels and magnets attached to the body can be used. Among them, magnetic wheels are commonly used for inspecting ferromagnetic pipes. While current robots are suitable for large pipes, they are not practical for smaller ones. To address this gap, a small-sized robot equipped with a magnetic wheel system that ensures both strong attachment and smooth movement along vertical ferromagnetic surfaces is developed. The robot’s magnetic adhesion performance was analyzed through simulations using finite element method magnetics and validated through laboratory experiments. The results show an average error of only 8.25% between simulation and real-world tests, confirming the system’s reliability for external pipe inspection.
Volume: 14
Issue: 3
Page: 331-344
Publish at: 2025-12-01

Enhancing informatics teacher training in Kazakhstan through dual education and specialized educational platforms

10.11591/ijere.v14i6.34236
Alima Seitaliyeva , Nurzhan Shyndaliyev , Dinara Kalmanova , Assemgul Kaipova , Kaussar Mukhtarkyzy
This study addresses the gap between traditional informatics teacher training in Kazakhstan and the practical demands of modern classrooms. It explores the integration of dual education and the informaticedu.kz digital platform as a means to enhance methodological and practical competencies among future teachers. A mixed-methods design was used, involving 24 students from Pavlodar Pedagogical University. Data were collected through structured questionnaires and qualitative interviews. Quantitative responses were analyzed using descriptive statistics, t-tests, and correlation analysis, while qualitative data underwent thematic analysis. The findings showed that the platform significantly supported lesson planning and methodological development, particularly among 4th-year students who rated the tool more positively than 3rd-year students. High correlations were found between understanding lesson structure and effective planning. However, participants reported a lack of interactive content such as case studies and problem-solving tasks. The results suggest that integrating dual education with specialized digital platforms enhances informatics teacher training. Still, to maintain relevance and effectiveness, platforms must evolve to include more interactive and adaptive features tailored to different training stages.
Volume: 14
Issue: 6
Page: 5003-5013
Publish at: 2025-12-01

Unveiling the emotional labor of overseas Filipino international teachers

10.11591/ijere.v14i6.34579
Leomar O. Baylosis , Ivy F. Amante , Rovy M. Banguis , Aldin Paul S. Genovia , Shem A. Cedeño
Emotional labor at work typically manifests through surface acting and deep acting. This phenomenological study examines the emotional labor experienced by 15 international Filipino teachers working in the United States, Saudi Arabia, and Thailand. Guided by self-determination theory (SDT), the research explores their reasons for teaching abroad, as well as the challenges they face and how they navigate them emotionally. A qualitative design was employed using semi-structured interviews and framed narratives. Each participant engaged in one individual interview and one focus group discussion. Data were analyzed using interpretative phenomenological analysis (IPA) to generate key themes. Findings show that deep acting involves emotional control, display of positive emotions, emotional exhaustion, experience of negative emotions, and emotional indifference toward self. In contrast, surface acting includes masking emotions, projecting artificial feelings, and withdrawal behaviors. The five major themes emerged as contributing factors to emotional labor: cultural adjustment, language barrier, professional challenges, limited support networks, and work-life balance. Coping strategies identified include emotional regulation, positive cognitive response, support from family and peers, and participation in recreational activities. These nuanced findings offer important insights for international teacher preparation, emotional well-being, and future research on cross-cultural educational contexts.
Volume: 14
Issue: 6
Page: 4628-4637
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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