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28,719 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

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

Environmental and psychological influences on adolescents’ self-concept: teacher-student relationship as a moderator

10.11591/ijere.v14i6.34518
Ting Chen , Jamalsafri Saibon
Adolescence is a critical stage for the development of self-concept and psychological resilience. However, the impact of environmental and psychological factors on adolescents’ self-concept through psychological resilience has not been fully explored. Meanwhile, the discussion on whether the teacher-student relationship moderates the relationship between psychological resilience and self-concept is relatively rare. Based on cognitive-behavioral and social learning theories, this study collected data from 404 Chinese adolescents through a questionnaire survey. It employed partial least squares structural equation modeling (SEM) to test the hypotheses. The study found that environmental and psychological factors significantly influence adolescents’ psychological resilience, and psychological resilience mediates the relationship between environmental and psychological factors and self-concept. Moreover, the teacher-student relationship moderates psychological resilience and self-concept, particularly the positive teacher-student relationship, significantly promoting adolescents’ self-concept. This research highlights the critical influence of psychological resilience and teacher-student relationships in shaping adolescents’ self-concept. It provides empirical support for educational practice, highlighting the key role of environment, psychological factors, and good teacher-student relationships in adolescents’ mental health and self-concept development.
Volume: 14
Issue: 6
Page: 4528-4539
Publish at: 2025-12-01

Influences of educational and personal contexts on self-efficacy and job satisfaction of public elementary school teachers

10.11591/ijere.v14i6.33510
Ellaine Joy G. Eusebio , Philip R. Baldera , Aljay Marc C. Patiam , Charton F. Sombria , Jacel Ruz F. Gan , Connie G. Castillo
Enhancing teachers’ performance and sense of fulfillment in their roles is essential for advancing educational quality and promoting their overall well-being. This study investigates the determinants of teachers’ self-efficacy within a supportive school culture, as well as the factors influencing their job satisfaction, focusing on both educational and personal contexts among public elementary school teachers within a supportive school culture, focusing on educational and personal contexts. Utilizing a sample of 97 teachers from 13 schools in the Philippines, the research employs a causal-comparative design and surveys to gather data. The Kruskal-Wallis test results indicate no significant differences in self-efficacy and job satisfaction across age groups. The Mann-Whitney U test reveals a significant difference in self-efficacy between male and female teachers, with the latter reporting higher levels, while no significant gender differences were observed in job satisfaction. Likewise, no significant differences were found across career stages in both efficacy and satisfaction. A multivariate analysis of variance reveals that a supportive school culture has a significant impact on teachers’ self-efficacy and also on their job satisfaction. These results emphasize the critical role of nurturing a supportive school environment to enhance teacher well-being and effectiveness. The study provides valuable insights and practical recommendations for improving educational quality and teacher satisfaction through targeted interventions in school culture and opportunities for career advancement.
Volume: 14
Issue: 6
Page: 4468-4477
Publish at: 2025-12-01

Exploring feature selection method for microarray classification

10.11591/ijece.v15i6.pp5584-5593
Muhammad Zaky Hakim Akmal , Devi Fitrianah
Effectively selecting features from high-dimensional microarray data is essential for accurate cancer detection. This study explores the pivotal role of feature selection in improving the accuracy of classifying microarray data for ovarian cancer detection. Utilizing machine learning techniques and microarray technology, the research aims to identify subtle gene expression patterns that indicate ovarian cancer. The research explores the utilization of principal component analysis (PCA) for dimensionality reduction and compares the effectiveness of feature selection techniques such as artificial bee colony (ABC) and sequential forward floating selection (SFFS). The dataset used in this study comprises of 15154 genes, 253 instances, and 2 classes related to ovarian cancer. Through a comprehensive analysis, the study aims to optimize the classification process and improve the early detection of ovarian cancer. Moreover, the study presents the classification accuracy results obtained by PCA, ABC, and SFFS. While PCA achieved an accuracy of 96% and SFFS yielded a classification accuracy of 98%, ABC demonstrated the highest classification accuracy of 100%. These findings underscore the effectiveness of ABC as the preferred choice for feature selection in improving the classification accuracy of ovarian cancer detection using microarray data.
Volume: 15
Issue: 6
Page: 5584-5593
Publish at: 2025-12-01

Explainable fault diagnosis using discrete grey wolf optimization algorithm for photovoltaic system

10.11591/ijece.v15i6.pp5286-5296
Slimani Hassina , Chouhal Ouahiba , Beddiaf Yassine , Mahdaoui Rafik , Haouassi Hichem , Hamdi Roumaissa
The present article introduces the discrete grey wolf optimization algorithm (DGWOA), a novel variant of the grey wolf optimizer (GWO). DGWOA integrates discrete optimization techniques with explainable artificial intelligence (XAI) methodologies. This approach aims to overcome limitations associated with traditional fault diagnosis methods, such as limited accuracy in identifying complex patterns and low interpretability. Furthermore, it mitigates early convergence problems commonly encountered in optimization algorithms and enhances adaptability to discrete classification challenges. The DGWOA algorithm is designed to generate interpretable classification rules for fault detection through a stochastic search strategy. The explainability provided by the model not only enhances decision-making transparency but also improves diagnostic efficiency and predictive accuracy. The proposed algorithm was evaluated using a photovoltaic system dataset and benchmarked against established rule-based classifiers. DGWOA consistently achieved a classification accuracy of 99.48% and a precision of 100%, demonstrating its effectiveness in enhancing fault detection. Moreover, the interpretability of the generated classification rules contributes to the generation of outcomes that are both actionable and comprehensible to decision-makers.
Volume: 15
Issue: 6
Page: 5286-5296
Publish at: 2025-12-01

Computationally efficient pixelwise deep learning architecture for accurate depth reconstruction for single-photon LiDAR

10.11591/ijece.v15i6.pp5934-5941
Yu Zhang , Yiming Zheng
This work introduces a compact deep learning architecture for depth image reconstruction from time-resolved single-photon histograms. Unlike most deep learning approaches that mainly rely on 3D convolutions, our network is implemented purely with 1D convolutions without assistance from other sensors or pre-processing. Both synthetic and real datasets were used to evaluate the accuracy of our model for challenging signal-to-background ratios (SBRs), ranging from 5:1 to 1:1. Conventional maximum likelihood (ML) and another photon-efficient optimization-based algorithm were adopted for performance comparisons. Results from synthetic data show that our model achieves lower mean absolute error (MAE). Additionally, results from real data indicate that our model exhibits better reconstruction for high-ambient effects and provides better spatial information. Unlike existing 3D deep learning models, we process pixel-wise histograms continuously, rather than splitting the point cloud and stitching them afterward, which saves memory and computational resources, thereby laying a foundation for real-world embedded applications.
Volume: 15
Issue: 6
Page: 5934-5941
Publish at: 2025-12-01

Trends of unmanned aerial vehicles in smart farming: a bibliometric analysis

10.11591/ijece.v15i6.pp5746-5758
Alfred Thaga Kgopa , Sikhosonke Manyela , Bessie Baakanyang Monchusi
This paper presents a review of the trends of unmanned aerial vehicles (UAV) in agriculture using a bibliometric analysis. This bibliometric analysis shows that 1676 articles were accessed from the Elsevier Scopus database between 2013 and 2023. Our findings indicate research related to UAVs in agriculture has surged over the years, but the adoption and acceptance of smart farming technology in sub-Saharan Africa remains inert. This study employed VosViewer in data analysis and bibliometrics. Our findings show that China leads all countries and followed by the United States on UAV publications in smart farming research foci. Our findings indicate that UAVs are impactful in improving crop growth, crop health monitoring, and may be beneficial to small-holder farmers with increased yields. We recommend that sub-Saharan Africa nations accelerate collaboration with China and United States in advancing climate smart agriculture practices to mitigate food insecurity risks.
Volume: 15
Issue: 6
Page: 5746-5758
Publish at: 2025-12-01

Designing, developing and analyzing of a rectangular-shaped patch antenna at 3.5 GHz for 5G applications at S band

10.11591/ijece.v15i6.pp5422-5432
Sukanto Halder , Md. Sohel Rana , Md Abdul Ahad , Md. Shehab Uddin Shahriar , Md. Abdulla Al Mamun , Md. Mominur Rahaman , Omer Faruk , Md. Eftiar Ahmed
This research study focuses on the design and analysis of two distinct patch antennas for 5G applications at 3.5 GHz. Rogers RT5880 served as the foundational material for antenna designs I and II. A 50 Ω feed line is utilized to supply both antennas. According to the calculations, Design I exhibits a reflection coefficient (S11) of -32.98 dB, a voltage standing wave ratio of 1.045, a gain of 7.81 dBi, an efficiency of 89.2%, and a surface current of 66.82 A/V. Design II has a reflection coefficient (S11) of 34.98 dB, voltage standing wave ratio (VSWR) of 1.036, gain of 8.78 dBi, efficiency of 89.87%, and surface current of 62.7 A/V. Among the two antenna designs, design II outperformed design I, and the results indicate that the antenna fulfilled the designated purpose. The novelties of the proposed paper are to design two different patch antennas using same materials and highlight the performance of the design parameters. Design II is proficient in supporting 5G services owing to its advantageous performance. In addition, S11 of the antenna is reduced to bring the VSWR value is close to 1. Also, improve gain, directivity and efficiency by bringing the antenna impedance matching close to 50 Ω.
Volume: 15
Issue: 6
Page: 5422-5432
Publish at: 2025-12-01

Solar-powered boost-fly back converter for efficient warehouse monitoring with flack droid

10.11591/ijict.v14i3.pp802-810
S. Sivajothi Kavitha , D. Usha , V. Jamuna
Warehouses serve as essential infrastructure for storing a wide array of goods and are utilized by various entities. Implementing a sophisticated warehouse management system (WMS) represents a pinnacle of technological advancement. Effective warehouse maintenance is paramount, benefiting both consumers and producers alike. Typically, warehouses store items such as medicine, chemicals, food, and electronics, requiring controlled conditions of temperature and humidity. Monitoring these factors is essential to comply with regulations and maintain internal quality standards. This paper focuses on optimizing warehouse management to meet customer demands and streamline processes for packaging and production teams. Additionally, it proposes the integration of droid technology within warehouses to monitor the parameters and mitigate fire hazards, thereby enhancing the efficiency and safety of goods storage. This proactive approach not only ensures the integrity of stored products but also contributes to cost-saving measures within the warehouse. This paper introduces an innovative method to achieve a substantial increase in voltage output in a DC-DC converter while avoiding the need for excessively high duty ratios. The converter’s operation is governed by a single pulse width modulation (PWM) signal, employing a fractional-order proportional-integral-derivative controller (FOPID) for regulating the power switch. By merging boost-forward-fly back (BFF) converter topologies, the design achieves a remarkable voltage gain. Moreover, the converter efficiently recycles energy stored in the leakage inductance of the coupled inductor, thereby reducing voltage stress and minimizing power losses and thus enhancing overall converter efficiency.
Volume: 14
Issue: 3
Page: 802-810
Publish at: 2025-12-01
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