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28,428 Article Results

Multilevel and multisource data fusion approach for network intrusion detection system using machine learning techniques

10.11591/ijece.v15i4.pp3938-3948
Harshitha Somashekar , Pramod Halebidu Basavaraju
To enhance the performance of network intrusion detection systems (NIDS), this paper proposes a novel multilevel and multisource data fusion approach, applied to NSL-KDD and UNSW-NB15 datasets. The proposed approach includes three various levels of operations, which are feature level fusion, dimensionality reduction, and prediction level fusion. In the first stage features of NSL-KDD and UNSW-NB15 both datasets are fused by applying the inner join joint operation by selecting common features like protocol, service and label. Once the data sets are fused in the first level, linear discriminant analysis is applied for 12 feature columns which is reduced to a single feature column leading to dimensionality reduction at the second level. Finally, in the third level, the prediction level fusion technique is applied to two neural network models, where one neural network model has a single input node, two hidden nodes, and two output nodes, and another model having a single input node, three hidden nodes, and two output nodes. The outputs obtained from these two models are then fused using a prediction fusion technique. The proposed approach achieves a classification accuracy of 97.5%.
Volume: 15
Issue: 4
Page: 3938-3948
Publish at: 2025-08-01

Synthesizing strategies and innovations in combating land degradation: a global perspective on sustainability and resilience

10.11591/ijai.v14.i4.pp3133-3142
Gangamma Hediyalad , Ashoka Kukkuvada , Govardhan Hegde Kota
This paper presents a comprehensive examination of land degradation, a critical environmental challenge with far-reaching implications for agricultural productivity, ecosystem sustainability, and socio-economic stability worldwide. With the backdrop of escalating human population pressures and the exacerbating impact of climate change. It delves into the causes and consequences of soil erosion, desertification, salinization, and biodiversity loss, highlighting the interplay between natural processes and anthropogenic activities. Through a detailed review of literature spanning various remediation technologies, conservation practices, and policy frameworks, the paper critically assesses the effectiveness of current land management approaches, including the utilization of biosurfactants, remote sensing technologies, and agroforestry systems. Furthermore, it identifies significant research gaps and future directions, emphasizing the need for quantitative assessments, exploration of socio-economic impacts, and evaluation of restoration techniques. By offering evidence-based recommendations for policymakers and practitioners, this paper contributes to the global dialogue on sustainable land management and aims to catalyze action towards halting the advance of land degradation, ensuring food security, and preserving biodiversity for future generations. This work not only advances our understanding of land degradation challenges but also outlines a path forward for research, policy, and practice in the pursuit of environmental sustainability and resilience.
Volume: 14
Issue: 4
Page: 3133-3142
Publish at: 2025-08-01

Deep transfer learning for classification of ECG signals and lip images in multimodal biometric authentication systems

10.11591/ijai.v14.i4.pp3160-3171
Latha Krishnamoorthy , Ammasandra Sadashivaiah Raju
Authentication plays an essential role in diverse kinds of application that requires security. Several authentication methods have been developed, but biometric authentication has gained huge attention from the research community and industries due to its reliability and robustness. This study investigates multimodal authentication techniques utilizing electrocardiogram (ECG) signals and face lip images. Leveraging transfer learning from pre-trained ResNet and VGG16 models, ECG signals and photos of the lip area of the face are used to extract characteristics. Subsequently, a convolutional neural network (CNN) classifier is employed for classification based on the extracted features. The dataset used in this study comprises ECG signals and face lip images, representing distinct biometric modalities. Through the integration of transfer learning and CNN classification, improving the reliability and precision of multimodal authentication systems is the primary objective of the study. Verification results show that the suggested method is successful in producing trustworthy authentication using multimodal biometric traits. The experimental analysis shows that the proposed deep transfer learning-based model has reported the average accuracy, F1-score, precision, and recall as 0.962, 0.970, 0.965, and 0.966, respectively.
Volume: 14
Issue: 4
Page: 3160-3171
Publish at: 2025-08-01

Load frequency control for integrated hydro and thermal power plant power system

10.11591/ijece.v15i4.pp3583-3592
Vu Tan Nguyen , Thinh Lam-The Tran , Dao Huy Tuan , Dinh Cong Hien , Vinh Phuc Nguyen , Van Van Huynh
Persistent electrical supply requires the power systems to be stable and reliable. Against varying load conditions, control strategies such as load frequency control (LFC) is a key mechanism to protect its stability. Traditional control strategies for LFC often face challenges due to system uncertainties, external disturbances, and nonlinearities. This paper presents an advanced approach to control load frequency and enhancing LFC in power systems by using sliding mode control (SMC). SMC offers powerful stability and robustness versus nonlinearities and perturbation, making it a promising approach for addressing the limitations of conventional control methods. We contemporary a comprehensive analysis of the SMC approach tailored for LFC, including the strategy and employment of the control algorithm. The proposed method makes use of a sliding/gliding surface to enable the system trajectories to be continuous on this surface despite parameter variations and external disturbances. Simulation results demonstrate significant improvements in frequency stability and system performance compared to conventional proportional-integral-derivative (PID) controllers. The paper also includes a comparative analysis of SMC with other modern control techniques, highlighting its advantages in terms of robustness and adaptability.
Volume: 15
Issue: 4
Page: 3583-3592
Publish at: 2025-08-01

Efficient high-gain low-noise amplifier topologies using GaAs FET at 3.5 GHz for 5G systems

10.11591/ijece.v15i4.pp3833-3842
Samia Zarrik , Abdelhak Bendali , Elmahdi Fadlaoui , Karima Benkhadda , Sanae Habibi , Mouad El Kobbi , Zahra Sahel , Mohamed Habibi , Abdelkader Hadjoudja
Achieving a gain greater than 18 dB with a noise figure (NF) below 2 dB at 3.5 GHz remains a formidable challenge for low-noise amplifiers (LNAs) in sub-6 GHz 5G systems. This study explores and evaluates various LNA topologies, including single-stage designs with inductive source degeneration and cascade configurations, to optimize performance. The single-stage topology with inductive source degeneration achieves a gain of 18.141 dB and an NF of 1.448 dB, while the cascade-stage common-source low-noise amplifier with inductive degeneration achieves a gain of 32.714 dB and a noise figure of 1.563 dB. These results underscore the importance of GaAs FET technology in meeting the demanding requirements of 5G systems, specifically in the 3.5 GHz frequency band. The advancements demonstrated in gain, noise figure, and linearity affirm the viability of optimized LNA topologies for high-performance 5G applications, supporting improved signal quality and reliability essential for modern telecommunication infrastructure.
Volume: 15
Issue: 4
Page: 3833-3842
Publish at: 2025-08-01

Psychological and pedagogical conditions for the formation of the image of the future special educator

10.11591/ijere.v14i4.33541
Gulshat Koshzhanova , Kairat Ibragim , Mukhamejan Tusseyev , Arzu Gurbanova
In contemporary educational paradigms, the formation of a positive image of teachers plays a crucial role in students’ learning. However, the psychological and pedagogical conditions that contribute to the formation of the professional image of teachers have not been sufficiently investigated. The use of innovative teaching methods and effective communication strategies, are key to improving the professional image of teachers and can significantly improve teacher training in higher education institutions. The objective of this study is to investigate the impact of psychological and pedagogical factors on the professional image of special educators, assess the effectiveness of innovative teaching methods in the training process of teachers. The study was experimental in nature and employed a mixed-methods approach, incorporating both quantitative and qualitative data collection methods, including interviews and surveys. The experiment involved 200 participants (180 students and 20 teachers), randomly selected from three universities in Kazakhstan. The results indicated that innovative teaching methods significantly improved teachers’ perceptions of their professional image, evidenced by an increase in the average score of participants from 68.5 to 85.4 (p<0.001). Data analysis also revealed a positive correlation between the teacher’s image and factors such as the use of interactive technologies and communication strategies.
Volume: 14
Issue: 4
Page: 3148-3161
Publish at: 2025-08-01

The challenges of pre-calculus in times of change

10.11591/ijere.v14i4.30057
Maura A. E. Pilotti , Russina A. Eltoum , Hanadi M. Abdelsalam , Arifi Waked
Sustainable science, technology, engineering, and mathematics (STEM) education rests not only on gender equity in educational opportunities but also on the academic attainment of female students. In Saudi Arabia, women enrolled in engineering and computer science are both newcomers and a minority. The present study compared the performance of female undergraduate students in pre-calculus (a prerequisite for STEM programs) before and after the pandemic. It also examined changes in students’ perceived difficulty and value of pre-calculus, the stress experienced in it, and their determination to pursue STEM. In this study, pre-calculus performance declined after the pandemic. Furthermore, the course was perceived as more difficult and stressful, as well as less valuable. Nevertheless, students’ determination to continue in STEM was undeterred. After the pandemic, students reported mostly utilitarian motives for pursuing STEM degrees (i.e., career opportunities), and concerns about their ability to balance work and personal lives in STEM professions. The key challenge was a personal issue (i.e., balancing professional and personal lives) rather than a social issue (e.g., the persistent gender imbalance in STEM fields). These findings suggest that interventions supporting intrinsic motivation are key to students’ engagement and persistence in academic pursuits and thus to their ability to be academically and professionally successful in STEM.
Volume: 14
Issue: 4
Page: 3027-3041
Publish at: 2025-08-01

Renewable energy impact integration in Moroccan grid-load flow analysis

10.11591/ijece.v15i4.pp3632-3648
Safaa Essaid , Loubna Lazrak , Mouhsine Ghazaoui
This paper analyzes the behavior of a Moroccan electric transportation system in the presence of an integration of renewable energy sources, which represents a significant challenge due to their intermittent nature. The aim is to evaluate the performance of the transportation system in various situations and possible configurations. The current study enables the calculation of power flow in the network using the Newton-Raphson method under the MATLAB/Simulink software. To achieve this, a series of power flow simulations were conducted on a 5-bus Moroccan electrical network, examining four distinct scenarios. In addition, this article offers an evaluation of the power flow performance of the same electric transportation system with varying percentages of renewable energy penetration. In order to provide a complete critical analysis, many simulations were conducted to obtain the voltage and active power profile generated at different bus locations, as well as an evaluation of the losses in the studied network.
Volume: 15
Issue: 4
Page: 3632-3648
Publish at: 2025-08-01

Ensemble of convolutional neural network and DeepResNet for multimodal biometric authentication system

10.11591/ijece.v15i4.pp4279-4295
Ashwini Kailas , Madhusudan Girimallaih , Mallegowda Madigahalli , Vasantha Kumara Mahadevachar , Pranothi Kadirehally Somashekarappa
Multimodal biometrics technology has garnered attention recently for its ability to address inherent limitations found in single biometric modalities and to enhance overall recognition rates. A typical biometric recognition system comprises sensing, feature extraction, and matching modules. The system’s robustness heavily relies on its capability to effectively extract pertinent information from individual biometric traits. This study introduces a novel feature extraction technique tailored for a multimodal biometric system utilizing electrocardiogram (ECG) and iris traits. The ECG helps to incorporate the liveliness related information and Iris helps to produce the unique pattern for each individual. Therefore, this work presents a multimodal authentication system where data pre-processing is performed on image and ECG data where noise removal and quality enhancement tasks are performed. Later, feature extraction is carried out for ECG signals by estimating the Heart rate variability feature analysis in time and frequency domain. Finally, the ensemble of convolution neural network (CNN) and DeepResNet models are used to perform the classification. The overall accuracy is reported as 0.8900, 0.8400, 0.7900, 0.8932, 0.87, and 0.97 by using convolutional neural network-long short-term memory (CNN-LSTM), support vector machine (SVM), random forest (RF), CNN, decision tree (DT), and proposed MBANet approach respectively.
Volume: 15
Issue: 4
Page: 4279-4295
Publish at: 2025-08-01

Real-time machine learning-based posture correction for enhanced exercise performance

10.11591/ijece.v15i4.pp3843-3850
Anish Khadtare , Vasistha Ved , Himanshu Kotak , Akhil Jain , Pinki Vishwakarma
Poor posture and associated physical health problems have grown more common as technology use increases, especially during workout sessions. Maintaining proper posture is essential to increasing the efficacy of your workouts and avoiding injuries. The research paper presents the development of a machine-learning model designed to provide real-time posture correction and feedback for exercises such as squats and planks. The model uses MediaPipe for precise real-time posture estimation and OpenCV for analyzing video frames. It detects poor posture and provides users with instant corrective feedback on their posture by examining the angles between important body parts, such as the arms, knees, back, and hips. This innovative method enables a thorough evaluation of form without requiring face-to-face supervision, opening it up to a wider audience. The model is trained on real-world workout datasets of people performing exercises in different positions and postures to ensure that posture detection is reliable under various user circumstances. The system utilizes cutting-edge machine-learning algorithms to demonstrate scalability and adaptability for future training types beyond squats and planks. The main goal is to provide users with a model that increases the efficacy of workouts, lowers the risk of injury, and encourages better exercise habits. The model's emphasis on usability and accessibility makes it potentially a vital tool for anyone looking to enhance their posture and general fitness levels.
Volume: 15
Issue: 4
Page: 3843-3850
Publish at: 2025-08-01

Two-step majority voting of convolutional neural networks for brain tumor classification

10.11591/ijece.v15i4.pp4087-4098
Irwan Budi Santoso , Shoffin Nahwa Utama , Supriyono Supriyono
Brain tumor type classification is essential for determining further examinations. Convolutional neural network (CNN) model with magnetic resonance imaging (MRI) image input can improve brain tumor classification performance. However, due to the highly variable shape, size, and location of brain tumors, increasing the performance of tumor classification requires consideration of the results of several different CNN models. Therefore, we proposed a two-step majority voting (MV) on the results of several CNN models for tumor classification. The CNN models included InceptionV3, Xception, DensNet201, EfficientNetB3, and ResNet50; each was customized at the classification layer. The initial step of the method is transfer-learning for each CNN model. The next step is to carry out two steps of MV, namely MV on the three CNN model classification results at different training epochs and MV on the results of the first step. The performance evaluation of the proposed method used the Nickparvar dataset, which included MRI images of glioma, pituitary, no tumor, and meningioma. The test results showed that the proposed method obtained an accuracy of 99.69% with a precision and sensitivity average of 99.67% and a specificity of 99.90%. With these results, the proposed method is better than several other methods.
Volume: 15
Issue: 4
Page: 4087-4098
Publish at: 2025-08-01

Multi-layer convolutional autoencoder for recognizing three-dimensional patterns in attention deficit hyperactivity disorder using resting-state functional magnetic resonance imaging

10.11591/ijece.v15i4.pp3965-3976
Zarina Begum , Kareemulla Shaik
Attention deficit hyperactivity disorder (ADHD) is a neurological disorder that develops over time and is typified by impulsivity, hyperactivity, and attention deficiency. There have been noticeable changes in the patterns of brain activity in recent studies using functional magnetic resonance imaging (fMRI). Particularly in the prefrontal cortex. Machine learning algorithms show promise in distinguishing ADHD subtypes based on these neurobiological signatures. However, the inherent heterogeneity of ADHD complicates consistent classification, while small sample sizes limit the generalizability of findings. Additionally, methodological variability across studies contributes to inconsistent results, and the opaque nature of machine learning models hinders the understanding of underlying mechanisms. We suggest a novel deep learning architecture to overcome these issues by combining spatio-temporal feature extraction and classification through a hierarchical residual convolutional noise reduction autoencoder (HRCNRAE) and a 3D convolutional gated memory unit (GMU). This framework effectively reduces spatial dimensions, captures key temporal and spatial features, and utilizes a sigmoid classifier for robust binary classification. Our methodology was rigorously validated on the ADHD-200 dataset across five sites, demonstrating enhancements in diagnostic accuracy ranging from 1.26% to 9.6% compared to existing models. Importantly, this research represents the first application of a 3D Convolutional GMU for diagnosing ADHD with fMRI data. The improvements highlight the efficacy of our architecture in capturing complex spatio-temporal features, paving the way for more accurate and reliable ADHD diagnoses.
Volume: 15
Issue: 4
Page: 3965-3976
Publish at: 2025-08-01

Enhancing voltage stability of transmission network using proportional integral controlled high voltage direct current system

10.11591/ijece.v15i4.pp3593-3602
Chibuike Peter Ohanu , Uche C. Ogbuefi , Emenike Ejiogu , Tole Sutikno
The contingencies experienced in transmission power networks often lead to unstable voltage profiles, challenging grid reliability and stability. This research aim is to enhance voltage stability using a proportional-integral (PI) controlled high voltage direct current (HVDC) system on a real life 330 kV network. The Newton-Raphson (NR) method is used for power flow analysis of the test network, and stability analysis identified Makurdi bus as the candidate bus for improvement due to its low eigenvalue and damping ratio. Application of a balanced three-phase fault at this bus resulted in a minimum voltage of 0.70 per unit (p.u.), falling outside the statutory voltage limit requirements of 0.95 to 1.05 p.u. The PI-based HVDC system was then applied along the Makurdi to Jos transmission line, which has a low loading capacity. The application of this model optimized the system response to disturbances, significantly improve voltage stability and raised the minimum voltage profile on the network to 0.80 p.u. This demonstrates 10% voltage profile improvement from the base case and reaffirms the effectiveness of the PI-based HVDC system in enhancing voltage stability during major disturbances. This research highlights the potential of integrating control systems into power networks to improve voltage stability and ensure reliable operation, even during large disturbances.
Volume: 15
Issue: 4
Page: 3593-3602
Publish at: 2025-08-01

Enhancing multi-class text classification in biomedical literature by integrating sequential and contextual learning with BERT and LSTM

10.11591/ijece.v15i4.pp4202-4212
Oussama Ndama , Ismail Bensassi , Safae Ndama , El Mokhtar En-Naimi
Classification of sentences in biomedical abstracts into predefined categories is essential for enhancing readability and facilitating information retrieval in scientific literature. We propose a novel hybrid model that integrates bidirectional encoder representations from transformers (BERT) for contextual learning, long short-term memory (LSTM) for sequential processing, and sentence order information to classify sentences from biomedical abstracts. Utilizing the PubMed 200k randomized controlled trial (RCT) dataset, our model achieved an overall accuracy of 88.42%, demonstrating strong performance in identifying methods and results sections while maintaining balanced precision, recall, and F1-scores across all categories. This hybrid approach effectively captures both contextual and sequential patterns of biomedical text, offering a robust solution for improving the segmentation of scientific abstracts. The model's design promotes stability and generalization, making it an effective tool for automatic text classification and information retrieval in biomedical research. These results underscore the model's efficacy in handling overlapping categories and its significant contribution to advancing biomedical text analysis.
Volume: 15
Issue: 4
Page: 4202-4212
Publish at: 2025-08-01

An in-depth analysis of a tutoring solution by digital technology

10.11591/ijece.v15i4.pp4058-4073
Soukaina Nai , Amal Rifai , Abdelalim Sadiq , Bahaa Eddine Elbaghazaoui
In Morocco, the dropout rate in primary and secondary education remains high due to environmental, social, familial, and educational factors. To address this issue, students rely on private tutoring or online platforms. However, socio-economic disparities make private tutoring inaccessible to many, while technical and pedagogical challenges limit the effectiveness of online platforms, deepening educational inequalities. This article proposes a nationwide participatory tutoring approach involving educational administration and teachers to ensure equitable and quality learning. We analyze existing models to identify their limitations and propose a structured tutoring system tailored to different student profiles. This system is based on a specific algorithm that defines skill assessment, remediation, and progress tracking. Unified modeling language UML is used to structure and present our approach in detail. Then, we compare current Moroccan platforms, particularly Massar, with our system, evaluating student engagement, pedagogical monitoring, curriculum alignment, and remediation effectiveness. Finally, we discuss our results, highlighting our system’s potential to reduce learning gaps, improve education, and significantly decrease the dropout rate in Morocco.
Volume: 15
Issue: 4
Page: 4058-4073
Publish at: 2025-08-01
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