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29,939 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

Adaptive multi-radio quality of service model using neural network approach for robust wireless sensor network transmission in multipath fading environment

10.11591/ijece.v15i4.pp3795-3802
Galang Persada Nurani Hakim , Dian Widi Astuti , Ahmad Firdausi , Huda A. Majid
Wireless sensor network loss in wireless data transmission is one of the problems that needs attention. Interference, fading, congestion, and delay are some factors that cause loss in wireless data transmission. This paper used an adaptive multi-radio model to enhance the wireless data transmission to be more robust to disturbance in a multipath fading environment. A neural network approach was used to generate the adaptive model. If we use 433 MHz as our carrier frequency with 250 kHz bandwidth and 12 spreading factors, we can get signal noise ratio (SNR) for 20 meters at about -9.8 dB. Thus, we can use the adaptive model to enhance the WSN wireless data transmission's SNR to 9 dB, automatically changing the radio configuration to 797.1 MHz frequency, with 378.1 bandwidth and 7.111 for spreading factor. Based on the result, the wireless data transmission link has been successfully enhanced using the proposed adaptive model for wireless sensor networks (WSN) in a multipath fading environment.
Volume: 15
Issue: 4
Page: 3795-3802
Publish at: 2025-08-01

Blockchain and internet of things synergy: transforming smart grids for the future

10.11591/ijece.v15i4.pp4239-4248
Mouad Bensalah , Abdellatif Hair , Reda Rabie
Conventional smart grid systems face challenges in security, transparency, and efficiency. This study addresses these limitations by integrating blockchain and internet of things (IoT) technologies, presenting proof-of-concept implemented on an Orange Pi 4 single-board computer. The realized prototype demonstrated secure and transparent energy transaction management with consistent throughput between 7.45 and 7.81 transactions per second, and efficient resource utilization across varying transaction volumes. However, scalability challenges, including a linear increase in processing time with larger block sizes, emphasize the need for optimized consensus mechanisms. The findings underscore the feasibility of blockchain-based smart grids in resource-constrained settings, paving the way for advancements in peer-to-peer energy trading, decentralized energy storage, and integration with artificial intelligence for dynamic energy optimization. This work contributes to developing secure, efficient, and sustainable energy systems.
Volume: 15
Issue: 4
Page: 4239-4248
Publish at: 2025-08-01

Breast cancer identification using a hybrid machine learning system

10.11591/ijece.v15i4.pp3928-3937
Toni Arifin , Ignatius Wiseto Prasetyo Agung , Erfian Junianto , Dari Dianata Agustin , Ilham Rachmat Wibowo , Rizal Rachman
Breast cancer remains one of the most prevalent malignancies among women and is frequently diagnosed at an advanced stage. Early detection is critical to improving patient prognosis and survival rates. Messenger ribonucleic acid (mRNA) gene expression data, which captures the molecular alterations in cancer cells, offers a promising avenue for enhancing diagnostic accuracy. The objective of this study is to develop a machine learning-based model for breast cancer detection using mRNA gene expression profiles. To achieve this, we implemented a hybrid machine learning system (HMLS) that integrates classification algorithms with feature selection and extraction techniques. This approach enables the effective handling of heterogeneous and high-dimensional genomic data, such as mRNA expression datasets, while simultaneously reducing dimensionality without sacrificing critical information. The classification algorithms applied in this study include support vector machine (SVM), random forest (RF), naïve Bayes (NB), k-nearest neighbors (KNN), extra trees classifier (ETC), and logistic regression (LR). Feature selection was conducted using analysis of variance (ANOVA), mutual information (MI), ETC, LR, whereas principal component analysis (PCA) was employed for feature extraction. The performance of the proposed model was evaluated using standard metrics, including recall, F1-score, and accuracy. Experimental results demonstrate that the combination of the SVM classifier with MI feature selection outperformed other configurations and conventional machine learning approaches, achieving a classification accuracy of 99.4%.
Volume: 15
Issue: 4
Page: 3928-3937
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

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

A ten-year retrospective (2014-2024): Bibliometric insights into the study of internet of things in engineering education

10.11591/ijece.v15i4.pp4213-4226
Zakiah Mohd Yusoff , Siti Aminah Nordin , Norhalida Othman , Zahari Abu Bakar , Nurlaila Ismail
This article presents a comprehensive ten-year retrospective analysis (2014-2024) of the evolving landscape of internet of things (IoT) studies within engineering education, employing bibliometric insights. The pervasive influence of IoT technologies across diverse domains, including education, underscores the significance of examining its trajectory in engineering education research over the past decade. Recognizing the dynamic nature of this intersection is crucial for educators, researchers, and policymakers to adapt educational strategies to IoT-induced technological shifts. Addressing this imperative, the study conducts a detailed bibliometric review to identify gaps, trends, and areas necessitating further exploration. Methodologically, the study follows a framework involving a comprehensive search of Scopus and Web of Science databases to identify relevant articles. Selected articles undergo bibliometric analysis using the Biblioshiny tool, supplemented by manual verification and additional analysis in Excel. This approach facilitates robust evaluation of citation patterns, co-authorship networks, keyword trends, and publication patterns over the specified timeframe. Anticipated outcomes include the identification of seminal works, key contributors, influential journals, and science mapping. The study aims to unveil emerging themes, track research trends, and provide insights into collaborative networks shaping IoT discourse in engineering education. This analysis offers a roadmap for future research directions, guiding educators and researchers toward fruitful avenues of exploration.
Volume: 15
Issue: 4
Page: 4213-4226
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

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

Instance segmentation for PCB defect detection with Detectron2

10.11591/ijece.v15i4.pp4172-4180
Aravalli Sainath Chaithanya , Lavadya Nirmala Devi , Putty Srividya
Printed circuit boards (PCBs) are essential in modern electronics, where even minor defects can lead to failures. Traditional inspection methods struggle with complex PCB designs, necessitating automated deep learning techniques. Object detection models like Faster R-CNN and YOLO rely on bounding boxes for defect localization but face overlap issues, limiting precise defect isolation. This paper presents a segmentation-based PCB defect detection model using Detectron2’s Mask R-CNN. By leveraging instance segmentation, the model enables pixel-level defect localization and classification, addressing challenges such as shape variations, complex structures, and occlusions. Trained on a dataset of 690 COCO-annotated images, the model underwent rigorous experimentation and parameter tuning. Evaluation metrics, including loss functions and mean average precision (mAP), assessed performance. Results showed a steady decline in loss values and high precision for defects like mouse bites and missing holes. However, performance was lower for complex defects like spurs and spurious copper. This study highlights the effectiveness of instance segmentation in PCB defect detection, contributing to improved quality control and manufacturing automation.
Volume: 15
Issue: 4
Page: 4172-4180
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

Multi-class pneumonia detection using fine-tuned vision transformer model

10.11591/ijece.v15i4.pp3996-4003
Khushboo Trivedi , Chintan B. Thacker
Distinguishing between the various forms of pneumonia (bacterial, viral, fungal, and normal) using chest X-rays is a major problem in global health. Conventional approaches to pneumonia identification frequently depend on laborious and error-prone manual interpretation. Current machine learning (ML) models, like convolutional neural networks (CNNs), have demonstrated some success, but they frequently fail on jobs requiring multi-class classification or generalization. The potential of vision transformer (ViT) models, fine-tuned to address these limitations, is explored. The approach enhances the accuracy of pneumonia classification into four distinct classes by leveraging the attention mechanism in vision transformers (ViTs). Fine-tuning with a tagged chest X-ray dataset improves the algorithm's ability to detect subtle variations in pneumonia types. The findings demonstrate the model's effectiveness in multi-class pneumonia diagnosis, achieving a significant performance improvement with 98% accuracy across the four classes. This work highlights the promise of vision transformers in medical imaging, enabling the development of improved and scalable pneumonia classification methods.
Volume: 15
Issue: 4
Page: 3996-4003
Publish at: 2025-08-01

An approach for predicting brain tumor with machine learning techniques

10.11591/ijece.v15i4.pp4332-4340
PSRB Shashank , L. Anand , R. Pitchai
The medical industry relies heavily on image processing for tumor diagnosis. Medical imaging is an ever-evolving and intricate field. Brain tumor (BT) is extremely frequent and may cause death. A BT develops when brain cells divide and grow out of control. The prognosis for people with BT can be greatly improved and the survival rate can be increased if the tumor is detected early. A single individual's brain magnetic resonance imaging (MRI) scan comprises of multiple slices through the 3D anatomical perspective. As a result, extracting tumor from MRI scans is a difficult and time-consuming laborious task. Because of the risks associated with biopsies, an MRI-based automated BT categorization is a safer alternative. The scientific profession has worked tirelessly from the beginning of the millennium to develop an automatic BT segmentation and classification system. Therefore, there is a large body of work in the field dedicated to the study of BT research through machine learning (ML) techniques. The review paper summaries the publicly accessible benchmark datasets typically used and compares various processing approaches, feature extraction (FE), segmentation, and classification algorithms for BT. The report also emphasizes the challenges of BT detection. Our hope is that this survey will provide researchers, clinicians, and other interested parties will gain an in-depth understanding of BT segmentation and classification using ML.
Volume: 15
Issue: 4
Page: 4332-4340
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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