Articles

Access the latest knowledge in applied science, electrical engineering, computer science and information technology, education, and health.

Advanced Search
Filter Icon

Filters article

Years

FAQ Arrow
Single Year
Range Year
0
0

Source Title

FAQ Arrow

Authors

FAQ Arrow

28,428 Article Results

Techno-economic analysis of a 4 MW solar photovoltaic capacity expansion in a remote Indonesian village

10.11591/ijece.v15i4.pp4133-4147
Agie Maliki Akbar , Fahmy Rinanda Saputri , David Tee
As of the end of 2022, Indonesia’s electrification ratio reached 99.63%, reflecting significant progress. However, the province of East Nusa Tenggara lags behind with an electrification ratio below 90%, indicating a considerable gap in energy access. This challenge is particularly evident in Oelpuah village, where frequent power outages occur due to the inadequacy of the existing 5 MW solar farm. This study proposes addressing this shortfall by expanding the solar farm capacity by an additional 4 MW. Comprehensive feasibility studies were conducted, evaluating solar radiation, natural disaster risks, and land use. The analysis, supported by PVSyst simulations, identified a suitable site with high radiation levels, though it is not entirely free from disaster risks. The design requires 13,500 solar panel modules, each with a capacity of 330 Wp, and seven 500 kW inverters. Optimal system performance is achieved with a 15-degree panel tilt and a 0-degree azimuth, aligning with the site's location south of the equator. This expansion could supply electricity to up to 4,014 households, each with a typical power usage of 0.825 kW. The study highlights the need for further research to enhance electricity coverage across Indonesia.
Volume: 15
Issue: 4
Page: 4133-4147
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

Survey on 3D biometric traits for human identification

10.11591/ijai.v14.i4.pp3143-3152
Divya Gangachannaiah , Mamatha Aruvanalli Shivaraj , Honganur Chandrasekharaiah Nagaraj , Prasanna Gururaj Paga
Individuals are verified and identified using Biometric technology based on their biological or behavioral traits. Biometric-based personal authentication systems are more reliable and user friendly, overruns the traditional personal authentication systems. The physiological biometric traits get abraded due to aging and massive work, while the behavioral biometric traits are having high variations due to external factors such as fatigue, and mood. Among the physiological biometric traits, Finger geometry patterns are widely deployed authentication system reason being its stability, user acceptability and uniqueness. Recent trends in Biometrics attempt to incorporate 3D domain traits, 3D reconstruction is done using 2D multiple images. 3D images are usually more robust and illumination invariant as compared to their 2D counterparts. 3D reconstruction algorithms are compared by finding mean square error (MSE).
Volume: 14
Issue: 4
Page: 3143-3152
Publish at: 2025-08-01

Near-infrared spectroscopy and machine learning to detect olive oil type: a systematic review

10.11591/ijece.v15i4.pp4120-4132
Leonardo Ledesma Ortecho , Enrique Romero José , Christian Ovalle , Heli Alejandro Cordova Berona
The present study evaluates the effectiveness of visible/near-infrared spectroscopy (VIS/NIR) combined with machine learning in olive oil type detection. A search strategy based on the population, intervention, comparison, and outcome (PICO) framework was employed to formulate specific equations used in Scopus, ScienceDirect, and PubMed databases. After applying exclusion criteria, 53 studies were included in the review following preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. The reviewed studies demonstrate that VIS/NIR spectroscopy coupled with machine learning allows rapid and accurate identification of different types of olive oil, highlighting the detection of fatty acids, polyphenols, and other vital compounds. However, variability in samples and processing conditions present significant challenges. Although the results are promising, further research is required to fully validate the efficacy and feasibility of this technology in industrial settings. This review provides a comprehensive overview of the advances, challenges, and opportunities in this field, highlighting the need to optimize machine learning models and standardize analysis procedures for practical application in the food industry.
Volume: 15
Issue: 4
Page: 4120-4132
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

Machine learning approaches to cybersecurity in the industrial internet of things: a review

10.11591/ijece.v15i4.pp3851-3866
Melanie Heier , Penatiyana W. Chandana Prasad , Md Shohel Sayeed
The industrial internet of things (IIoT) is increasingly used within various sectors to provide innovative business solutions. These technological innovations come with additional cybersecurity risks, and machine learning (ML) is an emerging technology that has been studied as a solution to these complex security challenges. At time of writing, to the author’s knowledge, a review of recent studies on this topic had not been undertaken. This review therefore aims to provide a comprehensive picture of the current state of ML solutions for IIoT cybersecurity with insights into what works to inform future research or real-world solutions. A literary search found twelve papers to review published in 2021 or later that proposed ML solutions to IIoT cybersecurity concerns. This review found that federated learning and semi-supervised learning in particular are promising ML techniques being proposed to combat the concerns around IIoT cybersecurity. Artificial neural network approaches are also commonly proposed in various combinations with other techniques to ensure fast and accurate cybersecurity solutions. While there is not currently a consensus on the best ML techniques to apply to IIoT cybersecurity, these findings offer insight into those approaches currently being utilized along with gaps where further examination is required.
Volume: 15
Issue: 4
Page: 3851-3866
Publish at: 2025-08-01

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

Myoelectric grip force prediction using deep learning for hand robot

10.11591/ijai.v14.i4.pp3228-3240
Khairul Anam , Dheny Dwi Ardhiansyah , Muchamad Arif Hana Sasono , Arizal Mujibtamala Nanda Imron , Naufal Ainur Rizal , Mochamad Edoward Ramadhan , Aris Zainul Muttaqin , Claudio Castellini , Sumardi Sumardi
Artificial intelligence (AI) has been widely applied in the medical world. One such application is a hand-driven robot based on user intention prediction. The purpose of this research is to control the grip strength of a robot based on the user’s intention by predicting the grip strength of the user using deep learning and electromyographic signals. The grip strength of the target hand is obtained from a handgrip dynamometer paired with electromyographic signals as training data. We evaluated a convolutional neural network (CNN) with two different architectures. The input to CNN was the root mean square (RMS) and mean absolute value (MAV). The grip strength of the hand dynamometer was used as a reference value for a low-level controller for the robotic hand. The experimental results show that CNN succeeded in predicting hand grip strength and controlling grip strength with a root mean square error (RMSE) of 2.35 N using the RMS feature. A comparison with a state-of-the-art regression method also shows that a CNN can better predict the grip strength.
Volume: 14
Issue: 4
Page: 3228-3240
Publish at: 2025-08-01

Integrating time-frequency features with deep learning for lung sound classification

10.11591/ijece.v15i4.pp3737-3747
Su Yuan Chang , Marni Azira Markom , Zhi Sheng Choong , Arni Munira Markom , Latifah Munirah Kamaruddin , Erdy Sulino Mohd Muslim Tan
Deep learning has transformed medical diagnostics, especially in analyzing lung sounds to assess respiratory conditions. Traditional methods like CT scans and X-rays are impractical in resource-limited settings due to radiation exposure and time consumption, while conventional stethoscopes often lead to misdiagnosis due to subjective interpretation and environmental noise. This study evaluates deep learning models for lung sound classification using the International Conference on Biomedical Health Informatics 2017 dataset, comprising 920 annotated samples from 126 subjects. Pre-processing includes down sampling, segmentation, normalization, and audio clipping, with feature extraction techniques like spectrogram and Mel-frequency cepstral coefficients (MFCC). The adopted automatic lung sound diagnosis network (ASLD-Net) model with triple feature input (time domain, spectrogram, and MFCC) achieved the highest accuracy at 97.25%, followed by the dual feature model (spectrogram and MFCC) at 95.65%. Single-input models with spectrogram and MFCC performed well, while the time domain input alone had the lowest accuracy.
Volume: 15
Issue: 4
Page: 3737-3747
Publish at: 2025-08-01

A comparative study of deep learning-based network intrusion detection system with explainable artificial intelligence

10.11591/ijece.v15i4.pp4109-4119
Tan Juan Kai , Lee-Yeng Ong , Meng-Chew Leow
In the rapidly evolving landscape of cybersecurity, robust network intrusion detection systems (NIDS) are crucial to countering increasingly sophisticated cyber threats, including zero-day attacks. Deep learning approaches in NIDS offer promising improvements in intrusion detection rates and reduction of false positives. However, the inherent opacity of deep learning models presents significant challenges, hindering the understanding and trust in their decision-making processes. This study explores the efficacy of explainable artificial intelligence (XAI) techniques, specifically Shapley additive explanations (SHAP) and local interpretable model-agnostic explanations (LIME), in enhancing the transparency and trustworthiness of NIDS systems. With the implementation of TabNet architecture on the AWID3 dataset, it is able to achieve a remarkable accuracy of 99.99%. Despite this high performance, concerns regarding the interpretability of the TabNet model's decisions persist. By employing SHAP and LIME, this study aims to elucidate the intricacies of model interpretability, focusing on both global and local aspects of the TabNet model's decision-making processes. Ultimately, this study underscores the pivotal role of XAI in improving understanding and fostering trust in deep learning -based NIDS systems. The robustness of the model is also being tested by adding the signal-to-noise ratio (SNR) to the datasets.
Volume: 15
Issue: 4
Page: 4109-4119
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

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

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
1 2 3
...
1895 1896
Show 57 of 1896

Discover Our Library

Embark on a journey through our expansive collection of articles and let curiosity lead your path to innovation.

Explore Now
Library 3D Ilustration