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30,096 Article Results

Variance-k-means++: A deterministic centroid initialization method based on variance for enhanced clustering stability

10.11591/ijece.v16i3.pp1434-1448
Widodo Widodo , Jiel Vayyad Ramadhan , Muhammad Ficky Duskarnaen , Via Tuhamah Fauziastuti , Chelsea Zaomi Pondayu , Mada Rekadarma Septianda
K-means++ is developed to improve the performance of k-means when choosing a starting centroid. However, both algorithms in clustering still select an initial centroid randomly. Randomly selecting initial centroids has the potential to produce unstable clusters. This paper proposes a deterministic centroid initialization method called variance-k-means++, which utilizes statistical properties—mean and variance—to generate pseudo-centroids and derive initial centroids. The method aims to improve clustering stability and reduce the number of iterations. For the initial study, we used low-dimensional data to conduct the experiment series. Then, we employed two baseline methods for benchmarking, k-means and k-means++. The results show that variance-k-means++ outperformed the baseline method on average. Evaluating in Davies-Bouldin Index (DBI) and convergence analysis, we obtained DBI values at 0.756 and 0,771 for vertical and horizontal variance k-means++ with Iris dataset. At the same time, baseline methods have 0.802 and 0.830 for k-means++ and k-means, respectively. In convergence analysis, the results are 5.158 for vertical and 5.474 for horizontal, while baseline methods are 9.000 and 8.842. The primary contribution of this study lies in its achievement of minimizing the number of iterations while enhancing cluster stability.
Volume: 16
Issue: 3
Page: 1434-1448
Publish at: 2026-06-01

A survey of retrieval algorithms in ad and content recommendation systems

10.11591/ijece.v16i3.pp1518-1530
Yu Zhao , Fang Liu , Yuan Yuan , Yifan Dang
This paper presents a survey of retrieval algorithms used in advertising recommendation and organic content recommendation systems. Modern digital platforms rely on retrieval-based models to efficiently match users with relevant advertisements or personalized content. This survey reviews key techniques including inverted index methods, collaborative filtering, content-based filtering, hybrid recommendation models, and the two-tower neural network architecture widely used in large-scale recommendation systems. The paper compares the objectives, data utilization strategies, and evaluation metrics of ad targeting and organic retrieval systems. Practical challenges such as cold-start problems, data quality, scalability, and privacy considerations are also discussed. This survey further highlights the growing connection between industrial recommendation pipelines and emerging retrieval mechanisms used in large language model (LLM) systems. This survey provides insights into the design principles of modern retrieval systems and outlines future research directions at the intersection of recommendation systems and LLM.
Volume: 16
Issue: 3
Page: 1518-1530
Publish at: 2026-06-01

Sepsis detection using biomarkers and machine learning

10.11591/ijece.v16i3.pp1286-1297
Tuan Anh Vu , Dang Hoai Bac , Minh Tuan Nguyen
Life-threatening dysfunction of organs, known as sepsis, is caused by an imbalanced response of host to infection. In this work, an efficient algorithm is proposed to address vital biomarkers for identification of sepsis using immune-related differential expression genes. A total of 16 gene datasets are processed for the extraction of a gene intersection between different gene datasets and the immune-related gene group, which improve the generalization of the final detection algorithm due to diversity of the input data. A novel gene selection method using sequential forward gene selection, machine learning, and ranked genes based on their importance calculated by a random forest model. A subset of 36 potential immune-related genes, which are identified as the biomarkers from 560 input genes, show an efficiency of the proposed gene selection algorithm. The biomarkers are validated the performance using various machine learning and deep learning related to sepsis diagnosis. The highest statistical performance is shown for the random forest model using the biomarkers as the input with an accuracy of 96.83%, sensitivity of 98.86%, specificity of 86.70%, and AUC of 98.67%. The proposed detection algorithm includes a random forest model and 36 biomarkers, which is simple, effective, and reliable for the applications in clinic environments.
Volume: 16
Issue: 3
Page: 1286-1297
Publish at: 2026-06-01

Analytic algebraic Riccati solution for a robust control system: application to 2-DOF arm robot

10.11591/ijece.v16i3.pp1159-1174
Menad Meriem , Ahmed Foitih Zoubir , Mokhtari Abdellah
An analytic solution to the Riccati algebraic equation has been investigated by employing eigenvalue–eigenvector techniques combined with the Gram–Schmidt orthogonality process. An analytic solution to the Riccati algebraic equation has been investigated by employing eigenvalue–eigenvector techniques combined with the Gram–Schmidt orthogonalization process. The applied method is used to improve robust control of second and third-order state-dependent systems by handling nonlinearities. An H∞ controller is designed in this context via backstepping technique to enhance robustness and reduce computational effort. The effectiveness of this method has been demonstrated on a two-degree-of-freedom (2-DOF) robotic manipulator arm. Simulation results validate the performance of the controller, showing improved tracking accuracy, disturbance rejection, and overall system stability, thereby confirming the efficiency and applicability of the combined analytic Riccati algebraic equation and H∞ backstepping approach for nonlinear robotic systems.
Volume: 16
Issue: 3
Page: 1159-1174
Publish at: 2026-06-01

AI-enabled energy-aware routing approach for future-wireless sensor networks

10.11591/ijece.v16i3.pp1543-1561
Shamsher Singh , Mandeep Kumar
Next-generation wireless sensor networks (WSNs) demand intelligent, energy-aware communication mechanisms capable of sustaining long-term operation in environments with varying conditions and strict resource limitations. Traditional routing protocols often fail to optimize energy consumption under varying network densities, heterogeneous traffic patterns, and environmental uncertainties. This research proposes an AI-enabled energy-efficient routing protocol (AI-EERP) designed to enhance network lifetime, stability, and data delivery performance in next-generation WSNs. The protocol integrates machine learning–based node selection, adaptive clustering, and predictive residual-energy estimation to make optimized routing decisions in real time. Using AI-driven models, AI-EERP dynamically adjusts routing paths based on energy patterns, link quality, and network topology changes. The simulation outcomes clearly indicate that the proposed approach achieves notable gains in energy efficiency, packet delivery reliability, and network lifetime when compared with traditional routing protocols, including LEACH, PEGASIS, and HEED. The proposed approach establishes a robust and scalable framework for future intelligent WSN deployments across applications including smart cities, precision agriculture, environment-focused applications and automated industrial operations.
Volume: 16
Issue: 3
Page: 1543-1561
Publish at: 2026-06-01

Smart water distribution for smart cities based on Internet of Things

10.11591/ijece.v16i3.pp1655-1668
Amal Douli , Khelifa Benahmed , Belkacem Draoui
Against an unprecedented water crisis in our country, balancing water supply and demand is necessary for a secure and sustainable water supply. This challenge requires systems capable of delivering the necessary quantities while conserving resources. Numerous research initiatives focus on addressing water distribution challenges with the help of smart water systems to optimize network operations and minimize water demand. Based on these advancements, this paper proposes a new smart water distribution system for southwest of Algeria. The system integrates the Internet of Things (IoT), information and communication technologies, and smart technologies to address critical attributes for enhancing efficiency. To achieve the efficient management of two-way flows (both water and data) based on water demand and its availability, two innovative architectures have been proposed, using various measurements of water quantity and quality parameters. Algorithms to automate and optimize water distribution are also proposed. According to obtained results, performance has improved, with an accuracy rate of over 98%. These results establish the suggested system as a strong option for intelligent and sustainable water resource management by demonstrating its efficacy and durability.
Volume: 16
Issue: 3
Page: 1655-1668
Publish at: 2026-06-01

Using the technology theory to adoption virtual reality among university students

10.11591/ijece.v16i3.pp1485-1492
Ghaliya AlFarsi , Raghad M. Tawafak , Roy Mathew , Sohail Iqbal Malik , Abir AlSideiri
Virtual reality is a technology field that has become an integral part in most areas of life. Before the 20th century, virtual reality consisted primarily of artificial illusions. Students encounter early obstacles in learning and the current virtual reality (VR) learning mechanism. The research is based on previous studies by filling in the blank by observing the problems that students were facing. The second main point of this research was unified theory using model of technology acceptance and use. This paper focuses on the adoption of a virtual reality learning model in order to improve student academic performance. The results of this paper prove that hypotheses have a positive impact on the factors to use the proposed model.
Volume: 16
Issue: 3
Page: 1485-1492
Publish at: 2026-06-01

GAN-augmented vision transformer with balanced synthetic data generation for robust rice leaf disease detection

10.11591/ijece.v16i3.pp1307-1318
Saiful Islam , Md. Nasim Akhtar , M. Mahadi Hassan , A. N. M. Rezaul Karim , Israt Binteh Habib
Early and accurate identification of rice leaf diseases is essential for sustainable crop management; however, many existing convolutional neural networks (CNNs) based solutions struggle with class imbalance and limited robustness when applied to real-field data. In this work, a generative adversarial network (GAN) augmented vision transformer (ViT) framework is introduced to overcome these limitations. A deep size representative samples for underrepresented disease categories, resulting in a more balanced training dataset and achieving a Fréchet inception distance (FID) score of 18.6. The balanced dataset is then used to train a vision transformer model that leverages self-attention to capture global contextual features of rice leaf images. Experimental evaluation across ten disease classes shows that the proposed approach attains an overall classification accuracy of 96.5%, exceeding the performance of several established CNN architectures. Additionally, the model demonstrates strong generalization capability on an external field dataset, achieving 94.8% accuracy. To validate real-world applicability, the trained model is deployed on a Jetson Nano edge device, where it delivers efficient inference performance suitable for practical agricultural applications. The findings indicate that combining GAN-based data augmentation with transformer-based learning provides a reliable and scalable solution for rice leaf disease detection.
Volume: 16
Issue: 3
Page: 1307-1318
Publish at: 2026-06-01

Techno-economic assessment of gas engine power plants penetration in a power grid

10.11591/ijape.v15.i2.pp535-545
Adelhard Beni Rehiara , Frederik Haryanto Sumbung
This paper presents a techno-economic assessment of integrating engine power plants into a power grid, using the snake optimization (SO) algorithm to solve the multi-objective optimal power flow (OPF) problem. The study focuses on four key objectives: minimizing fuel costs, reducing voltage deviation, enhancing voltage stability, and minimizing active power losses. Simulations conducted on the 38-bus of Manokwari grid system demonstrate that the SO algorithm significantly improved performance in all areas. Fuel costs were reduced to 2.003 million USD/h while maintaining a stable voltage profile. Voltage deviation was reduced to 0.5577 p.u., ensuring better voltage consistency across the grid. Voltage stability was enhanced with a minimized Lmax value of 0.0200 p.u., and active power losses were reduced to 0.3423 MW, reflecting a notable increase in system efficiency. These findings demonstrate the effectiveness of integrating gas engine power plants, which led to noticeable improvements in operational efficiency and grid stability.
Volume: 15
Issue: 2
Page: 535-545
Publish at: 2026-06-01

Surface passivation-induced enhancement of light absorption in photoanodes for quantum dot-based solar cells

10.11591/ijape.v15.i2.pp948-954
Ho Minh Trung , Le Xuan Thuy
Quantum dot-sensitized solar cells hold promise for low-cost, high-efficiency photovoltaic applications; however, instability due to quantum dot degradation and poor interfacial charge transport remain key challenges. In this study, a copper-doped Zn(S,Se) passivation layer was chemically synthesized and applied onto TiO₂/CdS/CdSe@Cu photoanodes. The goal was to shield quantum dots from corrosive polysulfide electrolytes and enhance photon absorption. The morphology, structure, and optical characteristics of the Zn(S,Se):Cu layers were systematically analyzed using field-emission scanning electron microscopy (FESEM), energy-dispersive X-ray spectroscopy (EDX), X-ray diffraction (XRD), and UV-Vis spectroscopy. J-V measurements demonstrated that the ZnSe:Cu-coated photoelectrode achieved a higher power conversion efficiency (5.31%) than the ZnS:Cu counterpart (4.5%). Moreover, electrochemical impedance spectroscopy revealed a lower charge transfer resistance (Rct2 = 331 Ω), indicating improved electron transport and reduced recombination. These findings highlight the potential of Zn(S,Se):Cu layers in enhancing the stability and efficiency of quantum dot-sensitized solar cells, paving the way for more durable and efficient solar energy devices.
Volume: 15
Issue: 2
Page: 948-954
Publish at: 2026-06-01

A comprehensive review of sound source localization methods for robotics

10.11591/ijra.v15i2.pp257-266
Muhammad Akmal Aliff , Emerson Joseph Raja
Sound source localization (SSL) is a key technology in robotics that allows machines to detect and locate auditory cues in real time. This review provides a thorough examination of SSL techniques classified into classical, artificial intelligence (AI), and hybrid methods. Classical methods, which account for 44% of reviewed studies, excel in computational efficiency and reliability under controlled conditions but have limitations in dynamic environments. AI methods, which account for 16% of studies, use deep learning to adapt to complex scenarios, but they require large datasets and computational resources. Hybrid methods, which combine classical signal processing and AI, are the most robust and accurate, with an average accuracy of 97.45%. The review also looks at the role of microphone arrays in SSL performance, revealing that systems with ten or more microphones achieve the highest accuracy of 99.23%, while single- and dual-microphone systems still perform competitively (97.60% and 97.21%, respectively). These findings suggest that hybrid methods combined with larger microphone arrays are the most effective SSL solution in robotics, balancing precision and adaptability. This paper discusses current SSL trends, challenges, and future research directions, providing insights for the development of advanced auditory systems capable of reliable performance in dynamic, real-world environments.
Volume: 15
Issue: 2
Page: 257-266
Publish at: 2026-06-01

A risk-constrained SARSA–FIS hybrid decision architecture with adaptive exploration control

10.11591/ijece.v16i3.pp1531-1542
Joni Fat , Parwadi Moengin , Pudji Astuti , Sally Cahyati
Algorithmic trading systems operate in highly dynamic and uncertain environments where learning-based decision agents must balance adaptability with strict risk control. Reinforcement learning (RL) methods provide adaptive policy optimization but often suffer from unstable exploration and limited interpretability in financial markets. This study proposes a risk-constrained SARSA–FIS hybrid decision architecture with adaptive exploration control for algorithmic trading. The framework integrates a compact SARSA-based reinforcement learning environment with a Sugeno-type fuzzy inference system (FIS) that converts reinforcement signals into interpretable trading decisions. Exploration follows a decaying ε-greedy policy with a drawdown-triggered reset mechanism to maintain bounded risk exposure during learning. The system was implemented as a MetaTrader 5 Expert Advisor and evaluated on the GBPUSD currency pair using historical market data. Experimental results show that the hybrid framework improves trading performance compared with a rule-based baseline. During a six-month out-of-sample evaluation, the system achieved a net profit of 90 USD and a profit factor of 1.35, compared with 10 USD and 1.02 for the baseline. Extended one-year testing confirmed stable profitability and controlled drawdown behavior. The results demonstrate that integrating reinforcement learning, fuzzy decision mapping, and explicit risk constraints provides a practical approach for developing adaptive trading agents.
Volume: 16
Issue: 3
Page: 1531-1542
Publish at: 2026-06-01

An internet of things-telemedicine platform empowered by 5G mobile networks for Tunisian Rural places

10.11591/ijece.v16i3.pp1261-1271
Ibrahim Monia , Dadi Mohamed Bechir , Rhaimi Belgacem Chibani
With the advent of Internet of Things (IoT) technologies, offering new possibilities for remote healthcare delivery, the medicine sector has undergone significant advancements in recent years. New tools are used, and diagnostics have become more accurate. We suggest creating a platform that can be extended for several applications. This platform has been realized to attest and demonstrate how IoT technology offers devices that could be integrated to provide novel services like remote consultations. Our proposed platform contains novel functionalities such as real-time video calls, instantaneous messaging, live notifications, vital signs monitoring, and electronic health record access. This is accomplished with enhanced qualities of remote healthcare services. Added to this, healthcare access equity will be guaranteed. The paper emphasizes the potential of Laravel 11 as a framework offering powerful features for creating modern and high-performance applications. We have integrated Laravel Reverb, a powerful real-time communication package, to provide seamless real-time communication with users. With our application, notifications and interactions are dynamically created. This allows instant updates to delivery and engages the user experience. The database was designed based on the latest version of MySQL 8, coupled with the advanced capabilities of PHP 8.2. This combination provides unparalleled performance, scalability and reliability. Added to that, IoT’s technology usage helps to improve healthcare access and delivery, especially in underserved areas. Human and machine cooperation is a main factor of the 5th industry level. This is widely respected by our platform. This offers great help, especially for those isolated and underserved areas, as we hope.
Volume: 16
Issue: 3
Page: 1261-1271
Publish at: 2026-06-01

Enhancing sEMG finger gesture recognition using optimized 1D-convolutional neural network

10.11591/ijece.v16i3.pp1576-1587
Daniel Sutopo Pamungkas , Sumantri K. Risandriya
Robust and precise finger gesture recognition using surface electromyography (sEMG) is essential for developing intuitive prosthetic control systems. However, sEMG signals are inherently stochastic and non-stationary, posing significant challenges for high-accuracy classification in fine-grained movements. This study proposes an optimized 1D convolutional neural network (1D-CNN) framework for classifying 20 distinct fine-grained finger gestures using raw sEMG data from an 8-channel wearable Myo Armband sensor. Unlike traditional methods that rely on manual feature engineering, the proposed 1D-CNN performs end-to-end learning to automatically extract temporal features. The research specifically investigates the impact of temporal windowing strategies, ranging from 400 to 750 ms, on model performance. Experimental results demonstrate that the optimized 1D-CNN achieves a peak test accuracy of 94.4% with a 550 ms window size, demonstrating the model’s robustness across complex gesture classes and significantly outperforming the baseline principal component analysis- support vector machine (PCA-SVM) method which only attained 73.0% accuracy. While the model achieved perfect classification (100%) for index, middle, and little finger movements, a performance drop was observed in thumb recognition (50%) due to muscular crosstalk from deeper anatomical layers. These findings indicate that the integration of optimized windowing and 1D-CNN architectures provides a highly reliable solution for complex large-scale gesture recognition, offering a robust foundation for the next generation of multi-functional prosthetic hands.
Volume: 16
Issue: 3
Page: 1576-1587
Publish at: 2026-06-01

Enhancing Bitcoin price forecasting: a comparative analysis of advanced time series models with hyperparameter optimization

10.11591/ijict.v15i2.pp535-544
Amine Batsi , Mohamed Biniz , Rachid El Ayachi
This paper evaluates state-of-the-art time series forecasting to predict next day Bitcoin prices via distinct architectures and methodologies in a real-time setting. We study six advanced models, KAN, TimesNet, NBEATS, NHITS, PatchTST and BiTCN, applied to a Jan 1, 2023, to Dec 1, 2024. We simulate real world applications via a rolling forecast strategy, in which we predict daily prices from the most recent data. The dataset consists of daily Bitcoin closing prices and data preprocessing and integrity checks for its constituent data. Additionally, rigorous accuracy and reliability were investigated using performance metrics such as the MAE, RMSE, MAPE, and R². NBEATS and NHITS were the top performers, achieving an R² score of 0.967, explaining complex patterns in volatile cryptocurrency data. The specific importance of model architecture and further hyperparameter optimization in achieving higher forecasting accuracy is highlighted in this study. The practical implications of these findings for the advancement of time series forecasting in financial markets are leveraged here, where timely and accurate forecasts are critical.
Volume: 15
Issue: 2
Page: 535-544
Publish at: 2026-06-01
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