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30,033 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

Hybrid deep learning (ILeS-Net) for lung cancer classification in cloud-IoT healthcare systems

10.11591/ijece.v16i3.pp1588-1607
Affrose Affrose , Cheruku Sandesh Kumar , Archek Praveen Kumar
This study presents a cloud–Internet of Things (cloud-IoT) based intelligent decision support framework for lung cancer classification and treatment recommendation, centered on a hybrid deep learning model termed ILeS-Net. Computed tomography (CT) images from a benchmark dataset are first preprocessed using Gaussian filtering to enhance image quality. Cancerous regions are identified using an Improved BIRCH (I-BIRCH) segmentation approach, followed by feature extraction using shape descriptors, color features, and Improved local Gabor XOR pattern (I-LGXP) textures. The extracted features are classified using ILeS-Net, which integrates Improved LeNet-5 and SqueezeNet architectures to achieve improved classification performance with reduced overfitting. Based on the classification results, the framework provides supportive recommendations to assist clinical decision-making. Experimental results demonstrate that the proposed ILeS-Net model achieves a maximum accuracy of 0.951, outperforming several conventional and state-of-the-art methods. The cloud–IoT integration further enables scalable, real-time, and secure data processing, highlighting the framework’s potential for practical computer-aided lung cancer diagnosis.
Volume: 16
Issue: 3
Page: 1588-1607
Publish at: 2026-06-01

Designing and evaluating a community-based digital dictionary system for the Balinese language: An IT innovation adoption study

10.11591/ijece.v16i3.pp1369-1381
Cokorda Pramartha , Madek Jeani Purnama , Ida Bagus Gede Sarasvananda , I Wayan Arka , Ni Luh Watiniasih
Regional and vulnerable languages increasingly depend on digital tools to remain visible and usable in everyday life, yet many dictionary initiatives are described mainly in terms of content or interface features rather than evaluated as information-system innovations. This paper presents an exploratory design science study of a community-based Balinese digital dictionary that supports bidirectional Balinese-Indonesian lookup, Latin and Balinese Unicode script, speech-level information, part-of-speech tagging, related-word search, and role-based contribution workflows. The platform is implemented as a web-based system with a three-tier architecture and relational database. To evaluate adoption readiness, 40 users completed representative tasks and then responded to an adapted Moore and Benbasat IT innovation adoption instrument covering seven constructs. The results show high ease of use, relative advantage, and compatibility, indicating strong functional value and fit with user routines. Image and visibility are moderate, while result demonstrability and visibility show lower reliability and are therefore interpreted as exploratory indicators. The study contributes both a documented digital-dictionary artefact for Balinese language support and a reusable evaluation approach for other early-stage community-facing information and communication technology (ICT) systems. The findings suggest that wider uptake depends not only on technical quality, but also on institutional visibility, outreach, and continued content enrichment.
Volume: 16
Issue: 3
Page: 1369-1381
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

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

Bioelectricity generation and physicochemical evolution of a substrate with sheep compost in microbial fuel cells in a high Andean area

10.11591/ijece.v16i3.pp1085-1096
Joel Colonio , Elvis Carmen , Arlitt Lozano , Alizze Colonio
The recovery of organic waste, such as sheep compost, is a key strategy for energy valorization. This study evaluated its potential as a substrate in microbial fuel cells (MFCs) using zinc (anode) and copper (cathode) electrodes and analyzed the evolution of its physicochemical properties, using soil samples from a high Andean area of the Chacapampa district, Peru. Two configurations of ground-mounted MFCs in series were compared: C1 (16 reactors of 400 g) and C2 (8 reactors of 800 g), maintaining a total mass of 6.4 kg. The C2 configuration was significantly more efficient, generating a median power of 819.53 μW, more than double the 380.92 μW of C1 (p=0.002). The final physicochemical analysis revealed that the process transforms the substrate, increasing electrical conductivity and phosphorus availability, although potassium decreased. It is important to note that due to the use of reactive metal electrodes, the system operates as a hybrid microbial-galvanic cell, where the zinc anode is consumed. It is concluded that sheep compost is an effective substrate and that consolidating the volume in fewer reactors optimizes electrochemical performance, although long-term environmental impacts regarding zinc accumulation must be monitored.
Volume: 16
Issue: 3
Page: 1085-1096
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

Prostate magnetic resonance imaging/transrectal ultrasound registration using vision transformer and convolutional neural network

10.11591/ijece.v16i3.pp1188-1198
Hanae Mahmoudi , Hiba Ramadan , Jamal Riffi , Hamid Tairi
Multimodal registration of 3D medical images (3D-MReg) plays a key role in several medical applications and remains a very challenging task as it deals with multimodal images and volumetric objects at the same time. Recently, convolutional neural networks (CNNs) based approaches have been proposed to solve 3D-MReg. However, these techniques cannot preserve the global spatial context required for accurate affine registration since they rely on convolution and regional clustering operations. To solve these problems, we propose a supervised approach that combines both CNN and the vision transformer (ViT) to predict a dense displacement field (DDF). In a first step, our method investigates the power of ViT to capture global voxels dependencies for initial rigid alignment. Then we exploit the force of CNNs to focus on local details within pre-aligned concatenated input 3D moving and fixed images and estimate DDF, which is then applied to the moving labels. Our method has been validated in a prostate magnetic resonance imaging/transrectal ultrasound (MRI/TRUS) dataset and achieved promising results compared to previous work based on only CNNs.
Volume: 16
Issue: 3
Page: 1188-1198
Publish at: 2026-06-01

Utilizing phase congruency technique in reception performance optimization of UWB signals in multipath fading channels

10.11591/ijece.v16i3.pp1272-1285
Nadir Mohamed Abdelaziz
Ultra-wideband (UWB) technology enables high-data-rate communications and centimeter-accurate indoor localization but suffers severe degradation in multipath fading channels due to dense multipath components, narrowband interference (NBI), and low signal-to-noise ratios (SNR). Conventional energy-based detection methods, including Rake receivers, fail under these conditions due to amplitude sensitivity. This paper introduces a phase congruency (PC)-based selective Rake (S-Rake) receiver that exploits phase alignment across frequencies rather than signal magnitude for robust feature detection. The proposed method computes PC metrics via Hilbert transforms and sub-band decomposition to identify phase-aligned multipath components, guiding S-Rake finger selection (4, 8, and 128 fingers) and time-of-arrival (TOA) estimation. Simulations using 6th-derivative Gaussian pulses over IEEE 802.15.3a CM4 channels (NLOS, 4-10 m) with AWGN and IEEE 802.11a interference (SIR=-30 dB to 0 dB) demonstrate that PC-based S-Rake achieves 4 dB SNR gain at BER=10⁻⁴ over conventional Rake under high interference. DS-UWB with PC outperforms TH-UWB by 3× lower BER at SIR=-30 dB. Increasing Rake fingers from 4 to 128 reduces BER by >40% and improves TOA accuracy by 62% (RMSE: 1.8 ns → 0.68 ns). PC maintains BER=10⁻³ at SIR=0 dB where conventional methods fail. Results establish PC as a transformative paradigm for interference-resilient UWB applications including IoT localization and 5G-coexistent communications.
Volume: 16
Issue: 3
Page: 1272-1285
Publish at: 2026-06-01

Radar-based gesture recognition simulation for unmanned aerial vehicles command interpretation

10.11591/ijece.v16i3.pp1227-1235
Denny Dermawan , Freddy Kurniawan , Yenni Astuti , Paulus Setiawan , Lasmadi Lasmadi , Uyuunul Mauidzoh , Bambang Sudibya
Radar-based gesture recognition has emerged as a robust alternative to vision-based systems, particularly in environments where lighting and privacy pose challenges. This study presents a simulation approach for recognizing hand gestures to control unmanned aerial vehicles (UAVs) using radar signals. Five discrete gestures, i.e., TakeOff, Land, MoveForward, TurnLeft, and stop, were defined and modeled in MATLAB to generate synthetic radar signals. From each sample, four time-frequency domain features were extracted: duration, maximum amplitude, dominant frequency, and root mean square (RMS). A dataset of 500 samples (100 per class) was classified using three supervised learning models: support vector machine (SVM), k-nearest neighbors (k-NN), and decision tree. The k-NN classifier achieved the highest accuracy of 96%, demonstrating the feasibility of lightweight classifiers for gesture recognition using low-complexity features. These results highlight the potential of radar-based interfaces to replace traditional remote controls in UAV operation. The proposed simulation framework contributes to the development of intuitive, non-contact human-machine interaction systems.
Volume: 16
Issue: 3
Page: 1227-1235
Publish at: 2026-06-01

Artificial intelligence-based battery management systems in electric vehicles: models, optimization, and future directions

10.11591/ijece.v16i3.pp1645-1654
Hassan Kassem , Tariq Bishtawi
The electric vehicle (EV) depends on the capabilities and durability of the main element of the car — the battery. Conventional battery management systems (BMS) can generally be challenged with regards to state estimation and lifespan forecasting in the face of complicated real-world scenarios. To address these limitations, this study examines how artificial intelligence (AI) has the potential to transform BMS operations. We introduce an in-depth discussion of AI-controlled BMS by examining the state-of-the-art models of precise state-of-charge and state-of-health estimation. The paper also goes into details of how machine learning and deep learning methods can optimize charging strategy, improve thermal management, and predictive diagnostics. The comparison between the data-driven solutions and the traditional methods is going to reveal that there is a high safety, efficiency, and battery life improvement. Lastly, we map the way ahead, taking into consideration issues such as edge computing, explainable AI, and the way of making the BMS a truly self-optimizing system, essential to the next generation of electric cars.
Volume: 16
Issue: 3
Page: 1645-1654
Publish at: 2026-06-01

Enhancing road damage detection performance using the YOLOv9 model

10.11591/ijict.v15i2.pp616-624
Muhammad Farkhan Adhitama , Sutikno Sutikno , Rismiyati Rismiyati
Roads are essential infrastructure that support community mobility, and their condition significantly impacts road user safety. However, manual road damage detection remains inefficient, time-consuming, costly, and prone to human error. To address this issue, this study proposed the YOLOv9 model for automated road damage detection and explored parameter combinations to optimize its performance. The proposed solution leverages the YOLOv9 model, which offers enhanced detection speed and accuracy compared to previous YOLO versions, due to its improved backbone and dynamic label assignment techniques. The method uses pre-trained weights and performs parameter tuning to adapt the model for identifying common road defects, including potholes, longitudinal, lateral, and alligator cracks. A publicly available dataset of road condition images was used for training and evaluation. Experimental results demonstrated that the optimized YOLOv9 model achieved a mean average precision (mAP) of 62.8%, indicating a promising ability to detect multiple types of road damage accurately. This study highlights the potential of YOLOv9 as an effective tool for road monitoring systems, contributing to proactive maintenance strategies and more efficient infrastructure management.
Volume: 15
Issue: 2
Page: 616-624
Publish at: 2026-06-01

A mHealth adoption model for diabetes self-management: patient-centered insights from UNRWA clinics

10.11591/ijict.v15i2.pp553-564
Saleem Mohammad Faraj , Haw Yuan Kang , Raja Rina Raja Ikram , Lizawati Salahuddin
This study develops and validates a mobile health (mHealth) adoption model to enhance diabetes self-management among type 2 diabetes mellitus (T2DM) patients in UNRWA primary healthcare clinics across Palestinian refugee camps. This study fills a gap in research on mHealth adoption in low-resource settings by combining the technology acceptance model (TAM), task-technology fit (TTF), and self-efficacy theory (SET). A descriptive, cross-sectional design was employed using a structured, validated questionnaire administered to 503 T2DM patients. Reliability analysis yielded high internal consistency (Cronbach’s α = 0.808–0.966). Structural equation modeling (SEM) using SPSS and AMOS validated the model fit, evidenced by a comparative fit index (CFI) of 0.941 and a root mean square error of approximation (RMSEA) of 0.048. Out of the eleven factors that were examined, Perceived Usefulness had the most positive impact on self-care management (β = 0.67, p < 0.001), while Task Requirement had the least. Notably, Perceived Self-Efficacy showed no significant effect on behavior (p > 0.05). These findings highlight usability, usefulness, and tool functionality as central to promoting mHealth use. The validated model can be modified for other chronic disease settings in comparable healthcare environments and provides practical advice for creating patient-centered mHealth interventions.
Volume: 15
Issue: 2
Page: 553-564
Publish at: 2026-06-01

An machine learning-enhanced reconfigurable software defined radio architecture for adaptive 5G wireless systems

10.11591/ijict.v15i2.pp699-706
Vijaya Bhaskar Chalampalem , Sancarapu Nagaraju , Venkata Vara Prasad , R. Kiran Kumar , Shanmugham Balasundaram
This paper presents a machine learning (ML)-enhanced software defined radio (SDR) architecture optimized for adaptive 5G wireless communication. The system incorporates predictive ML algorithms to enable real-time modulation selection, finite impulse response (FIR) filter reconfiguration, and spectrum adaptation based on dynamic channel parameters such as bit error rate (BER), received signal strength indicator (RSSI) and signal-to-noise ratio (SNR). A decision tree classifier and a deep Q-learning agent dynamically determine optimal modulation schemes (BPSK, QPSK, 16-QAM, OQAM) and filter tap configurations (4/8/16 taps), ensuring efficient communication under varying network conditions. Implemented on a Xilinx Zynq SoC using Verilog for datapath design and Python for ML control via AXI4-Lite, the architecture achieves a maximum operating frequency of 182.4 MHz, 40.7% logic utilization, and only 122.3 mW power consumption. Compared to existing SDR implementations, the system demonstrates a 17% frequency improvement, 28% power reduction, and 21% area savings. Real-time electrocardiogram (ECG) transmission confirms the system’s adaptability, achieving BER < 10⁻³ at 22 dB SNR and < 10⁻⁵ at 26 dB. These results affirm the viability of the proposed ML-SDR for edge-based biomedical and ultra-reliable low-latency communications (URLLC) applications in 5G networks.
Volume: 15
Issue: 2
Page: 699-706
Publish at: 2026-06-01

Coastline segmentation on Landsat 8 OLI images using majority voting with deep learning models

10.11591/ijict.v15i2.pp588-596
Nur Nafiiyah , Salwa Nabilah , Nur Azizah Affandy , Rifky Aisyatul Faroh , Esa Prakasa
Coastlines are highly dynamic due to both natural processes and anthropogenic factors, including global warming and sea level rise. Accurate coastline segmentation is essential for effective monitoring and management. Although previous studies have applied deep learning for coastline detection, many existing models still suffer from instability across scenes, blurred boundaries, and segmentation artifacts, indicating that model generalization remains a challenge. This study aims to develop a more robust coastline segmentation approach by introducing an automated majority voting strategy that integrates three deep learning models: ResNet50, ResNet18, and MobileNet-V2. Landsat 8 OLI imagery is used for training and testing. The Jaccard index results show that ResNet18, ResNet50, and MobileNet-V2 achieved scores of 0.96, 0.98, and 0.95 respectively, while the proposed majority voting method also achieved 0.98. Despite the producing a similar numerical score to the best individual model (ResNet50), the ensemble method improves segmentation consistency by reducing artifacts such as unwanted peripheral shapes and cracks within land areas. These findings demonstrate that combining multiple segmentation outputs yields more stable and reliable coastline detection than using single models. Future work will apply this approach to broader Indonesian coastal regions to further assess its generalizability across diverse shoreline conditions.
Volume: 15
Issue: 2
Page: 588-596
Publish at: 2026-06-01
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