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

30,185 Article Results

Artificial intelligence in orthodontics: modeling decision support systems for treatment planning

10.11591/ijai.v15.i1.pp97-105
Sowmya Lakshmi Belur Subramanya , Advaith Vijaya Mohan , Achala Varsha Vishlavath Premalatha , Manchikanti Varunsai
Orthodontic treatment planning involves complex clinical decision-making that can benefit from artificial intelligence (AI). This study evaluates machine learning and deep learning models—including random forest, AdaBoost, gradient boosting, and artificial neural networks (ANNs)—for predicting orthodontic treatment strategies using a dataset of 612 anonymized patient records with 66 clinically validated features across four categories (extraction, non-extraction, functional appliance, and orthopedic case). Preprocessing included imputation, normalization, and the synthetic minority oversampling technique (SMOTE) for class imbalance, while performance was assessed via 10-fold cross-validation. Results showed that ANNs achieved the highest balanced accuracy (0.83), F1-score (0.84), and receiver operating characteristic area under the curve (ROC-AUC) (0.90), outperforming ensemble and baseline models. Shapley additive explanations (SHAP) analysis confirmed clinically meaningful predictors such as vertical face proportions and mandibular plane angle, enhancing interpretability. Although promising, the study is limited by its single-institution dataset and lack of external validation. Future research should incorporate multicenter, multimodal datasets and interpretable-by-design frameworks to enable clinically trusted AI decision-support systems in orthodontics.
Volume: 15
Issue: 1
Page: 97-105
Publish at: 2026-02-01

Improved disturbance rejection of induction motor drives using PI–VGSTASM control and torque disturbance estimation

10.12928/telkomnika.v24i1.27459
Ngoc; Industrial University of Ho Chi Minh City Thuy Pham , Duc; Industrial University of Ho Chi Minh City Thuan Le , Thanh; Industrial University of Ho Chi Minh City Tinh Pham
Induction motor (IM) drives often suffer performance degradation under load variations and parameter uncertainties when using conventional proportional–integral (PI)- based field-oriented control (FOC). To address these issues, this study proposes a composite control framework combining a PI regulator in the speed loop with a Lyapunov-based variable-gain super twisting algorithm (VGSTA) for the inner current loops to enhance robustness against disturbances and parameter variations. In addition, a load torque observer is developed to estimate unknown disturbances in real time and generate an equivalent compensation current, thereby improving disturbance rejection. Unlike existing approaches, the proposed strategy achieves a balance between simplicity, robustness, and smooth control by integrating classical PI control with higher-order sliding mode techniques and adaptive observer dynamics. Furthermore, the controller and observer gains are optimized using particle swarm optimization (PSO) to improve convergence and reduce overshoot under uncertain conditions. Simulation results demonstrate accurate speed regulation, effective chattering reduction, and reliable operation under load and parameter variations. Due to its low computational complexity and high robustness, the proposed method is well suited for industrial drive systems and electric mobility applications.
Volume: 24
Issue: 1
Page: 329-342
Publish at: 2026-02-01

Comparison methods in a decision support system for determining JavaScript frameworks

10.12928/telkomnika.v24i1.27241
Rofif Aghna; Sunan Kalijaga State Islamic University Yogyakarta Fakhri Diya , Agus; Sunan Kalijaga State Islamic University Yogyakarta Mulyanto
The selection of an appropriate JavaScript framework in web-based software development often leads to errors when the chosen framework is incompatible with the design. The ability to make decisions quickly, accurately, and precisely is therefore a key factor in successful software design. Addressing this need, the present study analyzes the accuracy of the analytical hierarchy process-weight product (AHP-WP), analytical hierarchy process-technique for order preference by similarity to ideal solution (AHP TOPSIS), and analytical hierarchy process-simple multi-attribute rating technique (AHP-SMART) methods in determining the most suitable JavaScript framework according to the International Organization for Standardization (ISO) 9126 classification. To evaluate accuracy, the mean absolute percentage error (MAPE) was applied as a cost function to measure the error percentage of each method. The analysis was conducted on ten popular JavaScript frameworks selected based on their popularity and usage trends. The evaluation considered six quality criteria: functionality, reliability, usability, efficiency, maintainability, and portability. The results show the ranking of each alternative for all methods. Accuracy measurement using MAPE revealed that the AHP-WP method produced the smallest error percentage (37.77645%), compared to AHP-TOPSIS (47.12566%) and AHP-SMART (46.4041%). Accordingly, the AHP-WP method is recommended for decision support system (DSS) development.
Volume: 24
Issue: 1
Page: 95-110
Publish at: 2026-02-01

Distorted born iterative method reconstruction in high-noise environments using KNN-based machine learning denoising

10.12928/telkomnika.v24i1.27401
Nguyen Quang; Vietnam Academy of Science and Technology Huy , Nguyen Truong; Vietnam Academy of Science and Technology Thang
Ultrasound tomography reconstruction using the distorted born iterative method (DBIM) is sensitive to measurement noise, which degrades image fidelity and slows convergence. We propose integrating a k-nearest neighbors (KNN) denoising step within each DBIM iteration to suppress noise adaptively while preserving structural edges. Simulations with a circular cylindrical target and transmit/receive geometry (12×12) were conducted at signal-to-noise ratio (SNR) levels of 6 dB, 3 dB, and 1 dB. Compared with conventional DBIM employing Tikhonov regularization, the KNN-filtered DBIM reduces normalized reconstruction error by up to 57.2% at 1 dB and shows faster error decay over successive iterations. The method is training-free, computationally lightweight, and preserves fine structural details. These properties make KNN-filtered DBIM attractive for noisy or resource-constrained imaging environments. Future work will validate the approach on experimental data and explore adaptive K selection.
Volume: 24
Issue: 1
Page: 206-218
Publish at: 2026-02-01

Enhancing reflective elements of intelligent reflective surfaces in 6G communications using artificial intelligence

10.12928/telkomnika.v24i1.27307
Jehan Kadhim Shareef; University of Thi-Qar Al-Safi , Abbas Thajeel Rhaif; University of Thi-Qar Alsahlanee
The dynamic landscape of 6G communication networks necessitates innovative strategies to address energy inefficiency and signal degradation in densely populated regions with limited line-of-sight (LOS) coverage. A novel technology known as an intelligent reflecting surface (IRS) has emerged; it can dynamically modify the characteristics of electromagnetic waves to enhance signal propagation. Unfortunately, current IRS models frequently neglect the balance between energy efficiency (EE) and the quantity of reflective elements (N) in Rayleigh fading scenarios. This study introduces an algorithm called dynamic-static particle swarm optimization (DS-PSO) aimed at improving EE and decreasing the quantity of reflective components in the performance optimization of IRS. The research assesses the proposed model in comparison to single-input single-output (SISO) systems, conventional IRS models, and IRS models from prior studies within a realistic urban framework. The optimized IRS, which only uses seven reflective elements, has a peak EE of 366 Mbit/Joule. This is a big improvement over IRS models from earlier research, as shown by the numbers. The findings indicate that artificial intelligence (AI)-driven optimization can enhance IRS technology for sustainable and efficient 6G networks.
Volume: 24
Issue: 1
Page: 22-33
Publish at: 2026-02-01

Technology levels in artificial intelligence robotics and industrial automation: impacts and implications

10.12928/telkomnika.v24i1.27253
Ratna; Universitas Esa Unggul Yulika Go , Agnes; National Research and Innovation Agency (BRIN) Sondita Payani , Siti; Universitas Hasanuddin Rabiatul Adawiyah , Ogi; National Research and Innovation Agency (BRIN) Gumelar
Robotics technology has progressed rapidly since its debut in 1922, evolving from simple programmable automation to highly sophisticated systems. This study employs a hybrid methodology, combining qualitative analysis of key robotic components manipulators, controllers, end effectors, and geometric configurations with quantitative comparison of performance metrics to classify robots according to their technological level (low-tech versus high tech). The findings show clear distinctions across these levels. Low-tech robots typically achieve positioning accuracy of about 0.025 mm and rely mainly on single electric motor actuation, making them suitable for simple, repetitive tasks. In contrast, high-tech robots can perform complex operations with positioning accuracy of up to 3 mm, integrating multiple actuation systems such as electric, pneumatic, and hydraulic mechanisms for enhanced flexibility and control. Moreover, high-tech robots exhibit greater manipulative capabilities and advanced control systems that enable multi axis and adaptive operations not feasible for low-tech counterparts. These results demonstrate how the technological level directly shapes a robot’s precision, actuation complexity, and functional range, providing a clear framework for selecting appropriate robotic solutions in both industrial and research settings.
Volume: 24
Issue: 1
Page: 175-183
Publish at: 2026-02-01

Implementation of markerless augmented reality and cyber physical-social systems for smart tourism application

10.12928/telkomnika.v24i1.27414
Ilham; Institut Teknologi Sumatera Firman Ashari , Fanesa; Institut Teknologi Sumatera Hadi Permana , Muhammad; Universitas Muhammadiyah Malang Zainal Arifin , Purwono; Institut Teknologi Sumatera Prasetyawan
Lampung province holds substantial tourism potential that remains underutilized due to fragmented information and limited promotional strategies. This study introduces a smart tourism application integrating markerless augmented reality (AR) with cyber-physical-social systems (CPSS), representing the first implementation of its kind for location-based tourism in the region. The novelty lies in the hierarchical coordinate transformation architecture (HCTA), a multi-layer computational framework employing the Haversine formula to achieve high-precision mapping of geographic coordinates into AR-optimized perceptual views. The system was evaluated for geolocation accuracy, resource utilization, backend scalability, AR rendering robustness, and user experience. Results show strong performance: geolocation tests across seven destinations yielded a mean error rate of 1.5%; AR operations remained efficient with 8–10% central processing unit (CPU) and 140–160 MB random access memory (RAM) usage; and rendering was stable across 360° device orientation. Backend tests confirmed scalability, sustaining 56 requests per second with zero failures under 100 concurrent users. A user study with 20 participants using the user experience questionnaire-short (UEQ-S) revealed highly positive outcomes, with overall scores 2.275, all within the Excellent benchmark. These findings confirm that the application is not only technically robust and efficient but also engaging and enjoyable, offering a scalable framework for immersive smart tourism ecosystems.
Volume: 24
Issue: 1
Page: 71-94
Publish at: 2026-02-01

SELLA: An IoT-based smart shopping trolley with real-time RFID tracking and automated checkout

10.12928/telkomnika.v24i1.27464
Hadj; Djillali Liabes University of Sidi Bel Abbes Zerrouki , Salima; Djillali Liabes University of Sidi Bel Abbes Azzaz-Rahmani
The contemporary retail sector faces a persistent challenge in enhancing in store customer experience, primarily due to inefficiencies at checkout. This paper presents smart e-cart for lean logistics application (SELLA), a smart shopping trolley system engineered to eliminate this bottleneck. The system’s architecture is centered on a Raspberry Pi 4 microcontroller, orchestrating an ultra-high frequency (UHF) radio frequency identification (RFID) subsystem for instantaneous, non-line-of-sight product identification, and a responsive 7-inch touchscreen graphical user interface (GUI) developed in PyQt. The core contribution lies in developing a self contained shopping solution with integrated payment processing, supported by comprehensive performance validation. We present a detailed methodology, including the system’s multi-threaded software architecture and core operational algorithm. Experimental evaluation demonstrates a mean tag detection accuracy of 98.2% under optimal conditions, a robust user interface (UI) latency of under 500 ms, and an average central processing unit (CPU) utilization of 28%, proving system efficiency. Comparative analysis confirms that SELLA’s integration of on-trolley automated payment and detailed performance metrics represents a significant advancement over existing prototypes. The system provides a validated, high-performance solution for next-generation smart retail environments.
Volume: 24
Issue: 1
Page: 184-195
Publish at: 2026-02-01

Optimization and techno-economic analysis of hybrid renewable systems in Nigeria

10.12928/telkomnika.v24i1.27499
Lambe; Kwara State University Mutalub Adesina , Jamiu; Kwara State University Lawal , Olalekan; Kwara State University Ogunbiyi , Abdulwaheed; Kwara State University Musa , Bilkisu; Kwara State University Jimada Ojuolape , Monsurat; Kwara State University Omolara Balogun , Bashiru; Kwara State University Olalekan Ariyo
Rising electricity demand, fossil fuel depletion, and environmental concerns highlight the need for sustainable rural electrification. The Elenjere community in Kwara State, Nigeria, depends on costly diesel generation and limited grid access, creating an urgent demand for reliable and affordable alternatives. This study designs and optimizes a hybrid renewable energy system (HRES) for the community using hybrid optimization model for electric renewables (HOMER) Pro simulation. The proposed system combines photovoltaic (PV), wind turbines (WT), battery storage (BAT), inverter (INV), and a diesel generator (DG) as backup. Field data on load demand, solar radiation, and wind speed were used for realistic modeling. System performance was evaluated using levelized cost of energy (LCOE), net present cost (NPC), and system capital cost (SCC). Results show the PV/WT/BAT/INV/GEN configuration achieved the lowest LCOE of USD 0.455/kWh, an NPC of USD 2.98 million, and 86.2% renewable penetration, significantly reducing diesel use. Sensitivity analysis revealed that reducing battery costs and increasing PV capacity could lower the LCOE to USD 0.227–0.325/kWh. The study demonstrates how modest wind resources (4.19 m/s at 10 m) complement PV in low-wind regions while addressing inflation realism (25.5% discount rate, foreign exchange (FX) volatility). Future work will include dynamic control simulation and lifecycle analysis to enhance scalability and sustainability.
Volume: 24
Issue: 1
Page: 343-358
Publish at: 2026-02-01

Performance analysis of a multi-level inverter fed permanent magnet synchronous motor for electric vehicles

10.12928/telkomnika.v24i1.27234
Donepudi; Aditya University Tata Rao , Bhimaraju; Aditya University Pemmanaboidi Srihari Datta , Uma Phanendra; Aditya University Kumar Chaturvedula , Kondala; Aditya University Rao Parasa , Mummidi Parvateeswara; Aditya University Subba Raju
Electric vehicle (EV) drive systems utilizing permanent magnet synchronous motors (PMSMs) often encounter performance limitations due to switching losses, voltage stress, and harmonic distortion. To address these challenges, this paper presents a compact 31-level multilevel inverter (MLI) topology designed to enhance drive efficiency and power quality. The proposed inverter minimizes switching devices and driver circuits, resulting in reduced total harmonic distortion (THD), lower voltage stress, and improved waveform fidelity. Advanced control strategies are employed to further optimize performance. field-oriented control (FOC) ensures precise torque and flux regulation, while direct torque control (DTC) delivers rapid transient response. To mitigate torque ripple and variable switching frequency inherent in conventional DTC, adaptive predictive control (APC) is integrated to refine switching behavior and enhance dynamic stability. Simulation studies conducted in MATLAB/Simulink demonstrate the effectiveness of the proposed system, revealing significant improvements in torque smoothness, reduced THD (0.85%) and elevated efficiency under variable load conditions. This integrated solution offers a practical and scalable approach for next-generation EVs, contributing to greater reliability, energy utilization, and overall system performance.
Volume: 24
Issue: 1
Page: 302-312
Publish at: 2026-02-01

Advanced microwave imaging and artificial neural networks for early detection and localization of breast tumors

10.12928/telkomnika.v24i1.27126
Abdelfettah; University of Mustapha Stambouli Miraoui , Lotfi; School of Applied Sciences Tlemcen Merad , Djalal; LARATIC Laboratory at National Institute of Telecommunications and Information Technology and Communication (ENSTTIC) Ziani-Kerarti
This study investigates the detection and localization of breast tumors based on dielectric property differences between cancerous and normal tissues. A microwave imaging technique integrated with artificial neural networks (ANNs) is proposed as a noninvasive alternative to conventional screening methods such as mammography and magnetic resonance imaging (MRI). A breast model with a 2.5 mm spherical tumor was designed using CST Microwave Studio. Simulation results show that the ANN achieves a detection rate close to 100%, providing negative outputs for tumor-free cases and positive outputs for cases with tumors. Additionally, ANN outputs strongly correlate with the actual tumor positions in the simulated environment. These findings suggest that microwave imaging combined with ANNs offers a cost effective, radiation-free, and patient-friendly solution for the early detection and localization of breast cancer, with promising potential for clinical translation.
Volume: 24
Issue: 1
Page: 240-248
Publish at: 2026-02-01

A decoupling-based multivariable H∞ controller for PMSM speed and current regulation

10.12928/telkomnika.v24i1.27515
Farid; Higher School of Applied Sciences of Tlemcen Oudjama , Mohammed; University of Tlemcen Messirdi , Mokhtar; University Centre of Maghnia Bourdim , Abelmadjid; University of Tlemcen Boumédiène
High precision speed regulation of the permanent magnet synchronous motor (PMSM) is a critical challenge in modern industrial applications, including electric vehicles and traction systems. This task is significantly affected by external disturbances, such as variable load torque, as well as physical phenomena often neglected in analytical models, such as magnetic circuit saturation or thermal variations in electrical parameters. In this context, conventional control methods often fail to ensure both dynamic performance and robustness. This paper proposes a multivariable H∞ control strategy based on field-oriented control (FOC) and d/q decoupling to design a robust and high-performance controller. The diagonal multiple-input multiple-output (MIMO) model, linking the direct-axis voltage𝑣𝑑to the current 𝑖𝑑and the quadrature-axis voltage 𝑣𝑞to the rotational speed 𝜔𝑟, is derived directly from the decoupling principles of FOC, without relying on linearization around an operating point or modeling of parametric uncertainties. The H∞ controller is synthesized using the standard configuration, with carefully selected weighting functions to ensure dynamic performance, closed-loop stability, and effective disturbance rejection. Numerical simulations demonstrate that the proposed controller achieves accurate speed reference tracking, fine current regulation, and fast load disturbance rejection, confirming its effectiveness and robustness. This approach provides an advanced alternative to conventional control methods by fully exploiting the multivariable structure of the system.
Volume: 24
Issue: 1
Page: 293-301
Publish at: 2026-02-01

A practical approach to Candi Siwa 3D reconstruction with COLMAP and Nerfstudio

10.12928/telkomnika.v24i1.27212
Helena; Prasetiya Mulya University Widiarti , Rokhmat; Prasetiya Mulya University Febrianto , Agung; Prasetiya Mulya University Alfiansyah
We demonstrate a practical approach for large-scale object three-dimensional (3D) reconstruction with freely available frameworks, COLMAP and Nerfstudio. We performed the reconstruction of a temple named Candi Siwa, located at Prambanan Site, which is situated between Central Java and Yogyakarta Province, Indonesia. We utilized COLMAP and Nerfstudio as platforms for 3D reconstruction from images captured by an everyday smartphone. In the 3D model construction process, COLMAP generates a dense point cloud, whereas Nerfstudio generates a scene from source images. We selected 96 images of Candi Siwa to perform reconstruction using COLMAP. As a result, a 3D model for the temple with a clear structure and color was observed. A scene rendered in MP4 format was also generated using Nerfstudio. Additionally, we performed the 3D reconstruction from 150 images taken by the public and found them insufficient for constructing the object. This occurred despite the number of images being larger than those used in the previous reconstruction. The results indicate that the success of a crowdsourcing project for reconstructing a large-scale object should consider not only the number of images but also the variation in point of view and the completeness of the whole structure.
Volume: 24
Issue: 1
Page: 249-257
Publish at: 2026-02-01

Optimized edge-aware frequency-guided filtering for robust image denoising

10.12928/telkomnika.v24i1.27338
Iman; Karabuk University Elawady , Ahmet Reşit; Karabuk University Kavsaoğlu , İsmail Rakıp; Karabuk University Karaş
The problem of denoising intrusion is still of great concern in computational imaging because of the trade-off between noise reduction and image structure and details recovery. This paper proposes an optimized edge-aware fast adaptive guided filter (E-FAGF) combining wavelet-domain decomposition, edge-awareness, and lightweight deep learning for efficient and effective denoising. The biorthogonal wavelet transform is employed to decompose noisy images into low- and high-frequency sub bands and an improved edge-attention map for selective high-frequency denoising. Regularization parameters are estimated pixel-wise by a compact convolutional neural network (CNN), allowing spatial-varying filtering to be done with multi-scale processing. The resultant E-FAGF consistently outperforms the state of the art on this dataset: on BSD500 for speckle and Gaussian noise (peak signal-to-noise ratio (PSNR) of 39.63 dB and 33.97 dB, respectively), and competitive performance for Poisson noise (30.84 dB) a large margin compared to the reference bilateral and non-local means. Our method maintains high structural similarity (up to 0.97 in structural similarity index measure (SSIM)), runs at 0.015 seconds per 512×512 image on graphics processing unit (GPU), and can be applied without dataset specific training. These results suggest the possibility of E-FAGF to achieve a balance between classical efficiency and learning-based adaptability, thereby forming a new scenario to combine fast and reliable image restoration for actual scenarios.
Volume: 24
Issue: 1
Page: 219-227
Publish at: 2026-02-01

Optimized IMC with GWO algorithm and variable switching function for voltage regulation of SEPIC converter

10.12928/telkomnika.v24i1.27330
Reza; Shahrood University of Technology Fazeli , Mohammad; Shahrood University of Technology Haddad Zarif , Mahmoud; Islamic Azad University Zadehbagheri , Tole; Universitas Ahmad Dahlan Embedded System and Power Electronics Research Group Sutikno
With the growing application of single-ended primary-inductor converter (SEPIC) converters in power electronic systems, precise output voltage regulation under uncertainties and nonlinear conditions remains a significant challenge. Although internal model control (IMC) effectively addresses issues arising from unstable zeros and fixed time delays in non-minimum phase systems, its performance can degrade under large transient errors or sudden disturbances, leading to control signal saturation and instability. In this study, a modified IMC scheme is proposed, which integrates a variable switching function into the control structure. This addition enhances the robustness of the system by dynamically adapting the control effort to mitigate abrupt changes in the control signal and stabilize the output voltage. Furthermore, it prevents controller saturation during large-signal deviations, thereby improving transient response and maintaining system stability. The design parameters of the controller are optimized using the gray wolf algorithm to achieve an optimal balance between voltage overshoot, settling time, and closed-loop stability. Simulation results under various operating conditions confirm the superior performance of the proposed control method compared to conventional IMC.
Volume: 24
Issue: 1
Page: 258-270
Publish at: 2026-02-01
1 2 3
...
2012 2013
Show 72 of 2013

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