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31,456 Article Results

Parametric optimization of microchannel heat exchanger using socio-inspired algorithms

10.11591/ijai.v14.i6.pp5303-5310
Vikas Gulia , Aniket Nargundkar
Miniaturized products and systems have emerged as game-changing innovations with huge potential in the modern period with increasing emphasis on sustainable development and green energy. Automotive, astronomical, electronics, and medical research are just a few of the industries where micro electro mechanical systems (MEMS) have found use. In addition to that, microchannel heat exchangers (MCHX) have been created in response to the growing demand for effective cooling solutions for these small systems. Optimization of these MCHX is important for improving the overall system efficiency. In this work, two popular socio inspired evolutionary algorithms viz. teaching learning-based optimization (TLBO) and cohort intelligence (CI) are applied for optimizing three objectives such as power density, compactness factor, and heat transfer with pressure drop (HTPD) for air-water MCHX. The results obtained are significantly improved when compared with genetic algorithm (GA). Moreover, both the techniques are observed to be robust. This study investigates the use of socio-inspired artificial intelligence (AI) algorithms to support the design and optimization of heat exchangers, highlighting their potential to address complex engineering challenges more efficiently.
Volume: 14
Issue: 6
Page: 5303-5310
Publish at: 2025-12-01

AI-MG-LEACH: investigation of MG-LEACH in wireless sensor networks energy efficiency applied the advanced algorithm

10.11591/ijece.v15i6.pp5080-5090
Hicham Ouldzira , Alami Essaadoui , Mustapha EL Hanine , Ahmed Mouhsen , Hassane Mes-Adi
Wireless sensor networks (WSNs) play a crucial role in data collection across various fields like environmental monitoring and industrial automation. The energy efficiency of these networks, powered by limited-capacity batteries, is key to their performance. Clustering protocols such as low- energy adaptive clustering hierarchy (LEACH) are widely used to optimize energy consumption. To enhance LEACH’s performance, MG-LEACH was introduced, improving cluster head selection to extend network lifespan. This study compares MG-LEACH with AI-MG-LEACH, which incorporates artificial intelligence (AI) to further improve energy efficiency by selecting cluster heads based on factors like residual energy. Simulations show AI-MG-LEACH reduces energy consumption, extends network life, and enhances data reliability, outperforming MG-LEACH.
Volume: 15
Issue: 6
Page: 5080-5090
Publish at: 2025-12-01

Citizens’ electronic satisfaction factors in electronic government services: an empirical study from Kuwait

10.11591/ijece.v15i6.pp5690-5698
Abdullah Alshehab , Ali Alfayly , Naser Alazemi
This study investigates the dimensions of service quality provided by Kuwait’s “Sahel” electronic government (e-government) application and their impact on user satisfaction among citizens and residents. Adopting a quantitative methodology based on the modified electronic government quality (e-GovQual) model, data were collected from 1,064 respondents over four weeks, assessing user experiences across usability, reliability, responsiveness, security, and efficiency dimensions. Results indicate moderate overall satisfaction, with particularly high ratings for transparency and ease of use, yet notable concerns regarding trust and data security. Satisfaction with reliability and technical support was moderate, signaling areas for improvement. The study recommends enhancing the user interface for intuitive navigation, improving real-time data synchronization between governmental entities, providing efficient technical support, and strengthening security measures to build user trust. These recommendations are crucial for advancing Kuwait’s e-government effectiveness. Future research should explore causal relationships among service quality dimensions and incorporate technical assessments by information and communication technology (ICT) experts to further enhance user satisfaction.
Volume: 15
Issue: 6
Page: 5690-5698
Publish at: 2025-12-01

Escalating QoS by firefly optimization of CGSTEB routing protocol with subordinate energy alert gateways

10.12928/telkomnika.v23i6.27007
R.; Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Madonna Arieth , Ramya; Vellore Institute of Technology (VIT) Govindaraj , Subrata; Sri Venkateswara College of Engineering and Technology (A) Chowdhury , Thu; Hanoi University of Industry Thi Nguyen , Tran; Phenikaa University Duc-Tan
Wireless sensor networks (WSNs) comprise large numbers of sensor nodes that are highly constrained by limited battery power, making energy-efficient routing essential for sustaining network lifetime and service quality. Among existing solutions, the general self-organized tree-based energy balancing (GSTEB) pro tocol with clustering has been widely adopted for energy-aware communication. However, GSTEB and its clustered variant often suffer from energy imbalance, high packet loss, and reduced quality of service (QoS) due to excessive load on cluster heads (CHs). To address these challenges, this paper introduces an enhanced routing framework that integrates firefly optimization with clustered GSTEB(CGSTEB)andintroduces subordinate energy alert gateways (SEAGs). The firefly algorithm is applied to optimize CH selection through a fitness func tion that balances residual energy and node proximity, ensuring efficient cluster formation and adaptive load distribution. Meanwhile, SEAGs establish a two hop communication model between CHs and the base station (BS), reducing CH energy consumption and preventing premature node failures. Simulation exper iments conducted in NS2 demonstrate that the proposed firefly-CGSTEB with SEAG significantly improves QoS metrics, including network lifetime, energy utilization, throughput, and packet loss rate, compared with conventional CG STEB. These results confirm the effectiveness of combining metaheuristic opti mization with gateway-assisted routing for resilient and energy-efficient WSNs.
Volume: 23
Issue: 6
Page: 1718-1728
Publish at: 2025-12-01

Mixed attention mechanism on ResNet-DeepLabV3+ for paddy field segmentation

10.12928/telkomnika.v23i6.26829
Alya; University of Indonesia Khairunnisa Rizkita , Masagus Muhammad; University of Indonesia Luthfi Ramadhan , Yohanes Fridolin; University of Indonesia Hestrio , Muhammad Hannan; University of Indonesia Hunafa , Danang Surya; National Research and Innovation Agency Candra , Wisnu; University of Indonesia Jatmiko
Rice cultivation monitoring is crucial for Indonesia, where paddy field areas de clined by 2.45% according to the Central Bureau of Statistics due to land func tion changes and shifting crop preferences. Regular monitoring of paddy field distribution is essential for understanding agricultural land utilization by farmers and landowners. Satellite imagery has become increasingly common for agricul tural land observation, but traditional neural networks alone provide insufficient segmentation accuracy. This study proposes an enhanced deep learning architec ture combining residual network (ResNet)-DeepLabV3+ with coordinate atten tion (CA) and spatial group-wise enhancement (SGE) modules. The attention mechanisms establish direct connections between context vectors and inputs, enabling the model to prioritize relevant spatial and spectral features for precise paddy field identification. The CA module enhances spectral feature discrim ination, whereas the SGE improves spatial characteristic representation. The experimental results demonstrate superior performance over the baseline meth ods, achieving intersection over union (IoU) of 0.85, dice coefficient of 0.89, and accuracy of 0.95. The proposed mixed attention mechanism significantly improves the accuracy and efficiency of automatic crop area identification from satellite imagery.
Volume: 23
Issue: 6
Page: 1611-1625
Publish at: 2025-12-01

Power loss reduction and stability enhancement of power system through transmission network reconfiguration

10.11591/ijece.v15i6.pp6012-6026
Titus Terwase Akor , Theophilu Chukwudolue Madueme , Chibuike Peter Ohanu , Tole Sutikno
The power network faces several challenges as electricity usage rises and the frequency of partial and total grid disruptions is of great concern. This paper addresses the problem of voltage instability and high-power losses in transmission network, which threatens the stability of the power grid. The MATLAB R2023a/MATPOWER 5.0 is used to develop a model and analyze using the Newton-Raphson load flow method. The analysis reveals a marginal voltage violation at Bus 13 (below 0.95 p.u.). To enhance stability and efficiency, the network was reconfigured using a hybrid whale algorithm and particle swarm optimization (WAPSO) approach, incorporating new transmission lines (5-8 and 13-14) to improve connectivity and reduce congestion. The reconfiguration reduced active power losses by 29.5% (from 36.013 to 25.371 MW) and reactive power losses by 29.8% (from 301.30 to 211.59 MVAr). The system demonstrated first swing stability, with rotor angles remaining below π/2 (1.5669 rad maximum deviation) and fault clearance within the critical clearing time (0.2 s). Optimized exciter gains and a damping coefficient of 1.5 p.u. ensured effective oscillation suppression and stable generator voltages at 1.05 p.u. The hybrid WAPSO approach proved effective in optimizing voltage and rotor angle stability, enabling the network to meet a 24.086 p.u. load demand while enhancing overall grid reliability.
Volume: 15
Issue: 6
Page: 6012-6026
Publish at: 2025-12-01

An insight on using deep learning algorithm in diagnosing gastritis

10.12928/telkomnika.v23i6.27191
Ragu; Universiti Tun Hussein Onn Malaysia (UTHM) P. J. , Ashok; Universiti Tun Hussein Onn Malaysia (UTHM) Vajravelu , Muhammad Mahadi; Universiti Tun Hussein Onn Malaysia (UTHM) bin Abdul Jamil , Syed Riyaz; NITTE University Ahammed
Chronic autoimmune gastritis (CAG) is a condition in which the stomach membrane is significantly impacted by inflammation. Despite the availability of numerous modern medical techniques, the detection of this condition continues to be a difficult challenge. White light endoscopy (WLE) has been employed to diagnose gastritis, but it has been subject to certain constraints. This technique is most effective when executed by an endoscopist who possesses a high level of expertise. In the present day, WLE is frequently accompanied by artificial intelligence (AI) due to its superior ability to detect defects that lead to damage. Recently, there has been a substantial increase in the efficacy of AI in conjunction with the expertise of endoscopists in the detection of CAG. The 25,216 intriguing case studies were examined in the eight selected studies. The collection comprised 84,678 frames and 10,937 images. The AI was 94% sensitive (95% CI: 0.88-0.97, I2 = 96.2%) and 96% specific (95% CI: 0.88-0.98, I2 = 98.04%). The receiver operating characteristic curve had an area of 0.98 (95% confidence interval: 0.96–0.99). A camera is highly effective when combined with AI to assist in the identification of CAG and is advantageous for clinical review.
Volume: 23
Issue: 6
Page: 1528-1542
Publish at: 2025-12-01

Improved copy-move forgery detection through multilevel clustering

10.11591/ijai.v14.i6.pp5279-5289
Doaa Gamal Abdelazem , Hala H. Zayed , Ahmed Taha
Copy move forgery detection (CMFD) based on keypoints remains a widely used technique; however, it often struggles to effectively identify small and smoothly tampered regions within images. This paper introduces a CMFD method that enhances detection accuracy by integrating a double-matching process with advanced region localization techniques. Delaunay triangles formed by accelerated KAZE (AKAZE) and scale-invariant feature transform (SIFT) features are matched in the double-matching process to identify suspicious regions. To ensure sufficient keypoint pairs, the set of matching triangles is iteratively expanded to include neighboring triangles, covering the entire tampered area. Subsequently, a second matching with a looser threshold is performed on the vertices. In the region localization process, the multilevel density-based spatial clustering of applications with noise (DBSCAN) effectively handles scenarios involving multiple copied regions with varying sizes. Using the standard MICC-F600 and COVERAGE datasets, experiments demonstrate that the proposed CMFD method is robust and achieves better performance than state-of-the-art baselines. 
Volume: 14
Issue: 6
Page: 5279-5289
Publish at: 2025-12-01

Solar powered internet of things-based heart rate monitoring system employing electrocardiogram signal analysis

10.11591/ijece.v15i6.pp5942-5953
Suziana Ahmad , Ahmad Alif Ahmad Aina , Shahrul Hisyam Marwan , Rosziana Hashim , Nurul Syuhada Shari
Electrocardiogram (ECG) test is used to record the electrical activity of a human heart for determining any problems with irregular heartbeat patterns and other cardiovascular conditions. This project deals with the implementation of an Internet of things (IoT) enabled ECG monitoring system with solar supply that can identify heart rate deviations from normal values (40 BPM, 80 BPM and 120 BMP) utilizing simulated ECG signals. The ECG data acquisition is done by using KL-76001 biomedical measurement training system, KL-75001 ECG module and multiparameter simulator MS400. The acquired ECG signals are processed through Python software to detect R-peaks and R-R interval. The counts of these R-R peaks are utilized in conjunction with the Blynk IoT platform, employing an ESP8266 module for monitoring via a mobile application and LCD display. The system was tested for detecting and monitoring three heart conditions which are bradycardia, normal, and tachycardia and successfully demonstrated alert capabilities for these conditions.
Volume: 15
Issue: 6
Page: 5942-5953
Publish at: 2025-12-01

Stability analysis and robust control of cyber-physical systems: integrating Jacobian linearization, Lyapunov methods, and linear quadratic regulator control via LMI techniques

10.11591/ijece.v15i6.pp5276-5285
Rachid Boutssaid , Abdeljabar Aboulkassim , Said Kririm , El Hanafi Arjdal , Youssef Moumani
Stability issues in cyber-physical systems (CPS) arise from the challenging effects of nonlinear dynamics relation to multi-input, multi-output systems. This research proposed a robust control framework that combines Jacobian linearization, Lyapunov stability analysis, and linear quadratic regulator (LQR) control via linear matrix inequalities (LMIs). The robust methodology does the following: it applies linearization on the dynamics of the CPS; it establishes the stability of the system using Lyapunov functions and LMIs; and it designs an LQR controller. The proposed framework was validated through a comparison between the behavior of a linearized and nonlinear model. The autonomous vehicle application showed: a settling time of 20 seconds; an overshoot of 3.8187%; and a steady-state error of 2.688×10⁻⁷. The proposed framework is robustly demonstrated and has applications to areas in automation and smart infrastructure. Future work includes optimizing the design of weighting matrices and developing adaptive control features.
Volume: 15
Issue: 6
Page: 5276-5285
Publish at: 2025-12-01

Particle swarm optimization-optimized integrator backstepping for the control of electric wheelchairs velocity

10.12928/telkomnika.v23i6.27516
Djamila; University of ORAN 1 Boubekeur , Khayreddine; Tlemcen University Saidi , Mohammed; Tlemcen University Messirdi , Abelmadjid; Tlemcen University Boumédiène
Most people suffering from temporary or permanent disabilities rely on wheelchairs or electric powered wheelchairs (EPW) to maintain autonomy of movement. To address different EPW control challenges, several studies have investigated this kind of robot. This paper focuses on the optimization of the integrator backstepping control parameters of the EPW. The system operates using two permanent magnet synchronous motors (PMSM), noted for their great efficiency, substantial torque, minimal noise, and robustness. At first, the dynamic model for both EPW-motors is showned. After that, a nonlinear integrator backstepping command based on Lyapunov’s second technique, which combines the choice of the energy function with the control laws, was applied to the resulting global model. To ensure optimal performance, the control parameters were tuned by means of an optimization approach. Specifically, the particle swarm optimization technique (PSO) was employed to search for the optimal parameters (gains) of the integrator backstepping controller. In order to assess the performance of the optimized backstepping–based control approach, numerical simulations were conducted to illustrate the evolution of both electrical and mechanical velocity- related variables.
Volume: 23
Issue: 6
Page: 1646-1656
Publish at: 2025-12-01

Exploring feature selection method for microarray classification

10.11591/ijece.v15i6.pp5584-5593
Muhammad Zaky Hakim Akmal , Devi Fitrianah
Effectively selecting features from high-dimensional microarray data is essential for accurate cancer detection. This study explores the pivotal role of feature selection in improving the accuracy of classifying microarray data for ovarian cancer detection. Utilizing machine learning techniques and microarray technology, the research aims to identify subtle gene expression patterns that indicate ovarian cancer. The research explores the utilization of principal component analysis (PCA) for dimensionality reduction and compares the effectiveness of feature selection techniques such as artificial bee colony (ABC) and sequential forward floating selection (SFFS). The dataset used in this study comprises of 15154 genes, 253 instances, and 2 classes related to ovarian cancer. Through a comprehensive analysis, the study aims to optimize the classification process and improve the early detection of ovarian cancer. Moreover, the study presents the classification accuracy results obtained by PCA, ABC, and SFFS. While PCA achieved an accuracy of 96% and SFFS yielded a classification accuracy of 98%, ABC demonstrated the highest classification accuracy of 100%. These findings underscore the effectiveness of ABC as the preferred choice for feature selection in improving the classification accuracy of ovarian cancer detection using microarray data.
Volume: 15
Issue: 6
Page: 5584-5593
Publish at: 2025-12-01

Catalysing precision in bone x-ray analysis for image detection and classification: the triple context attention model advancement

10.11591/ijai.v14.i6.pp4957-4970
Tabassum N. Sultana , Nagaratna P. Hegde , Asma Parveen
Accurate detection and classification of fractures in bone x-ray images are crucial for effective medical diagnosis and treatment. In this study, we propose the triple context attention model (TCAN) as a novel approach to address the challenges in this domain. TCAN offers several key contributions that significantly enhance the accuracy and efficiency of bone x-ray image recognition and classification. Firstly, TCAN introduces the coordination attention mechanism, which considers both horizontal and vertical positional data during the recognition process. Secondly, TCAN mitigates the common issue of mislabelling fractures in bone x-ray images, particularly in the you only look once (YOLO) model, due to the absence of positional data during training. Thirdly, TCAN efficiently enhances positional data by focusing on weights, and increasing feature dimension while maintaining a manageable model size. This allows for effective utilization of positional data without computational overhead. Lastly, TCAN combines the visual attention network (VAN) with its capabilities, resulting in a comprehensive system that can handle diverse image dimensions and accurately classify various types of fractures across different body regions. Overall, TCAN presents a promising advancement in medical image analysis, improving fracture detection accuracy and classification efficiency in bone x-ray images, thus aiding in more effective clinical decision-making.
Volume: 14
Issue: 6
Page: 4957-4970
Publish at: 2025-12-01

Reconfigurable ultra-wideband hexagonal antenna with two notched-band features for wireless applications

10.12928/telkomnika.v23i6.27047
Khaled; Azzaytuna University B. Suleiman , Akrem; College of Computer Technology Zawiya Asmeida , Shipun; UTHM University Anuar Hamzah , Mohd Shamian; UTHM University bin Zainal
Owing to the demand for frequency agility, a switchable ultra-wideband (UWB) hexagonal antenna was developed in this study. The proposed antenna features two notch filters introduced by two U-shaped slots on the patch to reduce interference from other wireless networks by rejecting the unique frequency bands. In addition, the proposed antenna comprises a hexagonal radiator attached to a feeding 50 Ω standard microstrip line. To fabricate the antenna prototype, a substrate (Rogers RT/Duroid 5880) with loss tangent and relative permittivity values of 0.0009, and 2.2, respectively, was used. Frequency and pattern reconfigurability were achieved by changing the electrical equivalent circuit of two positive-intrinsic-negative (PIN) diodes sandwiched within two U-shaped slots. The evaluation confirmed that the antenna operated within the D1&D2-ON configuration across the entire UWB range while, effectively filtering the wireless body area network (WBAN) (6.10–6.56 GHz) and radar application (9.16–10.79 GHz) bands when both diodes were OFF. The radiation efficiency and gain reached values of 92.9 % and 7.5 dB, respectively. The proposed design offers a robust performance with enhanced interference rejection. This makes it suitable for modern cognitive radio systems.
Volume: 23
Issue: 6
Page: 1439-1448
Publish at: 2025-12-01

Retrieval-augmented generation for Arabic legal information: the family code case study

10.12928/telkomnika.v23i6.27400
Jamal; Abdelmalek Essaâdi University Hrimech , Mohammed; Abdelmalek Essaâdi University Mghari , Youssef; Abdelmalek Essaâdi University Zaz
This document describes the implementation and evaluation of a retrieval-augmented generation (RAG) system to improve access to and understanding of Moroccan law, particularly the family code in Arabic. The research addresses the drawbacks of the widely used linguistic model applied to complex legal terminology in Arabic and aims to help citizens access crucial legal data. We built a new custom dataset with 2.5 k question-answer pairs while preprocessing and using the BGE-m3 embedding model in this experiment. Performance metrics, such as mean reciprocal rank (MRR), Recall@k, and F1-score, indicate that the RAG approach is effective compared to the use of standalone large language models (LLMs). Moreover, an evaluation on metrics such as the blue score, fidelity, response relevance, and contextual relevance indicated that the matching of meanings and context were well captured, which signifies a very good semantic understanding. The research highlights the need for language-specific model specialization in Arabic and presents its main challenges, such as dialectal variations and appropriate evaluation measures. The results indicate that well-developed RAG systems offer a promising approach to improving access to legal information in Arabic-speaking practice communities and to guiding future research and development in this field.
Volume: 23
Issue: 6
Page: 1495-1505
Publish at: 2025-12-01
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