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31,377 Article Results

Ensuring effective cervical cancer diseases diagnosis system using ensemble machine learning model

10.11591/ijai.v15.i3.pp2410-2422
Oluwatobi Akinlade , Jafar Abdollahi , Misan Paul Etchie , Sunday Adeola Ajagbe , Oluwaseyi Omotayo Alabi , Bambo Ayo Adeyanju
Worldwide, cervical cancer is a major public health concern, and early detection is essential for successful management and treatment. Ensemble machine learning (ML) has emerged as a promising approach for improving the accuracy and reliability of cervical cancer diagnosis systems. This study evaluated the performance of various ensemble ML models for cervical cancer diagnosis using a large dataset of cervical cell images. The performance of different ensemble models was compared, including random forest (RF), gradient boosting (GB), and stacking, with conventional ML models, such as logistic regression and support vector machine (SVM). The bagging ensemble model developed reported training of 0.991667, 0.827586, 1.0, and 0.90566 for accuracy, precision, recall, and F-measure (F1-score), respectively. Furthermore, the interpretability of ensemble models was investigated using feature importance and partial dependence plots. The interpretability analysis revealed that ensemble models can provide valuable insights into the key features and factors that contribute to cervical cancer diagnosis. In conclusion, the findings suggest that ensemble ML is a promising approach for developing accurate and reliable cervical cancer diagnosis systems and improving the interpretability of these models.
Volume: 15
Issue: 3
Page: 2410-2422
Publish at: 2026-06-01

ValveHealthNet: a light deep learning model for accurate valvular heart disorder detection

10.11591/ijai.v15.i3.pp2643-2654
Ausilah Alfraihat , Wafaa Al-Sharu , Ali Mohammad Alqudah
Valvular heart disease (VHD) is a significant global health issue, contributing to increased morbidity and mortality rates, particularly in aging populations. Current diagnostic methods, such as echocardiography and manual auscultation, face limitations in accessibility and accuracy, particularly in resource-constrained environments. This study introduces ValveHealthNet, a lightweight deep learning model designed to classify various VHDs using heart sound recordings. Leveraging a dataset of over 10,000 heart sounds, minimal preprocessing was applied by converting the audio signals into power spectra before feeding them into a convolutional neural network (CNN) combined with a bidirectional long short-term memory (BiLSTM) network. This model achieved impressive results, with an accuracy of 98% in training and testing and 98.4% through 10-fold cross-validation. This highly efficient model can be used in embedded systems, providing a cost-effective, AI-driven solution for early detection of VHD in settings where advanced diagnostic tools may be unavailable.
Volume: 15
Issue: 3
Page: 2643-2654
Publish at: 2026-06-01

Performance comparison of deep learning models for concrete crack detection on mobile devices

10.11591/ijai.v15.i3.pp2811-2825
Sarapee Chunkaew , Somporn Ruang-On , Prawit Nuengmatcha , Kritaphat Songsri-in
Concrete crack detection is essential for structural maintenance, yet traditional manual inspection methods are time-consuming and require specialized expertise. While deep learning offers promising solutions, existing models often demand high computational resources unsuitable for mobile deployment. This research evaluates three convolutional neural network (CNN) architectures, namely mobile network (MobileNet), visual geometry group-16 (VGG-16), and residual network-50 (ResNet-50), to identify an optimal model for practical mobile-based crack detection. A dataset of 1,634 images was collected from online databases and field documentation, categorized into 10 classes across three severity levels: i) severe cracks requiring urgent repair (30%); ii) cracks requiring monitoring (40%); and iii) minor cracks (30%). The models were trained using standardized parameters with 224×224-pixel RGB input, rectified linear unit (ReLU) activation, and softmax classification. Systematic parameter optimization was conducted across epochs, learning rate, dropout rate, and optimizer selection, with stochastic gradient descent (SGD) identified as the optimal optimizer. Experimental results demonstrate that MobileNet achieves the best performance with 80% accuracy and a compact model size of 13.1 megabytes. This study concludes that MobileNet provides an optimal balance between detection accuracy and computational efficiency, enabling practical field deployment for automated concrete crack detection, with expert verification recommended for critical structural assessments.
Volume: 15
Issue: 3
Page: 2811-2825
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

Vision transformer and hybrid models for Malayalam handwritten word recognition

10.11591/ijai.v15.i3.pp2655-2663
Anju Arangil Thazhath , Binu Poothakuzhiyil Chacko , Mohamed Basheer Kizhakke Parambath
Transformer-based architectures and attention mechanisms have revolutionized the field of image recognition. This study focuses on offline handwritten Malayalam word recognition, addressing the lack of publicly available datasets for this low-resource language. A new Malayalam word dataset (MWD) comprising 20,850 samples across 139 classes was developed to support research in this domain. The vision transformer (ViT) was employed for advanced feature extraction, and multiple recognition models—feed-forward neural network (FFNN), global average pooling (GAP), bidirectional long short-term memory (BiLSTM), and attention based feed-forward neural network (AFFNN)—were evaluated. Among these, AFFNN achieved the highest accuracy of 98.56%, establishing the proposed vision transformer-based attention handwritten word recognition (ViTA-HWR) model as a robust framework for handwritten Malayalam word recognition and valuable contribution to regional language processing.
Volume: 15
Issue: 3
Page: 2655-2663
Publish at: 2026-06-01

Study on the design and comparison of permanent magnet synchronous motors for electric vehicle applications

10.11591/ijece.v16i3.pp1107-1117
Pham Ngoc Sam , Tran Duc Chuyen
In this research, the authors present a study analysis and compares two types of embedded internal permanent magnet synchronous motors (IPMSM) with U-type and V-type magnet configurations using finite element method (FEM) modeling to apply these motors to the currently popular electric vehicle industry. Parameters such as magnetic flux density, torque, cogging torque, back electromotive force (back-EMF), torque oscillation, and harmonic components were analyzed and compared; thereby identifying the advantages and disadvantages of the two IPMSM structures. Specifically, the V-type IPMSM motor offers higher efficiency, more stable torque, and a higher quality back electromotive force waveform with lower losses, making it suitable for high-performance applications such as electric vehicles and industrial automation. Meanwhile, the U-type structure has lower cogging torque, suitable for low-speed applications or those requiring high precision. Simulation results from the ANSYS Maxwell software show that the IPMSM motor is energy-efficient, has high power density, and operates smoothly, allowing for rapid acceleration, long range, compact configuration, and low maintenance; it uses permanent magnets on the rotor to eliminate losses, making electric vehicles lighter and more efficient than traditional motors.
Volume: 16
Issue: 3
Page: 1107-1117
Publish at: 2026-06-01

Prediction of new student admissions to higher education using support vector machines

10.11591/ijai.v15.i3.pp2484-2493
Neni Purwati , Windya Harieska Pramujati , A. Aviv Mahmudi , Mira Febriana Sesunan , Yahya Yahya
Higher education institutions across various regions operate using systems that generate large amounts of data. This data is stored and utilized for strategic decision-making, providing significant business value to these institutions. Support vector machine (SVM) has become popular due to its strong generalization capability, high prediction accuracy, and faster training speed. SVM employs kernels as tuning parameters. This study aims to enhance the accuracy of student admissions prediction in higher education institutions using the SVM classification model. The SVM model was applied to a dataset comprising 5,936 records with four attributes and was evaluated using the use training set, 10-fold cross-validation, and percentage splits of 70%–30% and 80%–20%. Initially, the SVM-kernel model achieved high accuracy but failed to identify any true positive instances, indicating its inability to detect the minority “not accepted” class due to severe class imbalance. After applying class balancing techniques, the model’s performance improved significantly in terms of area under the curve (AUC), F-measure, and Matthews correlation coefficient (MCC), reflecting a more balanced classification between majority and minority classes. The SVM with Pearson VII function-based universal kernel (PUK) and classifier version 4.5 (C4.5) models achieved the best performance, indicating that class balancing effectively enhances both sensitivity and fairness in predictive classification.
Volume: 15
Issue: 3
Page: 2484-2493
Publish at: 2026-06-01

Flashover of a polluted high voltage insulator under electric field distribution

10.11591/ijece.v16i3.pp1097-1106
Zainab Abdullah , Izham Zainal Abidin , Miszaina Osman , Nurulazmi Abd. Rahman , Muhammad Shafiq
This study investigates the effect of surface pollution on a single-unit 11 kV glass suspension insulator using two-dimensional (2D) axisymmetric simulations in COMSOL Multiphysics. The developed model incorporates the electrical properties of glass, cement, steel electrodes, surrounding air, and a uniform pollution layer, with an applied AC voltage of 11 kV under quasi-static conditions. Simulation results demonstrate pronounced electric field intensification in the polluted configuration, particularly at the air–glass–cap triple junction region, where localized electrical stress is significantly higher compared to the clean condition. While the clean insulator operates within IEC 60383 recommended limits, the polluted model exhibits elevated peak electric field magnitudes, indicating increased flashover vulnerability. The findings highlight the strong influence of surface contamination, material permittivity, and geometric configuration on electric field distribution along the creepage path. This study establishes a reliable and computationally efficient predictive framework for optimizing insulator design, improving maintenance strategies, and enhancing the long-term reliability of high-voltage transmission systems, especially in pollution-prone environments.
Volume: 16
Issue: 3
Page: 1097-1106
Publish at: 2026-06-01

Comparing global and variety-specific ensemble models for avocado maturity prediction with near-infrared

10.11591/ijai.v15.i3.pp2300-2313
Christian Ovalle , Jhon Garcia Jimenez , Jose Briones Zuñiga
Ensuring accurate, non-destructive maturity classification of avocados is critical to supply chain optimization in agro-industrial systems. This study presents a predictive framework that integrates near-infrared (NIR) spectroscopy with ensemble stacking machine learning (ML) to enhance the precision of avocado ripeness assessment. The proposed methodology compares global versus variety-specific models for 'Hass' and 'Fuerte' avocado types, leveraging spectral data (900–1,700 nm) and multiple base classifiers, including random forest (RF), gradient boosting (GB), support vector machines (SVMs), decision trees (DT), k-nearest neighbors (KNN), and categorical boosting (CatBoost), combined via linear regression as a meta-learner. Experimental results revealed that the stacking models outperformed individual learners, with variety-specific GB model achieving the highest performance (Matthews correlation coefficient (MCC) =0.679, area under the curve (AUC) =0.931). These findings highlight the critical importance of varietal specificity in model calibration and demonstrate how ensemble strategies can improve robustness, scalability, and interpretability in intelligent agricultural systems. The proposed model provides a computationally efficient solution for real-time quality control and supports the deployment of AI-powered systems within agricultural supply chains in developing regions.
Volume: 15
Issue: 3
Page: 2300-2313
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

Residual reinforcement learning for disturbance-resilient control under modeling uncertainties

10.11591/ijece.v16i3.pp1175-1187
Abolanle Adetifa , Rexcharles Enyinna Donatus , Daniel Udekwe
Modern control systems must operate reliably in the presence of modeling uncertainties and external disturbances, conditions under which conventional fixed-gain controllers often exhibit performance degradation. This paper proposes a residual reinforcement learning framework for disturbance-resilient pitch-rate control of an aircraft longitudinal model. A classical proportional-integral-derivative (PID) controller is employed as a stabilizing baseline, while a deep deterministic policy gradient (DDPG) agent learns a bounded residual control signal to compensate for unmodeled dynamics and external perturbations. To promote favorable transient behavior, the learning process incorporates transient-aware and reference-model-based reward shaping, while actuator constraints are enforced within the environment dynamics. Simulation results demonstrate that the proposed residual controller achieves a superior balance between response speed, overshoot, and tracking accuracy compared with both the standalone PID controller and a pure DDPG-based controller. In particular, the residual architecture significantly reduces overshoot and tracking error while preserving fast transient response and providing robust disturbance rejection under large pitching moment disturbances. These results indicate that residual reinforcement learning offers a practical and effective approach for enhancing robustness and performance in safety-critical flight control applications.
Volume: 16
Issue: 3
Page: 1175-1187
Publish at: 2026-06-01

Cutting-edge algorithms for reliable failure prediction in metro train systems

10.11591/ijai.v15.i3.pp2269-2277
Sana Chakri , Naoual Mouhni , Faouzia Ennaama
This study investigated various machine learning algorithms on dataset for failure prediction within metro train systems. The data indicated strong linear relationships within the dataset, making linear models such as support vector machines (SVMs) viable, as well as logistic regression analysis. For example, the least absolute shrinkage and selection operator (LASSO) regularization method used in feature selection had profound implications, leading to enhanced performance through the identification of pertinent attributes. Some advanced models like gradient boosting machines (GBMs), convolutional neural networks (CNNs), and kernel SVMs were found to outperform the conventional methods because they are capable of recognizing any complicated trends or non-linear relationships present in data sets. Combining strong learners can produce an ensemble model that improves forecast performance, while top-performing models are used in the ensemble method to enhance prediction accuracy. These findings would help professionals in the metro train industry choose appropriate machine learning methods to support preventive maintenance strategies, minimizing costs while enhancing operational effectiveness and safety.
Volume: 15
Issue: 3
Page: 2269-2277
Publish at: 2026-06-01

AMAC-LW: Adaptive medium access control for long range wide area network with energy-aware routing

10.11591/ijece.v16i3.pp1626-1644
Sowmya M. , S. Meenakshi Sundaram , Pandiyanathan Murugesan , Santhosh Kumar K. S. , Tejaswini R. Murgod
To enhance the performance of long range wide area network (LoRaWAN), a routing algorithm and a novel medium access control (MAC) layer protocol are required. In addition to addressing scalability and security issues, the protocol seeks to improve communication efficiency, dependability, and power consumption. It presents a dynamic routing method that reduces energy consumption by utilizing machine learning processes, adaptive routing tactics, and route optimization approaches. Simulations in a range of deployment situations are used to assess the suggested solutions. These results imply that the suggested protocol and routing scheme have the potential to greatly enhance the sustainability, energy efficiency, and performance of LoRaWAN-based Internet of Things networks. The effectiveness of the proposed solutions is evaluated through extensive simulations across diverse deployment scenarios. The results demonstrate that the proposed MAC protocol achieves a throughput of 350 bps, outperforming conventional protocols that typically reach only 220 bps. Latency is reduced to 50 ms from 85 ms, energy consumption is decreased to 2.5 joules from 4.5 joules, and the packet delivery ratio (PDR) is improved to 95%, compared to 75% in existing approaches. These findings highlight the potential of the proposed protocol and routing scheme to significantly enhance the performance, energy efficiency, and sustainability of LoRaWAN-based IoT networks.
Volume: 16
Issue: 3
Page: 1626-1644
Publish at: 2026-06-01

Navigating the new frontier: large language models and their implications for education

10.11591/ijai.v15.i3.pp2141-2152
Laila Boullous , Mustapha Hain , Adil Chergui , Brahim Elbhiri
This survey characterizes the contributions of large language models (LLMs) to technology enhanced learning by relating their capabilities to actual educational functions, making comparisons with traditional models of language. The contributions for this study are: i) introduce an education centered taxonomy that classifies LLM use by four key functions personalization and adaptivity, assessment and evaluation, profiling and prediction, and intelligent tutoring with illustrations from deployed systems and tools; ii) give a domain-based comparison of where LLMs outperform traditional models (sentiment analysis with sarcasm, context-aware question answering, and abstractive summarization) and why those advantages will mean something to e-learning practice; iii) synthesize six cross-cutting risks, including computational cost/carbon, privacy, bias and hallucination, labor displacement, interpretability, and the limits of human-like judgment, and provide practical design/research implications; and iv) report on a transparent review protocol that got the initial corpus down to 50 key articles, allowing for modifications and future updates from other interested researchers. In sum, the discussion about LLMs in education has been pushed past the broad strokes to a situation where there is a comprehensive vocabulary for what LLMs can do, and how they may or may not responsibly improve learning experiences, educator workflows, and systems/learning design in e-learning.
Volume: 15
Issue: 3
Page: 2141-2152
Publish at: 2026-06-01

Yarn inspection and sorting system using robotic vision and machine learning

10.11591/ijai.v15.i3.pp2325-2336
Emmanuel Agung Nugroho , Joga Dharma Setiawan , Deni Kurnia , Nanang Roni Wibowo
The increasing demand for automation in the textile industry, particularly in quality inspection processes, underscores the need for intelligent and cost effective solutions. Conventional methods of yarn classification and sorting remain labor-intensive, time-consuming, and susceptible to human error, resulting in inconsistent quality control. This study introduces an automated system for yarn inspection and sorting that integrates robotic vision, machine learning, and position-based visual servoing (PBVS) for real-time motion control. The proposed system combines Raspberry Pi-based machine learning with computer vision utilizing a 4-degree-of-freedom (4-DOF) robotic manipulator and a webcam, enabling precise pick-and-place operations based on yarn classification into four categories: good, striped, moldy, and dirty. Experimental results validate the system’s effectiveness, achieving an average deviation of 0.375 mm along the x-axis, 0.69 mm along the y-axis, and 0.675 mm along the z-axis, resulting in an overall position error of 0.58 mm. These results demonstrate the system’s robustness and reliability in dynamic industrial environments. The novelty of this research lies in leveraging a low-cost embedded architecture with advanced visual servoing for textile automation, reducing operational errors, improving efficiency, and supporting industry 4.0 adoption.
Volume: 15
Issue: 3
Page: 2325-2336
Publish at: 2026-06-01
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