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30,938 Article Results

Fuzzy integral fault-tolerant control of an activated sludge process

10.11591/ijra.v15i2.pp473-487
Ahmed Sami Hamana , Mounir Bekaik , Messaoud Ramdani
This paper presents a fuzzy integral fault-tolerant controller (FIFTC) for robust regulation of substrate and dissolved oxygen in activated sludge processes (ASP). The nonlinear dynamics of the process are represented using an augmented Takagi–Sugeno (TS) fuzzy model, which includes an additional vector representing the integral state to improve tracking accuracy. A fuzzy proportional-integral (PI) observer is employed to estimate states and detect actuator faults, particularly in the aeration system. Controller and observer gains are computed by solving linear matrix inequalities (LMIs), while an H∞ performance criterion, defined by the parameter, ensures effective disturbance attenuation and bounds the error energy. In the simulation, we considered actuator faults of the loss of effectiveness (LOE) type. Simulation results demonstrate that FIFTC significantly outperforms classical linear quadratic regulator (LQR) in terms of tracking accuracy, robustness, and fault tolerance, even under partial actuator failures and external disturbances. The proposed FIFTC control strategy, which leverages fuzzy modeling, robust observers, and LMI-based optimization, provides significant benefits, primarily by improving efficiency, reducing energy consumption, and enhancing robustness.
Volume: 15
Issue: 2
Page: 473-487
Publish at: 2026-06-01

Integrating artificial intelligence and Internet of Things for solid waste management: a review

10.11591/ijra.v15i2.pp388-396
Aditya Karle , Tejas Ramdas Pagare , Mohammed Ashaz Arkati , Prathmesh Prafull Tarapurkar , Praveen Kumar Bhojane
The increasing pace of urbanization and industrial growth has intensified the challenges of solid waste management, demanding intelligent, data-driven, and sustainable solutions. This review explores how the combined application of artificial intelligence (AI) and the Internet of Things (IoT) is revolutionizing conventional waste management practices into intelligent, automated, and responsive systems. Through a comprehensive review of 43 scholarly publications, case analyses, and technical studies, this paper emphasizes how AI-based methods—such as learning algorithms, image recognition, and data-driven prediction—improve waste sorting precision, recycling performance, and material recovery efficiency-enhance waste segregation accuracy, recycling efficiency, and resource recovery. Simultaneously, IoT-based systems employing sensors, cloud platforms, and smart bins enable real-time waste monitoring, dynamic routing, and optimized collection logistics. Emerging technologies like blockchain for waste traceability, robotics for automated sorting, and advanced analytics for decision-making are also examined. Despite these advancements, challenges related to scalability, interoperability, cost, and data privacy persist. This review identifies current research gaps, proposes future directions, and emphasizes the importance of integrating AI and IoT with circular economy principles under Industry 5.0 to achieve sustainable, efficient, and human-centric waste management solutions.
Volume: 15
Issue: 2
Page: 388-396
Publish at: 2026-06-01

Design of beefsteak tomato harvesting robot system in greenhouse

10.11591/ijra.v15i2.pp353-364
Thien An Dinh , So Nam Phung , Tri Cong Phung
One challenge for tomato harvesting robots is that some of the tomato stems were not detectable because they were hidden behind the leaves or other obstacles. The primary objective of this research is to design, simulate, and experiment with a tomato harvesting robot and propose an improved detection algorithm to overcome the above problem. The suggested detection algorithm is designed to first detect the tomato fruit itself, and if the stem is not visible, the system will automatically adjust the camera's viewing angle to provide a better perspective and uncover the hidden stem. Simulation and experimental tests were carried out in a real tomato greenhouse to evaluate the cutting and holding mechanism, as well as the camera-based detection algorithm. These experimental results confirmed the effectiveness of the gripper and detection system and revealed several challenges in the harvesting algorithm. By integrating advanced algorithms for tomato detection and harvesting, this robot will reduce damage to the tomatoes, ensuring higher quality and yield.
Volume: 15
Issue: 2
Page: 353-364
Publish at: 2026-06-01

Evaluation of machine learning approach in modelling and forecasting real gross domestic product growth: a comparative study

10.11591/ijece.v16i3.pp1339-1349
Moiz Qureshi , Muhammad Ismail , Nawaz Ahmad , Ibrar Hussain , Abbas Ali Ghoto , Jolita Vveinhardt
This study aims to provide an efficient and accurate machine-learning approach for modelling and forecasting the real gross domestic production (GDP) in the context of Pakistan. The study forecasts Pakistan's GDP growth rate using different forecasting models, such as naïve, seasonal naïve (SNaive), smoothing, and k-nearest neighbors (k-NN). Machine learning algorithms provide additional advice for data-driven decision-making. According to the findings, the k-NN-based forecasting gives minimum mean absolute percentage error (MAPE), root mean square error (RMSE), and mean absolute error (MAE) compared to the other three models. Economic policymakers can use accurate models to measure significant economic activity and formulate plans. The results indicate that the model produced accurate projections of future GDP levels for Pakistan.
Volume: 16
Issue: 3
Page: 1339-1349
Publish at: 2026-06-01

Integrating Sustainable Development Goals into educational information systems: toward a theoretical model for sustainable school management

10.11591/ijece.v16i3.pp1350-1359
Veri Arinal , Miswanto Miswanto , Kiki Setiawan , Agus Tanti Rahayu
This research addresses the critical challenge of implementing Sustainable Development Goal (SDG) 4, "Quality education," in Indonesian secondary schools. While national policies exist, schools lack a systematic digital tool to plan, monitor, and evaluate sustainability-based activities against concrete SDG indicators. To bridge this gap, this study employs a six-cycle design science research (DSR) methodology to develop a theoretical model for a sustainable education information system. The model is designed to integrate SDG principles into school management, enabling systematic data handling, adaptive curriculum functions, and real-time monitoring. A web-based prototype was developed using a React.js frontend and Node.js backend and evaluated through a mixed-methods approach. Data from interviews with 15 administrators and surveys of 97 teachers (yielding a usability satisfaction score of 4.34/5) validated the model’s effectiveness in making educational administration more efficient, transparent, and quality-oriented. The resulting artifact serves as a foundational technical and managerial reference for schools, education offices, and policymakers to leverage information technology in fostering a sustainable, participatory learning culture aligned with the SDGs.
Volume: 16
Issue: 3
Page: 1350-1359
Publish at: 2026-06-01

Sub-X-band reconfigurable antenna network with graphene slots

10.11591/ijece.v16i3.pp1249-1260
Hassna Agoumi , Seddik Bri , Youssef El Amraoui , Adil Saadi
This paper presents the design and analysis of a graphene-slotted hexagonal microstrip patch antenna and its extension to a compact 4×4 planar array operating in the sub-X-band. The objective of this work is to demonstrate that graphene-based electrical reconfigurability can be extended from a single antenna element to an array configuration while improving radiation performance. The proposed antenna integrates graphene slots etched into the radiating patch, where reconfigurability is achieved by electrically tuning the graphene conductivity through an external gate voltage Vg. The single antenna operates around 9.4 GHz with an impedance bandwidth of 400 MHz and a peak gain of 6 dB. The design is then extended to a 4×4 array with an inter-element spacing of approximately 1.2 wavelengths. The array operates in the 9–10 GHz range, provides a bandwidth of 380 MHz, and achieves a maximum gain of 13.08 dB. The results confirm that graphene-enabled reconfigurability can be preserved at the array level without increasing structural complexity.
Volume: 16
Issue: 3
Page: 1249-1260
Publish at: 2026-06-01

Stacking ensemble techniques for automated peripheral blood cell classification using Inception v3 features

10.11591/ijai.v15.i3.pp2247-2259
Marwa Mawfaq Mohamedsheet Al-Hatab , Maysaloon Abed Qasim , Nawar A. Sultan
Robust distinction of blood cells is crucial in clinical evaluation. Manual examination is slow and exposed to errors. This work investigates using machine learning (ML) techniques for automated classification of eight categories of peripheral blood cell types from multi-color images. The Inception v3 network was used to extract features, a split of 66%/34% were used to evaluate the model along with 20-fold cross-validation. To reduce computational complexity, principal component analysis (PCA) was used to reduce the 2048-dimensional feature vectors to 100 components. Among all classifiers used, the highest performance without using PCA was achieved using the support vector machine (SVM) with an accuracy equal to 93.4% and an area under the curve (AUC) of 0.996. Using PCA, affected monocytes and immature granulocytes most due to the slight reduction in the accuracy and AUC which became 90.1% and to 0.991 respectively. Results were further enhanced when a stacked ensemble of neural network (NN), logistic regression (LR), and SVM were used, achieving an accuracy of 95.2% and an AUC of 0.998. The obtained findings confirmed the effectiveness of using stacked ensembles in providing a robust, high accuracy framework for automated blood cell classification, while PCA efficiently reduced dimensions with minimal performance loss.
Volume: 15
Issue: 3
Page: 2247-2259
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

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

Advanced inferential statistics and data mining for chlorophyll distribution clustering

10.11591/ijai.v15.i3.pp2081-2091
Felix Reba , Toha Saifudin , Rimuljo Hendradi
This study proposes an integrated statistical framework to analyze chlorophyll distribution in marine environments by combining probability distribution modeling, goodness-of-fit (GoF) evaluation, and machine learning-based clustering. Eight probability distribution models—half normal, inverse Gaussian, Rician, Birnbaum–Saunders, Nakagami, extreme value, t location-scale, and stable—were evaluated using observational chlorophyll-a data from the Copernicus Marine Service. Model performance was assessed through the Kolmogorov–Smirnov (KS) and Anderson Darling (AD) GoF tests, along with five statistical information criteria. The results indicate that the inverse Gaussian and extreme value distributions consistently offered the best statistical fit and ecological relevance across varying sample sizes. Clustering analysis, performed using the k-means algorithm and validated via the silhouette index, further confirmed the robustness of these two models in forming stable and well-separated clusters. In contrast, the half-normal distribution showed poor performance and instability, especially with smaller sample sizes. The proposed taxonomy and spatial visualizations enable empirical classification of model behavior and support integration into real-time marine decision support systems (DSS) for ecosystem monitoring. Overall, the study contributes to the development of accurate, data-driven analytical tools that aid sustainable marine resource management, aligned with sustainable development goal (SDG) 14 on marine ecosystem protection.
Volume: 15
Issue: 3
Page: 2081-2091
Publish at: 2026-06-01

Multilingual signs recognition using recurrent neural network

10.11591/ijai.v15.i3.pp2494-2510
Thouseef Ulla Khan , Dileep Marichi Ramachandra
Recognition of sign language is a crucial step towards providing individuals with hearing and speech impairment meaningful communication, but the fact that there are a number of distinct sign languages and gestures remain complex makes it a challenge to the current automated systems. The present paper describes a real-time multilingual sign language recognition system that is based on a recurrent neural network with long short-term memory (RNN-LSTM) with hand landmark MediaPipe-based hand landmark detection to successfully receive spatial and temporal gesture features. The proposed system was trained and tested over a self-collected set of alphabet gestures of the Chinese, American, and Indian sign language, including one hand and two-hand gestures, and was run with Keras with extensive performance evaluation metrics. The strength and generalization abilities of the suggested approach as part of different gesture patterns and variations in users are confirmed by experimental outcomes that indicate high recognition rates of 99.58%, 99.62%, and 99.63% of the Chinese, American, and Indian sign languages, respectively. These results demonstrate the promise of the given framework as a dedicated assistive system of communication and give it a solid base to continue its development to the point of the system of the continuous sign language recognition (CSLR) and multimodal translators.
Volume: 15
Issue: 3
Page: 2494-2510
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

Machine learning-driven analysis of user bandwidth allocation and performance in 5G heterogeneous network: a survey

10.11591/ijece.v16i3.pp1236-1248
Pang Wai Leong , Raymond Chia , Phang Swee King , Goh Hui Hwang , Chan Kah Yoong , Chung Gwo Chin
A key foundation of 5G heterogeneous networks (HetNets) is the use of network slicing, which divides bandwidth into multiple logical networks and accounts for each function’s requirements. Currently, various machine learning (ML) models are being implemented into the network slicing algorithm to allocate bandwidth dynamically. The network slicing algorithm analyzes the traffic and allocates bandwidth based on the current services using a network-centric approach. However, limited work is found on further studying the impact of user-centric algorithms in bandwidth allocation. This paper presents the network slicing used in 5G and the limitations of these algorithms. A detailed review of user-centric bandwidth allocation algorithms is presented, along with a critical review of ML algorithms for traffic prediction and resource allocation decisions. Finally, the technology gaps and opportunities of the existing works are reported, and the direction for further research of ML in user-centric bandwidth allocation algorithms is tabulated.
Volume: 16
Issue: 3
Page: 1236-1248
Publish at: 2026-06-01

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

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

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

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