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29,196 Article Results

Internet of things heatstroke detection device

10.11591/ijece.v16i1.pp535-544
Swati Patil , Rugved Ravindra Kulkarni , Karishma Prashant Salunkhe , Vidit Pravin Agrawal
The increasing frequency and intensity of heat waves due to climate change underscore the critical need for proactive measures to prevent heat stroke, a life-threatening condition affecting individuals of all demographics, with vulnerability among the elderly and outdoor workers. In response to this pressing public health challenge, we present the internet of things (IoT) based heat stroke prevention device, a comprehensive solution leveraging a suite of sensors including temperature, atmospheric, pulse rate, blood pressure, and gyroscope sensors, seamlessly integrated with an ESP32 microcontroller and Firebase's real-time database. Central to the device's functionality is a random forest classifier machine learning model, trained on historical data and user-specific parameters, to accurately predict the likelihood of heat stroke onset in real-time. Rigorous testing and validation procedures demonstrate the device's high accuracy and reliability in sensor measurements, data transmission, and model performance. The accompanying web-based dashboard provides users with intuitive access to their current health metrics, including temperature, humidity, blood pressure, pulse rate, and personalized predictions for heat stroke risk. This innovative device serves as a versatile tool for public health agencies, occupational safety programs, and individuals seeking to safeguard their well-being in the face of escalating temperatures and climate uncertainties.
Volume: 16
Issue: 1
Page: 535-544
Publish at: 2026-02-01

An information retrieval system for Indian legal documents

10.11591/ijece.v16i1.pp246-255
Rasmi Rani Dhala , A V S Pavan Kumar , Soumya Priyadarsini Panda
In this work, a legal document retrieval system is presented that estimates the significance of the user queries to appropriate legal sub-domains and extracts the key documents containing required information quickly. In order to develop such a system, a document repository is prepared comprising the documents and case study reports of different Indian legal matters of last five years. A legal sub-domain classification technique using deep neural network (DNN) model is used to obtain the relevance of the user queries with respective legal sub-domains for quick information retrieval. A query-document relevance (QDR) score-based technique is presented to rank the output documents in relation to the query terms. The presented model is evaluated by performing several experiments under different context and the performance of the presented model is analyzed. The presented model achieves an average precision score of 0.98 and recall score of 0.97 in the experiments performed. The retrieval model is assessed with other retrieval models and the presented model achieves 13% and 12% increase average accuracy with respect to precision scores and recall measures respectively compared to the traditional models showing the strength of the presented model.
Volume: 16
Issue: 1
Page: 246-255
Publish at: 2026-02-01

Application of deep learning and machine learning techniques for the detection of misleading health reports

10.11591/ijece.v16i1.pp373-382
Ravindra Babu Jaladanki , Garapati Satyanarayana Murthy , Venu Gopal Gaddam , Chippada Nagamani , Janjhyam Venkata Naga Ramesh , Ramesh Eluri
In the current era of vast information availability, the dissemination of misleading health information poses a considerable obstacle, jeopardizing public health and overall well-being. To tackle this challenge, experts have utilized artificial intelligence methods, especially machine learning (ML) and deep learning (DL), to create automated systems that can identify misleading health-related information. This study thoroughly investigates ML and DL techniques for detecting fraudulent health news. The analysis delves into distinct methodologies, exploring their unique approaches, metrics, and challenges. This study explores various techniques utilized in feature engineering, model architecture, and evaluation metrics within the realms of machine learning and deep learning methodologies. Additionally, we analyze the consequences of our results on enhancing the efficacy of systems designed to detect counterfeit health news and propose possible avenues for future investigation in this vital area.
Volume: 16
Issue: 1
Page: 373-382
Publish at: 2026-02-01

Accessibility in e-government portals: a systematic mapping study

10.11591/ijece.v16i1.pp357-372
Mohammed Rida Ouaziz , Laila Cheikhi , Ali Idri , Alain Abran
In recent years, several researchers have investigated the challenges of accessibility in e-government portals and have contributed to many proposals for improvements. However, no comprehensive review has been conducted on this topic. This study aimed to survey and synthesize the published work on the accessibility of e-government portals for people with disabilities. We carried out a review using a systematic mapping study (SMS) to compile previous findings and provide comprehensive state-of-the-art. The SMS collected studies published between January 2000 and March 2025 were identified using an automated search in five known databases. In total, 112 primary studies were selected. The results showed a notable increase in interest and research activities related to accessibility in e-government portals. Journals are the most widely used publication channel; studies have mainly focused on evaluation research and show a commitment to inclusivity. “AChecker” and “Wave validator” are the most used accessibility evaluation tools. The findings also identified various accessibility guidelines, with the most frequently referenced being the web content accessibility guidelines (WCAG). Based on this study, several key implications emerge for researchers, and addressing them would be beneficial for researchers to advance e-government website accessibility in a meaningful way.
Volume: 16
Issue: 1
Page: 357-372
Publish at: 2026-02-01

Students performance clustering for future personalized in learning virtual reality

10.11591/ijece.v16i1.pp297-310
Ghalia Mdaghri Alaoui , Abdelhamid Zouhair , Ilhame Khabbachi
This study investigates five clustering algorithms—K-Means, Gaussian mixture model (GMM), hierarchical clustering (HC), k-medoids, and spectral clustering—applied to student performance in mathematics, reading, and writing to support the development of virtual reality (VR)-based adaptive learning systems. Cluster quality was assessed using Davies-Bouldin and Calinski-Harabasz indices. Spectral clustering achieved the best results (DBI = 0.75, CHI = 1322), followed by K-Means (DBI = 0.79, CHI = 1398), while HC demonstrated superior robustness to outliers. Three distinct student profiles—beginner, intermediate, and advanced—emerged, enabling targeted adaptive interventions. Supervised classifiers trained on these clusters reached up to 99% accuracy (logistic regression) and 97.5% (support vector machine (SVM)), validating the discovered groupings. This work introduces a novel, data-driven methodology integrating unsupervised clustering with supervised prediction, providing a practical framework for designing immersive VR learning environments.
Volume: 16
Issue: 1
Page: 297-310
Publish at: 2026-02-01

Hybrid neurocontrol of irrigation of field agricultural crops

10.11591/ijece.v16i1.pp206-215
Aleksandr S. Kabildjanov , Aziz M. Usmanov , Dilnoza B. Yadgarova
This study investigates a conceptual framework for a hybrid intelligent control system designed to optimize the irrigation practice for field crops via fertigation technologies. This research is aimed at enhancing irrigation management through the improvement of the prediction, optimization, and regulation processes. This is achieved through the incorporation of modern computational intelligence with advanced deep learning based neural networks, evolutionary optimization algorithms, and the adaptive neuro-fuzzy technique. This hybrid control framework is made up of interconnected sets of monitoring and decision-making modules. These include subsystems for evaluation of soil conditions, monitoring of plant growth and physiological development, assessment of environmental and climatic conditions, and measurements of the intensity of solar radiation. Additional systems address the preparation of the fertigation mixture and control of intelligent decision-making processes. For this system, the overall control policy is rendered through a predictive neurocontrol approach with execution on a computer platform. A recurrent deep neural model, long short-term memory (LSTM) type, provides crop growth and development parameter predictions through the ability to explore temporal dependencies in agricultural processes. Optimization in the predictive control feedback is accomplished through genetic algorithms in an adaptive manner.
Volume: 16
Issue: 1
Page: 206-215
Publish at: 2026-02-01

Parameter-efficient fine-tuning of small language models for code generation: a comparative study of Gemma, Qwen 2.5 and Llama 3.2

10.11591/ijece.v16i1.pp278-287
Van-Viet Nguyen , The-Vinh Nguyen , Huu-Khanh Nguyen , Duc-Quang Vu
Large language models (LLMs) have demonstrated impressive capabilities in code generation; however, their high computational demands, privacy limitations, and challenges in edge deployment restrict their practical use in domain-specific applications. This study explores the effectiveness of parameter efficient fine-tuning for small language models (SLMs) with fewer than 3 billion parameters. We adopt a hybrid approach that combines low-rank adaptation (LoRA) and 4-bit quantization (QLoRA) to reduce fine-tuning costs while preserving semantic consistency. Experiments on the CodeAlpaca-20k dataset reveal that SLMs fine-tuned with this method outperform larger baseline models, including Phi-3 Mini 4K base, in ROUGE-L. Notably, applying our approach to the LLaMA 3 3B and Qwen2.5 3B models yielded performance improvements of 54% and 55%, respectively, over untuned counterparts. We evaluate models developed by major artificial intelligence (AI) providers Google (Gemma 2B), Meta (LLaMA 3 1B/3B), and Alibaba (Qwen2.5 1.5B/3B) and show that parameter-efficient fine-tuning enables them to serve as cost-effective, high-performing alternatives to larger LLMs. These findings highlight the potential of SLMs as scalable solutions for domain-specific software engineering tasks, supporting broader adoption and democratization of neural code synthesis.
Volume: 16
Issue: 1
Page: 278-287
Publish at: 2026-02-01

Artificial intelligence of things solution for Spirulina cultivation control

10.11591/ijece.v16i1.pp488-504
Abdelkarim Elbaati , Mariem Kobbi , Jihene Afli , Abdelrahim Chiha , Riadh Haj Amor , Bilel Neji , Taha Beyrouthy , Youssef Krichen , Adel M. Alimi
In the evolving field of Spirulina cultivation, the integration of the internet of things (IoT) has facilitated the optimization of spirulina growth and significantly enhanced biomass yield in the culture medium. This study outlines a control open-pond system for Spirulina cultivation that employs generative artificial intelligence (AI) and edge computing within an IoT framework. This transformative approach maintains optimal conditions and automates tasks traditionally managed through labor-intensive manual processes. The system is designed to detect, acquire, and monitor basin data via electronic devices, which is then analyzed by a large language model (LLM) to generate precise, context-aware recommendations based on domain-specific knowledge. The final output comprises SMS notifications sent to the farm manager, containing the generated recommendations, which keep them informed and enable timely intervention when necessary. To ensure continued autonomous operation in case of connectivity loss, pre-trained TinyML models were integrated into the Raspberry Pi. These models display alarm signals to alert the farm owner to any irregularities, thereby maintaining system stability and performance. This system has substantially improved the growth rate, biomass yield, and nutrient content of Spirulina. The results highlight the potential of this system to transform Spirulina cultivation by offering an adaptable, autonomous solution.
Volume: 16
Issue: 1
Page: 488-504
Publish at: 2026-02-01

Optimal investment framework of static VAr compensators in distribution system based on life cycle cost

10.11591/ijece.v16i1.pp76-88
Nguyen Hien Trung , Vu Van Thang
The distribution system planning and operating present significant challenges due to low voltage, high impedance, and large load density, which lead to substantial power losses and low voltage quality. To address this challenge, the paper proposes an optimal framework for the simultaneous determination of the placement and sizing of static VAr compensators (SVCs) in DSs. The proposed model is formulated as an optimization problem that minimizes the life cycle cost, while accounting for the varying lifespans and investment times of SVCs. The framework incorporates hourly load variation and employs full alternating current (AC) power flow analysis to improve the accuracy of results. Additionally, it considers the dependency of the reactive power injected by SVCs on the DSs and incorporates the discrete rated capacities of SVCs to ensure practical feasibility and enhance the accuracy of compensation power, effect of DSs. The proposed approach is validated using a modified 33-bus IEEE test system implemented in the general algebraic modeling system (GAMS). Numerical results from multiple case studies confirm the feasibility and high performance of the proposed model.
Volume: 16
Issue: 1
Page: 76-88
Publish at: 2026-02-01

Stochastic planning of multi-bus hydrothermal systems using the scenario tree technique

10.11591/ijece.v16i1.pp49-64
Martha Patricia Camargo-Martínez , Ricardo Rincón Ballesteros , Fabian Salazar-Caceres , Andrés F. Panesso H. , Harrynson Ramírez-Murillo , Osvaldo Añó
Hydrothermal operation planning (HTOP) is a complex, large-scale optimal control problem. Traditionally, mathematical programming is used to solve it; however, metaheuristic techniques have emerged as an alternative approach. However, even in the context of current technological developments, the models developed to date generally require simplifications in the formulation. In particular, in medium-term planning, they have used a deterministic model or simplified transmission lines into a single bus. However, this approach leads to conservative and unrealistic solutions that may result in either oversizing or underutilization of resources. Therefore, this work proposes a methodology for incorporating uncertainties into the HTOP problem with a multi-bus topology. It was tested in a three-bus system, where linear functions are applied to simplify the production of hydroelectric plants and the cost of thermal units. The methodology incorporated well-established techniques in an implicit stochastic optimization (ISO) model, using a tree of 50 scenarios to model the hydrological series, which is solved with linear programming (LP). The results were validated with the costs of the 10000 generated series, showing an error of 5.07%. Additionally, the solutions were compared with an adapted metaheuristic technique for this problem to explore models applicable to more complex formulations.
Volume: 16
Issue: 1
Page: 49-64
Publish at: 2026-02-01

Tiny machine learning with convolutional neural network for intelligent radiation monitoring in nuclear installation

10.11591/ijece.v16i1.pp404-413
Istofa Istofa , Gina Kusuma , Firliyani Rahmatia Ningsih , Joko Triyanto , I Putu Susila , Atang Susila
This study focuses on developing an intelligent radiation monitoring system capable of operating on a low-power single-board computer (Raspberry Pi) for deployment in remote monitoring stations within nuclear facility environments. The proposed system utilizes a radionuclide identification method based on tiny machine learning (TinyML) with a convolutional neural network (CNN) architecture. The radionuclide dataset was acquired through measurements of standard radiation sources, with variations in distance, exposure time, and combinations of multiple sources-including Cs-137, Co-60, Cs-134, and Eu-152. The radiation intensity data from detector measurements were structured into a response matrix and subsequently converted into a grayscale image dataset for model training. Keras is used to design and train machine learning models, while Tensor Flow Lite is used to model size reduction. Experimental results demonstrate that the developed model achieves an accuracy of 99.338% for Keras model trained on computer and 84.568% after deployment on the Raspberry Pi. Furthermore, this study successfully designed and embedded the TinyML model into an environment radiation monitoring system at the PUSPIPTEK nuclear installation.
Volume: 16
Issue: 1
Page: 404-413
Publish at: 2026-02-01

A novel stretched-compressed exponential low-pass filter and its application to electrocardiogram signal denoising

10.11591/ijece.v16i1.pp230-245
Roberto de Fazio , Bassam Al-Naami , Yahia Rawash , Abdel-Razzak Al-Hinnawi , Awad Al-Zaben , Paolo Visconti
The study investigates a novel stretched-compressed exponential low-pass (SCELP) filter to denoise electrocardiogram (ECG) signals. As an extension of Gaussian filter and unlike other denoising filters, the SCELP filter utilizes the stretched-compressed exponential function (SCEF) in the convolution kernel, being the Gaussian function its particular case. A MATLAB implementation is provided with a single parameter (β), which allows to modify the filter strength, to increase the signal-to-noise ratio (SNR) and reduce the mean squared error (MSE). The SCELP filter’s advantages over traditional denoising filters (i.e., Gaussian, Mittag–Leffler, and Savitzky-Golay filters) were assessed on 100 ECG signals, 50 normal and 50 abnormal (affected by sleep apnea), provided by the PhysioNet dataset. The SCELP filter’s efficacy in rejecting noise was evaluated as the β parameter varies, quantifying the filters' performance in terms of mean SNR and MSE to determine the optimal β value. The obtained results showed that the SCELP filter's best performances are achieved for β equal to = 1.6 (i.e., 16.9508 dB and 13.7574 dB SNR values, and 0.01025 and 0.01178 MSE values for normal and abnormal ECGs, respectively). Furthermore, the SCELP filter was tested on ECG signals with added white noise; compared to Gaussian, Mittag–Leffler, and Savitzky-Golay filters, the SCELP filter yields better performance regarding SNR (16.495 and 14.940 dB) and MSE (0.0106 and 0.0114) values, for normal and abnormal ECGs, respectively, suggesting its applicability for ECG signals' denoising.
Volume: 16
Issue: 1
Page: 230-245
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

Systematic review of artificial intelligence applications in predicting solar photovoltaic power production efficiency

10.11591/ijece.v16i1.pp463-476
M. Rizki Ikhsan , Muhammad Modi Lakulu , Ismail Yusuf Pannesai , Muhammad Rizali , Bayu Nugraha , Liliana Swastina
The global energy crisis and climate change demand more accurate and efficient renewable energy forecasting methods. Solar photovoltaic (PV) systems offer abundant clean energy but their efficiency is highly affected by weather variability, requiring advanced predictive models. This systematic review of 69 studies published between 2020 and 2024 evaluates artificial intelligence (AI) and machine learning (ML) applications in PV forecasting, with a focus on hybrid algorithms such as convolutional neural network-long short-term memory (CNN-LSTM). Results demonstrate that hybrid models consistently outperform traditional statistical methods and standalone AI approaches by capturing spatiotemporal patterns more effectively, achieving significant error reductions and improving reliability. A notable gap identified is the limited integration of consumer behavior into forecasting models, despite evidence that incorporating demand-side patterns enhances accuracy. Challenges also remain in data availability, scalability across diverse climates, and computational requirements. This review contributes by synthesizing recent advances and emphasizing consumer integration as an underexplored but critical dimension for future research. The findings provide a foundation for developing more precise, resilient, and scalable PV forecasting models, supporting optimized energy management and accelerating the transition toward sustainable energy systems.
Volume: 16
Issue: 1
Page: 463-476
Publish at: 2026-02-01

Machine learning-based prediction of moisture and oxygen in a large power transformer with online monitoring validation

10.11591/ijece.v16i1.pp1-9
Osama T. Ghazal , Mohammed S. Assaf
This study presents a predictive modeling approach for monitoring moisture and dissolved oxygen dynamics in a newly commissioned high-capacity power transformer. Using over 48,000 real-time observations collected across three years via an advanced online monitoring device installed on a 326 MVA generator step-up transformer (GSUT), machine learning models were developed to estimate moisture and oxygen concentrations based on correlated operational parameters. Multiple regression-based algorithms were trained and evaluated using performance metrics including root mean squared error (RMSE), mean absolute error (MAE), and coefficient of determination (R²). Linear regression achieved superior performance with an RMSE values as low as 0.05888 ppm for oxygen and 0.0153 ppm for moisture. The models were further validated using data from a sister transformer, demonstrating generalizability and reliability across similar transformer units. This work contributes a scalable and accurate solution for real-time transformer health assessment, with practical implications for predictive maintenance strategies in power utilities.
Volume: 16
Issue: 1
Page: 1-9
Publish at: 2026-02-01
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