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29,325 Article Results

Microbubble size and rise velocity measurement in dissolved air flotation system

10.11591/ijece.v16i1.pp174-186
Jeimmy Adriana Muñoz Alegría , Jesús Emilio Pinto Lopera , Elena Muñoz-España , Juan Fernando Flórez-Marulanda
Water reuse and resource recovery are priority environmental goals under increasing water scarcity and climate stress. Dissolved air flotation (DAF) is widely applied in municipal, industrial, and decentralized treatment trains because fine microbubbles (MB) enhance solids removal efficiency. Accurate, low-cost characterization of MB size and rise velocity is therefore valuable for process monitoring and optimization. This study develops and validates a smartphone-based, computer-vision pipeline for laboratory-scale DAF systems. After camera calibration and lens un-distortion, each video sequence (235 frames per run) is processed through grayscale conversion, median, Gaussian, and local-Laplacian filtering, gamma correction, and Otsu thresholding, followed by morphological refinement. Circular Hough transform then identifies MB candidates, providing their diameters and centroid locations. These detections are then linked frame-to-frame using a distance-gated nearest-neighbor tracker with dynamic memory allocation to accommodate new MBs under turbulent, bubble-clustering conditions. Rise velocity is computed from interframe centroid displacement and frame interval. The system reliably tracked up to 32 microbubbles simultaneously per video. Across four operating pressure/airflow combinations, mean MB diameters ranged 95.47–216.42 µm and mean rise velocities 9.40×10³–2.76×10⁴ µm/s. The approach is low cost, computationally lightweight, and suitable for rapid MB characterization to support DAF monitoring, optimization, and research.
Volume: 16
Issue: 1
Page: 174-186
Publish at: 2026-02-01

IDPS: A machine learning framework for real-time intrusion detection and protection system for malicious internet activity

10.11591/ijece.v16i1.pp437-449
Raisa Fabiha , Stein Joachim Reberio , Zubayer Farazi , Fernaz Narin Nur , Shaheena Sultana , A. H. M. Saiful Islam
With the increasing frequency and complexity of cyber threats, there is a pressing need for effective real-time solutions to detect and prevent malicious activities. This study introduces a novel machine learning-based architecture for real-time cybersecurity to enhance accurate identification and prevention of malicious cyber activities. The proposed framework combines advanced machine learning algorithms with Wireshark network traffic analysis to effectively detect and classify a wide range of cyberattacks, providing timely and actionable insights to cybersecurity professionals. A core component of this system is a prototype blocker, which is seamlessly integrated with Cisco infrastructure, enabling proactive intervention by blocking suspicious IP addresses in real-time. In addition, a user-friendly web application enhances system operability by offering intuitive data visualization and analytical tools, enabling rapid and informed decision-making. This comprehensive approach not only strengthens network security and protects digital assets but also equips defenders with the capability to respond effectively to the dynamic landscape of cyber threats.
Volume: 16
Issue: 1
Page: 437-449
Publish at: 2026-02-01

New control strategy for maximizing power extraction in the grid-connected CHP-PV-Wind hybrid system

10.11591/ijece.v16i1.pp36-48
Othmane Maakoul , Abdellah Boulal
This work represents a significant contribution to the advancement of modern electrical systems by combining advanced control strategies with robust protection solutions to address the challenges of the energy transition. It focuses on the integration of renewable energy within electrical grids, with particular attention to wind energy, cogeneration (CHP), and photovoltaic energy. The main contributions include the development of innovative methods to enhance system stability and improve energy quality. This is achieved notably through the use of advanced control algorithms, such as the synchronously rotating frame (SRF) transformation, applied to converters and voltage source converter-based high voltage direct current (VSC-HVDC) systems. These approaches enable precise voltage regulation, optimized power flow management, and significant reduction of harmonic distortion. The paper also explores novel techniques, such as control based on the ANFIS algorithm, to improve voltage regulation, current stability, and converter efficiency. Finally, an effective protection solution against voltage faults is proposed, ensuring the stable and reliable transfer of energy produced by offshore wind farms to onshore grids.
Volume: 16
Issue: 1
Page: 36-48
Publish at: 2026-02-01

Application of the model reference adaptive system method in sensorless control for elevator drive systems using 3-Phase permanent magnet synchronous motors

10.11591/ijece.v16i1.pp149-157
Tran Van Khoi , An Thi Hoai Thu Anh , Tran Trong Hieu
Improving sensorless control performance in elevator drive systems using three-phase permanent magnet synchronous motors (PMSM) has become increasingly popular to reduce costs and enhance system stability. The primary operation of the elevator involves motor mode when the cabin moves upward and shifts to generator mode or braking mode under the influence of gravity when moving downward. This presents significant challenges for sensorless control. To address these issues, the model reference adaptive system (MRAS) based on the mathematical d-q axis model of the PMSM is proposed to estimate rotor speed and position. Combined with field-oriented control (FOC), this method optimizes performance and precisely controls motor torque without requiring physical sensors. Additionally, a low-pass filter is employed to process input signals, such as voltage and current, to improve estimation accuracy and optimize speed response. Simulation results from MATLAB/Simulink demonstrate highly accurate speed responses, particularly under continuous load variations.
Volume: 16
Issue: 1
Page: 149-157
Publish at: 2026-02-01

Facial emotion recognition under face mask occlusion using vision transformers

10.11591/ijece.v16i1.pp395-403
Ashraf Yunis Maghari , Ameer M. Telbani
Facial emotion recognition (FER) systems face significant challenges when individuals wear face masks, as critical facial regions are occluded. This paper addresses this limitation by employing vision transformers (ViT), which offer a promising alternative with reduced computational complexity compared to traditional deep learning methods. We propose a ViT-based FER framework that fine-tunes a pre-trained ViT architecture to enhance emotion recognition under mask-induced occlusion. The model is fine-tuned and evaluated on the AffectNet dataset, which originally represents eight emotion categories. These categories are restructured into five broader classes to mitigate the impact of occluded features. The model’s performance is assessed using standard metrics, including accuracy, precision, recall, and F1 score. Experimental results demonstrate that the proposed framework achieves an accuracy of 81%, outperforming several state-of-the-art approaches. These findings highlight the potential of vision transformers in recognizing emotions under masked conditions and support the development of more robust FER systems for real-world applications in healthcare, surveillance, and human–computer interaction. This work introduces a scalable and effective approach that integrates self-attention, synthetic mask augmentation, and emotion class restructuring to improve emotion recognition under facial occlusion.
Volume: 16
Issue: 1
Page: 395-403
Publish at: 2026-02-01

Autonomous mobile robot implementation for final assembly material delivery system

10.11591/ijece.v16i1.pp158-173
Ahmad Riyad Firdaus , Imam Sholihuddin , Fania Putri Hutasoit , Agus Naba , Ika Karlina Laila Nur Suciningtyas
This study presents the development and implementation of an autonomous mobile robot (AMR) system for material delivery in a final assembly environment. The AMR replaces conventional transport methods by autonomously moving trolleys between the warehouse, production stations, and recycling areas, thereby reducing human intervention in repetitive logistics tasks. The proposed system integrates a laser-SLAM navigation approach, customized trolley design, RoboShop programming, and robot dispatch system coordination, enabling real-time route planning, obstacle detection, and material scheduling. Experimental validation demonstrated high accuracy in path following, with root mean square error values ranging between 0.001 to 0.020 meters. The AMR achieved an average travel distance of 118.81 meters and a cycle time of 566.90 seconds across three final assembly stations. The overall efficiency reached 57%, primarily due to reduced idle time and optimized material replenishment. These results confirm the feasibility of AMR deployment as a scalable and flexible intralogistics solution, supporting the transition toward Industry 4.0 smart manufacturing systems.
Volume: 16
Issue: 1
Page: 158-173
Publish at: 2026-02-01

Deep learning architecture for detection of fetal heart anomalies

10.11591/ijece.v16i1.pp414-422
Nusrat Jawed Iqbal Ansari , Maniroja M. Edinburgh , Nikita Nikita
Research has demonstrated that artificial intelligence (AI) techniques have shown tremendous potential over the past decade for analyzing and detecting anomalies in the fetal heart during ultrasound tests. Despite their potential, the adoption of these algorithms remains limited due to concerns over patient privacy, the scarcity of large well-annotated datasets and challenges in achieving high accuracy. This research aims to overcome these limitations by proposing an optimal solution. Two methods such as deterministic image augmentation techniques and Wasserstein generative adversarial network with gradient penalty (WGAN-GP) showcase the framework's capacity to seamlessly and effectively expand original datasets to 14 times and 17 times respectively, thereby effectively tackling the problem of data scarcity. It uses an annotation tool to precisely categorize anomalies identified in the echocardiogram dataset. Segmentation of the annotated data is done to highlight region of interest. Nine distinct fetal heart anomalies are identified with respect to the fewer covered in existing research. This study also investigates the state-of-the-art architectures and optimization techniques used in deep learning models. The results clearly indicate that the ResNet-101 model demonstrated superior precision accuracy of 99.15%. To ensure the reliability of the proposed model, its performance underwent thorough evaluation and validation by certified gynecologists and fetal medicine specialists.
Volume: 16
Issue: 1
Page: 414-422
Publish at: 2026-02-01

Credit card fraud data analysis using proposed sampling algorithm and deep ensemble learning

10.11591/ijece.v16i1.pp311-320
Aye Aye Khine , Zin Thu Thu Myint
Credit card fraud detection is challenging due to the severe imbalance between legitimate and fraudulent transactions, which hinders accurate fraud identification. To address this, we propose a deep learning-based ensemble model integrated with a proposed sampling algorithm based on random oversampling. Unlike traditional methods, the proposed sampling algorithm addresses the oversight of parameter selection and manages class imbalance without eliminating any legitimate samples. The ensemble framework combines the strengths of convolutional neural networks (CNN) for spatial feature extraction, long short-term memory (LSTM) networks for capturing sequential patterns, and multilayer perceptrons (MLP) for efficient classification. Three ensemble strategies—Weighted average, unweighted average, and unweighted majority voting—are employed to aggregate predictions. Experimental results show that all ensemble methods achieve perfect scores (1.00) in precision, recall, and F1-score for both fraud and non-fraud classes. This study demonstrates the effectiveness of ensemble model with optimized sampling approach for robust and accurate fraud detection.
Volume: 16
Issue: 1
Page: 311-320
Publish at: 2026-02-01

Evaluating plant growth performance in a greenhouse hydroponic salad system using the internet of things

10.11591/ijece.v16i1.pp505-517
Chonthisa Rattanachu , Wiyuda Phetjirachotkul , Isara Chaopisit , Kronsirinut Rothjanawan
Hydroponic salad cultivation is becoming increasingly popular. However, a common challenge is the lack of time to maintain hydroponic vegetables due to other responsibilities. This study presents a hydroponic system based on the internet of things (IoT) technique, designed to save time by enabling remote control through a mobile application connected to a NodeMCU microcontroller. Various sensors are integrated with the NodeMCU for real-time monitoring and automation. The study also explores the use of RGB LEDs, which significantly accelerated plant growth and reduced cultivation time. A comparative experimental design was employed to evaluate the growth rate of green oak salad vegetables under two different greenhouse systems. The primary factor compared was the greenhouse system type, with plant growth rate as the outcome variable. Each treatment was replicated 10 times. F-tests were used to statistically determine significant differences in growth rates between the two systems across measured intervals. Results showed that the automated greenhouse system produced the highest leaf width and plant weight values. The use of RGB LEDs reduced the cultivation period from 45 days to 30 days, enabling more planting cycles and ultimately increasing overall yield.
Volume: 16
Issue: 1
Page: 505-517
Publish at: 2026-02-01

Generalization of reactive power definition for periodical waveforms

10.11591/ijece.v16i1.pp102-110
Grzegorz Kosobudzki , Leszek Ładniak
The article presents a selection of reactive power definitions, which are applicable for implementation in energy meters. For sinusoidal current and voltage waveforms, all provided dependencies yield equivalent reactive power values. However, in the presence of distorted current and voltage, the power values are determined by the applied method (algorithm). Standardization requirements for reactive energy meters stipulate metrological verification under sinusoidal conditions. The selection of an optimal reactive power definition remains a problematic and ongoing subject of debate within the field. The paper shows that a generalized unique definition of additive reactive power derives from the definition of active power. Unlike active power, reactive power must be independent of the conversion of electric energy into work and heat. This independence is achieved if one of the waveforms – the current in the scalar voltage and current product (specifying active power) – is replaced by a special orthogonal waveform. An orthogonal waveform can be derived through either differentiation or integration. Reactive power obtained by this method is an additive within the system. When differentiation is employed, the reactive power for a nonlinear resistive load with a unique, time-invariant current-voltage characteristic will be zero. Some other properties of reactive power defined in this way are presented. This method is straightforward to implement in reactive energy meters.
Volume: 16
Issue: 1
Page: 102-110
Publish at: 2026-02-01

Efficiency enhancement of off-grid solar system

10.11591/ijece.v16i1.pp111-120
Satish Kumar , Asif Jamil Ansari , Anil Kumar Singh , Deepak Gangwar
This paper presents the design and implementation of a sensor-enabled off-grid solar charge controller aimed at maximizing the utilization of renewable energy. The proposed system integrates solar and load power sensors to minimize solar energy wastage. A microcontroller is employed to efficiently monitor and regulate battery voltage, solar power generation, and load demand. This system is designed to optimize solar energy usage, reduce dependency on the electrical grid, and lower electricity bills. Additionally, a main supply controller board with a display is introduced, along with a smart scheduler for appliance management. Prior to deployment, total solar power wastage was recorded at 93.1 watts per day. After implementing the proposed solution, wastage was reduced to 13.1 watts per day—reflecting an 85.92% reduction. These results confirm the system’s effectiveness in reducing energy loss, increasing self-consumption, and promoting energy sustainability in off-grid environments. It is important to note that this value may vary based on factors such as temperature, cloud cover, fog, and irradiation levels.
Volume: 16
Issue: 1
Page: 111-120
Publish at: 2026-02-01

An enhanced improved adaptive backstepping–second-order sliding mode hybrid control strategy for high-performance electric vehicle drives

10.11591/ijece.v16i1.pp121-134
Huu Dat Tran , Ngoc Thuy Pham
This paper proposes an enhanced hybrid speed control strategy, termed improved adaptive backstepping–second-order sliding mode (IABSSOSM), for six-phase induction motor (SPIM) drives in electric vehicle (EV) propulsion systems. The proposed method combines the systematic design framework of Backstepping in the outer speed and flux loops with a second-order sliding mode controller in the inner current loop. An innovation of the approach is the integration of a variable-gain super-twisting algorithm (VGSTA), which dynamically adjusts the control effort based on disturbance levels, thereby minimizing chattering and enhancing robustness against system uncertainties. To further improve disturbance rejection, a predictive torque estimator is incorporated using a forward Euler discretization, enabling accurate torque prediction and proactive compensation. This hybrid structure significantly improves convergence speed, enhances reference speed tracking accuracy, and ensures fast and precise torque response, and its strong resilience to external load disturbances, system parameter variations enable stable and reliable operation under challenging conditions. The effectiveness of the proposed approach is validated through comprehensive simulations using the MATLAB/Simulink.
Volume: 16
Issue: 1
Page: 121-134
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

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
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