Articles

Access the latest knowledge in applied science, electrical engineering, computer science and information technology, education, and health.

Advanced Search
Filter Icon

Filters article

Years

FAQ Arrow
Single Year
Range Year
0
0

Source Title

FAQ Arrow

Authors

FAQ Arrow

31,377 Article Results

Dung-beetle-optimization algorithm-based P-I-D controller for a separately excited DC-motor

10.11591/ijeecs.v43.i1.pp93-102
Kerrache Soumia , Haidas Mohammed
Understanding the proportional, integral, and derivative (P-I-D) control system is crucial for optimizing its parameters to achieve the desired system performance. The proportional gain determines how aggressively the system responds to the error, while the integral gain helps to eliminate any steady state error. The derivative gain plays a role in stabilizing the system by damping out any oscillations caused by sudden changes in the error. P-I-D control is a widely used control technique in various engineering applications, including the control of DC-motors, which is refers to adjust the motor’s input voltage, current, speed or position in order to achieve a desired output. One approach to tuning P-I-D parameters is the ziegler nichols (ZN) methods, where the first one involves to plot the step response of the model’s open loop with its tangent line. The other method conists systematically increasing the gains until the system becomes unstable, and then adjusting the gains to find the ultimate gain and ultimate period. One of the main advantages of metaheuristic algorithms is their ability to quickly converge to near-optimal solutions without getting stuck in local optima. This is achieved by using a combination of exploration and exploitation strategies to efficiently search through the solution space. Within our study, we seek to incorporate the Dung-Beetle based optimization algorithm (DBO) to adjust the P-I-D controller for a separately excited DC-motor’s (SEDCM) speed control using MATLAB-software relies on the objective functions: the integral absolue error (IAE), the integral squared error (ISE) and the integral time absolue error (ITAE). The results obtained are compared in their best performances on rise time, settling time, overshoot, peak response and peak time.
Volume: 43
Issue: 1
Page: 93-102
Publish at: 2026-07-01

Electrical engineering in the era of autonomous intelligence: building sustainable, resilient, and self-evolving energy systems

10.11591/ijeecs.v43.i1.pp1-6
Tole Sutikno
Electrical engineering is entering a transformative era in which autonomous intelligence is becoming an integral component of modern energy infrastructures rather than merely an auxiliary computational tool. The convergence of advanced power electronics, renewable energy technologies, intelligent sensing, edge computing, artificial intelligence, and high-speed communications is enabling electrical systems to evolve from passive and centrally controlled networks into adaptive, resilient, and self-evolving ecosystems. This editorial discusses the emerging paradigm of autonomous electrical engineering, where future power systems are expected to perceive operating conditions, learn from historical and real-time data, predict disturbances, and autonomously optimize their performance while maintaining reliability, security, and sustainability. Beyond conventional objectives such as efficiency and stability, next-generation electrical systems must address increasing renewable penetration, distributed energy resources, electrified transportation, cyber-physical security, and climate resilience. The editorial also highlights several promising research directions, including AI native power system operation, autonomous microgrids, digital twins, physics-informed intelligence, trustworthy and explainable AI, intelligent power electronic converters, and coordinated human–AI decision-making. These developments position electrical engineering as a foundational discipline for achieving sustainable development and future energy transition, while emphasizing that autonomous intelligence should augment engineering expertise to create safer, more reliable, and environmentally responsible electrical infrastructures.
Volume: 43
Issue: 1
Page: 1-6
Publish at: 2026-07-01

MobileNetV2 with transfer learning for brain tumor classification

10.11591/ijeecs.v43.i1.pp281-298
Aziz Srai
This document presents a deep learning-based approach for the automatic classification of brain tumors from magnetic resonance imaging (MRI) images, using the lightweight MobileNetV2 model combined with transfer learning and fine-tuning techniques. The study aims to address the constraints of resource-limited medical environments, where model speed and lightweight design are as important as accuracy. The dataset used is a fusion of three public databases (Figshare, SARTAJ, and BR35H), comprising 4,480 images for training and 1,600 for testing, divided into four classes: glioma, meningioma, pituitary tumor, and healthy brain. The methodology includes several steps: resizing the images to 224×224 pixels, normalizing pixel values between 0 and 1, augmenting the data through random rotations, shifts, and zooms to avoid overfitting, and then extracting features using MobileNetV2 pre-trained on ImageNet. The strategy adopted comprises two phases: first, transfer learning where only the layers added at the top of the model are trained for 10 epochs, then fine-tuning consisting of unfreezing the last 20 layers of the base model and retraining them with a reduced learning rate for 5 epochs. The results obtained show an overall accuracy of 86.56% after fine-tuning, with a macro-mean area under the ROC curve (AUC) of 0.9645, indicating excellent discriminatory power. The confusion matrix reveals that the "no tumor" class achieves perfect performance (400 out of 400), while the "meningioma" class remains the most difficult to classify, often confused with gliomas and pituitary tumors. Compared to more resource-intensive models like VGG16, ResNet50, or EfficientNet, MobileNetV2 offers an optimal balance between performance and lightweight design, with a significantly lower number of parameters, making it particularly well-suited to resource-constrained environments. The authors conclude that this approach provides reliable diagnostic support for radiologists, accelerating tumor detection without replacing medical expertise. Future directions include clinical validation on multi-center data, integration of an automated segmentation step, and exploration of newer architectures such as attention mechanisms.
Volume: 43
Issue: 1
Page: 281-298
Publish at: 2026-07-01

Coral classification in underwater images using a dual-branch deep learning framework

10.11591/ijeecs.v43.i1.pp207-218
Pracharat Sa-ngadsup , Chawan Koopipat
Automated coral classification from underwater imagery is essential for large-scale reef monitoring but remains challenging due to color attenuation, illumination variability, and differences between texture-focused close- range images and morphology-focused colony-level observations. To address these challenges, this study proposes a dual-branch convolutional neural network that integrates information from the CIELAB (LAB) color space with structural descriptors derived from the discrete wavelet transform (DWT). RGB images are first converted to the LAB color space to separate luminance and chromatic components in separate channels, enabling the model to exploit color and lightness information more explicitly. Structural information is extracted from the luminance channel using wavelet decomposition to capture high-frequency morphological patterns. The two representations are processed through parallel convolutional branches and fused at the feature level for classification. Experiments conducted on a unified coral dataset containing texture dominant and morphology-focused imagery across 14 classes show that the proposed method achieves 96.52% accuracy on the texture-focused RSMAS dataset and 88.33% accuracy on the morphology-focused structure RSMAS dataset, reducing the cross-domain performance gap from 22.46% to 8.19% compared with RGB baselines, demonstrating improved cross domain robustness for coral classification under heterogeneous underwater imaging conditions.
Volume: 43
Issue: 1
Page: 207-218
Publish at: 2026-07-01

HawkNet: an intelligent bio-inspired optimization based patch wise adaptive U-Net framework for retinal blood vessel segmentation in fundus images

10.11591/ijeecs.v43.i1.pp157-178
Saba Sheiba , Saba Sheiba
The accurate segmentation of retinal blood vessels plays a pivotal role in the early diagnosis and monitoring of health issues like diabetes, high blood pressure, and glaucoma. Traditional machine learning techniques such as matched filtering, morphological processing, and edge detection have a tough time to see tiny blood vessels clearly because eye images often have uneven lighting, poor contrast, and blood vessels that twist and turn in complex ways. To overcome these challenges, this work introduces an improved Patch-wise U-Net method with Harris Hawk optimization (HHO). The patch system split fundus images into overlapping patches, enabling the network to focus on localized vessel features such as fine capillaries, bifurcations, and vessel boundaries that are often missed in global segmentation. Meanwhile, HHO is employed to fine-tune the U-Net’s hyperparameters and learning weights, achieving faster convergence and enhanced segmentation accuracy without manual tuning. To establish generalizability and reliability, the proposed framework is rigorously tested on two standard retinal image repositories DRIVE, and STARE. Experimental evaluation demonstrates significant improvement in key performance metrics, including accuracy of 97.8%, precision 95.8%, recall 96.5%, f1-score 97.3%, IoU of 96.2%, and dice coefficient (DC) 94.3%, highlighting the model’s capability to capture thin and thick vessels structures while minimizing false detections. Additionally, qualitative segmentation further confirm that the proposed framework effectively preserves the continuity and morphology of thin and thick vessels, even in regions with low contrast or uneven illumination. Overall, the proposed method visual inspection has revealed that the suggested method can segment thin and thick vessels with greater accuracy than previous methods. It also demonstrates its potential for real-life clinical application.
Volume: 43
Issue: 1
Page: 157-178
Publish at: 2026-07-01

Advanced state machine-sliding mode current control energy management for multi-source DC microgrid

10.11591/ijeecs.v43.i1.pp63-77
Hamza Rezigue , Mabrouk Khemliche , Samia Latreche , Badreddine Kanouni , Hamza Khemliche
This work addresses effective power management in a multi-source DC microgrid. An innovative energy management technique utilizing a state machine control (SMC) integrated with a sliding mode current control (SMCC) has been developed. This approach offers advantages through its equitable distribution of power among sources, storage devices, and demand loads, thereby optimizing the flow rate, discharge, and charge cycles of energy storage devices; additionally, it improves the response time of PEMFC power across various states in comparison to a conventional PI controller. The SMC-SMCC has proposed nine scenarios, categorized into three state of charge (SOC) situations, to fulfill a predetermined set of parameters for the operation of the DC microgrid. Simulation studies conducted with a precise model in MATLAB/Simulink have demonstrated that the proposed SMC-SMCC is proficient in achieving effective power sharing, rapid DC link voltage control in terms of stability, and maintaining the SOC within its constraints; additionally, the SMC-SMCC exhibits a quicker response time compared to conventional SMC-PI.
Volume: 43
Issue: 1
Page: 63-77
Publish at: 2026-07-01

A review of field-programmable gate array-based biomedical signal processing for public health applications

10.11591/ijres.v15.i2.pp320-338
Tole Sutikno , Aiman Zakwan Jidin , Lina Handayani
Biomedical signal processing is essential for modern diagnostics, monitoring, and preventive healthcare in public health and mobile health (mHealth) systems. Signals such as electroencephalography (EEG), electromyography (EMG), and heart rate variability (HRV) offer vital insights into brain, muscle, and cardiovascular health. However, achieving real-time, energy-efficient, and scalable processing remains challenging for conventional hardware such as central-processing units (CPUs), graphics-processing units (GPUs), and application-specific integrated circuits (ASICs). Field-programmable gate arrays (FPGAs) provide a promising alternative through their reconfigurability, parallelism, and adaptability to dynamic biomedical workloads. This review examines FPGA-based implementations for EEG, EMG, and HRV processing, focusing on key metrics including latency, throughput, and power efficiency. It also discusses design strategies such as low-power optimization, hardware–software co-design, and FPGA-based machine learning acceleration, with attention to data integrity and security in medical contexts. Integration with wearable, portable, and telemedicine platforms is explored, alongside comparative analyses with traditional computing architectures. The paper identifies challenges in power–performance trade-offs, design complexity, and clinical validation, and highlights emerging directions such as artificial intelligence (AI)-driven FPGA platforms, neuromorphic design, and sustainable low-cost solutions for large-scale health monitoring. Overall, FPGA-based biomedical signal processing emerges as a foundation for intelligent, efficient, and accessible next-generation public-health technologies.
Volume: 15
Issue: 2
Page: 320-338
Publish at: 2026-07-01

Graph neural network-based biomedical misinformation detection with semantic consistency analysis

10.11591/ijres.v15.i2.pp439-449
Siva Dhievaraj , Agusthiyar Ramu
Conventional misinformation detection approaches primarily rely on textual features and deep learning (DL) classifiers, which often fail to capture complex relationships among biomedical entities and the underlying scientific context of health claims. To address this limitation, this study proposes a graph neural network (GNN)-based biomedical misinformation detection framework that integrates knowledge graph propagation with semantic consistency verification. Initially, key biomedical entities such as diseases, treatments, and biological processes are extracted and mapped into a structured biomedical knowledge graph (BKG) to represent semantic relationships. A graph attention network (GAT) is then employed to model relational dependencies and propagate contextual information across connected entities, enabling the detection of hidden inconsistencies in biomedical claims. The proposed model is evaluated using benchmark biomedical misinformation datasets, including Reliable COVID-19 News Dataset, 2021 (ReCOVery), COVID-19 Healthcare Misinformation Dataset, 2020 (CoAID), and 2018–2020 biomedical health news corpus (HealthStory). Experimental results demonstrate that the proposed framework achieves an average detection accuracy of 96.3%, outperforming conventional long short-term memory (LSTM), convolutional neural networks (CNN), and transformer-based models in terms of precision, recall, and F1-score. The findings highlight that integrating structured biomedical knowledge with graph-based reasoning significantly enhances the reliability and interpretability of misinformation detection systems.
Volume: 15
Issue: 2
Page: 439-449
Publish at: 2026-07-01

Revolutionizing night-time object detection in autonomous vehicles with SCL-YOLOv11 and ROA optimization

10.11591/ijres.v15.i2.pp534-552
Kondapalli Sri Vijaya , Gokula Krishnan Vasudevan , Pinagadi Venkateswara Rao , Therasa Michael , Balasubramanian Lalithambigai , Boddula Prathusha Laxmi
Accurate object detection under low-light conditions is a critical requirement for reliable perception in autonomous driving systems. However, night-time environments often suffer from poor illumination, noise, and reduced feature visibility, which significantly degrade the performance of conventional object detection models. To address this challenge, this paper proposes spatial contrast learning (SCL)-you only look once version 11 (YOLOv11), an enhanced object detection framework designed for night-time scenarios. The proposed approach integrates SCL to improve feature discrimination in dark regions and employs the revolution optimization algorithm (ROA) for effective model parameter optimization. The framework is evaluated on three benchmark night-time datasets, ExDark, LLVIP, and BDD100K, to assess its detection performance. Experimental results demonstrate that the proposed model achieves a mAP@50 of 72.9%, improving the baseline YOLOv11 by 9.5% while also reducing inference latency by 18.3%. Comparative evaluations with existing detectors further confirm that the proposed method provides improved accuracy and efficiency for night-time object detection. These results indicate that the proposed framework can enhance perception reliability for autonomous driving applications operating in low-light environments.
Volume: 15
Issue: 2
Page: 534-552
Publish at: 2026-07-01

Development of internet of things-based exoskeleton for monitoring elbow rehabilitation therapy

10.11591/ijres.v15.i2.pp553-562
Geevanthran A/L Vegurgama , Mohd Razali Mohamad Sapiee , Khalil Azha Mohd Annuar
The elbow joint is a complex articulation comprising the humeroulnar and humeroradial joints, facilitating flexion-extension movements essential for daily activities. Leveraging advancements in connected systems and paradigms such as the internet of things (IoT), this study proposes an affordable, effective, and IoT-enabled one-degree-of-freedom (1DOF) elbow exoskeleton for home-based rehabilitation. The exoskeleton is designed to provide a natural range of elbow movements (flexion and extension) while enabling real-time monitoring of rehabilitation progress through mobile applications and web servers. The system collects qualitative data on elbow movements, which are critical in rehabilitation therapy, and enables patients to save their rehabilitation status for future reference. This data can be accessed by doctors remotely, ensuring continuity of care. For patients unable to lift their arm independently, a servomotor provides mechanical assistance, enabling them to achieve desired angles for rehabilitation. The IoT platform generates real-time graphs, offering detailed insights into the recovery process through data analysis. This project is a significant advancement in clinical and healthcare settings, as it reduces dependency on human support or physiotherapists. By integrating IoT technology, the proposed exoskeleton ensures effective, autonomous, and data-driven rehabilitation for elbow joint recovery.
Volume: 15
Issue: 2
Page: 553-562
Publish at: 2026-07-01

Predicting student academic outcomes from e-learning interaction data using hybrid machine learning models

10.11591/ijres.v15.i2.pp259-268
Sajithunisa Hussain , Jayachandran Jeyachidra
The rapid growth of digital learning platforms has generated large volumes of student interaction data, providing opportunities for intelligent prediction of academic outcomes. Beyond educational analytics, such prediction tasks are relevant for reconfigurable systems, embedded platforms, very large scale integration (VLSI) accelerators, and internet of things (IoT)-enabled edge devices in smart learning environments. This study proposes a hybrid machine learning framework for predicting student performance using the e-learning student reactions dataset, which captures engagement patterns, behavioral responses, and interaction dynamics. Eight classifiers— eXtreme gradient boosting (XGBoost), K-nearest neighbors (KNN), decision tree (DT), random forest (RF), support vector machine (SVM), multilayer perceptron (MLP), radial basis function (RBF), and deep neural network (DNN)—are evaluated using both an 80–20 train–test split and K-fold cross-validation to assess accuracy and generalization. Results show the RBF model achieves the highest accuracy of 1.00, demonstrating its ability to capture complex, nonlinear behavior. From a systems perspective, the framework can be mapped onto field programmable gate arrays (FPGAs) or embedded devices, leveraging parallel computation for low-latency inference, and integrated with IoT-enabled smart classrooms for real-time edge analytics. These findings confirm that hybrid machine learning models not only improve student performance prediction but also serve as practical workloads for reconfigurable, embedded, and VLSI-based intelligent systems in digital education.
Volume: 15
Issue: 2
Page: 259-268
Publish at: 2026-07-01

High-performance approximate MAC multiplier using majority logic compressors for CNNs

10.11591/ijres.v15.i2.pp269-280
Selvarasan Radhakrishnan , Sudhagar Govindhaswamy , Rasadurai Kumaravel
This research presents an optimized multiple accumulate (MAC) unit multiplier design for efficient convolutional neural network (CNN) operations. This design mainly focuses on making the multiplier systems smaller by using approximate majority compressor methods instead of the usual and traditional approximate methods. The traditional approximate multiplier compressor techniques are leads to increases in logic size, critical path delay, and power consumption; however, the proposed research mitigates these problems and solves them with a novelty-based approach in the Dadda multiplier technique. The novelty of this approach is to reduce the number of stages in the multiplier design using 4:2, 5:2, and 7:2 compressors. This compressor is designed with an approximate method using majority logic; compared to this traditional method, the proposed majority approximate compressor method processed less error differences in multiplication output. The proposed approaches resulted in significant reductions in area, power, and delay relative to traditional multipliers. This research compared seven unique comparisons of MAC-based multiplier architecture, and it will have been developed in Verilog hardware description language (HDL) and synthesized on the Xilinx Vertex-5 FPGA, providing reductions of 58.4% in lookup table (LUT) and 76.2% in occupied slices, and proving less power consumption. This design is a highly suitable approach for real-time CNN and digital signal processing (DSP) applications.
Volume: 15
Issue: 2
Page: 269-280
Publish at: 2026-07-01

Analysis of a compact wideband DGS-inspired octagonal patch antenna for sub-6 GHz 5G and IoT wireless systems

10.11591/ijres.v15.i2.pp479-489
Komalavalli Subramanian , Divya Subramani , Sornalatha Ravindran , Muthu Manickam Anbarasu , Mohan Chinnasamy , Anita Daniel
The proposed compact wideband antenna is developed to meet the increasing demand for efficient and miniaturized radiators in sub-6 GHz fifth generation (5G) and internet of things (IoT) wireless systems. The design features an octagonal radiating patch integrated with modified H-shaped slots to enhance the current path and impedance matching, while a graded defected ground structure (DGS) is introduced to improve bandwidth (BW) and suppress unwanted surface wave effects. Fabricated on an FR4 substrate and energised by a simple stripline feed, the antenna maintains a compact size of 18×15 mm² without compromising performance. It achieves a wide fractional BW of 42.81% spanning 3.1–5.8 GHz, with a resonance centered at 4.6 GHz and obtained reflection coefficient of −36 dB, indicating excellent impedance matching. Additionally, the suggested antenna provides the maximum gain of 3.14 dB and an overall radiation efficiency of 80.5%, demonstrating stable radiation characteristics while making it ideal for small, low-profile 5G and IoT communication devices.
Volume: 15
Issue: 2
Page: 479-489
Publish at: 2026-07-01

A real-time multi-modal deep learning framework for student attentiveness assessment in online learning environments

10.11591/ijres.v15.i2.pp450-460
Rajasekaran Mariswamy , P.V. Praveen Sundar
The rapid growth of online learning platforms has increased the need for intelligent systems capable of monitoring student attentiveness in real time to improve learning effectiveness and adaptive instruction. This paper proposes a multi-modal deep learning framework for attentiveness assessment by integrating visual, behavioral, and temporal information extracted from online classroom interactions. The proposed system consists of four major components, namely data acquisition, preprocessing and normalization, deep feature extraction with temporal learning, and attentiveness evaluation with analytics generation. Visual and spatial characteristics are learned using a convolutional neural network (CNN), while temporal behavioral patterns are captured through a long short-term memory (LSTM) network to model sequential engagement dynamics. The framework is designed to operate in both real-time and offline modes, enabling live monitoring during virtual classes as well as post-session analysis of recorded lectures. The computational pipeline is optimized through fixed-point processing, parallel convolution execution, and latency-aware temporal modeling, making it suitable for field programmable gate array (FPGA)-based and embedded implementations under constrained computational resources. Experimental evaluation conducted on an in-house dataset demonstrates that the proposed framework achieves 92.9% classification accuracy and a 91.9% F1-score, while maintaining strong generalization capability on cross-dataset benchmarks. Furthermore, latency analysis shows an average processing time of 31.6 ms per frame, enabling near real-time inference at approximately 30 frames per second.
Volume: 15
Issue: 2
Page: 450-460
Publish at: 2026-07-01

ESP-NOW based multi-node internet of things system for agricultural solar dryers with hybrid offline-online monitoring

10.11591/ijres.v15.i2.pp426-438
Rahmat Siswanto , Putri Dewintari , Sapar Sapar
Existing internet of things (IoT) systems for agricultural solar dryers rely on continuous internet connectivity, limiting deployment in remote rural areas with unreliable infrastructure. This study develops and validates a multi-node IoT architecture using ESP-NOW peer-to-peer communication that enables infrastructure-independent operation with optional ThingSpeak cloud synchronization. The system integrates ESP32 nodes, SHT41 sensors, relay-controlled actuators, and a hybrid solar-battery-grid power supply, deployed at a cocoa processing facility in South Sulawesi, Indonesia. Field evaluation confirmed: >95% packet delivery at 50 m, <10 ms latency, 94.3% cloud synchronization reliability, and 96% upload timing precision within ±2 s (σ=0.96 s). The dual-mode architecture sustained continuous local monitoring and actuator control during all network outages, with autonomous cloud reconnection requiring no manual intervention. Drying trials showed a -40 57% reduction in cocoa drying duration (3–4 days vs. 5–7 days baseline) through automated chamber control (40–60 °C; RH <60%). Energy analysis yielded an intensity of 0.12–0.17 kWh/kg dried output, with the IoT subsystem consuming less than 1% of total drying energy. The validated architecture provides a deployable, offline-capable, and energy-efficient solution for post-harvest monitoring in infrastructure-constrained environments, with applicability to diverse crop drying and storage scenarios.
Volume: 15
Issue: 2
Page: 426-438
Publish at: 2026-07-01
1 2 3
...
2091 2092
Show 55 of 2092

Discover Our Library

Embark on a journey through our expansive collection of articles and let curiosity lead your path to innovation.

Explore Now
Library 3D Ilustration