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

30,468 Article Results

Adaptive fractional-order PID-controlled DVR optimized by zebra algorithm for harmonic suppression

10.11591/ijeecs.v42.i3.pp786-796
Milind Paraye , Rajendra G. Sutar
Dynamic voltage restorers (DVRs) are widely employed to mitigate power quality disturbances in modern power grids. Existing DVR control strategies frequently struggle to adequately suppress harmonic distortions and voltage sags due to nonlinear grid behaviour, rapidly varying disturbances, and limited tuning flexibility. We suggest a grid-connected DVR with an adaptive fractional order proportional integral derivative (FOPID) controller whose parameters are improved using an improved zebra algorithm (IZA) in order to close this gap. The IZA algorithm is used to improve the FOPID controller parameters, ensuring rapid convergence and superior accuracy. The effectiveness of the proposed system is assessed under two different operating conditions. In case 1, the harmonic compensation is analyzed, in which the DVR reduces systemic harmonic disturbances. The results reveal that the proposed controller reduces the total harmonic distortion (THD) from 1.36% to 0.01% while maintaining a constant voltage amplitude of around 0.9986 V, demonstrating strong harmonic suppression capability. Voltage sag mitigation is assessed in Case 2. The load voltage is effectively restored from 0.722 V to 0.9986 V by the DVR, which also reduces THD from 32.97% to 1.6% by injecting the required compensatory current. Overall, the results confirm that the adaptive FOPID–IZA controlled DVR significantly improves power quality and voltage stability in grid-connected systems by effectively mitigating both harmonic distortion and voltage sags.
Volume: 42
Issue: 3
Page: 786-796
Publish at: 2026-06-10

A transfer learning approach for real-time detection and classification of Indonesian coins

10.11591/ijeecs.v42.i3.pp856-864
Nur Hadisukmana , R. B. Wahyu , Stewart Qiu
Automated currency recognition plays an important role in banking automation, retail systems, and assistive technologies. While banknote recognition has been extensively studied, coin recognition remains challenging due to small object size, metallic reflectance, visual similarity across denominations, and circulation-induced wear. This study proposes a real-time system for detecting and classifying Indonesian coins using a transfer learning–based deep learning approach. A curated dataset was developed to address the lack of publicly available training data for this domain. The model was initialized with pretrained weights and fine-tuned to adapt to the specific coin classification task. Experimental evaluation on an unseen test set demonstrates high detection accuracy while maintaining real time inference performance. Qualitative analysis under challenging conditions—including glare, low illumination, occlusion, and coin wear— reveals operational limitations and defines robustness boundaries. The findings confirm that frozen-backbone transfer learning provides an effective and computationally efficient strategy for adapting state-of-the-art object detectors to low-resource, domain-specific currency recognition tasks.
Volume: 42
Issue: 3
Page: 856-864
Publish at: 2026-06-10

A multicriteria collaborative decision support system for multidisciplinary medical coordination meetings

10.11591/ijeecs.v42.i3.pp753-766
Souad Madouri , Kaouter Labed , Kawther Makhlouf , Djamila Hamdadou , Anis Ayoub Amara , Aya Aouimer
Multidisciplinary team meetings (MDTMs) are central to cancer care. However, consensus can be hard to reach because specialists rely on diverse expertise and uncertain, multi-criteria clinical data. In this paper, we propose a group decision support system (GDSS) that integrates a multi-agent system (MAS) with multi-criteria decision making (MCDM) to structure interactions, aggregate expert preferences, enable real-time evaluation of options based on criteria, and transparently prioritize patients for discussion and intervention. Each specialist is represented by an agent that evaluates cases against shared criteria, while an embedded negotiation protocol enables exchanges and concessions to resolve conflicts and build consensus. We evaluated the GDSS using simulated breast cancer MDTM scenarios generated from a synthetic dataset of MDTM records. Experimental results demonstrate rapid convergence toward a consensual patient prioritization within a few negotiation iterations; in our experiments, agreement on the highest risk patient was reached after four rounds. Sensitivity analysis on subjective inputs, including criteria weights and preference profiles, produced minor changes in the resulting ranking, indicating robustness and stability to preference variations. The system maintains low computational complexity and short execution times, improving the transparency and consistency of MDTM recommendations. These outcomes confirm effectiveness and scalability for complex multidisciplinary clinical decisions.
Volume: 42
Issue: 3
Page: 753-766
Publish at: 2026-06-10

Metaheuristic optimization of wind turbine farm siting in power grids: a comparative study of PSO and GA

10.11591/ijeecs.v42.i3.pp666-677
Taha Rachdi , Yahia Saoudi , Larbi Chrifi-Alaoui , Ayachi Errachdi
This paper addresses the optimal integration of wind turbines into distribution networks with the aim of reducing active power losses and improving voltage stability. Two metaheuristic optimization methods genetic algorithm (GA) and particle swarm optimization (PSO) are applied to determine the optimal siting and sizing of wind turbines in the IEEE 14-bus system. The problem is formulated as a multi-objective function combining loss minimization and voltage profile enhancement under standard network constraints. Simulation results using MATLAB/PSAT show that both algorithms improve system performance compared to the base case, with PSO providing superior loss reduction and voltage stability. Wind variability is represented through a Weibull distribution to reflect realistic operating conditions. The study demonstrates the effectiveness of metaheuristic optimization for renewable integration and highlights PSO’s stronger robustness. The work contributes a comparative evaluation of GA and PSO, supported by stability analysis and realistic wind modelling.
Volume: 42
Issue: 3
Page: 666-677
Publish at: 2026-06-10

Trust-based secure routing in IoT networks using machine learning for enhanced anomaly detection and risk mitigation

10.11591/ijict.v15i2.pp839-849
Sangeetha Krishnaswamy , Arulanandam Karalagan
The rapid growth of the internet of things (IoT) has led to the development of new challenges in ensuring secure and reliable data transmission. This paper proposes a trust-based secure routing protocol (TBSRP) designed to mitigate security threats such as routing attacks in IoT networks. The core innovation lies in the dual-layer trust evaluation mechanism, which combines reputation-based trust and behavioral analysis to dynamically adjust routing decisions based on real-time performance and historical behavior of network nodes. To enhance security, the protocol incorporates an adaptive threshold mechanism that adjusts trust criteria based on observed network conditions and an anomaly detection system utilizing machine learning (ML) algorithms for real-time monitoring of node behavior. Experimental evaluation demonstrates that TBSRP outperforms existing protocols (such as Ad hoc on-demand distance vector (AODV), trust-based AODV (TB-AODV), energy-efficient secure routing (ESR), and Secure AODV (SEC-AODV)) in key performance metrics, including packet delivery ratio (PDR), end-to-end delay, throughput, and routing overhead. The proposed protocol exhibits strong resilience to the increasing number of malicious nodes and varying network conditions, making it highly effective for securing IoT networks. This work contributes to the development of adaptive, scalable, and secure routing protocols for IoT environments, with the potential for further optimization through advanced ML techniques and real-world implementation.
Volume: 15
Issue: 2
Page: 839-849
Publish at: 2026-06-01

Multiclass classification using variational quantum circuit on benchmark dataset

10.11591/ijict.v15i2.pp578-587
Muhammad Hamid , Bashir Alam , Om Pal
Classification is a major task in data science. Data classification is required in many industries such as healthcare, transport, and finance. Noisy intermediate-scale quantum (NISQ) era. Quantum computers are capable of solving complex data challenges and can be used for the classification of the data with minimum features. In this regard, quantum neural networks are being used extensively for data classification. In this paper, we employ variational quantum circuits for the task of multiclass classification. A hybrid approach is used for building the neural network. In which quantum circuits are used for the feedforward architecture, while in back-propagation, parameters are updated using a classical optimizer on classical computers. We have successfully demonstrated multiclass classification using the proposed approach on benchmark data sets. Our results show that variational quantum circuit (VQC) are a promising candidate for classification problems with fewer features. We have performed experiments on International Business Machines Corporation (IBM) quantum hardware and simulators.
Volume: 15
Issue: 2
Page: 578-587
Publish at: 2026-06-01

Diabetic retinopathy detection using SWIN transformer

10.11591/ijict.v15i2.pp750-758
Sheetal J. Nagar , Nikhil Gondaliya
Diabetic retinopathy (DR) is a diabetes related eye disorder that damages the retina. DR is among the most specific complications of diabetes. A vital challenge for automated detection systems in medical image diagnosis is to minimize the false negative rate for patients’ timely treatment. This paper presents a novel strategy employing the shifted window (SWIN) Transformer for efficiently modeling local and global visual information to address this challenge. We have proposed our work to maximize the true positive ratio and minimize the false negative ratio for the automated process of diagnosing the level of DR, so that patients with positive signs of DR can be predicted most accurately and can save vision. The results suggest that SWIN Transformer architecture, along with the contrast-limited adaptive histogram equalization (CLAHE) technique, provides a robust option for developing a reliable DR detection system. The results indicate that the proposed approach achieves 96% weighted recall across all the levels of DR detection and 97.45% validation accuracy for the eyePACS DR detection dataset, as well as 99% weighted recall across all the levels of DR detection, along with 99.26% validation accuracy for APTOS 2019 Blindness Detection dataset. Thus, this study aimed to develop a DR detection system focused on minimizing false negatives using the SWIN transformer.
Volume: 15
Issue: 2
Page: 750-758
Publish at: 2026-06-01

Enhancing support vector machine performance using particle swarm optimization for sentiment analysis

10.11591/ijict.v15i2.pp523-534
Christofer Satria , Anthony Anggrawan , Peter Wijaya Sugijanto , Husain Husain , I Nyoman Yoga Sumadewa , Victoria Cynthia Rebecca
Recently, social media has established itself as a leading platform in various sectors. Meanwhile, text extraction and sentiment analysis classification have attracted significant attention in research. Regrettably, traditional sentiment analysis often falls short of accurately capturing sentiment nuances. At the same time, machine learning has enabled more effective sentiment analysis, data mining, and classification, as well as the development of models that incorporate artificial intelligence. Therefore, the purpose of this study is to optimize sentiment analysis of public opinion in social media regarding Grand Prix motorcycle racing (MotoGP) and World Superbike (WSBK) events using machine learning and an optimized machine learning method. This study applies the support vector machine (SVM) machine learning method and enhances its performance through optimization by integrating it with the particle swarm optimization (PSO) algorithm. This study found that the SVM method achieved 80.15% accuracy, 75.63% recall, and 76.89% F1-score. In contrast, the SVM method combined with PSO achieves accuracies of 81.82%, 79.9%, and 79.62% for recall, precision, and F1-score, respectively, in classifying the sentiment of sporting events. The implications suggest that applying Hybrid SVM with PSO significantly enhances classification accuracy in sentiment analysis.
Volume: 15
Issue: 2
Page: 523-534
Publish at: 2026-06-01

Performance improvement of DC microgrids via adaptive neuro-fuzzy inference system -optimized AI-tuned fractional order proportional-integral-derivative controllers

10.11591/ijict.v15i2.pp797-804
Debani Prasad Mishra , Sarita Samal , Manas Ranjan Sahu , Sonna Murari , Piyuskant Das , Surender Reddy Salkuti
This paper presents a novel approach to enhance the dynamic performance of direct current (DC) microgrids using an artificial intelligence (AI)-tuned fractional order proportional-integral-derivative (FO-PID) controller, further optimized through an adaptive neuro-fuzzy inference system (ANFIS). Conventional PID controllers tend to fail when it comes to dealing with microgrid environment-related non-linearities and uncertainties, particularly under changing load and generation situations. To remedy this, the suggested approach combines AI-tuned tuning algorithms for selecting initial parameters, and then ANFIS optimization to fine-tune the FOPID gains adaptively for better control precision. The performance of the hybrid control approach is tested through MATLAB simulations on a generic DC microgrid model that includes distributed energy resources, power electronic converters, and dynamic loads. Comparative evaluation against standard PID and independent FOPID controllers verifies remarkable advantages in terms of voltage regulation, stability, and transient response in various operating conditions. Amongst the achieved outcomes, it highlights the strength of the proposed ANFIS-optimized AI-tuned FOPID controller as a smart and robust strategy for real-time control of DC microgrids.
Volume: 15
Issue: 2
Page: 797-804
Publish at: 2026-06-01

A review of sensemaking design elements: towards an affordances typology

10.11591/ijict.v15i2.pp488-496
Fadzlin Ahmadon , Murni Mahmud , Muna Azuddin
This study explores the intersection of interaction design and sensemaking within digital systems, aiming to identify and categorize key affordances that enhance user sensemaking. Starting with a focused literature review, key design elements such as tagging and annotation are identified, important for effective sensemaking in interaction design. Drawing on Maier's construct of affordances, the behaviours of these design elements are analyzed to derive specific affordances integral to enhancing user experience. The primary objective is to develop a generalized affordance typology that supports sensemaking across various digital systems. This typology organizes the derived affordances into broad themes such as effortless discovery, expressive freedom, collaborative engagement, cognitive support, insight enhancement, and user empowerment. This typology serves as a tool for interaction designers, facilitating the application of these themes in various design scenarios to create more intuitive and effective digital environment for sensemaking.
Volume: 15
Issue: 2
Page: 488-496
Publish at: 2026-06-01

Utilizing the machine learning-driven techniques used to ECG dataset for predicting coronary heart disease

10.11591/ijict.v15i2.pp719-728
Mohd Osama , Rajesh Kumar , Chandrakant Kumar Singh
The worldwide cause of mortality is cardiovascular heart disease. The automatic prediction of heart disease can be made to possible for accurate detection in initial stage. In recent year, the artificial intelligence approaches giving promising outcomes in predicting various types of cardiovascular conditions. The main focous of this work is to implementation of various machine learning techniques used to predict cardiovascular heart disease (CHD) using electrocardiogram (ECG) datasets. ECG provide the electrical Signal from the heart that identify the presence of disease or not. The preprocessing method are used for improving the quality of ECG signals and extract the features from ECG of patients. There are several well-established machine learning techniques, including support vector machine (SVM) and K-nearest neighbour (KNN)., logistic regression and decision tree classifier used for prediction of the disease. So, our finding of this paper will provide the new understanding regarding CHD prediction using different machine learning techniques. The Decision Tree-based machine learning model demonstrated excellent performance, achieving 98% accuracy, 96% precision, 100% recall, and an F1-score of 97%, which is better than rest of other comparative machine learning models. Finaly expermental results shows that decision tree approach providing better outcome amongs all the algorithms with respect to all above mensioned parameter.
Volume: 15
Issue: 2
Page: 719-728
Publish at: 2026-06-01

Manufacturing mycelium moulds under controlled conditions using IoT

10.11591/ijict.v15i2.pp880-890
Subbulakshmi V. , Jeevaa Katiravan , Parvathy M. , Sridevi S.
In the process of making plastics, potentially dangerous substances like colourants or stabilisers are added. One example is phthalates, which are used to make PVC. The ecology is significantly impacted by the way plastic products are disposed of as well. The majority of plastics can take a long time to biodegrade lengthy time to break down if disposed of in a landfill. The issue of plastic trash is getting worse. Plastic is incredibly valuable due to its cheap availability and low cost of production; however, its recyclability has been oversold. Mycelium mould is a fantastic substitute for plastic. Mycelium is more efficient in terms of biodegradability and sustainability compared to plastic. The properties of Mycelium include heat insulation, fire resistance, water resistant, acoustic insulation, low weight, vegan meat, beauty products, and mainly bio-degradable. All these features make mycelium our only last chance to win the war against the plastic with greater potential than the other alternatives for plastics available in the market currently. Here, we have shown how mycelium can be grown in the most efficient way ever without any contamination and faster growth cycle. The primary goal is to lower the cost of mycelium mould, lessen mycelium spoiling, and accelerate its growth cycle by offering an ideal growing.
Volume: 15
Issue: 2
Page: 880-890
Publish at: 2026-06-01

Machine learning centered energy optimization in mobile edge computing: a review

10.11591/ijict.v15i2.pp465-476
Chandapiwa Mokgethi , Tshiamo Sigwele , Kabo Clifford Bhende , Aone Maenge , Selvaraj Rajalakshmi
Current literature reviews on machine learning-based approaches for mobile edge computing (MEC) energy optimization often lack in-depth gap analysis and fail to identify trends or offer actionable insights. Most focus narrowly on comparing MEC frameworks without critically evaluating or benchmarking prior research. This review contributes by addressings these gaps via analysis of existing reviews and related studies, with a focus on ML models, research objectives, evaluation metrics, datasets, tools, and gap identification. The review method follows a systematic literature review (SLR) using the PRISMA framework for transparency and reproducibility. Key findings reveal persistent challenges in energy consumption, computational overhead, cost, and poor performance in accuracy, QoS, latency, scalability, and carbon footprint. Deep reinforcement learning (DRL) emerges as the most commonly used model (55%), while TensorFlow (35%) is the most adopted tool, valued for its flexibility and robust community support. The AudioSet dataset is frequently used (28%) due to its compatibility. However, methodology limitations include dependency on study quality and exclusion of grey literature, context sensitivity. The review concludes by recommending advanced solutions such as serverless computing, liquid cooling, containerization, software-defined power, quantum computing, and blockchain to drive future MEC energy optimization.
Volume: 15
Issue: 2
Page: 465-476
Publish at: 2026-06-01

A new modified characteristic equation for optimal coordination of directional overcurrent relays

10.11591/ijict.v15i2.pp789-796
Neelakanteshwar Rao Battu , Surender Reddy Salkuti
The integration of distributed generation (DG) into power systems is increasing to meet the requirements of the utility system. Renewable energy sources are given priority due to their clean energy and high consistency advantages. Integration of DG into the system makes the bi-directional flow of current. Directional type overcurrent relays are usually used for protection of lines associated with bidirectional power flows. The installation of DGs, (especially, inverter-based) invites challenges to the existing protection schemes. A new modified characteristic equation-based approach is proposed in this paper to obtain the faster operational time of relays. The relay coordination scheme proposed in this paper is applied to an 8-bus test system integrated with the solar-based photovoltaic integrated distributed generator (PVIDG). The comparative analysis between the conventional and proposed approaches is done.
Volume: 15
Issue: 2
Page: 789-796
Publish at: 2026-06-01

Hybrid deep neural network model for aspect and opinion extraction with multi-head attention-driven sentiment analysis

10.11591/ijict.v15i2.pp769-777
Abhinandan Shirahatti , Ramesh Medar , Vijay Rajpurohit , Sanjeev Kaulgud , Mrutyunjaya Mathad Shivamurthaiah
Finding and extracting significant features from review sentences is known as aspect triplet extraction, and it provides succinct information on the elements that users have addressed. This method makes sentiment analysis and opinion mining easier, which helps to provide an adequate understanding of user opinions in reviews. This research presents a novel approach to achieve aspect-sentiment triplet-extraction (ASTE) using a deep neural network and transformer-based multi-head attention model. The proposed hybrid model adopts a pipeline methodology, concurrently extracting opinions and aspects while performing sentiment classification. The study addresses the intricate challenge of identifying triplets that capture nuanced relationships between terms and sentences, employing a deep neural network for joint extraction of aspects and opinions using a sequential tagging method. Sentiment classification is seamlessly integrated into the pipeline, treating sentiment recognition as a classification task, and aspect and opinion extraction as text-extraction challenges. Evaluations was out experimentally on the SemEval 2016 restaurant dataset demonstrate the effectiveness of the model, despite issues with unequal distribution of data.
Volume: 15
Issue: 2
Page: 769-777
Publish at: 2026-06-01
1 2 3
...
2031 2032
Show 5 of 2032

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