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29,922 Article Results

Voltage stability investigation: enabling large-scale renewable energy integration in Tamil Nadu’s grid

10.11591/ijeecs.v40.i1.pp47-56
Chelladurai Chandarahasan , Edwin Sheeba Percis
The integration of renewable energy sources (RES) such as wind and solar, characterized by their inherent intermittency and variability, poses notable challenges to the stability and reliability of power systems. This research addresses these challenges by conducting a detailed load flow, voltage sensitivity and stability analysis at a significant extra high voltage (EHV) pooling station under high-RES penetration. Employing advanced simulation methodologies, the study evaluates the effects of RES integration on voltage profiles and the system’s capacity to sustain equilibrium under steady-state operations. The findings highlight that substantial RES integration induces considerable voltage fluctuations and reactive power disparities. To counter these effects, static var compensators (SVCs) were deployed, demonstrating their efficacy in enhancing voltage stability and providing essential reactive power support. The study confirms the pivotal role of SVCs in alleviating voltage sensitivity to RES fluctuations, thereby promoting a more stable and reliable power supply. This research underscores the importance of strategic reactive power management devices in enabling a seamless transition towards a renewable-dominant energy landscape.
Volume: 40
Issue: 1
Page: 47-56
Publish at: 2025-10-01

Automating electronic document management design: a model-driven approach using business process

10.11591/ijeecs.v40.i1.pp216-224
Soufiane Hakkou , Redouane Esbai , Yasser Lamlili El Mazoui Nadori
Model-driven architecture (MDA) is a useful approach for designing enterprise information systems through structured models. This study applies MDA to electronic document management (EDM) systems, which are essential for improving document workflows and ensuring regulatory compliance. Organizations often face difficulties when converting business process models into software-ready designs. Current transformation methods are complex, involving multiple intermediate steps that increase effort and risk of errors. The objective of this work is to create a direct transformation from business process model and notation (BPMN) diagrams to unified modeling language (UML) class diagrams. This aims to improve automation, reduce modeling effort, and maintain consistency. The proposed methodology uses MDA principles and query/view/transformation (QVT) to automatically map BPMN elements to UML classes based on predefined rules. The approach is implemented within the eclipse modeling framework (EMF) and validated through a case study on EDM systems. The transformation successfully generates UML class diagrams that accurately represent BPMN-based business processes. The results demonstrate: increased automation, reducing manual effort in software modeling, improved model consistency, eliminating errors associated with multi-step transformations and enhanced business-IT alignment, providing a structured approach for business professionals and developers.
Volume: 40
Issue: 1
Page: 216-224
Publish at: 2025-10-01

Accurate plate number recognition based on Sobel edge detection and weighted morphological structuring element

10.11591/ijeecs.v40.i1.pp307-315
Rostam Affendi Hamzah , Ahmad Fauzan Kadmin , Khairul Azha A Aziz , Mohd Saad Hamid , Lim Then Sean
Number plate recognition (NPR) became a very important in our daily life because of the unlimited increase of cars and transportation systems which make it impossible to be fully managed and monitored by humans, examples are like traffic monitoring, tracking stolen cars, managing parking toll, red-light traffic violation enforcement, border, and customs checkpoints. Yet it is a very challenging problem, due to the diversity of plate formats, different scales, rotations, and non-uniform illumination conditions during image acquisition. The proposed system is simple but efficient for recognizing plate numbers. The Sobel edge detection and morphological process are used in the proposed framework with weighted structuring element. This approach simplifies the task by using the bounding box method for character segmentation to segment all the characters on the plate number. Template matching is then applied to recognize the numbers and characters. This approach produces accurate results as shown by the experimental process.
Volume: 40
Issue: 1
Page: 307-315
Publish at: 2025-10-01

Predict glucose values with DE algorithm optimized T-LSTM

10.11591/ijeecs.v40.i1.pp530-544
QingXiang Bian , Azizan As’array , XiangGuo Cong , Khairil Anas bin Md Rezali , Raja Mohd Kamil bin Raja Ahmad , Mohd Zarhamdy Md. Zain
The prevalence of diabetes is rising. According to the International Diabetes Federation (IDF) predictions, the number of diabetic patients worldwide will reach 608 million in 2030, accounting for approximately 11.3% of the total number of people in the world. To monitor and predict the future 1 hour glucose have a great significance meaning for patients. This research utilizes a differential evolution (DE) algorithm, an optimized hybrid model transformer and long short-term memory (T-LSTM) technologies to analyze historical data from continuous blood glucose monitoring (CGM) systems and equipment calibration values. The aim is to predict future blood sugar levels in patients, thereby helping to prevent episodes of hypoglycemia and hyperglycemia. The study tested the model using the CGM data from 8 patients at the Suzhou Municipal Hospital in Jiangsu Province, China. Results show that this DE-optimized T-LSTM model outperforms traditional models. The model's accuracy is evaluated using mean squared error (MSE), with MSE values recorded at 15, 30, and 45 minutes being 0.96, 1.54, and 2.31, respectively.
Volume: 40
Issue: 1
Page: 530-544
Publish at: 2025-10-01

Development and testing of a dedicated cooling system for photovoltaic panels

10.11591/ijece.v15i5.pp4387-4394
Omar Elkhoundafi , Rachid Elgouri
Solar energy is a viable alternative to fossil fuels, but its efficiency is limited by photovoltaic panel overheating, which causes a decrease in efficiency. This paper suggests a passive cooling method that incorporates aluminum heat sinks beneath the solar cells. This simple, low-cost device maximizes heat dissipation using natural convection. It requires no external energy. The goal is to provide a solution to the challenge of selecting an effective, sustainable, and flexible cooling system while considering technological, economic, and environmental constraints. Experimental results demonstrate that modules fitted with heatsinks experience an average 8.13 °C drop in temperature, as well as a 0.51 V rise in open-circuit voltage when compared to the reference panel. This increase demonstrates how well-designed passive solutions can dramatically improve the energy performance of solar panels. The study emphasizes the relevance of thermal design in photovoltaic system optimization and provides specific opportunities for the development of more efficient solar technologies, particularly in high-temperature situations.
Volume: 15
Issue: 5
Page: 4387-4394
Publish at: 2025-10-01

An improved conversation emotion detection using hybrid f-nn classifier

10.11591/ijeecs.v40.i1.pp490-498
Abhishek A. Vichare , Satishkumar L. Varma
Emotion recognition from text is a crucial task in natural language processing (NLP) with applications in sentiment analysis, human-computer interaction, and psychological research. In this study, we present a novel approach for text-based emotion recognition using a modified firefly algorithm (MFA). The firefly algorithm is a swarm intelligence method inspired by the bioluminescent communication of fireflies, and it is known for its simplicity and efficiency in optimization tasks. In this paper MFAbased model is evaluated on the international survey on emotion antecedents and reactions (ISEAR) dataset, which includes text entries categorized by various emotions. Experimental results indicate that our approach achieved promising outcomes. Specifically, the proposed method, which combines the firefly algorithm with a multilayer perceptron (MLP), attained an accuracy of 92.07%, surpassing most other approaches reported in the literature.
Volume: 40
Issue: 1
Page: 490-498
Publish at: 2025-10-01

Phishing URL prediction – two-phase model using logistic regression and finite state automata

10.11591/ijeecs.v40.i1.pp356-365
Nisha T N , Dhanya Pramod
The human factor in security is more important when they become the carriers of attacks on enterprises. Phishing attacks can be classified as insider attacks when the employees unintentionally participate in the attack propagation. Since complete user training is a myth, enterprises must implement detection tools for phishing attacks on their network perimeters. This research discusses a two-phase model for phishing URL detection, in which the first phase identifies the properties of URLs that detect phishing and their relative weight using logistic regression. The second phase checks the probability of a new URL being categorized as phishing using the knowledge achieved during the first phase using the dynamically created Finite state machines. The model defines a malicious score (MS), which can be used to check any URL in real-time to identify whether it is phishing or not. The model described in this work has been experimented with different benchmarking datasets to verify the performance. The model provided a decent result in classifying a URL as phishing or naive. The malicious score (MS) defined by this model can be used to evaluate any URL and can be used as a filtering mechanism for end-point phishing URL detection. The key contribution is towards developing a two-phase model which evaluates the URL with the help of self-crafted features without reliance on a feature set. This accommodates the model's hyper-competitive phishing URL detection area in cyber security.
Volume: 40
Issue: 1
Page: 356-365
Publish at: 2025-10-01

Machine identification codes of color laser printers: revisiting privacy and security

10.11591/ijeecs.v40.i1.pp137-145
Shreya Arora , Rajendra Kumar Sarin , Pooja Puri
Forging legal documents has been easier and faster with the advancement of technology. Printer identification has become a critical field for tracing criminals and validating the authenticity of documents. The current study uses a non-destructive method to detect and identify covert embedded hidden information (machine identification codes (MIC)). Samples were collected from popular brands, including Xerox and HP color laser printers, to attain this aim. Their printouts were then scanned at 600 dpi using a Konica Minolta scanner. Scanned images were subjected to graphic editors for linear and non-linear adjustments. Following this, yellow-toner dots were observed as a base pattern. Grayscale imaging with a computational approach to analyze the yellow dot patterns was utilized for intensity-focused analysis, with edge detection algorithms applied using Python to enhance and highlight the converted patterns in printed documents. The printouts from Xerox printers exhibited repeating patterns. However, no such detailed information was observed in prints from HP printers, even when analyzed using binary code for deductions. A notable variation was detected in the yellow tracking dots among both brands, which can be instrumental in identifying the origin of printouts and scanned images for forensic investigations. This methodology provides conclusive and dependable accuracy.
Volume: 40
Issue: 1
Page: 137-145
Publish at: 2025-10-01

Discount factor-based data-driven reinforcement learning cascade control structure for unmanned aerial vehicle systems

10.11591/ijece.v15i5.pp4542-4554
Ngoc Trung Dang , Quynh Nga Duong
This article investigates the discount factor-based data-driven reinforcement learning control (DDRLC) algorithm for completely uncertain unmanned aerial vehicle (UAV) quadrotors. The proposed cascade control structure of UAV is categorized with two control loops of attitude and position sub-systems, which are established the proposed discount factor-based DDRLC algorithm. Through the analysis of the Bellman function's time derivative from two perspectives, a revised Hamilton-Jacobi-Bellman (HJB) equation including a discount factor is developed. Then, in the view of off-policy consideration, an equation is formulated to simultaneously solve the approximate Bellman function and approximate optimal control law in the proposed DDRLC algorithm with guaranteed convergence. According to the modified state variables vector, the development of the discount factor-based DDRLC algorithm in each control loop is indirectly implemented by transforming the time-varying tracking error model into the time invariant system. Finally, a simulation study on the proposed discount factor-based DDRLC algorithm is provided to validate its effectiveness. To validate the tracking performance of the quadrotor, four performance indices are considered, including IAE_p=3.0527, IAE_Ω=0.1175, ITAE_p=1.8408, and ITAE_Ω=0.0144, where the subscript p denotes position tracking error and Ω denotes attitude tracking error.
Volume: 15
Issue: 5
Page: 4542-4554
Publish at: 2025-10-01

Advanced risk assessment using machine learning and sentiment analysis on log data

10.11591/ijai.v14.i5.pp3897-3905
Nidal Turab , Abdelrahman Abushattal , Jamal Al-Nabulsi , Hamza Abu Owida
Standard risk assessment approaches are sometimes time-consuming and subjective. In order to overcome these challenges an innovative method will be presented in this article by mixing sentiment analysis and machine learning (ML). The suggested technique improves the effectiveness, precision, and scope of risk insights when it comes to the detection of feelings in logs via the use of automated data collection. The research examines several different ML classifiers and makes use of a deep learning model that has been pre-trained to evaluate risks in logs that are multi-linguistic. This proves the adaptability and scalability of our technique when used in a multilanguage setting. This combination of sentiment analysis and ML are a significant advancement in comparison to traditional approaches since it enables real-time processing and delivers important insights into the management of organizational risks.
Volume: 14
Issue: 5
Page: 3897-3905
Publish at: 2025-10-01

Hybridized deep learning model with novel recommender for predicting criticality state of patient using MIMIC-IV dataset

10.11591/ijai.v14.i5.pp3926-3933
Sarika Khope , Deepali Kotambkar , Rama Vasantha Adiraju , Smita Suhas Battalwar
The contribution of machine learning towards prediction of critical state of patient is the prime focus of the current study. The review of current approaches of machine learning has been witnessed with various shortcomings. Hence, the proposed study adopts medical information mart for intensive care (MIMIC-IV) dataset in order to develop a novel analytical model that can predict the criticality state of patient in their next visit. The model has been designed by hybridizing convolution neural network (CNN) and long short-term memory (LSTM) which takes the discrete input of hospital and individual patient information in each visit. The concatenated feature is then subjected to a newly introduced recommender module which offers implicit feedback by assigning a ranking score. The final predictive outcome of study offers criticality rank. The study model is benchmarked with existing machine learning approaches to find 54% of increased accuracy and 70% of reduced processing time.
Volume: 14
Issue: 5
Page: 3926-3933
Publish at: 2025-10-01

Distributed formation control with obstacle and collision avoidance for humanoid robot

10.11591/ijeecs.v40.i1.pp108-117
Faisal Wahab , Bambang Riyanto Trilaksnono
Formation control has become a popular research topic in recent years. A common challenge in formation control is ensuring that robots can avoid obstacles and maintain a safe distance from one another to prevent collisions while forming a formation. In this research, a distributed formation control approach for a multi-robot system (MRS) with obstacle and collision avoidance is presented. The distributed formation control architecture is based on a consensus algorithm and consists of four layers: consensus tracking, consensus-based formation control, behavior, and physical robot layers. The system was implemented and evaluated through both simulations and experiments. Humanoid robots were used as the platform for these implementations. The result of the simulations and experiments show that the distributed formation control system successfully guided the robots into desired formation while also avoiding obstacles and preventing collisions with other robots.
Volume: 40
Issue: 1
Page: 108-117
Publish at: 2025-10-01

BonoNet: a deep convolutional neural network for recognizing bangla compound characters

10.11591/ijai.v14.i5.pp4171-4180
Kazi Rifat Ahmed , Nusrat Jahan , Adiba Masud , Nusrat Tasnim , Sazia Sharmin , Nusrat Jahan Mim , Imran Mahmud
The bangla alphabet includes vowels, consonants, and compound symbols. The compound nature of bangla is a product of combining two or more root bangla characters into one graph. They are difficult to differentiate because they have a sophisticated geometric shape and an immense variety of scripts used by different places and individuals. This is one of the greatest challenges in creating effective optical character recognition (OCR) systems for bangla. In this paper, a deep convolutional neural network (DCNN)-based system is presented to identify bangla compound characters with high precision. The model was trained using the AIBangla dataset. It has about 171 classes of bangla compound characters. A DCNN system, BonoNet, was designed to classify compound characters. BonoNet outperformed all other state-of-the-art architecture on the test set and improved over current state-of-the-art architecture methods. BonoNet will greatly improve the automation and analysis of the bangla language by accurately identifying these compound complex characters.
Volume: 14
Issue: 5
Page: 4171-4180
Publish at: 2025-10-01

Dynamic head pose estimation in varied conditions using Dlib and MediaPipe

10.11591/ijece.v15i5.pp4581-4592
Rusnani Yahya , Rozita Jailani , Nur Khalidah Zakaria , Fazah Akhtar Hanapiah
This paper presents the formulation and validation of a dynamic head pose estimation (HPE) algorithm, addressing challenges related to diverse conditions, complex poses, and partial obstructions. The study aims to create a robust algorithm that maintains high accuracy in real-time applications across varying conditions. The algorithm was implemented and assessed using Dlib and MediaPipe models. The study involved 30 participants in face and head without obstacles, face with obstacles and head with obstacles conditions. The results demonstrated impressive performance in both controlled and spontaneous head movement categories. The algorithm achieved an average accuracy of 93% for head pose estimation and 88% in detecting visual attention under spontaneous head movement categories. A correlation coefficient of 0.866 indicates a strong positive linear association between performance and attention accuracy, indicating that performance improvements are intricately linked to proportional increases in attention accuracy. However, this does not necessarily imply causation. The findings provide valuable insights into the effectiveness of the proposed algorithms in assessing visual attention and demonstrate their potential applications in healthcare monitoring, educational intervention, and driver monitoring systems. The significance of these results lies in the ability to advance human-computer interaction, enhance healthcare diagnostics, and offer innovative solutions across various domains.
Volume: 15
Issue: 5
Page: 4581-4592
Publish at: 2025-10-01

A hybrid intelligent model for prediction of coronary artery diseases using TabNet and multiclass SVM

10.11591/ijeecs.v40.i1.pp156-163
Niveditha Honnemadu Rudreshgowda , Balakrishna Kempegowda , Anitha Sammilan
Cardiovascular disease is one of the significant fatality-causing diseases in this era by affecting the heart and blood vessels. Cardio diseases are classified into coronary heart disease (CHD), heart failure, valve disease, and arrhythmias. Medical diagnosis of heart disease and treating the patient is a challenging process, where early detection can lead to decreased fatality. In this research, hybrid model-based prediction of CHD detection is developed by TabNet and multiclass support vector machine (SVM). We created our datasets for experimentation by visiting the hospitals in the Mysore and Mandya regions of Karnataka, India. Datasets consist of 16 features; the features are pre-processed to normalize, encode, and handle missing values to extract the aggregate features using TabNet, and the multiclass SVM model is trained to classify the disease based on the classes. The proposed hybrid model prediction performance was evaluated using various metrics such as accuracy, recall, precision, and F1-score.
Volume: 40
Issue: 1
Page: 156-163
Publish at: 2025-10-01
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