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

29,922 Article Results

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

Optimal sizing and performance evaluation of hybrid photovoltaic-wind-battery system for reliable electricity supply

10.11591/ijece.v15i5.pp4341-4354
Youssef El Baqqal , Mohammed Ferfra , Reda Rabeh
Given the advantages of hybrid renewable energy systems over single-source systems, this study proposes the optimal sizing and performance evaluation of a hybrid photovoltaic-wind battery system to meet the electricity demand of an isolated community in Dakhla, Morocco. The objective is to achieve an economical approach to electricity generation. Particle swarm optimization (PSO) and grey wolf optimizer (GWO) techniques were used to determine the optimal configuration of system components, including photovoltaic (PV) panels, wind turbines, and battery storage. The annual system cost (ACS) is minimized as the optimization objective, and the levelized cost of electricity (LCOE) is used for economic comparison. MATLAB serves as the platform for implementation and evaluation. Results demonstrate the convergence and effectiveness of PSO and GWO in delivering high-quality solutions. PSO, however, achieves superior system reliability with a lower loss of power supply probability (LPSP) during peak demand. The optimal configuration achieves a minimal LCOE of 0.1065 USD/kWh, representing a 33.44% reduction compared to the applicable rate. These findings highlight the potential of advanced optimization techniques to improve the economic and operational performance of hybrid renewable energy systems, making them a viable solution for rural electrification in regions with limited grid access.
Volume: 15
Issue: 5
Page: 4341-4354
Publish at: 2025-10-01

Analysis of solid oxide fuel cell systems for off-grid energy production

10.11591/ijeecs.v40.i1.pp18-33
Boudjemaa Mehimmedetsi , Abdellah Draidi , Billel Smaani
This work presents a simulation study of a 50-kW solid oxide fuel cell (SOFC) power supply system that provides electricity to residential users. Indeed, many decentralized applications rely on renewable energy sources not connected to the primary power grid. Moreover, fuel cell modelling and simulation are critical for promoting renewable energy as they eliminate the need for physical prototypes, saving time and money. We have also developed a reliable model for simulating self-contained SOFC fuel cells. The elaborated model includes the kinetics of electrochemical processes and accounts for voltage losses in SOFCs. Our fuel cells produce the necessary electrical current to charge the device. Also, our system has fuel cells, a DC/DC converter, and an inverter with LCL filters. These components connect the fuel cell system to other power electronics and the electrical load. Furthermore, a mathematical model of a dual inverter system describes its control method, including the proportional and integral parameters in the voltage and current loops has been derived. The proposed model and system could be helpful for a standalone load.
Volume: 40
Issue: 1
Page: 18-33
Publish at: 2025-10-01

Building change detection via classification in high-resolution aerial imagery

10.11591/ijai.v14.i5.pp4319-4331
Hayder Mosa Merza , Ihab Sbeity , Mohamed Dbouk , Zein Al-Abidin Ibrahim
This research investigates the detection of changes in building structures within high-resolution aerial images of Baghdad, Iraq, over two years, 2007 and 2024. Employing advanced remote sensing techniques and sophisticated image processing algorithms, this study aims to identify and quantify alterations in the urban landscape accurately by addressing the key challenges inherent in the image registration process, as well as the availability associated with change detection (CD) techniques. We examined the data collection strategies, evaluated matching methods, and compared CD approaches. Aerial images were accurately analyzed to detect changes in building footprints, construction activities, and destruction. We developed a comprehensive annotation methodology tailored to the complex urban environment of Baghdad. These findings emphasize the rapidly evolving nature of Baghdad’s urban fabric and the critical need for ongoing monitoring to inform urban planning and management strategies. The results demonstrate the efficacy of utilizing high-resolution aerial imagery with object-based CD techniques for detailed urban analysis. This research advances the existing knowledge by providing a robust framework for urban CD, with implications for enhancing urban planning and policy-making processes. Future research will focus on refining the annotation processes and incorporating additional data sources to enhance the accuracy and comprehensiveness of urban CD methodologies.
Volume: 14
Issue: 5
Page: 4319-4331
Publish at: 2025-10-01

Optimized fractional-order direct torque control with space vector modulation strategy for two-wheel-drive electric vehicles

10.11591/ijece.v15i5.pp4409-4420
Touhami Nawal , Ouled-Ali Omar , Mansouri Smail , Benhammou Aissa
Electric vehicles (EVs) are a sustainable and efficient transportation choice, offering zero emissions, lower operating costs, and advanced performance features like instant torque and regenerative braking. They promote energy independence, improve urban livability, and support the global shift toward cleaner, renewable energy-powered mobility, making them a future-proof investment. The electric motor is a critical component in electric vehicles (EVs), the importance of which lies in its high efficiency, instant torque delivery, and smooth operation, which enhances performance and energy use. This paper focuses on a two-wheel drive electric vehicle (TWD EV) configuration powered by an energy storage battery system (ESBS), driven by two permanent magnet synchronous motors (PMSMs), and controlled using direct torque control with space vector modulation (DTC-SVM). fractional-order proportional integral derivative (FOPID) controllers, optimized via the grey wolf optimizer (GWO) algorithm, are implemented for precise speed control of the PMSMs. An electronic differential (ED) is incorporated to ensure vehicle stability, safety, and performance. The simulation results show that the proposed GWO-FOPID controller gave super results by reducing electromagnetic torque overshoot by 33%, improves torque settling time by 55%, and achieves the lowest electromagnetic torque ripple of approximately ±1 Nm compared to conventional DTC-SVM and GWO-PID approaches. Additionally, it optimized speed overshoot and undershoot by 44%, significantly enhancing system performance, responsiveness, and drive smoothness. This novel combination of fractional-order control, metaheuristic optimization, and electronic differential integration marks a meaningful advancement in high-precision and efficient control for 2WD EVs.
Volume: 15
Issue: 5
Page: 4409-4420
Publish at: 2025-10-01

A centroid-based algorithm for measuring and tracking vehicle speed from a monocular camera using the YOLOv8 object detector

10.11591/ijeecs.v40.i1.pp437-449
Pankaj Kumar Gautam , Sanjeev Kumar
Accurate real-time vehicle speed measurement is crucial for enhancing road safety and advance intelligent transportation systems (ITS). This paper proposes a centroid-based tracking algorithm that integrates YOLOv8, a state-of-the-art object detector, with DeepSORT for robust multi-object tracking. By leveraging YOLOv8’s anchor-free detection and DeepSORT’s appearance-based association, the proposed method effectively mitigates occlusions and minimizes identity switches. Evaluations on the VS13 benchmark dataset reveal a 2–5% improvement in measurement accuracy as compared to existing solutions, while maintaining real-time performance at 30 FPS. The method demonstrates consistent reliability across different vehicle models, speeds, and lighting conditions, underscoring its adaptability to real-world traffic scenarios. Moreover, larger bounding boxes enhance tracking stability, reducing false detections. Overall, the approach’s low computational overhead and high accuracy position it as a practical solution for ITS applications in constrained environments.
Volume: 40
Issue: 1
Page: 437-449
Publish at: 2025-10-01

Enhancing facial landmark detection with ControlNet-based data augmentation

10.11591/ijece.v15i5.pp4907-4915
Kritaphat Songsri-in , Munlika Rattaphun , Sopee Kaewchada , Sunisa Kidjaideaw , Sangjun Ruang-On , Wichit Sookkhathon , Patompong Chabplan
Facial landmark detection plays a pivotal role in various computer vision applications, including face recognition, expression analysis, and augmented reality. However, existing approaches often struggle with accuracy due to the variations in lighting, poses, and occlusion. To address these challenges, this study explores the integration of ControlNet with Stable Diffusion to enhance facial landmark detection via data augmentation. ControlNet, an advanced extension of diffusion models, improves image generation by conditioning outputs on structured inputs such as landmark coordinates, enabling precise control over image attributes. By leveraging annotated landmark data from the 300W dataset, ControlNet synthesizes diverse facial images that supplement traditional training datasets. Experimental results demonstrate that ControlNet-based augmentation reduces the interocular normalized mean error (INME) in landmark detection from a baseline of 4.67 to a range of 4.63 to 4.74, with optimal parameter tuning yielding further accuracy gains. These findings highlight the potential of generative models in complementing discriminative approaches and improving robustness and precision in facial landmark detection. The proposed method offers a scalable solution for enhancing model generalization, particularly in applications requiring high-fidelity facial analysis. Future research can extend this framework to broader computer vision tasks that demand detailed feature localization and structured data augmentation.
Volume: 15
Issue: 5
Page: 4907-4915
Publish at: 2025-10-01

A multi-path routing protocol for IoT-based sensor networks

10.11591/ijeecs.v40.i1.pp225-235
Udaya Suriya Rajkumar Dhamodharan , Krishna Prasad Karani , Saranya Pichandi , Kavitha Palani , Sathiyaraj Rajendran
Internet of things (IoT) based sensors are to link a big number of low-cost and power-integrated devices in a reliable manner. Numerous military and adventurous applications are regulated by communication among IoT sensors. The multi-path routing protocol (MRP) approach presented in this research to enhance secure routing in IoT sensors is significant. This technique makes use of data transfer routing and the relationships between network components. It finds the most efficient route between the nodes that minimizes communication overhead and is both reliable and economical in terms of shortest duration. The particle swarm optimization (PSO) technique is used to find the shortest path that is most cost-effective. To reach the target node, end-to-end data transmission must transit via intermediary nodes, which are provided by the routing path node history. The optimal path is chosen by MRP from PSO, and it traces the path to identify the intermediate nodes. In the unlikely event of a crisis, MRP offers the most affordable backup route for data transfer. When compared to earlier techniques, the outcomes of these current approaches enhance network efficiency, balance energy consumption among nodes, and routing attacks.
Volume: 40
Issue: 1
Page: 225-235
Publish at: 2025-10-01

Technical-economic analysis for ON/OFF GRID solar photovoltaic system design

10.11591/ijeecs.v40.i1.pp93-107
Walter Naranjo Lourido , Oscar Mauricio Niño Archila , Daniel Eduardo Guarin Preciado , Omar Yesid Beltrán Gutierrez , Javier Eduardo Martínez Baquero
This manuscript presents a detailed techno-economic analysis of a hybrid solar photovoltaic (PV) system designed to operate in both grid-connected (ON GRID) and stand-alone (OFF GRID) modes. The study focuses on the Leonardo Da Vinci academic building at Universidad de Los Llanos, located in Villavicencio, Colombia, in the tropical Orinoquía region. Using local solar irradiance, temperature data, and real load profiles from the facility, the system was modeled to assess performance under true operating conditions. A key part of the system design involved a detailed shadow analysis to identify potential obstructions and optimize solar access. This step significantly improved the accuracy of energy yield predictions and contributed to long-term system reliability. Additionally, regression-based methods were used to determine optimal panel tilt angles and refine system sizing based on peak sun hours. Both ON GRID and OFF GRID configurations were evaluated in terms of energy output, levelized cost of electricity (LCOE), net present value (NPV), and internal rate of return (IRR). Results show that ON GRID systems are financially advantageous in urban environments with net metering, while OFF GRID systems are critical for ensuring energy autonomy in remote or underserved areas. The findings provide practical insights for the deployment of hybrid PV systems in institutional settings across equatorial regions.
Volume: 40
Issue: 1
Page: 93-107
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

Bone-Net: a parallel deep convolutional neural network-based bone fracture recognition

10.11591/ijece.v15i5.pp4692-4704
Md. Hasan Imam Bijoy , Nusrat Islam Kohinoor , Syeda Zarin Tasnim , Md Saidur Rahman Kohinoor
Many people suffer from bone fractures, which can result from minor accidents, forceful blows, or even diseases like osteoporosis or bone cancer. In the medical realm, accurately identifying bone fractures from X-ray images is paramount for effective diagnosis and treatment. To address this, a comparative study is conducted utilizing three distinct models: a traditional convolutional neural network (CNN), MobileNet-V2, and a newly developed parallel deep convolutional neural network (PDCNN). The primary aim is to evaluate and contrast these models in terms of precision, sensitivity, and specificity for diagnosing bone fractures. X-ray images of fractured and non-fractured bones are sourced from Kaggle and subjected to various image processing techniques to rectify anomalies. Techniques such as cropping, resizing, contrast enhancement, filtering, and augmentation are applied, culminating in canny edge detection. These processed images are then used to train and test models. The results showcased the superior performance of the newly developed PDCNN model, achieving an impressive accuracy of 92.89%, surpassing both the traditional CNN and pretrained MobileNet-V2 models. A series of ablation studies are conducted to fine-tune the hyperparameters of the PDCNN model, further validating its efficacy. Throughout the investigation, PDCNN consistently outperformed MobileNet-V2 and traditional CNN, underscoring its potential as an advanced tool for streamlining bone fracture identification.
Volume: 15
Issue: 5
Page: 4692-4704
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

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

A hybrid approach to phishing email detection: leveraging machine learning and explainable artificial intelligence

10.11591/ijece.v15i5.pp4865-4874
Tarek Zidan , Fadi Abu-Amara , Ahmad Hasasneh , Muath Sawaftah , Seth Griner
With the increasing use of emails in our daily lives, they have become a prime target of phishing attacks, posing a significant threat to users. Attackers pretend to be trusted sources and use email phishing attacks to trick people into clicking malicious links or opening attachments. The aim of these attacks is to obtain sensitive information, such as financial information, login credentials, and personally identifiable information. Emails have attributes including the URL, sender, subject, receiver(s), and body. This paper proposes a hybrid intelligence model that integrates machine learning algorithms (ML) and natural language processing (NLP) techniques for email phishing detection. Three ML algorithms are employed: logistic regression, decision tree, and random forest. In addition, a customized ChatGPT model has been developed to receive email classification results from the hybrid model. This model educates users on recognizing phishing emails by explaining email classifications, highlighting keywords, and offering security tips. The proposed approach to detecting phishing emails raises awareness and educates users on recognizing and reporting email phishing attacks.
Volume: 15
Issue: 5
Page: 4865-4874
Publish at: 2025-10-01

Performance evaluation of a high-gain 50 W DC-DC flyback boost converter for variable input voltage low-power photovoltaic applications

10.11591/ijece.v15i5.pp4520-4530
Muhammad Hafeez Mohamed Hariri , Lim Kean Boon , Tole Sutikno , Nor Azizah Mohd Yusoff
DC-DC boost converters are essential for stabilizing the voltage output of photovoltaic (PV) modules. This paper analyzes a unique 50 W high-gain DC-DC flyback boost converter for various input voltage PV applications. Scientific analysis was employed to determine suitable parameters for critical circuit components. Simulations were conducted to evaluate the proposed high-gain DC-DC boost converter's performance. Subsequently, a prototype of the high-gain DC boost converter was fabricated with a printed circuit board (PCB) size of 100×100 mm. The proposed prototype's performance is compared to that of conventional boost converters based on criteria such as input voltage, output voltage, component count, voltage stress, voltage gain, efficiency, and rated power. The results indicate that the proposed converter can achieve a 300 V output voltage with a 50 W power rating from variable input voltages ranging between 12 V and 36 V. The highest gain achieved was 25 with a 12 V input voltage, though at a lower power rating of 15 W. A peak efficiency of 84.30% was measured with a 24 V DC input voltage. The proposed converter's features, particularly its high step-up voltage gain, make it suitable for industrial and renewable energy applications.
Volume: 15
Issue: 5
Page: 4520-4530
Publish at: 2025-10-01
1 2 3
...
1994 1995
Show 109 of 1995

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