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31,456 Article Results

Enhancing network resilience and energy efficiency in the El Abiodh Sidi Cheikh grid through load flow analysis

10.11591/ijece.v15i5.pp4774-4784
Ali Abderrazak Tadjeddine , Soumia Djelaila , Ridha Ilyas Bendjillali , Sofiane Mohammed Bendelhoum , Abdelyamine Boukhobza , Salih Lachache
This study investigates the performance of the El Abiodh Sidi Cheikh (ESC) electrical grid, focusing on energy efficiency, system stability, and the integration of renewable energy sources. Numerical methods, including the Newton-Raphson (NR), accelerated Newton-Raphson (ANR), and fast decoupled (FD) load flow methods, were employed to evaluate power flow, voltage stability, and active power losses. Key results reveal that the NR method achieves the lowest power loss, with a minimal value of 2.32 MW, while voltage violations at specific nodes, such as buses 4 and 13, emphasize the necessity for voltage regulation. Analysis of the sun trajectory and temperature profiles highlights correlations between climatic conditions and energy demand, aiding renewable energy optimization. Additionally, photovoltaic (PV) measurements demonstrate diurnal variations in energy output, critical for enhancing renewable energy integration. These findings underscore the importance of advanced power flow analysis and strategic planning to ensure network resilience, energy efficiency, and reliability in the ESC region.
Volume: 15
Issue: 5
Page: 4774-4784
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

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

Energy yields and performance analysis of vertical and tilted oriented bifacial photovoltaic modules in tropical region

10.11591/ijece.v15i5.pp4508-4519
Rudi Darussalam , Agus Risdiyanto , Ant Ardath Kristi , Agus Junaedi , Noviadi Arief Rachman , Dalmasius Ganjar Subagio , Muhammad Kasim , Udin Komarudin , Ahmad Fudholi
This study experimentally investigates the performance of bifacial photovoltaic (bPV) modules under vertical and tilted orientations in a tropical region. Related studies are reviewed, then performance metrics including solar radiation, module temperature, bifaciality gain, and energy yield were monitored and analyzed over a specified period. The aim is to determine the optimal orientation for maximizing output power generation, temperature module, and understanding the bifaciality factor through real-world conditions. The experimental setup consisted of three different bifacial photovoltaic module configurations: two vertically mounted with facing east-west (E/W) and north-south (N/S) respectively, while the third was tilted 15 facing north. The study findings revealed that the tilted orientation produced the highest energy yield of 1951 Wh, followed by the vertical east-west (E/W) and vertical north-south (N/S) orientations with 1504 Wh and 609 Wh, respectively. While tilted bPV module benefit from higher irradiance, they also experience elevated temperatures (39% above ambient) compared to vertically bPV modules (8-21%). This can negatively affect efficiency, especially during peak solar hours. The results also show that differences in bPV installation orientation affect the bifaciality factor and gain. These findings offer valuable guidance for optimizing bPV system design and deployment in tropical regions with low latitude, supporting sustainable energy solutions.
Volume: 15
Issue: 5
Page: 4508-4519
Publish at: 2025-10-01

Applications of satellite information for rainwater estimation and usage: a comprehensive review

10.11591/ijece.v15i5.pp4671-4681
Laura Valeria Avendaño-García , Yeison Alberto Garcés-Gómez
Global climate change introduces significant uncertainty in water resource availability, making precipitation studies essential for societal sustainability. Satellite precipitation products (SPPs) have emerged as a vital alternative and complement to traditional meteorological station data for hydrological and climate research. This review examines scientific literature on SPP applications for daily, monthly, and annual rainfall estimations globally. Eleven widely used SPPs were identified, with the tropical rainfall measuring mission (TRMM) and climate hazards group infrared precipitation with station data (CHIRPS) standing out due to their frequent usage, high resolution, and extensive data records. A growing trend in research utilizes SPPs for hydrological studies and validates their estimates by contrasting satellite information with ground station measurements using continuous and categorical statistics. TRMM and CHIRPS, in particular, show precipitation accuracies closer to station data, influenced by local topography and climatology. Furthermore, SPP data, combined with geographic information systems (GIS), proves useful for identifying potential rainwater harvesting sites, offering an alternative information source to address water availability crises in drought-prone areas.
Volume: 15
Issue: 5
Page: 4671-4681
Publish at: 2025-10-01

Efficient mask region-based convolutional neural network-based architecture for COVID-19 detection from computed tomography data

10.11591/ijece.v15i5.pp4751-4761
Nader Mahmoud , Ashraf B. El-Sisi
The worldwide effect of the coronavirus disease (COVID-19) pandemic has been catastrophic, leading to a significant number of fatalities worldwide. In response to the outbreak, health care institutions have proposed the use of chest computed tomography (CT) as an important diagnosis tool for rapid diagnosis, leveraging deep learning approaches for disease detection. This paper aims to progress a robust methodology towards accurate diagnosis of COVID-19 based on deep learning approaches with chest CT images. We propose a mask region-based convolutional neural network (Mask R-CNN) model architecture that is well-trained and used to discriminate between COVID-19-infected and uninfected cases. In order to improve feature extraction, the proposed model incorporates a fuzzy color enhancement preprocessing technique that reduces image fuzziness and increases contrast. A publicly available chest CT dataset is considered for quantitative evaluation of the proposed architecture model, which includes various frontal image views of COVID-19 and non-COVID-19 cases. The proposed approach yielded an accuracy of 98.8% with 98.4% precision and 98.5% recall. Additionally, the proposed model architecture has been quantitatively evaluated in comparison with benchmark approaches, yielding superior performance in terms of conventional evaluation metrics.
Volume: 15
Issue: 5
Page: 4751-4761
Publish at: 2025-10-01

Comparative analysis of metaheuristic algorithms (genetic algorithm, artificial bee colony, differential evolution) in the design of substrate integrated waveguide dual bandpass filter

10.11591/ijece.v15i5.pp4682-4691
Souad Akkader , Abdennaceur Baghdad , Hamid Bouyghf , Aziz Dkiouak , Yassine Gmih
A well-optimized substrate integrated waveguide (SIW) filter can significantly enhance the performance of modern technologies, including wireless communication systems, radar, and sensors. The frequencies of 5 and 6 GHz play a crucial role in these applications. Metaheuristic algorithms such as genetic algorithm (GA), artificial bee colony (ABC), and differential evolution (DE) are effective for designing SIW filters specifically tailored to these needs. This paper evaluates the performance of evolutionary optimization techniques in the design of substrate integrated waveguide filters. The optimization focuses on achieving optimal impedance matching within the frequency range of 4 to 8 GHz. The attenuation constant serves as the cost function, guiding the optimization process to ensure reliable and accurate results from each algorithm. The filter parameters derived from the most efficient algorithm are verified using ANSYS HFSS, resulting in two bands with S11=-45 dB and S21=-0.2 dB in the first band, and S11=-28 dB and S21=-0.5 dB in the second band. Additionally, two transmission zeros with rejections of -23 and -12 dB are achieved at 6.4 and 7.08 GHz, respectively. These results highlight the practicality of SIW technologies in designing microwave circuits, particularly for internet of things (IoT) applications.
Volume: 15
Issue: 5
Page: 4682-4691
Publish at: 2025-10-01

A non-destructive approach for estimation of Hb, HCT and red blood cells using reflectance spectroscopic technique

10.11591/ijece.v15i5.pp4569-4580
P. Divyabharathi , Neelamegam Devarasu
Paediatric haematology involves the use of non-invasive methods and technologies to evaluate haematological parameters in children. These techniques attempt to offer precise measurements of blood constituents without the necessity of intrusive procedures such as venipuncture or blood draws, which can be difficult and unpleasant for paediatric patients. The data gathered from the elbow will be given priority for further investigations to find haematological profiles. Estimates of haemoglobin, haematocrit, and red blood cell count were done and compared against the values obtained using conventional methods. This method achieves an accuracy of 75.56% with high precision and specificity which makes the method particularly beneficial for paediatric applications, potentially due to physiological differences or enhanced calibration for younger populations. The sensitivity varies with red blood cells (RBC) showing the lowest true positive detection rate. Future work could focus on improving the sensitivity of these parameters to enhance the accuracy. Conventional techniques cannot monitor continuously and remotely, which is crucial for a point-of-care screening device in the current era. The proposed non-destructive technique offers the benefits of infection control, pain reduction, and minimal operational cum maintenance expenses, all while being portable and child friendly.
Volume: 15
Issue: 5
Page: 4569-4580
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

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

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

Language model optimization for mental health question answering application

10.11591/ijece.v15i5.pp4829-4836
Fardan Zamakhsyari , Agung Fatwanto
Question answering (QA) is a task in natural language processing (NLP) where the bidirectional encoder representations from transformers (BERT) language model has shown remarkable results. This research focuses on optimizing the IndoBERT and MBERT models for the QA task in the mental health domain, using a translated version of the Amod/mental_health_counseling_conversations dataset on Hugging Face. The optimization process involves fine-tuning IndoBERT and MBERT to enhance their performance, evaluated using BERTScore components: F1, recall, and precision. The results indicate that fine-tuning significantly boosts IndoBERT’s performance, achieving an F1-BERTScore of 91.8%, a recall of 89.9%, and precision of 93.9%, marking a 28% improvement. For the model, M-BERT’s fine-tuning results include an F1-BERTScore of 79.2%, recall of 73.4%, and precision of 86.2%, with only a 5% improvement. These findings underscore the importance of fine-tuning and using language-specific models like IndoBERT for specialized NLP tasks, demonstrating the potential to create more accurate and contextually relevant question-answering systems in the mental health domain.
Volume: 15
Issue: 5
Page: 4829-4836
Publish at: 2025-10-01

An efficient direction oriented block-based video inpainting using morphological operations and adaptively dimensioned search region with direction-oriented block-based inpainting

10.11591/ijece.v15i5.pp4705-4713
Shyni Shajahan , Y. Jacob Vetha Raj
Video inpainting is a technique in computer vision used to remove unwanted objects from video sequences while preserving visual consistency, so that modifications remain unnoticeable to the human eye. This paper presents an accurate video inpainting model based on the adaptively dimensioned search region with direction-oriented block-based inpainting (ADSR-DOBI) algorithm. The model operates in five main phases: preprocessing, background separation, morphological operations, object removal, and video inpainting. Initially, the input video is converted into frames, followed by preprocessing steps such as deionizing and resizing. These frames are then processed using a background subtraction module, where object localization and foreground detection are performed using the binomially distributed foreground segmentation network (BDFgSegNet) and morphological techniques. This results in segmented foreground objects tracked across frames. The object removal phase eliminates the identified foreground objects and defines the missing regions (holes) to be filled. The ADSR-DOBI algorithm is then applied to inpaint these regions seamlessly. Experimental results demonstrate that this approach outperforms existing state-of-the-art methods in both accuracy and efficiency.
Volume: 15
Issue: 5
Page: 4705-4713
Publish at: 2025-10-01

Fuzzy clustering optimization based artificial bee colony algorithm for brain magnetic resonance imaging image segmentation

10.11591/ijece.v15i5.pp4916-4932
Chakir Mokhtari , Mohammed Debakla , Boudjelal Meftah
In brain magnetic resonance imaging (MRI) analysis, image clustering is regarded as one of the most crucial tasks. It is frequently employed to estimate and visualize brain anatomical structures, identify pathological regions, and assist in guiding surgical procedures. Fuzzy c-means algorithm (FCM) is widely used in the MRI image segmentation process. However, it has been several weaknesses such as noise sensitivity, stuck in local optimum and issues with parameters initialization. To address these FCM problems, this paper presents a novel fuzzy optimization method that enhances brain MRI image segmentation by integrating the artificial bee colony (ABC) algorithm with FCM clustering techniques. The proposed method seeks to optimize multiple FCM parameters simultaneously, including the objective function, number of clusters, and cluster center values. The method was evaluated on both simulated and clinical brain MR images, with an emphasis on segmenting white matter, grey matter, and cerebrospinal fluid regions. Experimental results demonstrate significant improvements in segmentation accuracy, achieving a Jaccard similarity (JS) of nearly 1, a partition coefficient index (PCI) of 0.92, and a Davies-Bouldin index (DBI) of 0.41, outperforming other stats of the arts methods.
Volume: 15
Issue: 5
Page: 4916-4932
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
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