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29,939 Article Results

Maximum power point tracking technique based on the grey wolf optimization-perturb and observe hybrid algorithm for photovoltaic systems under partial shading conditions

10.11591/ijece.v15i4.pp3566-3582
Leghrib Bilal , Bensiali Nadia , Adjabi Mohamed
Photovoltaic panels represent the most abundant source of renewable energy and the cleanest form of electrical energy derived from the sun. However, partial shading can lead to the appearance of multiple local maximum power points (LMPP) in the power-voltage (P-V) characteristics of solar panels. This situation traps classical power maximization algorithms, such as perturb and observe (P&O) or incremental conductance, as these algorithms tend to deviate from the global maximum power point (GMPP), resulting in reduced electrical energy production. To overcome this major challenge in the electrical industry, we propose in this study a hybrid grey wolf optimization-perturb and observe hybrid (GWO-P&O) algorithm, designed to converge towards the global maximum power without being trapped in local peaks. To demonstrate its effectiveness, the proposed algorithm was simulated in MATLAB/Simulink under various complex and uniform partial shading conditions. Furthermore, a comparative study was conducted with the P&O and GWO algorithms to evaluate precision, tracking, response time, and efficiency. The simulation results revealed superior performance for the proposed technique, particularly in terms of constant tracking of the global peak, with efficiencies of 99.95% and 99.98% in the best cases, faster response times (ranging from 0.07 to 0.04 s), and minimal, almost negligible oscillations around the GMPP.
Volume: 15
Issue: 4
Page: 3566-3582
Publish at: 2025-08-01

Synchronized transform-aggregate model for big data analytics towards in distributed cloud ecosystem

10.11591/ijece.v15i4.pp4259-4267
Rajeshwari Dembala , Kavya Ananthapadmanabha , Shashank Dhananjaya
The massively generated data from various technologically advanced applications hosted in the cloud and internet of things (IoT) in present times calls for effective management towards balancing the demands of both service providers and users. The conventional usage of distributed frameworks for such big data management is witnessed with various ongoing challenges. Hence, this manuscript presents a novel analytical framework for big data that can offer reduced cost and reduced time demanded to evaluate the distributed big data from multiple data points in the cloud in an optimal way. The core ideology of this framework is to gain a synchronized optimality for cost and time for executing a task demanded for big data analytics complying with the constraints associated with task deadline. The proposed framework is capable of fine-tuning the positioning of task operation using transform and aggregate strategy to exhibit 37% reduced delay, 41% efficient task completion performance, and 28% reduced execution time in contrast to existing frameworks.
Volume: 15
Issue: 4
Page: 4259-4267
Publish at: 2025-08-01

Shearlet-based texture analysis and deep learning for osteoporosis classification in lumbar vertebrae

10.11591/ijece.v15i4.pp4318-4331
Poorvitha Hullukere Ramakrishna , Chandrakala Beturpalya Muddaraju , Bhanushree Kothathi Jayaramu , Shobha Narasimhamurthy
Osteoporosis is a bone disorder characterized by reduced bone density and increased fracture risk. It challenges society's health, remarkably among the elderly population. This research proposed an innovative method by combining Shearlet-transform (ST) spectral analysis with a deep learning neural network (DLNN) and a convolutional neural network (CNN), for osteoporosis classification in lumbar vertebrae (LV) L1-L4 of spine X-ray images. The ST enables precise extraction of texture features from images by capturing significant information regarding trabecular bone micro-architecture and bone mineral density (BMD) variations revealing in osteoporosis regions. These extracted features serve as input to a DLNN for automated classification of osteoporotic and non-osteoporotic vertebrae. Similarly, without extracting any features from ST image is directly used as an input to the CNN to classify the images. The experimental results highlight the framework's effectiveness, achieving 96% accuracy in osteoporosis image classification using CNN. Early and precise detection of osteoporosis, particularly in the lumbar vertebrae, is vital for effective treatment and fracture prevention. This study particularly emphasizes the potential and effectiveness of integrating image spectral analysis technique with NN, to improving diagnostic accuracy and clinical decision-making in osteoporosis management.
Volume: 15
Issue: 4
Page: 4318-4331
Publish at: 2025-08-01

Renewable energy impact integration in Moroccan grid-load flow analysis

10.11591/ijece.v15i4.pp3632-3648
Safaa Essaid , Loubna Lazrak , Mouhsine Ghazaoui
This paper analyzes the behavior of a Moroccan electric transportation system in the presence of an integration of renewable energy sources, which represents a significant challenge due to their intermittent nature. The aim is to evaluate the performance of the transportation system in various situations and possible configurations. The current study enables the calculation of power flow in the network using the Newton-Raphson method under the MATLAB/Simulink software. To achieve this, a series of power flow simulations were conducted on a 5-bus Moroccan electrical network, examining four distinct scenarios. In addition, this article offers an evaluation of the power flow performance of the same electric transportation system with varying percentages of renewable energy penetration. In order to provide a complete critical analysis, many simulations were conducted to obtain the voltage and active power profile generated at different bus locations, as well as an evaluation of the losses in the studied network.
Volume: 15
Issue: 4
Page: 3632-3648
Publish at: 2025-08-01

Ensemble of convolutional neural network and DeepResNet for multimodal biometric authentication system

10.11591/ijece.v15i4.pp4279-4295
Ashwini Kailas , Madhusudan Girimallaih , Mallegowda Madigahalli , Vasantha Kumara Mahadevachar , Pranothi Kadirehally Somashekarappa
Multimodal biometrics technology has garnered attention recently for its ability to address inherent limitations found in single biometric modalities and to enhance overall recognition rates. A typical biometric recognition system comprises sensing, feature extraction, and matching modules. The system’s robustness heavily relies on its capability to effectively extract pertinent information from individual biometric traits. This study introduces a novel feature extraction technique tailored for a multimodal biometric system utilizing electrocardiogram (ECG) and iris traits. The ECG helps to incorporate the liveliness related information and Iris helps to produce the unique pattern for each individual. Therefore, this work presents a multimodal authentication system where data pre-processing is performed on image and ECG data where noise removal and quality enhancement tasks are performed. Later, feature extraction is carried out for ECG signals by estimating the Heart rate variability feature analysis in time and frequency domain. Finally, the ensemble of convolution neural network (CNN) and DeepResNet models are used to perform the classification. The overall accuracy is reported as 0.8900, 0.8400, 0.7900, 0.8932, 0.87, and 0.97 by using convolutional neural network-long short-term memory (CNN-LSTM), support vector machine (SVM), random forest (RF), CNN, decision tree (DT), and proposed MBANet approach respectively.
Volume: 15
Issue: 4
Page: 4279-4295
Publish at: 2025-08-01

Enhancing logo security: VGG19, autoencoder, and sequential fusion for fake logo detection

10.11591/ijict.v14i2.pp506-515
Debani Prasad Mishra , Prajna Jeet Ojha , Arul Kumar Dash , Sai Kanha Sethy , Sandip Ranjan Behera , Surender Reddy Salkuti
This paper deals with a way of detecting fake logos through the integration of visual geometry group-19 (VGG19), an autoencoder, and a sequential model. The approach consists of applying the method to a variety of datasets that have gone through resizing and augmentation, using VGG19 for extracting features effectively and autoencoder for abstracting them in a subtle manner. The combination of these elements in a sequential model account for the improved performance levels as far as accuracy, precision, recall, and F1-score are concerned when compared to existing approaches. This article assesses the strengths and limitations of the method and its adapted comprehension of brand identity symbols. Comparative analysis of these competing approaches reveals the benefits resulting from such fusion. To sum up, this paper is not only a major contribution to the domain of counterfeit logo detection but also suggests prospects for enhancing brand security in the digital world.
Volume: 14
Issue: 2
Page: 506-515
Publish at: 2025-08-01

Analysis and modeling of a pneumatic artificial muscle system

10.11591/ijeecs.v39.i2.pp874-884
Vinh-Phuc Tran , Nhut-Thanh Tran , Chi-Ngon Nguyen , Chanh-Nghiem Nguyen
Hysteresis is a common challenge in achieving precise position control of pneumatic artificial muscles (PAMs). Accurate modeling of this phenomenon is essential for the development of efficient PAM control systems. This study evaluates four mathematical models for modeling PAM dynamics: Nonlinear AutoRegressive with eXogenous inputs (NARX), BoxJenkins (BJ), Prandtl-Ishlinskii (PI), and second-order underdamped system and one zero (P2UZ). To assess the effectiveness of these models, experiments were conducted with reference input signals of varying amplitudes. The accuracy and goodness of fit of these models were evaluated based on root mean square error (RMSE) and coefficient of determination. Results show that the P2UZ model achieved the highest fitness (97.15%) and the lowest RMSE (1.80 mm), followed closely by the NARX model with 96.83% fitness and an RMSE of 1.90 mm. The PI and BJ models demonstrated lower performance, with the BJ model showing the lowest fitness (90.79%) and the highest RMSE (3.25 mm). These findings provide valuable insights for improving PAM control and PAM-based automation systems by highlighting the strengths and limitations of each model.
Volume: 39
Issue: 2
Page: 874-884
Publish at: 2025-08-01

Enhancing the effectiveness of CAPTCHA using an improved visual cryptography scheme

10.11591/ijeecs.v39.i2.pp1121-1129
Chihi Hasnae , Chahboun Asaad
Traditional CAPTCHA systems, designed to distinguish humans from bots, are increasingly ineffective due to advancements in artificial intelligence (AI), particularly deep learning and optical character recognition (OCR) technologies, which enable bots to bypass these systems. This paper proposes a new CAPTCHA authentication method that combines enhanced visual cryptography with traditional techniques to improve security. Visual cryptography divides information into visually distinct shares, reinforcing CAPTCHA’s defenses against automated attacks, especially those using deep learning. This approach not only strengthens security but also improves user experience by adjusting the time required to complete CAPTCHA challenges, addressing usability concerns associated with traditional systems. Overall, the proposed method offers a more secure, efficient, and user friendly solution for online authentication.
Volume: 39
Issue: 2
Page: 1121-1129
Publish at: 2025-08-01

Enhancing acoustic environment classification for hearingimpaired individuals using hybrid CNN and RFE

10.11591/ijeecs.v39.i2.pp906-913
Sunilkumar M. Hattaraki , Shankarayya G. Kambalimath
Individuals who are deaf or hard of hearing experience considerable difficulties in distinguishing sounds in various acoustic environments, which affects their communication ability and overall quality of life. Existing auditory assistive technologies currently face challenges with real-time classification and adaptation to changing noise conditions, underscoring the need for more reliable and accurate classification models. This research bridges the existing gap by creating a hybrid classification framework that integrates convolutional neural networks (CNN) and random forest ensemble (RFE) to enhance the accuracy of environmental sound classification. The study utilizes Mel-frequency cepstral coefficients (MFCCs) for feature extraction and principal component analysis (PCA) for dimensionality reduction, thus facilitating the efficient processing of real-world audio data. The proposed methodology improves classification accuracy across various environmental conditions. Experimental evaluations demonstrate superior performance, achieving a training accuracy of 94.93% and a testing accuracy of 93.41%, thereby exceeding conventional machine learning methods. By overcoming limitations in existing models, this research contributes to the development of adaptive hearing assistance systems with enhanced noise classification capabilities. The results have significant implications for the development of smart hearing aids, real-time noise classification, and auditory scene analysis. Ultimately, this research enhances assistive hearing technologies, promoting greater accessibility, communication, and inclusion for hearing-impaired individuals, thus contributing positively to society.
Volume: 39
Issue: 2
Page: 906-913
Publish at: 2025-08-01

Artificial neural network based load flow analysis of radial distribution system in Kurdistan region

10.11591/ijeecs.v39.i2.pp761-773
Warda Hussein Ali , Dana O. Qader , Mohamed A. Hussein
Today electric energy is the most commonly used source in the world. Power flow (load flow) analysis is conciderd as the backbone of any power system analysis and design; they have a great necessity for operating systems, future planning, fault analysis, and contingency analysis. For better utilization of electrical power, off-line modeling and simulation of power systems using powerful software are essential and significant task especially in developing countries and regions. Therefore, this paper performs a comparison study of conventional and non-conventional load flow techniques for a 24-Bus radial distribution system in the governorate of Sulaymaniyah. The conventional power flow techniques include the Newton-Raphson (NR), and Gauss-Seidel (GS) techniques, while the nonconventional load flow technique utilizes the artificial neural network (ANN). Modeling, simulation, and analysis of the 24-Bus feeder are performed using MATPOWER simulation tool. The MATPOWER and neural network techniques are implemented independently, and it has been proved that ANN model efficiently estimated the power flow analysis for the system mentioned above, the high regression values of nearly 0.999 indicates that the ANN model can be used as an efficient tool to perform power flow analysis.
Volume: 39
Issue: 2
Page: 761-773
Publish at: 2025-08-01

Improved counterplan for interference in same-band information transmission and reception

10.11591/ijeecs.v39.i2.pp831-839
Eugene Rhee , Junhee Cho
Wireless communication technologies operating in the 2.4 GHz band, such as Wi-Fi, Bluetooth, ZigBee, and others, often face challenges related to mutual interference. These technologies share the same unlicensed frequency spectrum, which can lead to various types of interference, affecting performance, reliability, and data throughput. This paper addresses the issue of mutual interference in communications occurring within frequency bands commonly used in daily life. Through this, it conducts an in-depth study on information processing between wireless devices and the control of communication components. Specifically, it examines interference phenomena in the widely used 2.4 GHz band by analyzing communication methods where such interference is likely to occur. By investigating the characteristics of Wi-Fi, Bluetooth, and ZigBee, this study analyzes interference phenomena and proposes an algorithm to mitigate them. To mitigate this, this paper proposes a multi-layered method integrating adaptive filtering, dynamic frequency allocation, advanced error correction, and intelligent scheduling mechanisms.
Volume: 39
Issue: 2
Page: 831-839
Publish at: 2025-08-01

An implementation of GAN analysis for criminal face identification system

10.11591/ijeecs.v39.i2.pp963-972
Ayesha Sarosh , Govindu Komali , Vishnu Vardhan Battu , Laxmaiah Kocharla , Eswaree Devi Kopparavuri , Ooruchintala Obulesu , Praveen Mande , Amanulla Mohammad
In recent times, the criminal activities are growing at an exponential rate. For the prevention of crime, one of the main issues that are before the police are accurate identification of criminals and on the other hand the availability of police officers are not adequate. The most tedious task is tracking the suspect once a crime was committed. Over the years, several technical solutions have been presented to detect the criminals however most of them were not effective. One of the most significant characteristics for the identification of a person is face. Even identical twins have their own unique faces. Face identification is a challenging topic in computer vision because the human face is a dynamic entity with a high degree of visual variation. In this area, identification accuracy and speed are significant challenges. Hence to solve these issues, an implementation of generative adversarial network (GAN) analysis for criminal face identification system is presented. GAN is used for the identification of criminals. Recall, precision, accuracy, and F1-score are used to assess the performance of the presented technique. Compared to previous models, this model will achieve better performance for criminal face detection.
Volume: 39
Issue: 2
Page: 963-972
Publish at: 2025-08-01

Study of design thinking and software engineering integration in education and training

10.11591/ijeecs.v39.i2.pp1384-1398
Muhammad Ihsan Zul , Suhaila Mohd. Yasin , Dadang Syarif Sihabudin Sahid
Integrating design thinking (DT) with software engineering (SE) is widely applied in industry, serving as a reference for SE in education and training. The industry has various integration models, but researchers and educators mainly adapt them for education. A clear understanding of DT-SE integration models is essential to figuring out their implementation. This study examines existing DT-SE integration models, challenges, and integration methods using Kitchenham’s framework in education and training. The paper was collected from ScienceDirect, IEEEXplore, Scopus, ACM, SpringerLink, and Google Scholar, yielding 593 initial publications, with 43 selected for in-depth analysis. Findings indicate that the d.school model is the most widely adopted DT model. Key challenges include team dynamics, process management, complexity, and cultural factors. DT is integrated into requirements engineering (RE) due to its user-centered nature, though only two studies explicitly describe DT-SE integration models, both applied early in SE processes. These findings suggest educational practices align with industry trends in model adoption and integration focus. Educators and practitioners can use these insights to design or adapt integration models suitable for education and training by shaping curricula that emphasize user-centered design, collaboration, and the extension of DT practices beyond RE-strengthening its impact for education and training.
Volume: 39
Issue: 2
Page: 1384-1398
Publish at: 2025-08-01

Designing an automated matching model to enhance recruitment process

10.11591/ijeecs.v39.i2.pp1081-1091
Sahar Idwan , Ebaa Fayyoumi , Haneen Hijazi , Izzeddin Matar
Detecting qualified candidates for a vacant position is a difficult task, especially when there are numerous applicants. This delays team development in finding the appropriate individual at the right moment. Adopting a well-structured selection process will create opportunities for new aspects and ideas. In this paper, the matching job applicant (MJA) model is developed to assist all parties, the employers and the employees simultaneously by providing a fair, transparent unbiased solution constructed by using a mathematical machine. This provides a clear justification in the decision-making process in addition to advising the applicants with the most suitable positions that fits their qualifications.
Volume: 39
Issue: 2
Page: 1081-1091
Publish at: 2025-08-01

A curvilinear-based approach for sign-to-text conversion of Kannada deaf sign language

10.11591/ijeecs.v39.i2.pp1337-1349
Shantappa G Gollagi , Mahantesh Laddi , Suhas G K , Kalyan Devappa Bamane , Sulbha Yadav
This research addresses the challenge of translating Kannada sign language into text to improve communication for the deaf community. Existing methods, primarily shape-based approaches, often fail to accurately imprisonment the complexity of hand gestures, leading to reduced translation accuracy. This study proposes a curvilinear-based approach that leverages peak curvature features and contour evolution techniques to overcome these limitations. This method enhances the recognition and interpretation of sign language gestures while reducing processing overhead. Experimental results demonstrate that the proposed system significantly outperforms traditional methods, achieving higher precision and recall rates. The enhanced system provides a reliable solution for improving accessibility and communication for the deaf community. This research represents a significant step toward developing more inclusive digital communication tools, with future work focused on real-time processing and extending the system to other regional sign languages.
Volume: 39
Issue: 2
Page: 1337-1349
Publish at: 2025-08-01
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