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31,456 Article Results

Efficiency improvement of 50 Hz wireless photovoltaic power transfer using magnetic relay

10.11591/ijece.v15i2.pp1322-1331
Muhammad Irwanto , Nor Hanisah Baharudin , Yoga Tri Nugraha , Indra Nisja
A photovoltaic direct current (DC) power can be changed into AC power using an inverter. The inverter must be connected to an alternating current (AC) load using wire, which has drawbacks in terms of air space, wire cost, and an unattractive view of the sky. It is appropriate to suggest a wireless power transfer (WPT) system concept to replace the use of wires in the transfer of electrical power. The WPT system has been conducted by the previous researchers, but it is still in the frequency system of hundred, kilo, mega or gigahertz, thus it can only be applied on DC load after rectifying it, but cannot be applied for a normal AC load, also its distance is relative near. The modelling of wireless photovoltaic power transfer (WPVPT) with a 50 Hz system frequency is presented in this work. The four modelling components that create the WPVPT system are models of the PV module, the transmitter circuit, the magnetic relay, and the receiver circuit. The findings indicate that the efficiency of the proposed WPVPT system is 71.27% without a magnetic relay and 72.82% with a magnetic relay. It suggests that the use of magnetic relay can improve the WPVPT system's efficiency.
Volume: 15
Issue: 2
Page: 1322-1331
Publish at: 2025-04-01

Flexible hybrid graphene-based NFC tag antenna for temperature monitoring application

10.11591/ijeecs.v38.i1.pp227-242
Najwa Mohd Faudzi , Ahmad Rashidy Razali , Asrulnizam Abd Manaf , Nurul Huda Abd Rahman , Ahmad Azlan Ab Aziz , Syed Muhammad Hafiz , Suraya Sulaiman , Nora’zah Abdul Rashid , Amirudin Ibrahim , Aiza Mahyuni Mozi
A hybrid graphene-based material, composed of reduced graphene oxide (rGO) and silver nanoparticle (AgNP), has been proposed for a near field communication (NFC) tag antenna with an integrated, flexible temperature monitoring circuit. The limited availability of high-conductivity graphene-based materials in the market has restricted the use of graphene in NFC tag applications. Therefore, this paper proposes a hybrid graphene-based composition featuring a high conductivity of 3.95×106 S/m. The feasibility of this material for NFC tags had not been validated previously, which is the main motivation for this research. The synthesis of the materials, along with the design, fabrication, and characterization of the NFC tag, is also presented. Results show that the inkjet-printed tag achieves a good reading range of up to 3 cm and demonstrates robustness against bending from 60⁰ to 190⁰, maintaining a maximum reading range of 1.3 cm. Performance on various materials, such as plastic, paper, and carton, also shows minimal impact on frequency shifting. Additionally, the graphene-based NFC tag integrates well with the temperature circuit, effectively monitoring temperatures in the 20-60 ⁰C range in real-time. This makes the developed tag suitable for applications such as food safety monitoring systems through NFC-integrated packaging.
Volume: 38
Issue: 1
Page: 227-242
Publish at: 2025-04-01

Self-assessment of secondary school Islamic education teacher: validity and reliability of qualitative study

10.11591/ijere.v14i2.29438
Azwani Masuwai , Hafizhah Zulkifli , Mohd Isa Hamzah
Issues concerning the validity of qualitative studies are frequently debated among scholars. Interview techniques often lack standardized procedures, which can affect reliability, and eliminating biases in interviews is challenging. Ensuring reliability and validity is crucial for establishing research credibility and high measurement reliability. This study was conducted to explore the reliability and validity of qualitative study of self-assessment concept among Islamic education teachers towards continuous professionalism development. The qualitative study was involved six participants selected based on the purposive sampling technique using a semi-structured interview. This study aimed to validate interview data through participant verification, ensure the validity of qualitative themes through expert validation, and enhance reliability via data triangulation. Multiple methods were employed to achieve these goals, including interview protocol verification, pre-field study, data triangulation, field notes, participant verification, expert validation, and agreement assessed using the Cohen Kappa Index, along with an extended study period. The findings indicated that the study met it is objectives for validity and reliability according to the recommended strategies and techniques. The use of various validation methods and attention to language barriers contributed to the study’s robustness and offers valuable guidance for future research.
Volume: 14
Issue: 2
Page: 961-974
Publish at: 2025-04-01

Enhancing predictive modelling and interpretability in heart failure prediction: a SHAP-based analysis

10.11591/ijict.v14i1.pp11-19
Niaz Ashraf Khan , Md. Ferdous Bin Hafiz , Md. Aktaruzzaman Pramanik
Predictive modelling plays a crucial role in healthcare, particularly in forecasting mortality due to heart failure. This study focuses on enhancing predictive modelling and interpretability in heart failure prediction through advanced boosting algorithms, ensemble methods, and SHapley Additive exPlanations (SHAP) analysis. Leveraging a dataset of patients diagnosed with cardiovascular diseases (CVD), we employed techniques such as synthetic minority over-sampling technique (SMOTE) and bootstrapping to address class imbalance. Our results demonstrated exceptional predictive performance, with the gradient boosting (GBoost) model achieving the highest accuracy of 91.39%. Ensemble techniques further enhanced performance, with the voting classifier (VC), stacking classifier (SC), and Blending achieving accuracies of 91.00%. SHAP analysis uncovered key features such as time, Serum_creatinine, and Ejection_fraction, significantly impacting mortality prediction. These findings highlight the importance of transparent and interpretable machine learning models in healthcare decision-making processes, facilitating informed interventions and personalized treatment strategies for heart failure patients.
Volume: 14
Issue: 1
Page: 11-19
Publish at: 2025-04-01

Modelling and validation of hybrid series active filter for power quality enhancement using OPAL-RT simulator

10.11591/ijece.v15i2.pp1274-1288
Nitisha Shrivastava , Arjun Baliyan
It is a known fact that the quality of power is degrading at a very fast rate. The reason behind this is harmonics in the power supply. The main motive is to reduce the harmonic pollution in the system for various combinations of the load. The method proposed in this paper consists of hybrid series active power filter (HSAPF) which is a combination of an active power filter connected in series and the passive filter in parallel with the load. In order to generate the compensating signals, synchronous reference method has been used and the tuning of the proportional integral (PI) controller is performed with grey wolf optimization technique. It provides the optimized values of controller parameters that are very much necessary to improve not only the transient but also the steady state characteristics of the system. For comparison purposes the tuning of PI controller is also done using the well- known particle swarm optimization technique. A comparative simulation analysis is done in MATLAB as well as the results are verified by using OPAL-RT 4510 real time simulator which proves that the suggested topology gives better results in terms of total harmonic distortion (THD) reduction.
Volume: 15
Issue: 2
Page: 1274-1288
Publish at: 2025-04-01

Radionuclide identification system using convolution neural network for environmental radiation monitoring

10.11591/ijece.v15i2.pp2282-2290
Istofa Istofa , Gina Kusuma , Firliyani Rahmatia Ningsih , Joko Triyanto , I Putu Susila , Prawito Prajitno
Radionuclide identification is an important task for nuclear safety and security aspects, especially to environmental radiation monitoring systems. This study aims to build an automatic radionuclide identification system that can be applied in environmental radiation monitoring stations. The gamma energy spectrum was obtained by varying radionuclide types, measurement time and source distance using a scintillation detector. The dataset was collected by converting gamma energy spectrum into images, data pre-processing by removing background noise and normalizing the gamma spectrum. Automatic identification is demonstrated as a development method based on convolutional neural network (CNN) algorithm, where the images come from gamma-ray spectrum in the form of photoelectric peak characteristic. Three CNN architectures are used to train the model, which are VGG-16, AlexNet and Xception. The performance of each model is evaluated using accuracy, precision and recall to find the appropriate architecture. The most optimum results are shown by VGG-16 with an accuracy of 97.72%, a precision of 97.75% and a recall of 97.71%. The models are critically reviewed and it is concluded that the developed models can be further implemented on embedded devices utilizing the tiny machine learning (TinyML) platform in environmental radiation monitoring systems.
Volume: 15
Issue: 2
Page: 2282-2290
Publish at: 2025-04-01

Three-phase hybrid active power filter: an overview

10.11591/ijece.v15i2.pp1361-1371
Le Quang Binh , Chau Minh Thuyen
Hybrid active power filter (HAPF) is considered an effective solution for filtering harmonics and compensating reactive power in power systems. It is a combination of passive power filters and an active power filter to form many different structures depending on the system voltage and load characteristics. This paper aims to introduce an overall picture of the results of research on three-phase HAPF systems, such as: HAPF models, advantages, disadvantages, and scope of application of each model; methods for determining parameters of passive power filters; structure, response time, resonance, power loss and cost of types of output filters; inverter structure and commonly used pulse width modulation methods in HAPF; control strategies and used controllers are also listed and compared; the DC Link voltage stabilization is also summarized and analyzed. Finally, the existing problems and the development trends of HAPF.
Volume: 15
Issue: 2
Page: 1361-1371
Publish at: 2025-04-01

Embedded systems and artificial intelligence for enhanced humanoid robotics applications

10.11591/ijece.v15i2.pp1912-1923
Jalal Hdid , Oussama Lamsellak , Ahmad Benlghazi , Abdelhamid Benali , Ouafae El Melhaoui
This paper presents a method for collecting precise hand gesture (HG) data using a low-cost embedded device for an embedded artificial intelligence (EAI)-based humanoid robotics (HR) application. Despite advancements in the field, challenges remain in deploying cost-effective methods for accurately capturing and recognizing body gesture data. The ultimate objective is to develop humanoid robots (HRS) capable of better understanding human activities and providing optimal daily life support. In this regard, our approach utilizes a Raspberry Pi Pico microcontroller with a 3-axis accelerometer and a 3-axis gyroscope motion sensor to capture real- time HG data, describing ten distinct real-world tasks performed by the hand in experimental scenarios. Collected data is stored on a personal computer (PC) via a micro-python program, forming a dataset where tasks are classified using ten supervised machine learning (SML) models. Two classification experiments were conducted: the first involved predicting raw data, and the second applied normalization and feature extraction (FE) techniques to improve prediction performance. The results showed promising accuracy in the first phase (89% max), with further improvements achieved in the second phase (94% max). Finally, by employing similar methods, we can integrate highly trained machine learning (ML) models into embedded humanoid robotic systems, enabling real-time human assistance.
Volume: 15
Issue: 2
Page: 1912-1923
Publish at: 2025-04-01

Ensemble learning weighted average meta-classifier for palm diseases identification

10.11591/ijeecs.v38.i1.pp303-311
Sofiane Abden , Mostefa Bendjima , Soumia Benkrama
Crop diseases lead to significant losses for farmers and threaten the global food supply. The date palm, valued for its nutritional benefits and drought resistance in desert climates, is a vital export crop for many countries in the Middle East and North Africa, second only to hydrocarbons. However, various diseases pose a threat to this important plant. Therefore, early disease prediction using deep learning (DL) is essential to prevent the deterioration of date palm crops. The aim of this paper is to apply a robust ensemble method (EL) combining tree transfer learning (TL) models Resnet50, DenseNet201, and InceptionV3, and compares its performance with the CNN-SVM model and the tree TL models mentioned previously. The models were applied to a date palm dataset containing three classes: White scale, brown spot, and healthy leaf. The training and validation sets were applied to a public dataset, while the testing set was applied to a local dataset captured manually to check the model’s performance. As a result, we considered that the ensemble method gave very satisfactory results compared to other methods. Our hybrid model reached a testing accuracy of 98% while achieving an amazing training and validation accuracy of 99.94% and 98.14%, respectively.
Volume: 38
Issue: 1
Page: 303-311
Publish at: 2025-04-01

Enhancing financial cybersecurity via advanced machine learning: analysis, comparison

10.11591/ijai.v14.i2.pp1281-1289
Grace Odette Boussi , Himanshu Gupta , Syed Akhter Hossain
The financial sector is a prime target for cyber-attacks due to the sensitive nature of the data it handles. As the frequency and sophistication of cyber threats continue to rise, implementing effective security measures becomes paramount. In this paper we provide a comprehensive comparison of six prominent machine learning techniques utilized in the financial industry for cyber-attack prevention. The study aims to identify the best-performing model and subsequently compares its performance with a proposed model tailored to the specific challenges faced by financial institutions. This paper looks at using advanced machine learning methods to make cybersecurity stronger for financial institutions. The work explores the deployment of cutting-edge machine learning algorithms - logistic regression, random forest, support vector machines (SVM), K-nearest neighbour (KNN), naïve Bayes, extreme gradient boosting (XGBoost), and deep learning technique (Dense Layer) - to fortify the cybersecurity framework within financial institutions. Through a meticulous analysis and comparative study, we explore the efficacy, scalability, and practical implementation aspects of various machine learning algorithms tailored to address cybersecurity concerns. Additionally, we propose a framework for integrating the most effective machine learning models into existing cybersecurity infrastructure, offering insights into bolstering resilience against evolving cyber threats. In our comparison, XGBoost exhibited outstanding performance with an accuracy of 95%.
Volume: 14
Issue: 2
Page: 1281-1289
Publish at: 2025-04-01

Enhanced hippopotamus optimization algorithm for power system stabilizers

10.11591/ijeecs.v38.i1.pp22-31
Widi Aribowo , Toufik Mzili , Aliyu Sabo
This article presents techniques for modifying the power system stabilizer's (PSS) parameters. An enhanced version of the hippocampal optimization algorithm (HO) is presented here. HO represents a novel approach in metaheuristic methodology, having been inspired by the observed clinging behavior in hippos. The notion of the HO is defined using a trinary-phase model that includes their position updates in rivers or ponds, defensive techniques against predators, and mathematically described evasive methods. To confirm the efficacy of the recommended approach, this article provides comparison simulations of the PSS objective function and transient response. This study employs validation through a comparison between Original HO and conventional methods. Simulation results demonstrate that, when compared to competing algorithms, the suggested approach yields optimal results and, in some cases, exhibits fast convergence. It is known that, in comparison to the original HO approach, the recommended way can lower the average undershoot of the rotor angel and speed by 12.049% and 26.97%, respectively.
Volume: 38
Issue: 1
Page: 22-31
Publish at: 2025-04-01

IC-CGAN: Imbalanced class-conditional generative adversarial network with weighted loss function

10.11591/ijece.v15i2.pp1632-1646
Chaitra Ravi , Siddesh Gaddadevara Matt
This research proposes an advanced deep learning model that deals with the over-distribution of plant leaf disease classes by using an imbalanced class-conditional generative adversarial network (IC-CGAN) that is coupled with a weighted loss function. IC-CGAN model provides a solution to class imbalance through the synthesis of tomato leaf disease images and adding them to the dataset which as a consequence, improves the accuracy of disease detection. The weighted loss function essentially does a crucial job of solving the problem of imbalance in class during the training stage. Mixing of these models leads to the generation of realistic leaf disease synthetic images and balancing class distribution in the dataset, hence improving of tomato disease detection model’s accuracy. This study is another step toward the development of effective disease detection systems for agricultural purposes by addressing the concern of class imbalance with IC-CGAN through the vector-weighted loss function. The proposed IC-CGAN has a high chance of enhancing the disease detection at its early stage with a much higher level of accuracy (99.95%), precision (99.98%), recall (99.98%) and F1-score (99.98%) in tomato plant leaf disease detection.
Volume: 15
Issue: 2
Page: 1632-1646
Publish at: 2025-04-01

Efficient power optimized very-large-scale integration architecture of proportionate least mean square adaptive filter

10.11591/ijece.v15i2.pp2513-2522
Gangadharaiah Soralamavu Lakshmaiah , Narayanappa Chikkajala Krishnappa , Poornima Golluchinnappanahalli Ramappa , Divya Muddenahally Narasimhaiah , Umesharaddy Radder , Chakali Chandrasekhar
The focus on power optimization in embedded systems is especially important for embedded applications since it has brought in many methods and factors that are necessary for developing systems that are both power- and area-efficient. In contrast to the current delayed wavelet μ-law proportionate least mean square (DWMPLMS) and delayed least mean square (DLMS) algorithms, this work offers the development of adaptive filters based on the least mean square (LMS) method, which improves power and timing performance. In order to improve area and time efficiency, the proportionate least mean square (PLMS) algorithm's architecture has been modified to remove delay, add a proportionate gain block, design for a fixed length, include an approximate multiplier block, and swap out standard blocks for floating-point adder and divider blocks. According to a power and temporal comparison with the DWMPLMS and DLMS algorithms, field-programmable gate array (FPGA) synthesis reduces power usage by 95% for a 32-bit filter length in PLMS when compared to the above methods.
Volume: 15
Issue: 2
Page: 2513-2522
Publish at: 2025-04-01

Predicting peak demand for electricity consumption using time series data and machine learning model

10.11591/ijeecs.v38.i1.pp668-676
Suriya S. , Agusthiyar R.
Energy consumption is influenced by various factors, including the proliferation of electronic devices, technological advancements, economic growth, agricultural development, and population increase. Each of these factors contributes to the rising demand for energy. This paper addresses the challenge of predicting peak energy demand (ED) by utilizing historical time series data from the past five years, combined with temperature data from Tamil Nadu’s official sources. We employed feature engineering techniques to prepare the data for machine learning models, specifically XGBoost regressor, lasso, and ridge regression. The time series data was then analyzed using both univariate and multivariate models, including auto regressive integrated moving average (ARIMA) and vector autoregressive (VAR) models. The results show that our models can effectively forecast ED, providing critical insights for policymakers and stakeholders involved in energy planning and resource management.
Volume: 38
Issue: 1
Page: 668-676
Publish at: 2025-04-01

Secure financial application using homomorphic encryption

10.11591/ijeecs.v38.i1.pp595-602
Vijaykumar Bidve , Aruna Pavate , Rahul Raut , Shailesh Kediya , Pakiriswamy Sarasu , Koteswara Rao Anne , Aryani Gangadhara , Ashfaq Shaikh
In today’s digital age, the security and privacy of financial transactions are paramount. With the advent of technologies like homomorphic encryption, it is now possible to perform computations on encrypted data without the need to decrypt it first, offering a promising avenue for secure financial applications. This research paper explores the implementation and implications of utilizing homomorphic encryption in financial applications to safeguard sensitive data while maintaining computational integrity. By employing homomorphic encryption techniques, financial institutions can enhance the confidentiality of their clients’ information, protect against data breaches, and enable secure computations on encrypted data. The paper discusses the principles of homomorphic encryption, its applications in financial systems, challenges, and potential solutions. Additionally, it examines real-world examples and case studies where homomorphic encryption has been employed successfully, highlighting its effectiveness in ensuring the privacy and security of financial transactions. Overall, this paper aims to provide insights into the role of homomorphic encryption in creating secure financial applications and its potential to revolutionize the way sensitive financial data is handled and processed.
Volume: 38
Issue: 1
Page: 595-602
Publish at: 2025-04-01
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