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25,002 Article Results

Model of semiconductor converters for the simulation of an asymmetric loads in an autonomous power supply system

10.11591/ijece.v15i2.pp1332-1347
Saidjon Tavarov , Mihail Senyuk , Murodbek Safaraliev , Sergey Kokin , Alexander Tavlintsev , Andrey Svyatykh
This article is devoted to the development of computer model with semiconductor converters for the simulation of asymmetric loads allowing to solve the voltage symmetry problems under asymmetric loads (active and active-inductive) for isolated electric networks with renewable energy sources (mini hydroelectric power plants). A model of a symmetry device has been developed in the MATLAB/Simulink environment based on a proportional-integral controller and a relay controller - P. The effectiveness of their use depends on the load's nature. The implementation of a voltage converter is presented considering a three-phase inverter with discrete key switching at 120, 150, and 180 degrees with a purely active load. Based on the harmonic analysis of the three-phase voltage at discrete conversion, the value of the first harmonic is determined. Voltage transformations under active-inductive load at 120, 150, and 180 degrees are mathematically described. To determine the harmonic spectrum, an analysis of the fast Fourier transform for the three-phase voltage of a MATLAB/Simulink semiconductor converter was carried out. It is established that the alternating current output voltage is generated on the output side of the inverter of a three-phase voltage source through a three-phase load connected by a star with a harmonic suppression method.
Volume: 15
Issue: 2
Page: 1332-1347
Publish at: 2025-04-01

Key management for bitcoin transactions using cloud based key splitting technique

10.11591/ijece.v15i2.pp1861-1867
Amar Buchade , Nakul Sharma , Varsha Jadhav , Jagannath Nalavade , Suhas Sapate , Rajani Sajjan
Bitcoin wallet contains the information which is required for making transactions. To access this information, user maintains the secret key. Anyone with the secret key can access the records stored in bitcoin wallet. The compromise of the key such as physical theft, side channel attack, sybil attack, DoS attack and weak encryption can cause the access of transactional details and bitcoins stored in the wallet to the attacker. The cloud-based key split up technique is proposed for securing the key in blockchain technology. The key shares are distributed across virtual machines in cloud computing. The approach is compared to the existing key management approaches such as local key storage, keys derived from password and hosted wallet. It is observed that our approach is most suitable among the other key management approaches.
Volume: 15
Issue: 2
Page: 1861-1867
Publish at: 2025-04-01

Kafka-machine learning based storage benchmark kit for estimation of large file storage performance

10.11591/ijece.v15i2.pp1990-1999
Sanjay Kumar Naazre Vittal Rao , Anitha Chikkanayakanahalli Lokesh Kumar , Subhash Kamble
Efficient storage and maintenance of big data is important with respect to assuring accessibility and cost-friendliness to improve risk management and achieve an effective comprehension of the user requirements. Managing the extensive data volumes and optimizing storage performance poses a significant challenge. To address this challenge, this research proposes the Kafka-machine learning (ML) based storage benchmark kit (SBK) designed to evaluate the performance of the file storage system. The proposed method employs Kafka-ML and a drill-down feature to optimize storage performance and enhance throughput. Kafka-ML-based SBK has the capability to optimize storage efficiency and system performance through space requirements and enhance data handling. The drill-down search feature precisely contributes through reducing disk space usage, enabling faster data retrieval and more efficient real-time processing within the Kafka-ML framework. The SBK aims to provide transparency and ease of utilization for benchmarking purposes. The proposed method attains maximum throughput and minimum latency of 20 MBs and 70 ms, respectively on the number of data bytes is 10, as opposed to the existing method SBK Kafka.
Volume: 15
Issue: 2
Page: 1990-1999
Publish at: 2025-04-01

Buffers balancing of buffer-aided relays in 5G non-orthogonal multiple access transmission internet of things networks

10.11591/ijece.v15i2.pp1774-1782
Mohammad Alkhwatrah , Nidal Qasem
Buffer-aided cooperative non-orthogonal multiple access (NOMA) enhances the efficiency of utilizing the spectral by allowing more users to share the same re- sources to establish massive connectivity. This is remarkably attractive in the fifth generation (5G) and beyond systems, where a massive number of links is essential like in the internet of things (IoT). However, the capability of buffer co-operation in reducing the outage is limited due to empty and full buffers, where empty buffers can not transmit and full buffers can not receive data packets. Therefore, in this paper, we propose balancing the buffer content of the inter-connected relays, so the buffers that are more full send packets to the emptier buffers, hence all buffers are more balanced and farther from being empty or full. The simulations show that the proposed balancing technique has improved the network outage probability. The results show that the impact of the balancing is more effective as the number of relays in the network is increased. Further- more, utilizing the balancing with a lower number of relays may lead to better performance than that of more relays without balancing. In addition, giving the balancing different levels of priorities gives different levels of enhancement.
Volume: 15
Issue: 2
Page: 1774-1782
Publish at: 2025-04-01

A new data imputation technique for efficient used car price forecasting

10.11591/ijece.v15i2.pp2364-2371
Charlène Béatrice Bridge-Nduwimana , Aziza El Ouaazizi , Majid Benyakhlef
This research presents an innovative methodology for addressing missing data challenges, specifically applied to predicting the resale value of used vehicles. The study integrates a tailored feature selection algorithm with a sophisticated imputation strategy utilizing the HistGradientBoostingRegressor to enhance efficiency and accuracy while maintaining data fidelity. The approach effectively resolves data preprocessing and missing value imputation issues in complex datasets. A comprehensive flowchart delineates the process from initial data acquisition and integration to ultimate preprocessing steps, encompassing feature engineering, data partitioning, model training, and imputation procedures. The results demonstrate the superiority of the HistGradientBoostingRegressor for imputation over conventional methods, with boosted models eXtreme gradient boosting (XGBoost) regressor and gradient boosting regressor exhibiting exceptional performance in price forecasting. While the study’s potential limitations include generalizability across diverse datasets, its applications include enhancing pricing models in the automotive sector and improving data quality in large-scale market analyses.
Volume: 15
Issue: 2
Page: 2364-2371
Publish at: 2025-04-01

Situational leadership and its relationship to crisis management among Jordanian universities

10.11591/ijere.v14i2.30405
Omar Taissir Bataineh , Hytham Mohmmad Bany Issa , Zohair Hussein Al-Zoubi
This study investigates the extent of situational leadership practice and its correlation with crisis management among heads of academic departments in Jordanian universities, as perceived by faculty members. Employing a descriptive-correlational approach, the study sampled 405 faculty members in Jordanian universities for the spring semester of 2022/2023. To collect data situational leadership scales and crisis management scale were used. The findings revealed a high degree of situational leadership practice and crisis management effectiveness among department heads. Moreover, a statistically significant relationship between situational leadership and crisis management is observed. The study suggests selecting department heads with strong competencies and involving faculty members in crisis management courses to enhance overall effectiveness.
Volume: 14
Issue: 2
Page: 777-785
Publish at: 2025-04-01

OCNet-23: a fine-tuned transfer learning approach for oral cancer detection from histopathological images

10.11591/ijece.v15i2.pp1826-1833
Amatul Bushra Akhi , Abdullah Al Noman , Sonjoy Prosad Shaha , Farzana Akter , Munira Akter Lata , Rubel Sheikh
Oral squamous cell carcinoma (OSCC) is emerging as a significant global health concern, underscoring the need for prompt detection and treatment. Our study introduces an innovative diagnostic method for OSCC, leveraging the capabilities of artificial intelligence (AI) and histopathological images (HIs). Our primary objective is to expedite the identification process for medical professionals. To achieve this, we employ transfer learning and incorporate renowned models such as VGG16, VGG19, MobileNet_v1, MobileNet_v2, DenseNet, and InceptionV3. A key feature of our approach is the meticulous optimization of the VGG19 architecture, paired with advanced image preprocessing techniques such as contrast limited adaptive histogram equalization (CLAHE) and median blur. We conducted an ablation study with optimized hyperparameters, culminating in an impressive 95.32% accuracy. This groundbreaking research ensures accurate and timely diagnoses, leading to improved patient outcomes, and represents a significant advancement in the application of AI for oral cancer diagnostics. Utilizing a substantial dataset of 5,192 meticulously categorized images into OSCC and normal categories, our work pioneers the field of OSCC detection. By providing medical professionals with a robust tool to enhance their diagnostic capabilities, our method has the potential to revolutionize the sector and usher in a new era of more effective and efficient oral cancer treatment.
Volume: 15
Issue: 2
Page: 1826-1833
Publish at: 2025-04-01

Computer simulation of transition modes in flow reactors considering the multistage and reactions non-perfectness

10.11591/ijeecs.v38.i1.pp486-495
Aizhan Tazhikhanovna Kalbayeva , Zhanat Rysbayevna Umarova , Sevara Dzhumagaliyevna Kurakbayeva , Leyla Mukhamedjanovna Musabekova , Madamin A. Amandikov , Kuttybek E. Arystanbayev
Due to the variety of reaction types and schemes in chemical-technological apparatuses, a general engineering methodology to assessing how the transient modes and reactions multi-stage act the kinetics in conditions of occurrence of moving reaction fronts in flow apparatuses has not yet been developed. The paper devotes to constructing the mathematical models for several important cases of the problem mentioned, namely: for theoretically study the kinetic dynamics of the conversion process in a three-stage chemical reaction with an autocatalytic first stage and the presence of a mass source of one of the components. An original mathematical model for describing the chemisorption dynamics at the initial stage of forming a moving reaction front in flow-through apparatuses has been developed. A special algorithm and numerical solution for the initial absorption period have been constructed, and appropriate computer simulation has been implemented. The significant influence of multistage on the formation and on stability types of stationary states has been established. Expressions to evaluate the characteristics of the emerging oscillatory modes have been obtained too. The results can be used to assess the influence of control parameters on the reaction front movement speed.
Volume: 38
Issue: 1
Page: 486-495
Publish at: 2025-04-01

Fostering integrity among school principals’ ethical leadership: a comprehensive systematic review

10.11591/ijere.v14i2.30993
Mackencidy ak Bujang , Bity Salwana Alias , Azlin Norhaini Mansor
This systematic review explores the importance of promoting integrity in the ethical leadership of school principals, particularly when ethical lapses by educational leaders can impact outcomes. Increasing societal pressures and a complex educational environment highlight the importance of ethical leadership in shaping the school environment and student success. However, the lack of a complete understanding of how to effectively create ethical leadership remains a major concern. An extensive search of scholarly articles was conducted from reputable databases such as Scopus, Web of Science, and ERIC, focusing on studies published between 2020 and 2024. The flow of study based on PRISMA framework. The database found (n=19) final primary data was analyzed. The finding was divided into three themes which is academic integrity and ethics in education, leadership and integrity in educational management, and character and value education in schools. Comprehensive programs and administrative support are essential to fostering integrity in schools, while effective leadership plays an important role in shaping a conducive school environment. In conclusion, the need to foster integrity among school principals across disciplinary boundaries requires concerted efforts and innovative approaches to prepare ethical leaders to navigate the complexities of the contemporary educational landscape and inspire positive change.
Volume: 14
Issue: 2
Page: 1034-1043
Publish at: 2025-04-01

A qualitative case study of constructivist teaching at a high school in a northern area of Vietnam

10.11591/ijere.v14i2.32123
Pham Thi Kieu Oanh , Nguyen Van-Trao , Nguyen Huong Thi Mai
This study aims to identify the English high school teachers’ beliefs at a high school in the northern region of Vietnam regarding constructivist teaching (CT) and their actual classroom practices (CP). This paper summarizes results from a five-year Ph.D. study conducted with seven high-school English teachers within a qualitative case study research design with the help of three data collection instruments, including semi-structured and Stimulated recall interviews and direct classroom observations. Thematic analysis using MAXQDA was chosen for data analysis. The findings uncovered themes that emerged from the data, which might lay the foundation for classifying teachers into three groups: adaptive originators (AOs), neutral pragmatists (NPs), and traditional conservers (TCs). Most TCs’ teaching philosophies were teacher-centered and supportive of conventional teaching techniques. Still, the NPs acknowledged that instructional strategies needed to be changed. The AOs sought to change how teachers used CT; they actively put strategy into place to bring about meaningful change. These results were valuable references for high school teachers to assist their students with better constructivist instruction, thus enhancing the quality of teaching and learning English in the 4.0 era.
Volume: 14
Issue: 2
Page: 1436-1446
Publish at: 2025-04-01

Teacher technology usage, a catalyst for principal digital leadership practice

10.11591/ijere.v14i2.31914
Rui Zhu , Bity Salwana Alias , Mohd Izham Mohd Hamzah , Jamalullail Abdul Wahab
In the digital age, university principals and teachers share the same responsibility for improving educational digital transformation. However, their performance remains disadvantaged. This study aims to investigate the relationship between principal digital leadership and teacher technology usage, and explores how teacher technology usage contributes to principal digital leadership. The study employed a quantitative method, collecting data from 500 teachers across 25 universities in Jilin Province, China. Pearson correlation analysis examined the association between principal digital leadership and teacher technology usage, while multiple regression explored how teacher technology usage contributes to principal digital leadership practices. The results revealed a positive relationship between principal digital leadership and teacher technology usage. All four dimensions of teacher technology usage contribute to digital leadership practices, with the professional development dimension showing the greatest impact. Overall, these findings prove that teacher technology usage indeed catalyzes principal digital leadership practices, which contributes to understanding of the interaction in leadership and provides a new perspective for enhancing principal digital leadership. It implies that principals and teachers can grow together in their interactions. Therefore, strengthening teachers’ professional development can contribute to principal digital leadership practice.
Volume: 14
Issue: 2
Page: 1227-1234
Publish at: 2025-04-01

Development and instrument validation of Indonesian achievement motivation scale using the Rasch model

10.11591/ijere.v14i2.29374
Bambang Dibyo Wiyono , Nur Hidayah , Muhammad Ramli , Adi Atmoko , Amin Al-Haadi Shafie
This research involves the development of items on an achievement motivation scale that is used in improving the achievement motivation of senior high school students. No research on instrument development has been carried out on measuring the level of achievement motivation of senior high school students in Indonesia. Participants tested the development of 38 items and consisted of 1,909 respondents as students from senior high schools in the City of Surabaya. The utilized analysis technique was the Rasch model. Results of applying the Rasch analysis indicated that achievement motivation scale was good, proper, and appropriate in items to the model. The accomplishment motivation scale is a valid and dependable instrument for precisely determining pupils’ levels of accomplishment motivation. In light of the achievement motivation scale results, this study explores the consequences and suggests directions for future research on the use of guidance and counseling.
Volume: 14
Issue: 2
Page: 1427-1435
Publish at: 2025-04-01

Handwritten text recognition system using Raspberry Pi with OpenCV TensorFlow

10.11591/ijece.v15i2.pp2291-2303
Jamil Abedalrahim Jamil Alsayaydeh , Tommy Lee Chuin Jie , Rex Bacarra , Benny Ogunshola , Noorayisahbe Mohd Yaacob
Handwritten text recognition (HTR) technology has brought about a revolution in the way handwritten data is converted and analyzed. This proposed work focuses on developing a HTR system using deep learning through advanced deep learning architecture and techniques. The aim is to create a model for real-time analysis and detection of handwritten texts. The proposed deep learning architecture that is convolutional neural networks (CNNs), is investigated and implemented with tools like OpenCV and TensorFlow. The model is trained on large handwritten datasets to enhance recognition accuracy. The system’s performance is evaluated based on accuracy, precision, real-time capabilities, and potential for deployment on platforms like Raspberry Pi. The actual outcome is a robust HTR system that can convert handwritten text to digital formats accurately. The developed system has achieved a high accuracy rate of 91.58% in recognizing English alphabets and digits and outperformed other models with 81.77% mAP, 78.85% precision, 79.32% recall, 79.46% F1-Score, and 82.4% receiver operating characteristic (ROC). This research contributes to the advancement of HTR technology by enhancing its precision and utility.
Volume: 15
Issue: 2
Page: 2291-2303
Publish at: 2025-04-01

Enhancing spatiotemporal weather forecasting accuracy with 3D convolutional kernel through the sequence to sequence model

10.11591/ijece.v15i2.pp2022-2030
Renaldy Fredyan , Karli Eka Setiawan , Kelvin Asclepius Minor
Accurate weather forecasting is important when dealing with various sectors, such as retail, agriculture, and aviation, especially during extreme weather events like heat waves, droughts, and storms to prevent disaster impact. Traditional methods rely on complex, physics-based models to predict the Earth's stochastic systems. However, some technological advancements and the availability of extensive satellite data from beyond Earth have enhanced meteorological predictions and sent them to Earth's antennae. Deep learning models using this historical data show promise in improving forecast accuracy to enhance how models learn the data pattern. This study introduces a novel architecture, convolutional sequence to sequence (ConvSeq2Seq) network, which employs 3D convolutional neural networks (CNN) to address the challenges of spatiotemporal forecasting. Unlike recurrent neural network (RNN)--based models, which are time-consuming due to sequential processing, 3D CNNs capture spatial context more efficiently. ConvSeq2Seq overcomes the limitations of traditional CNN models by ensuring causal constraints and generating flexible length output sequences. Our experimental results demonstrate that ConvSeq2Seq outperforms traditional and modern RNN-based architectures in both prediction accuracy and time efficiency, leveraging historical meteorological data to provide a robust solution for weather forecasting applications. The proposed architecture outperforms the previous method, giving new insight when dealing with spatiotemporal with high density.
Volume: 15
Issue: 2
Page: 2022-2030
Publish at: 2025-04-01

A constrained convolutional neural network with attention mechanism for image manipulation detection

10.11591/ijece.v15i2.pp2304-2313
Kamagate Beman Hamidja , Fatoumata Wongbé Rosalie Tokpa , Vincent Mosan , Souleymane Oumtanaga
The information disseminated by online media is often presented in the form of images, in order to quickly captivate readers and increase audience ratings. However, these images can be manipulated for malicious purposes, such as influencing public opinion, undermining media credibility, disrupting democratic processes or creating conflict within society. Various approaches, whether relying on manually developed features or deep learning, have been devised to detect falsified images. However, they frequently prove less effective when confronted with widespread and multiple manipulations. To address this challenge, in our study, we have designed a model comprising a constrained convolution layer combined with an attention mechanism and a transfer learning ResNet50 network. These components are intended to automatically learn image manipulation features in the initial layer and extract spatial features, respectively. It makes possible to detect various falsifications with much more accuracy and precision. The proposed model has been trained and tested on real datasets sourced from the literature, which include MediaEval and Casia. The obtained results indicate that our proposal surpasses other models documented in the literature. Specifically, we achieve an accuracy of 87% and a precision of 93% on the MediaEval dataset. In comparison, the performance of methods from the literature on the same dataset does not exceed 84% for accuracy and 90% for precision.
Volume: 15
Issue: 2
Page: 2304-2313
Publish at: 2025-04-01
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