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31,456 Article Results

Enhancing vocational computer engineering education with a GPT-driven speech recognition tool

10.11591/ijeecs.v39.i1.pp564-574
Putra Utama Eka Sakti , Alva Hendi Muhammad , Asro Nasiri
This research investigates the effectiveness of an AI-driven speech recognition and GPT-powered learning tool in enhancing vocational students’ proficiency in computer networks. The study involved 100 students from vocational hig school, who used the prototype as part of their learning process. A pre-test/post-test design was employed to assess changes in proficiency, and students also provided feedback on the tool’s usability and impact. The results showed a consistent improvement in proficiency across all classes. A strong positive correlation was found between students’ feedback and their proficiency improvement, suggesting that students who rated the prototype as Very Helpful were more likely to see significant learning gains. However, the correlation between time spent using the tool and proficiency improvement was minimal, indicating that the quality of engagement with the tool was more important than the duration of usage. These findings highlight the prototype’s potential to improve vocational learning outcomes and underscore the importance of user satisfaction in driving success, with future refinements necessary to ensure the tool’s broader effectiveness across different learning contexts.
Volume: 39
Issue: 1
Page: 564-574
Publish at: 2025-07-01

Blockchain-based decentralized voting system with SHA-256 algorithm and facial recognition

10.11591/ijres.v14.i2.pp481-489
BJD Kalyani , Jaya Krishna Modadugu , Sarabu Neelima
Blockchain technology has completely changed the way data is stored and transactions are verified. It provides a dependable, transparent, and safe medium for communication and transaction validation. In order to solve the drawbacks of conventional electronic voting systems, the goal of this research project is to design a decentralized voting system based on blockchain technology. The suggested method offers an immutable and safe way to record and validate votes by utilizing the security and transparency capabilities of blockchain technology. The suggested approach provides an immutable and safe way to record and validate votes by utilizing the security and transparency capabilities of blockchain technology. This paper aims to provide a comprehensive process for digital identity authentication, create a voter interface that is compatible with Ethereum wallets, and apply smart contracts on the Ethereum network to speed up voter registration, ballot preparation, voting, and result tabulation. Additionally, this paper proposes to build up a multi-factor authentication system for election managers and validators to offer them safe and approved power over the voting process. By carefully examining the existing methods, this research highlights the flaws and weaknesses of traditional electronic voting systems and stresses the need for more trustworthy and secure voting technology. The proposed blockchain-based voting system offers an innovative solution to problems with voter fraud and election manipulation because of its irreversible blockchain record, which gives a high degree of transparency and integrity.
Volume: 14
Issue: 2
Page: 481-489
Publish at: 2025-07-01

An optimized simulated annealing memetic algorithm for power and wirelength minimization in VLSI circuit partitioning

10.11591/ijres.v14.i2.pp365-374
P. Rajeswari , Smitha Sasi
The development of physical architecture standards for very large scale integration (VLSI) single and multichip platforms is still in its early stages. To deal with the growing complexity of modern VLSI systems, it has become common practice to split large circuit architectures into smaller, easier-to-manage sub-circuits. Circuit partitioning improves parallel modeling, testing, and system performance by lowering chip size, number of components and interconnects, wire length (WL), and delays. VLSI partitioning's primary goal is to split a circuit into smaller blocks with as few connections as possible between them. This is frequently accomplished by recursive bi-partitioning until the required complexity level is reached. Thus, partitioning is a fundamental circuit design challenge. An efficient remedy that offers a heuristic method that explores the design space to iteratively enhance outcomes is evolutionary computation. In order to minimize WL, area, and interconnections, we provide an optimized simulated annealing memetic algorithm (OSAMA) that combines local search methods with evolutionary tactics. The efficiency of the method was evaluated using criteria like runtime, cost, delay, area, and WL. OSAMA's ability for effective partitioning is demonstrated by experimental results, which confirm that it dramatically lowers important design parameters in VLSI circuits.
Volume: 14
Issue: 2
Page: 365-374
Publish at: 2025-07-01

RIBATS: RSSI-based adaptive tracking system with ASEKF for indoor WSN

10.11591/ijeecs.v39.i1.pp225-234
Rafina Destiarti Ainul , Hendi Wicaksono Agung
Wireless indoor tracking systems face challenges due to environmental conditions and signal attenuation, affecting location accuracy, crucial in wireless sensor network (WSN) applications. Many tracking techniques rely on specific path loss models proposed by previous researches, but these models are susceptible to changes in environmental conditions, impacting estimation outcomes. In order to solve these problems, this paper propose adaptive tracking system using received signal strength indicator (RSSI) measurement parameter called as RIBATS. Adaptive in this system refers to the reliability of an algorithm for obtaining the accurate location without any path loss modelling at dynamic indoor environments. The enhancement of weighted centroid localization (eWCL) scheme calculates the location estimation only using RSSI data measurement without propagation characterisic determination. However, estimation result from eWCL still have high error at certain area. Hence, by defining a multiplier factor as adaptive scaled to the covariance matrix of EKF can eliminate distortion effects from eWCL called as adaptive scaled extended Kalman filter (ASEKF) algorithm. An effective variance estimation algorithm for adaptive indoor tracking system using eWCL and ASEKF combination achieve 0.82 meters mean square error (MSE) value with 55.67% error reduction. Then, without using multiplier scale factor at EKF algorithm only reduce previous eWCL at 3.78% with 1.78 meters MSE value.
Volume: 39
Issue: 1
Page: 225-234
Publish at: 2025-07-01

Enhancing scalability and efficiency in technological transaction utilizing dual-layer blockchain approach

10.11591/ijres.v14.i2.pp452-462
T. Kanimozhi , M. Inbavalli
The leather industry encounters significant challenges in integrating blockchain technology and smart contracts into its complex supply networks. Despite technological advancements, existing supply chain management systems suffer from inefficiencies, opacity, and vulnerabilities to fraud. Blockchain offers promising solutions such as immutable ledgers, decentralized governance, and smart contract automation. However, scalability limitations hinder the efficient handling of high transaction volumes, impacting procurement, production, inventory management, and distribution processes, leading to delays and increased costs. This research aims to address these challenges by exploring innovative approaches, including dual-layer blockchain architectures incorporating sharding and state channels, tailored to the unique needs of the leather industry. By overcoming scalability barriers, the research seeks to unlock the transformative potential of blockchain technology and smart contracts, enhancing transparency, traceability, and efficiency in leather supply chains while ensuring global interoperability and regulatory compliance. Through empirical validation and comparative analysis, this study provides understandings into the practical implementation of blockchain solutions within the leather industry, offering strategic guidance for sustainable supply chain management practices.
Volume: 14
Issue: 2
Page: 452-462
Publish at: 2025-07-01

An efficient DVHOP localization algorithm based on simulated annealing for wireless sensor network

10.11591/ijeecs.v39.i1.pp720-736
Omar Arroub , Anouar Darif , Rachid Saadane , My Driss Rahmani , Zineb Aarab
In the last decade, the research community has devoted significant attention to wireless sensor networks (WSNs) because they contribute positively to some critical issues encountered in nature and even in industry. On the other hand, localization is one of the most important parts of WSN. Hence, the conception of an efficient method of localization has become a hot research topic. Lastly, it has been invented, a set of optimal positioning methods that make locate a node with low cost and give precise results. In our contribution, we investigate the source of imprecision in the distance vectorhop (DVHOP) localization algorithm. However, we found the last step of DVHOP caused an imprecision in the calculation. Consequently, our work was to replace this step, aiming to reach satisfactory precision. For that purpose, we created three improved versions of this algorithm by adopting two meta-heuristic (simulated annealing, particle swarm optimization) and Fmincon solver dedicated to optimization in the field of WSN node localization. The experimental results obtained in this work prove the efficiency of simulated annealing (SA)-DVHOP in terms of accuracy. Furthermore, the enhanced algorithm outperforms its opponents by varying the percentage of anchors and the number of nodes.
Volume: 39
Issue: 1
Page: 720-736
Publish at: 2025-07-01

Influences of the Sm3+ -Eu3+ codoped Ba2Gd(BO3)2Cl phosphors on the commercial white light emitting diodes

10.11591/ijeecs.v39.i1.pp62-69
Luu Hong Quan , My Hanh Nguyen Thi
The color quality of current commercial white light emitting diodes (wLEDs) suffers low performance owing to the lack of the red-emission component. Developing quality and stable red-emission phosphors is feasible among various approaches to obtain the red spectral supplement for the w-LEDs in the pursuit of color quality improvement. In this paper, the Sm3+-Eu3+ codoped Ba2Gd(BO¬3)2Cl (BGBC:Sm-Eu) red phosphor was proposed for using in commercial w-LEDs. Its luminescence and influences on w-LED properties were simulated and presented. The solid-phase method was utilized for the fabrication of the phosphor. The results indicated that the phosphor emitted the strong emission in orange-red region with a peak centering at 593 nm. It can be caused by the proficient power shift between Sm3+ and Eu3+. In the w-LED package, the presence of BGBC:Sm-Eu phosphor stimulated the scattering efficiency to promote the blue-light conversion and extraction. The orange emission spectrum of the w-LED increased with the higher BGBC:Sm-Eu doping amount. The luminous strength of the w-LED was enhanced and so was the color temperature uniformity. The color rendering properties declined with high BGBC:Sm-Eu phosphor concentration owing to the red-light dominance over the light spectrum. The BGBC:Sm-Eu phosphor is a promising red phosphor for improving commercial w-LED color-temperature stability and luminosity. It also helps to obtain full-spectrum w-LED with high color rendition when combined with other blue-to-green luminescent materials.
Volume: 39
Issue: 1
Page: 62-69
Publish at: 2025-07-01

Pipelined reconfigurable architecture for 5G software-defined radio systems

10.11591/ijres.v14.i2.pp320-327
Vijaya Bhaskar Chalampalem , Munaswamy Pidugu , Sanacarapu Nagaraju
The filters are used to allow a specific band of frequencies. In a wireless communication, the filter is used to select the frequency of operation with a narrow or broad band. As the generations increase the amount of data handled increases drastically. 5G data rate can be significantly deliver up to 20 Gigabits per second while 4G communication data rate is handled in the order of 100 Megabits per second. Now the challenge becomes processing data at such a speed with low power and low area specifications. The filters that can configure themselves as per the data received are reconfigurable filters so that the bandwidth is saved. Also, when the pipelining is introduced, the reconfigurable filter improves the performance of the design. This paper details about the pipelined reconfigurable finite impulse response (RFIR) filter with the simplest algorithm with auto updating capability. The design is modelled in Verilog hardware description language (HDL) language, synthesized for Cyclone III field-programmable gate array (FPGA). The results prove that the proposed filter increases only slightly with respect to delay and power dissipation with a trade off in area and maximum possible clock frequency.
Volume: 14
Issue: 2
Page: 320-327
Publish at: 2025-07-01

Random forest method for predicting discharge current waveform and mode of dielectric barrier discharges

10.11591/ijeecs.v39.i1.pp101-109
Laiadi Abdelhamid , Chentouf Abdellah , Ezziyyani Mostafa
This study addresses the classification of Homogeneous and Filamentary discharge modes in dielectric barrier discharge (DBD) systems and predicts the Homogeneous current waveform using machine learning (ML). The motivation stems from the need for accurate modelling in non-thermal plasma systems. The problem tackled is distinguishing between these two modes and predicting the current waveform for Homogeneous discharge. A random forest classification algorithm is applied, using experimental features such as applied voltage, frequency, gas gap, dielectric material, and gas type. An exponential model is proposed for the discharge current, with Gaussian regression transforming the model’s parameters. The classification results are evaluated through a confusion matrix, showcasing 80% accuracy in distinguishing discharge modes. The regression analysis reveals strong Pearson correlation coefficients between predicted and experimental waveforms. In conclusion, the results demonstrate the efficacy of ML techniques in enhancing DBD system modelling, though improvements can be made by expanding the dataset and refining feature selection for better classification and prediction performance.
Volume: 39
Issue: 1
Page: 101-109
Publish at: 2025-07-01

Low-resolution image quality enhancement using enhanced super-resolution convolutional network and super-resolution residual network

10.11591/ijeecs.v39.i1.pp634-643
Mohammad Faisal Riftiarrasyid , Rico Halim , Andien Dwi Novika , Amalia Zahra
This research explores the integration of enhanced super-resolution convolutional network (ESPCN) and super-resolution residual network (SRResNet) to enhance image quality captured by low-resolution (LR) cameras and in internet of things (IoT) devices. Focusing on face mask prediction models, the study achieves a substantial improvement, attaining a peak signal-to-noise ratio (PSNR) of 28.5142 dB and an execution time of 0.34704638 seconds. The integration of super-resolution techniques significantly boosts the visual geometry group-16 (VGG16) model’s performance, elevating classification accuracy from 71.30% to 96.30%. These findings highlight the potential of super-resolution in optimizing image quality for low-performance devices and encourage further exploration across diverse applications in image processing and pattern recognition within IoT and beyond.
Volume: 39
Issue: 1
Page: 634-643
Publish at: 2025-07-01

Benchmarking spectral handoff rate performance in cognitive wireless networks with real multi-user access

10.11591/ijeecs.v39.i1.pp190-201
Cesar Hernández , Diego Giral , Fredy Martínez
Cognitive radio (CR) has proven to be an excellent alternative to the problem of inefficient spectrum use in wireless networks. However, the vast majority of proposals found in the current literature are restricted to the access of a single secondary user (SU) to the network, and the few proposals with multiple access do not take into account the access of other primary users (PUs) during the opportunistic transmission of the SU. The objective of this work is to perform a comparative evaluation of the spectral handoff (SH) rate in cognitive wireless networks with multi-user access in an environment with other PUs interacting. To carry out this evaluation, four SH models with better performance were selected: deep learning (DL), feedback fuzzy analytic hierarchy process (FFAHP), simple additive weighting (SAW), and Naïve Bayes (NB), which were validated according to the metric of the number of total handoffs, under four scenarios given by the combination of the following parameters: low spectral availability, high spectral availability, active presence of others SUs, and passive presence of others SUs. The results show that each model performs well according to the scenario in which it is executed, suggesting an adaptive multi-model as a proposal.
Volume: 39
Issue: 1
Page: 190-201
Publish at: 2025-07-01

Geographic information system for marine ecotourism and rural lifestyle in Prachuap Khiri Khan

10.11591/ijeecs.v39.i1.pp485-496
Sompond Puengsom , Jakkapong Polpong , Phisit Pornpongtechavanich
According to the Prachuap Khiri Khan Province tourism statistics report for 2023, there were 11,143,079 Thai and foreign tourists from January to December 2023, which increased by 1,395,195 people or 14.31 percent compared to 2022. Simultaneously, tourist attractions accumulated tourism income in 2023 totaling 44,241 million baht, marking an increase of 11,402.63 million baht or 34.72 percent from 2022. Despite this growth, tourist attractions that are popular with tourists remain centered in Hua Hin District due to a lack of publicity and insufficient information provided to tourists. Consequently, the researcher intended to develop a geographic information system (GIS) for marine ecotourism and rural lifestyles in Prachuap Khiri Khan Province to promote rural tourist attractions and distribute tourism income to the community. The system utilized the classification (precision and recall) model and was developed using ArcGIS and the web app builder ArcGIS. Findings from 8 experts in computers, information technology (IT), and GIS indicated that the overall system efficiency had an average of 4.54 and a standard deviation of 0.50. Additionally, results from the study on retrieval efficiency using the classification (precision and recall) model revealed a precision value of 0.90 and a recall value of 0.95.
Volume: 39
Issue: 1
Page: 485-496
Publish at: 2025-07-01

Virtual learning environment on satisfaction and academic performance of students in institutions of higher learning

10.11591/ijeecs.v39.i1.pp258-271
Odunayo Dauda Olanloye , Peter Adebayo Idowu , Abidemi Emmanuel Adeniyi , Afolake Afusat Badmus , Oluwasegun Julius Aroba
As a result of the COVID-19 outbreak in 2020, education institutions across the world had to come to a functional standstill since they had to protect their students from viral exposures thereby affecting academic activities. However, several institutions had to adopt online virtual learning environments (VLE) using basic information and communication technology tools to provide platforms for teaching and learning thereby mitigating the effects of the pandemic on the students. This study was focused on the identification of the various types of VLE tools that were adopted alongside the impact that these tools had on learning satisfaction and the academic performance of students of higher learning in Nigeria. This study adopted a purposive simple random selection of undergraduate students of the department of computer science who had adopted the use of VLE to learn during the period of the pandemic. The results of the study showed that the most popular VLE tools were Zoom, Google Classroom, WhatsApp, Telegram, Coursera, Google Forms and learning management systems (LMS) while the least popular VLE tools were Microsoft Teams, Moodle/Edmondo, and Google Meet. The results showed that the students agreed to their behavioral intention to use VLE, the impact of VLE on learning satisfaction, and the impact of VLE on academic performance alongside the existence of a positive correlation among the research variables.
Volume: 39
Issue: 1
Page: 258-271
Publish at: 2025-07-01

Real-time driver drowsiness detection based on integrative approach of deep learning and machine learning model

10.11591/ijeecs.v39.i1.pp592-602
Gowrishankar Shiva Shankara Chari , Jyothi Arcot Prashant
Driver drowsiness is a major factor that contributing to road accidents. Several researches are ongoing to detect driver drowsiness, but they suffer from the complexity and cost of the models. This paper introduces a hybrid artificial intelligence (AI)-driven framework integrating deep learning (DL) and machine learning (ML) models for real-time drowsiness detection. The system utilizes a robust DL model to classify driver states based on facial images and support vector machine (SVM) model is trained to develop a cost-efficient yet robust facial landmark detector to extract key features such as eye aspect ratio (EAR) and mouth aspect ratio (MAR). We also introduce a multi-stage decision fusion mechanism that combines convolutional neural network (CNN) probability scores with EAR/MAR thresholds to enhance detection reliability and reduce false positives. Experimental results demonstrate that the proposed model achieves 98% accuracy and F1-score, significantly outperforming traditional DL approaches. Additionally, the SVM-based landmark predictor shows improved efficiency with lower mean squared error (MSE) without having higher computational requirements.
Volume: 39
Issue: 1
Page: 592-602
Publish at: 2025-07-01

A Custom RISC-V Based Architecture for Real-Time ECG Feature Extraction

10.11591/ijres.v14.i2.pp%p
Vinayak Vikram Shinde , Sheetal Umesh Bhandari , Deepti Snehal Khurge , Satyashil Dasharath Nagarale , Ujwal Ramesh Shirode
The need for small, energy-efficient biomedical signal processing systems has grown along with the popularity of wearable health tracking systems. In order to facilitate real-time processing in wearable health-monitoring devices, this paper presents Register Transfer Level (RTL) design and simulation of a custom architecture for real-time Electrocardiogram (ECG) feature extraction. The architecture includes a Reduced Instruction Set Computer-V (RISC-V) based control core and dedicated hardware blocks such as Arithmetic and Logical Unit (ALU), Memory, Control Unit for processing ECG signals to extract ECG features such as R-peaks and heart rate. The design flow includes RTL design, synthesis, and validation. It uses ECG datasets and EDA tools. The purpose of this paper is to come up with a small, power-efficient solution for wearable tech to process biomedical data.
Volume: 14
Issue: 2
Page:
Publish at: 2025-06-10
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