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23,598 Article Results

Empowering E-learning through blockchain: an inclusive and affordable tutoring solution

10.11591/ijece.v14i5.pp5554-5565
Saadia Lgarch , Meriem Hnida , Asmaa Retbi
This study presents an innovative approach using the Ethereum blockchain to democratize access to tutoring services, advancing educational technology by bridging the affordability gap for learners with limited financial resources. This solution enables low-income learners to access tutoring services without significant expenses by eliminating intermediaries through smart contracts. Learners can directly book tutoring services based on fees and evaluations, ensuring a fair and accessible experience. The findings show that this approach reduces tutoring expenses and improves trust and accountability through transparent transactions and feedback mechanisms. The proposed system demonstrates how blockchain technology can foster a more equitable and efficient educational landscape, offering personalized
Volume: 14
Issue: 5
Page: 5554-5565
Publish at: 2024-10-01

A fuzzy logic scheme based on spread rate and population for pandemic vaccine allocation

10.11591/ijece.v14i5.pp5941-5948
Abdul Kareem , Varuna Kumara
This paper deals with a novel decision-making scheme for inferring the allocation of vaccines to the provincial health care authorities by the central health care authority of a country in pandemic scenarios. This novel scheme utilizes a fuzzy logic-based inference scheme that utilizes the spread rate and population of a province as inputs to infer the vaccination rate. The proposed scheme is evaluated on the coronavirus disease (COVID-19) data from six southern states of India during the first week of October 2020, collected from the database maintained by the Government of India. The findings demonstrate that the suggested plan, which takes population and spread rate into account, makes sure that enough vaccination doses are distributed to the provinces with a larger spread rate with a higher priority, and that immunizations are not delayed in provinces with controlled spread rates. Also, in due course, all territories will appropriately distribute enough vaccine supply to control the spread. Therefore, this plan strengthens the efforts to control the pandemic outbreaks by ensuring the proper and balanced delivery of vaccines in a timely, efficient, and objective manner.
Volume: 14
Issue: 5
Page: 5941-5948
Publish at: 2024-10-01

Utilizing digital elevation models and geographic information systems for hydrological analysis and fire prevention in Khuan Kreng peat swamp forest, Southern Thailand

10.11591/ijece.v14i5.pp5408-5419
Uraiwun Wanthong , Somporn Ruang-On , Nunticha Limchoowong , Phitchan Sricharoen , Panjit Musik
The objectives of this research were to create a topographic model using Mathematica and hydrologic model using ArcGIS for water management aimed at preventing forest fires in the Khuan Kreng peat swamp forest. Pan basin area in Kreng Sub-district, characterized by low mountains, where the Cha-Uat canal intersects the krajood forest, was revealed by the hydrographic model. Kreng Sub-district was traversed by three main streams: Khuan canal, Hua Pluak Chang canal, and Laem canal. Additionally, several tributary canals that interconnect, ultimately converging into the Cha-Uat Phraek Muang canal were identified. During the dry period, the water from these canals flowed into the Cha-Uat Phraek Muang canal. To mitigate the risk of fires, it was essential to install water table measuring devices and underground barrier gates at the drain points. This ensured the return of water from the Cha-Uat Phraek Muang canal to the Khuan Kreng peat swamp forest. Maintaining sufficient water table level was crucial, as the occurrence of fires was more likely when the water table dropped below the soil surface. When the swamp forest was adequately hydrated, wildfires were confined to a narrow area since they could only burn on the forest surface, which was easier to extinguish.
Volume: 14
Issue: 5
Page: 5408-5419
Publish at: 2024-10-01

Cattle weight prediction model using convolutional neural network and artificial neural network

10.11591/ijeecs.v36.i1.pp441-449
Yulianingsih Yulianingsih , Sri Nurdiati , Heru Sukoco , Cece Sumantri
The weight of livestock is a crucial metric for evaluating management efficacy, informing policy decisions, and determining the market value of animals. In certain scenarios, conventional methods such as physical weighing and measurement calculations can prove challenging, including the absence of livestock health records or weighing equipment. This research aims to develop a predictive model for estimating the live weight of cattle through visual assessments and metadata, including age and pixel count, utilizing a combination of convolutional neural network (CNN) and artificial neural network (ANN) methodologies. A total of 223 data were obtained from a local farm before augmentation. The model's predictive capability was successfully demonstrated, with its performance quantified by an average mean absolute percentage error (MAPE) of 10% on test data. This study demonstrates that through the combination of CNN and ANN, as well as optimal parameter tuning, efficient prediction of cattle weight can be achieved.
Volume: 36
Issue: 1
Page: 441-449
Publish at: 2024-10-01

Optimizing channel capacity for B5G with deep learning approaches in MISO-NOMA-HBF and BFNN

10.11591/ijeecs.v36.i1.pp205-213
Muhammad Atique Masud , Ahmed Al Amin , Md. Shoriful Islam , Vaskor Mostafa , Md. Wahiduzzaman
This study proposes the integration of a beamforming neural network (BFNN) and multiple-input single-output based non-orthogonal multiple access (MISO-NOMA) with hybrid beamforming (HBF) for cell edge users (CEU) in a millimeter wave (mmWave)-based beyond 5G cellular communication system. This system is referred to as MISO-NOMA-HBF-BFNN. The proposed scheme has been implemented to support multiple users simultaneously and also to considerably enhance and significantly improve the overall the sum channel capacity (SC) and user channel capacities. Additionally, the simulation results demonstrate the superiority of the proposed MISO-NOMA-HBF-BFNN scheme over the existing MISO-NOMA with HBF and MISO-OMA with HBFBFNN based schemes in terms of user capacities and SC.
Volume: 36
Issue: 1
Page: 205-213
Publish at: 2024-10-01

A framework for reusable domain specific software component extraction based on demand

10.11591/ijeecs.v36.i1.pp274-281
N Md Jubair Basha , Gopinath Ganapathy , Moulana Mohammed
The majority of organizations use an agile software development methodology. Standard analysis and design processes are abandoned due to the enormous demand of generating the product within time and budget. This may result in a lack of high-quality software while components are not constructively reused. The components are identified at a later stage in the majority of component approaches. To address such challenges, a methodology for extracting demand-based domain-specific software components from the repository was developed. The process for reusing current components is described in depth with various domain-specific components, and the suggested framework is for extracting demand-based reusable domain-specific software components.
Volume: 36
Issue: 1
Page: 274-281
Publish at: 2024-10-01

New droop-based control of parallel voltage source inverters in isolated microgrid

10.11591/ijece.v14i5.pp4856-4868
Timilehin F. Sanni , Ayokunle A. Awelewa , Anthony U. Adoghe , Adeola Balogun , Tobi Somefun
Microgrids, featuring distributed generators like solar energy and hybrid energy storage systems, represent a significant step in addressing challenges related to the greenhouse effect and outdated transmission infrastructures. The operation and control of islanded microgrids, particularly in terms of grid voltage and frequency, rely on the synchronization of multiple parallel inverters connected to the distributed generators. However, to determine the necessary grid parameters for effective control, the presence of circulating currents from unbalanced grid voltages arises as a challenge. This situation necessitates the development of a new approach to achieve phase angle locking for grid synchronization, with the aim of maintaining the voltage within acceptable limits in islanded microgrids. This objective is realized through the creation of a microgrid network model, design of an adaptive filter, utilizing the double second-order generalized integrator–phase-locked loop (DSOGI-PLL), for dynamic voltage transformation. The design is evaluated by simulation using MATLAB/Simulink. The primary goal is to investigate the DSOGI-PLL-based droop control and compare its performance with the conventional synchronous reference frame–phase-locked loop (SRF-PLL) control approach. Notably, the DSOGI-PLL successfully eliminates the ripples in phase angle estimation, consequently enhancing the quality of voltage output in the microgrid.
Volume: 14
Issue: 5
Page: 4856-4868
Publish at: 2024-10-01

Blood glucose prediction using non-invasive optical system based on photoplethysmography

10.11591/ijece.v14i5.pp5200-5208
Mohammed Anes Bereksi Reguig , Nassima Labdelli
Several people must frequently evaluate their blood glucose since it is an important indicator of health problems mainly diabetes. Different medical systems are commercialized to measure blood glucose levels; some are invasive others are noninvasive. The main purpose of this article is to develop a non-invasive device for measuring blood glucose levels based on the detection and analysis of the photoplethysmogram signal. The developed systems include an optical sensor to detect the photoplethysmography (PPG) signal, digitalizing and acquiring boards to a computer and a software program to process and analyze the digitalized PPG signal regarding some features extracted from its waveform. These features are the systolic amplitude Sa and the b/a amplitude ratio in the second derivative PPG (SDPPG) waveform. An invasive glucometer is also used along with the Sa and b/a ratio determined from the developed system to generate a calibration model which is used to deduce blood glucose level (BGL) values. The result showed that the calibration model using the b/a ratio is more accurate for non-invasive blood level measurement then that of Sa with a difference in glucose estimation around 2 mg/dl and with the correlation coefficient (R2) of the glucose level prediction between 0.8904 and 0.9775.
Volume: 14
Issue: 5
Page: 5200-5208
Publish at: 2024-10-01

Performance evaluation of a proposal for spectrum assignment based on combinative distance-based assessment multicriteria strategy

10.11591/ijece.v14i5.pp5308-5318
Cesar Hernandez , Diego Giral , Tania Vaca
Cognitive radio networks offer an alternative to low spectral availability in some frequency bands due to their high demand for frequency channels. This article proposes to improve the spectral assignment based on the combinative distance-based assessment multicriteria algorithm. The metrics obtained are compared with a simple additive weighting algorithm and a RANDOM selection. To establish the algorithm 's performance, five quality-of-service metrics are used: number of handoffs, number of failed handoffs, average bandwidth, average throughput, and cumulative average delay. From the analysis of the results obtained, combinative distance-based assessment (CODAS) presented the best result for the cost metrics with the lowest levels, and for the benefit metrics, the highest levels were obtained.
Volume: 14
Issue: 5
Page: 5308-5318
Publish at: 2024-10-01

New image encryption approach using a dynamic-chaotic variant of Hill cipher in Z/4096Z

10.11591/ijece.v14i5.pp5330-5343
Hicham Rrghout , Mourad Kattass , Younes Qobbi , Naima Benazzi , Abdellatif JarJar , Abdelhamid Benazzi
Currently, digital communication generates a considerable amount of data from digital images. Preserving the confidentiality of these images during transmission through network channels is of crucial importance. To ensure the security of this data, this article proposes an image encryption approach based on enhancing the Hill cipher by constructing pseudo-random matrices operating in the ring Z/212Z injected into a controlled affine transformation. This approach relies on the use of chaotic maps for generating matrices used in the encryption process. The use of the ring Z/212Z aims to expand the key space of our cryptosystem, thus providing increased protection against brute-force attacks. Moreover, to enhance security against differential attacks, a matrix of size (4×4), not necessarily invertible, is also integrated into a diffusion phase. The effectiveness of our technique is evaluated through specific tests, such as key space analysis, histogram analysis, entropy calculation, NPCR and UACI values, correlation analysis, as well as avalanche effect assessment.
Volume: 14
Issue: 5
Page: 5330-5343
Publish at: 2024-10-01

Enhanced accuracy estimation model energy import in on-grid rooftop solar photovoltaic

10.11591/ijece.v14i5.pp5970-5983
Alfin Sahrin , Imam Abadi , Ali Musyafa
Installing rooftop solar photovoltaic (PV) with an on-grid system benefits consumers because it can reduce imports of electrical energy from the grid. This study aims to model the estimation of energy imports generated from on-grid rooftop solar PV systems. This estimation model was carried out in 20 provincial capitals in Indonesia. The parameters used are weather conditions, orientation angle, and energy generated from the on-grid rooftop solar PV system. The value of imported energy is divided into three combinations based on the azimuth angle direction, which describes the type and shape of the roof of the building (one-direction, two-directions, and three-directions). Modeling was done using machine learning with neural network (NN), linear regression, and support vector machine. A comparison of the machine learning algorithm results is NN produces the smallest root mean square error (RMSE) value of the three. Model enhancement uses a grid search cross-validation (GSCV) to become the GSCV-NN model. The RMSE results were enhanced from 53.184 to 44.389 in the one-direction combination, 145.562 to 141.286 in the two-direction combination, and 81.442 to 76.313 in the three-direction combination. The imported energy estimation model on the on-grid rooftop solar PV system with GSCV-NN produces a more optimal and accurate model.
Volume: 14
Issue: 5
Page: 5970-5983
Publish at: 2024-10-01

Modelling a neural network for analysing the results of segmentation of satellite images

10.11591/ijeecs.v36.i1.pp614-621
Mira Kaldarova , Akerke Akanova , Akgul Naizagarayeva , Albina Kazanbayeva , Nazira Ospanova
The study's relevance lies in addressing inaccuracies within satellite image segmentation, necessitating the development and implementation of neural network models for automated segmentation. The purpose of study is to develop a model of a neural network for training with data obtained from the segmentation of satellite images. The basis of the methodological approach in study is a combination of methods of system analysis of neural networks, which have had a substantial impact on the development of the computer vision industry, with an empirical study of the general principles of neural network modelling for the training on satellite images segmentation. In this study, the results were obtained, indicating that there is a fundamental possibility of developing and practical implementation of a neural network model to determine the quality of the obtained segmentation of images of agricultural fields. Satellite images of agricultural fields of the Republic of Kazakhstan are obtained, and segmentation of field images is performed using the developed neural network model for learning segmentation results. The practical importance of the results obtained in study lies in the possibility of their use in the development of functional models of neural networks for training the results of the segmentation of satellite images.
Volume: 36
Issue: 1
Page: 614-621
Publish at: 2024-10-01

Dual soft decoding of linear block codes using memetic algorithm

10.11591/ijece.v14i5.pp5263-5273
Rajaa Sliman , Ahmed Azouaoui
In this article we will approach the soft-decision decoding for the linear block codes, is a kind of decoding algorithms used to decode data to form better original estimated received message, it is considered as a NP-hard problem. In this article we present a new decoder using memetic algorithm such metaheuristic technic operates on the dual code rather than the code itself that aims to find the error caused when sending a codeword calculated from a message of k bits of information, the resulting codeword contains n bits, including the redundancy bits, the efficiency of an error-correcting code is equivalent to the ratio k/n, the rate is belong the interval [0,1]. Hence a good code is the one that ensures a certain error correcting capability at minimum ratio. The results proved that this approach using a combination of genetic algorithm and local search algorithm provides a sufficiently good solution to an optimization problem; the new decoder is applied on linear codes where the structure is given by a parity check matrix.
Volume: 14
Issue: 5
Page: 5263-5273
Publish at: 2024-10-01

A novel steady-state visually evoked potential-based brain-computer interfaces using trans-subject feature fusion approach

10.11591/ijeecs.v36.i1.pp392-400
Manjula Krishnappa , Madaveeranahally B. Anandaraju
A brain-computer interface (BCI) is a transformative technology that enables users to control external devices or communicate solely through the analysis of their brain activity. One promising aspect of BCIs is the utilization of steady-state visually evoked potentials (SSVEPs), a neurophysiological response in the brain that synchronizes with repetitive visual stimuli. This paper introduces a novel approach known as the trans-subject feature fusion approach (TFA), designed to improve SSVEP-based BCIs. This methodology streamlines data pre-processing, creates invariant SSVEP templates, and simplifies calibration, addressing key challenges that have hindered BCI adoption. By doing so, the main aim is to contribute to the advancement of BCIs, making them more accessible and efficient for a range of applications, from assistive technologies to healthcare, ultimately enhancing users’ communication, and control capabilities.
Volume: 36
Issue: 1
Page: 392-400
Publish at: 2024-10-01

A model proposal for enhancing cyber security in industrial IoT environments

10.11591/ijeecs.v36.i1.pp231-241
Atdhe Buja , Marika Apostolova , Artan Luma
The revolution of the industrial sector in the automated one has happened with the use of the Industrial Internet of things (IIoT). They are providing unprecedented possibilities for connection, and automation. Also, the ubiquitous of IIoT has brought new cyber security challenges, putting sensitive data at risk. This research paper proposes a comprehensive model for enhancing the cyber security of IIoT systems. Our model integrates various countermeasures, including a proactive assessment of security vulnerabilities, examination of identified vulnerabilities, categorizing data, delivery of comprehensive reports, and assurance of effective countermeasures based on a cost-benefit approach, aligned with industry standards and frameworks. The proposed model aims to address the need for the development of robust and resilient cyber security solutions for IIoT environments. This research work introduces the proposed model's main functions, integration, workflow, and references. With this research, we contribute to the enhancement of cyber security in the IIoT environment by proposing a model that assists with proactive assessment, effective response, and informed decision-making. We envision that the proposed model will support industrial organizations in securing their IIoT systems against cyber threats, ultimately have stability and secure industrial operations.
Volume: 36
Issue: 1
Page: 231-241
Publish at: 2024-10-01
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