Articles

Access the latest knowledge in applied science, electrical engineering, computer science and information technology, education, and health.

Advanced Search
Filter Icon

Filters article

Years

FAQ Arrow
Single Year
Range Year
0
0

Source Title

FAQ Arrow

Authors

FAQ Arrow

31,456 Article Results

Security in smart cities using YOLOv8 to detect lethal weapons

10.11591/ijai.v14.i2.pp945-953
Ederson Rodriguez-Rosas , Aron Castillo-Turpo , Kevin Acuna-Condori , Ernesto Paiva-Peredo
The increase in the illegal use of lethal weapons at a global level has reachedworrying figures, resulting in an increase in assaults and armed robberies. Based on the above, closed circuit television (CCTV) surveillance systems emerge as an alternative solution. Therefore, the use of artificial intelligence is explored in order to detect the presence of lethal weapons in images accurately. In this study, a convolutional neural network model YOLOv8 is trained. A database including 4104 images with the presence of lethal weapons is generated. The Google Colab platform is used for the training phase, since it provides us with a free graphic processing unit (GPU), and the YOLOv8x and YOLOv8n models are used for comparison. The results indicate an advantage when using the YOLOv8 models, and when comparing them with similar models already proposed in the studied literature, we can conclude that our model stands out with an accuracy of 89.56% in the detection of lethal weapons. In conclusion, we were able to obtain a model capable of detecting lethal weapons in CCTV images, in addition to being able to be used in applications that require real-time detection. 
Volume: 14
Issue: 2
Page: 945-953
Publish at: 2025-04-01

Factors influencing teacher performance in Shanghai high schools in COVID-19 pandemic

10.11591/ijere.v14i2.31992
Yuan Jiang , Mat Rahimi Yusof , Muhamad Dzahir Kasa
The performance of teachers in schools has become a questionable phenomenon during COVID-19 due to changing landscape of teaching. Since the performance of teachers is linked to performance of students, it is imperative to investigate the influencing factors for teachers’ performance. This study is conducted to examine the relationships between instructional leadership (IL), teaching autonomy (TA), school resources (SR), teacher self-efficacy (TSE), teacher performance (TP) and teaching workload (TW). A sample of 321 responses was collected using a Likert scale questionnaire. Partial least square–structural equation modeling (PLS-SEM) was used to analyze the data using Smart PLS 3. The findings of the study confirmed that TA, IL, and SR are significant antecedents of TSE. The study reports that TSE is a significant predictor of TP. The study reports that TSE is a significant mediator and TW is a significant moderator. The study suggests that TP can be improved with TSE which is improved by IL, TA, and SR. The school education administration in China is required to develop policies in line with this research to contribute to knowledge. The study recommends that the policymakers and practitioners should focus on school’s resources, IL and TA which significantly will improve the performance of teachers in China.
Volume: 14
Issue: 2
Page: 898-909
Publish at: 2025-04-01

A comparative analysis of exponential smoothing method and deep learning models for bitcoin price prediction

10.11591/ijai.v14.i2.pp1401-1409
Nrusingha Tripathy , Debahuti Mishra , Sarbeswara Hota , Mandakini Priyadarshani Behera , Gobinda Chandra Das , Sasanka Sekhar Dalai , Subrat Kumar Nayak
Blockchain technology is the foundation of cryptocurrencies, which are virtual currencies. The decentralized nature of cryptocurrencies has resulted in a significant reduction of central authority over them, which has implications for global trade and relations. The need for an effective model to anticipate the price of cryptocurrencies is essential due to their wide variations in value. Due to the shortcomings of conventional production forecasting, in this work, four distinct models were used. The deep learning models are the long short-term memory (LSTM) and bidirectional long short-term memory (Bi-LSTM), and both the Facebook-Prophet and Silverkite support the exponential smoothing technique. Silverkite is designed to handle a wide range of time series forecasting tasks. Considering past bitcoin information from January 2012 to March 2021, a period of nine years, we looked at the models. The Bi-LSTM model yields a 7.073 mean absolute error (MAE) and a 3.639 root mean squared error (RMSE). The Bi-LSTM model identifies the deviations that might draw attention and avert any problems.
Volume: 14
Issue: 2
Page: 1401-1409
Publish at: 2025-04-01

Improving water quality parameter prediction with multi-level linear regression model and hybrid feature selection

10.11591/ijece.v15i2.pp2381-2391
Aleefia Khurshid , Samruddhi Korke , Yudhir Kothari , Shruti Alone , Khushali Bais
Predicting and modeling the quality of water is essential to guarantee that the water is safe to drink. The chlorine content in water needs to be monitored in real-time to provide a consistent supply of drinkable water. Additionally, potassium and chlorine have a major impact on how appealing the water is, as they are important components that influence taste and odor. Therefore, to evaluate the levels of chlorine and potassium, this work presents a multivariable linear regression approach backed by a hybrid feature extraction method. To bridge the gap between the filter and wrapper approaches, a hybrid approach is used to remove unnecessary information and reduce processing time and complexity. Here the quantitative parameters, in conjunction with categorical parameters, are instrumental in enabling accurate prediction of two water quality parameters. The two developed multi-level regression (MLR) models for the prediction of potassium and chloride are useful when factors affecting water parameters fluctuate at the site level as well as over larger spatial or temporal scales giving consumers a visual representation of how each parameter influences prediction. The converged model outperforms in comparison with other machine learning algorithms with an MAE of 7.42e-15 for potassium and 3.72e-14 for chloride.
Volume: 15
Issue: 2
Page: 2381-2391
Publish at: 2025-04-01

Design of low power complementary metal-oxide semiconductor static random-access memory cell for embedded memories at three different technology nodes

10.11591/ijece.v15i2.pp1424-1433
Seerapu Venkatesh , Krishna Veni Sahukara
In this work, the complementary metal-oxide semiconductor (CMOS) static random-access memory (SRAM) cell is proposed using a hybrid model. It is designed by combining two different methods and simulated at different technologies which are 180, 90, and 45 nm. The proposed hybrid model SRAM cell has less power consumption. The power consumption results of the hybrid model SRAM cell are contrasted with the 6T CMOS SRAM, Stacked SRAM cell, and 8T SRAM cell at 180, 90, and 45 nm. Tanner tool was used for designing and simulating these different SRAM cell topologies at 180, 90, and 45 nm technology nodes. S-edit is used for designing circuit diagrams, T-edit is used for simulating spice net lists and W-edit is used for observing waveforms in Tanner tool. The hybrid SRAM cell at 45 nm got better power consumption results than other SRAM cell topologies at different technology nodes.
Volume: 15
Issue: 2
Page: 1424-1433
Publish at: 2025-04-01

A comprehensive overview of LLM-based approaches for machine translation

10.11591/ijeecs.v38.i1.pp344-356
Bhuvaneswari Kumar , Varalakshmi Murugesan
Statistical machine translation (SMT) used parallel corpora and statistical models, to identify translation patterns and probabilities. Although this method had advantages, it had trouble with idiomatic expressions, context-specific subtleties, and intricate linguistic structures. The subsequent introduction of deep neural networks such as recurrent neural networks (RNNs), long short-term memory (LSTMs), transformers with attention mechanisms, and the emergence of large language model (LLM) frameworks has marked a paradigm shift in machine translation in recent years and has entirely replaced the traditional statistical approaches. The LLMs are able to capture complex language patterns, semantics, and context because they have been trained on enormous volumes of text data. Our study summarizes the most significant contributions in the literature related to LLM prompting, fine-tuning, retrieval augmented generation, improved transformer variants for faster translation, multilingual LLMs, and quality estimation with LLMs. This new research direction guides the development of more efficient and innovative solutions to address the current challenges of LLMs, including hallucinations, translation bias, information leakage, and inaccuracy due to language inconsistencies.
Volume: 38
Issue: 1
Page: 344-356
Publish at: 2025-04-01

Enhancing terahertz patch antenna performance with metamaterials for biomedical applications

10.12928/telkomnika.v23i2.26226
Siraj; Moulay Ismail University Younes , Foshi; Moulay Ismail University Jaouad , Kaoutar; Ibn Zohr University Saidi Alaoui
This paper presents the performance enhancement of a terahertz (THz) patch antenna using the metamaterials (MTM). The antenna design features a rectangular patch with a modified ground structure, implemented on an FR4 substrate with dielectric properties of 4.4, a tan (δ) of 0.02, and a thickness of 1.6 µm. Operating at 4.92 THz, the antenna exhibits a -10 dB bandwidth of 0.25 THz (250 GHz), catering to diverse biomedical applications. To investigate the impact of incorporating MTM, the proposed MTM is positioned beneath the antenna at a separation of 12.8 µm. A comparative analysis of the antenna’s performance with and without MTM reveals a significant influence of MTM insertion. However, the results confirm the big influence of the addition of the MTM. As results, the return loss was improved from -23.16 dB to -44.73 dB. The gain was additionally elevated from 1.46 dB to 5.06 dB. The design and simulation of the antenna were carried out through high frequency structure simulator (HFSS) software.
Volume: 23
Issue: 2
Page: 267-274
Publish at: 2025-04-01

Optimized control and simulation of a grid-integrated photovoltaic and fuel cell hybrid power system

10.11591/ijece.v15i2.pp1411-1423
Amal Satif , Laamari Hlou , Rachid Elgouri
In this study, we propose an advanced power control strategy tailored for a grid-connected photovoltaic-hydrogen hybrid system aimed at ensuring reliable energy supply to consumers. This hybrid system integrates photovoltaic (PV) panels as the primary energy source supplemented by hydrogen fuel production through electrolysis of surplus PV-generated electricity. To optimize system flexibility and efficiency, we employ several advanced control techniques. A novel maximum power point tracking (MPPT) method, enhanced with a proportional-integral (PI) controller, surpasses conventional perturb and observe (P&O) techniques to maximize PV power output. For precise control of the three-phase photovoltaic inverter, we utilize a space vector pulse width modulation (SVPWM) algorithm. Synchronization with the utility grid is ensured by a phase-locked loop (PLL), maintaining phase coherence between the inverter output and grid supply. The primary goal is to develop a comprehensive and efficient control strategy for grid-connected hybrid systems, addressing both energy generation and storage challenges. MATLAB simulations validate the system's performance, demonstrating high energy conversion efficiency and robust control across varying conditions. This study underscores the potential of hybrid PV-hydrogen systems to provide sustainable and resilient energy solutions for future grid integration.
Volume: 15
Issue: 2
Page: 1411-1423
Publish at: 2025-04-01

Application of machine learning methods to analysis and evaluation of distance education

10.11591/ijece.v15i2.pp2172-2180
Ainur Mukhiyadin , Manargul Mukasheva , Ulzhan Makhazhanova , Aislu Kassekeyeva , Gulmira Azieva , Zhanat Kenzhebayeva , Alfiya Abdrakhmanova
In recent decades, distance learning has become an essential component of the modern educational system, providing students with flexibility and access to knowledge regardless of location. This paper discusses creating a hybrid machine-learning model for assessing the quality of distance learning based on survey data. The model combines two feature extraction methods: Term frequency-inverse document frequency (TF-IDF) and Word2Vec. Combining these methods allows for a more complete and accurate representation of text data, improving the quality of machine learning models. The study aims to develop and evaluate the effectiveness of the proposed hybrid model for analyzing survey data and assessing the quality of distance learning. The paper considers the tasks of collecting and preprocessing text data, experimentally comparing various feature extraction methods and their combinations, training and evaluating a machine learning model based on a combination of TF-IDF and Word2Vec features, as well as analyzing the results and assessing the effectiveness of the proposed model using various metrics. In conclusion, the prospects for further development and application of the proposed model in educational institutions to improve the quality of distance learning are discussed.
Volume: 15
Issue: 2
Page: 2172-2180
Publish at: 2025-04-01

Understanding the challenges faced by Orang Asli students in solving mathematics problems

10.11591/ijere.v14i2.29206
Abdul Hakim Abd Jalil , Abdul Halim Abdullah
The educational attainment of Orang Asli students, a minority group in Malaysia, particularly in mathematics, has been a persistent concern. Despite this, research on Orang Asli mathematics education, especially in the southern region of Peninsular Malaysia, remains scarce. This study scrutinized the academic performance of Orang Asli students in whole numbers and operations at level 2 of primary school, focusing on their challenges in solving word problems in mathematics. A total of 22 Orang Asli students from year 4 and 5 in two Orang Asli primary schools participated, undertaking a mathematics test comprising five-word problems. The study identified five prevalent errors among Orang Asli students: i) failure to comprehend the question; ii) misconceptions; iii) inability to grasp specific terms in the question; iv) carelessness; and v) poor reading skills. The findings are thoroughly discussed, and strategies to enhance Orang Asli students’ mathematical problem-solving abilities are proposed.
Volume: 14
Issue: 2
Page: 938-946
Publish at: 2025-04-01

A lightweight machine learning approach for denial-of-service attacks detection in wireless sensor networks

10.11591/ijece.v15i2.pp2089-2097
Mohamed Loughmari , Anass El Affar
Wireless sensor networks (WSNs) are increasingly prevalent in the Internet of Things ecosystem and have been used in several fields such as environmental monitoring, military, and healthcare. However, their limited resources and distributed architecture remain two main challenges: energy and security. Furthermore, denial of service (DoS) attacks are one of the principal cyber threats to WSNs. This research proposes a lightweight machine learning (ML) approach based on the extreme gradient boosting (XGBoost) model to detect these attacks in WSNs. Through an extensive investigation, we evaluate four prominent ML algorithms: random forest (RF), k-nearest neighbor (KNN), stochastic gradient descent (SGD), and XGBoost, using the WSN-DS dataset. In addition, we implement and investigate several feature selection techniques in order to have an improved version of the original dataset. Moreover, we evaluate the performance using various performance metrics, which include accuracy, precision, recall, F1-score, and processing time. The latter is a crucial consideration in WSN environments. For validation, we have employed 5-fold cross-validation to ensure robust and reliable results. The proposed model has achieved good performance in all metrics, with a maximum accuracy of up to 99.73%, and a 68% lower processing time compared to the other investigated classifiers.
Volume: 15
Issue: 2
Page: 2089-2097
Publish at: 2025-04-01

Topographic and flow direction model: a case study of Khuan Kreng peat swamp forest, Southern Thailand

10.11591/ijece.v15i2.pp1978-1989
Panjit Musik , Nunticha Limchoowong , Phitchan Sricharoen , Jintapat Nateewattana , Tanutta Amnuaywattanakul , Woravith Chansuvarn , Uraiwun Wanthong
Floods and droughts are contrasting natural phenomena. The risk of forest fires tends to be increased by the dry and hot conditions of the dry season. A topographic and flow direction model is aimed to be created using Mathematica and ArcGIS programs. The purpose of this model is to assist in water management to prevent forest fires in the Khuan Kreng peat swamp forest located in Nakhon Si Thammarat Province, Southern Thailand. Digital elevation models obtained from the Department of Land Development, representing altitude data of the terrain at a scale of 1:4,000, are utilized in this work. Using cellular automata principles with eight sub-cell flow pathways with a precision of 5×5 meters, identification was carried out. The Universal Transverse Mercator (UTM) coordinate system can store horizontal (X, Y) and vertical (Z) data in one cell, providing information about 2D and 3D topography. Our findings regarding flow direction are comparable to reference values for summer under dry conditions, where water mass is limited. The topographic model data was found to be compatible with data obtained from ArcGIS, Google Maps, and surveys. The ArcGIS flow modeling results are found to be suitable for flood simulation. The proposed method is applicable for regulating water use during droughts and preventing forest fires.
Volume: 15
Issue: 2
Page: 1978-1989
Publish at: 2025-04-01

Plant disease detection using vision transformers

10.11591/ijece.v15i2.pp2334-2344
Mhaned Ali , Mouatassim Salma , Mounia El Haji , Benhra Jamal
Plant diseases present a major risk to worldwide food security and the sustainability of agriculture, leading to substantial economic losses and hindering rural livelihoods. Conventional methods for disease detection, including visual inspection and laboratory-based techniques, are limited in their scalability, efficiency, and accuracy. This paper addresses the critical problem of accurately detecting and diagnosing plant diseases using advanced machine learning techniques, specifically vision transformers (ViTs), to overcome these limitations. ViTs leverage self-attention mechanisms to capture intricate patterns in plant images, enabling accurate and efficient disease classification. This paper reviews the literature on deep learning techniques in agriculture, emphasizing the growing interest in ViTs for plant disease detection. Additionally, it presents a comprehensive methodology for training and evaluating ViT models for plant disease classification tasks. Experimental results demonstrate the effectiveness of ViTs in accurately identifying various plant diseases across a balanced 55 classes dataset, highlighting their potential to revolutionize precision agriculture and promote sustainable farming practices.
Volume: 15
Issue: 2
Page: 2334-2344
Publish at: 2025-04-01

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

Dual band antenna design for 4G/5G application and prediction of gain using machine learning approaches

10.12928/telkomnika.v23i2.26233
Narinderjit; INTI International University Singh Sawaran Singh , Md. Ashraful; Daffodil International University Haque , Redwan; Daffodil International University A. Ananta , Md. Sharif; Daffodil International University Ahammed , Md. Abdul; Friedrich Schiller University Jena Kader Jilani , Liton; Pabna University of Science and Technology Chandra Paul , Rajermani; INTI International University Thinakaran , Malathy; INTI International University Batumalay , JosephNg; INTI International University Poh Soon , Deshinta; INTI International University Arrova Dewi
In this research, we disclose our findings from exploring a machine learning (ML) approach to enhancing the antenna’s performance in Industrial and Innovation contexts, particularly for4G and 5G (n77, n78) contexts. Methods for evaluating antenna performance utilizing simulation, the resistor, inductor, and capacitor (RLC) equivalent circuit model, and ML are discussed. Gain is a maximum of 6.56 dB and efficiency is about 97% for this antenna. The predicted antenna gain is calculated using an alternative supervised regression ML technique. Multiple measures, including as the variance score, R-square (R2), mean square error (MSE), and mean absolute error (MAE), can be used to assess an ML model’s performance. The linear regression (LR) model predicts profit with the fewest errors and highest accuracy of the five ML models. Finally, computer simulation technology (CST) and advanced design system (ADS) modeling findings, along with ML results, show that the proposed antenna is a promising option for 4G and 5G applications.
Volume: 23
Issue: 2
Page: 543-552
Publish at: 2025-04-01
1 2 3
...
2097 2098
Show 335 of 2098

Discover Our Library

Embark on a journey through our expansive collection of articles and let curiosity lead your path to innovation.

Explore Now
Library 3D Ilustration