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

Assessing risk factors for heart disease using machine learning methods

10.11591/ijece.v14i6.pp6734-6742
Natalya Maxutova , Jamalbek Tussupov , Kamilya Kedelbayeva , Assemgul Tynykulova , Zulfiya Balabayeva , Zauresh Yersultanova , Zhainagul Khamitova , Kamila Zhunussova
This paper examines various machine learning methods for assessing risk factors for cardiovascular diseases. To build predictive models, two approaches were used: the extreme gradient boosting (XGBoost) algorithm and a convolutional neural network (CNN). The focus is on analyzing the performance of each model in classification and regression tasks, as well as their ability to identify key biomarkers and risk factors such as cholesterol, ferritin, homocysteine and aspartate aminotransferase (AST) levels. XGBoost parameters have been optimized for working with tabular data, demonstrating high accuracy in risk prediction. The CNN model, despite the initial reduction in error on the training set, showed signs of overfitting when analyzing validation data. Performance evaluation using the metrics of mean squared error (MSE), coefficient of determination (R²), Akaike information criterion (AIC), and Bayesian information criterion (BIC) revealed significant differences between the models. The study results confirm the effectiveness of XGBoost in analyzing tabular data and summarizing risk factor knowledge, while the CNN model needs further optimization to handle sparse data. The work demonstrates the importance of choosing the right model architecture and training parameters to ensure reliable diagnosis of cardiovascular diseases.
Volume: 14
Issue: 6
Page: 6734-6742
Publish at: 2024-12-01

Optimizing the position of photovoltaic solar tracker panels with artificial intelligence using MATLAB Simulink

10.11591/ijai.v13.i4.pp4003-4018
Ricardo Linelson , Fahmy Rinanda Saputri
This research aims to apply an artificial intelligence (AI) system to control the position of photovoltaic (PV) panels to maximize the use of solar energy using the solar tracker. The implementation of AI algorithms to achieve optimal panel orientation, considering factors such as sunlight intensity and sun position is also discussed. The simulation results using matrix laboratory (MATLAB) Simulink can be observed on the scope, displaying the position control graph of the solar panel from sunrise to sunset. By employing proportional integral derivative (PID) control, the error is likely to be minimal, ensuring that the panel will continue to follow the sun until it sets at the maximum point of 4:00 PM. After that, the panel can be adjusted back or reset to the initial position at 6:00 AM for the following day. In a full-day simulation, the solar panel will follow the sun's movement from sunset to sunrise. At the basic level, sunrise occurs in the first hour at position 1.0, which is 6:00 AM in the minimum point at the bottom left corner of the curve, and sunset occurs in the afternoon at position 5.25, which is 4:00 PM at the maximum point in the top right corner of the curve.
Volume: 13
Issue: 4
Page: 4003-4018
Publish at: 2024-12-01

Optimal energy management of hybrid battery/supercapacitor storage system for electric vehicle application

10.11591/ijece.v14i6.pp6076-6085
Hegazy Rezk , Mujahed Al-Dhaifallah , Syed Muhammad Amrr , Abdullah Alharbi
A proposed approach for efficient energy management for lithium-ion battery and supercapacitor hybrid energy storage system is outlined in this study. The primary aim is to ensure the electric vehicle receives a stable and high-quality supply of electricity. The suggested management strategy focuses on maintaining the bus voltage at a consistent level while meeting the varying load demands with high-quality power across different scenarios. To achieve this, a management controller is employed, which utilizes a metaheuristics technique to define the parameters of the integral sliding mode control. The supercapacitor units are responsible for controlling the direct current (DC) bus, while the lithium-ion battery plays a role in balancing power distribution on the common line. This study evaluates the potential benefits of integrating salp swarm algorithm techniques into the controller's performance. The findings of the study suggest combining metaheuristic optimization methods with integral sliding mode control. Can lead to improvements in power quality. The proposed management algorithm not only efficiently allocates power resources but also safeguards them, ultimately ensuring a consistent and high-quality power supply for the electric vehicle.
Volume: 14
Issue: 6
Page: 6076-6085
Publish at: 2024-12-01

The impact of continuous professional development on teaching quality: a systematic review

10.11591/ijere.v13i6.30427
Josephine Ambon , Bity Salwana Alias , Aan Komariah , Azlin Norhaini Mansor
This article explores the impact of continuous professional development (CPD) on teaching quality. The article provides a comprehensive overview of CPD, focusing on pedagogical skills, classroom problem-solving abilities, and the mediating role of leadership within educational institutions. This study used PRISMA approach by analyzing 2,432 English-language publications from 2023 to 2024 taken from the Scopus, Web of Sciences (WoS), and ERIC databases. The data were collected, reviewed, and underwent a peer-review process before a systematic literature review. This article synthesizes findings from final articles (n=24) into five main themes: student perceptions and teaching quality, professional development (PD) and teacher learning, role of leadership in teacher development, assessment and evaluation in teaching, and collaboration and community in teaching. The review reveals that CPD significantly enhances teaching quality by improving pedagogical skills and problem-solving capabilities in the classroom. Furthermore, leadership within educational institutions plays a crucial role in facilitating the positive effects of CPD on teaching quality. By fostering an environment that supports PD, collaborative learning, and reflective practice, educational leaders can amplify the benefits of CPD. This study contributes to the understanding of how CPD and effective leadership together can elevate teaching standards and outcomes, offering valuable insights for policymakers, educators, and academic leaders aiming to enhance educational quality through PD.
Volume: 13
Issue: 6
Page: 3838-3847
Publish at: 2024-12-01

An automated power of hydrogen controlled filtration system for enhanced aquarium fish farming

10.11591/ijece.v14i6.pp6265-6270
Fabio Garcia , Daniel Martel , Ernesto Paiva-Peredo
The increasing popularity of fish keeping in aquariums and the need for electronic equipment to maintain an optimal environment. This article focuses on monitoring water purity to ensure fish health and longevity, addressing the issue of water pollution caused by chemicals and waste produced by fish. Solutions such as mechanical and biological filters are explored, highlighting the use of the mechanical filter composed of zeolite, ceramic rings, and activated carbon, which work to remove solid particles, toxic compounds, and pollutants from the aquarium water. The article presents the implementation of a mechanical filter controlled by a PIC18F4550 microcontroller using a pH sensor. The results indicate the stability of the pH of the water in the established range of 6.5 to 7.5, with a maximum error of 3% at the upper limit of the range and no error below the established lower limit. It is concluded that the system effectively maintains the desired levels and ensures the fish's health. A technological solution for monitoring and controlling water quality is presented, recognizing the possibility of improvements in aquaculture.
Volume: 14
Issue: 6
Page: 6265-6270
Publish at: 2024-12-01

Secure data transmission in power systems using blockchain technology

10.11591/ijece.v14i6.pp6170-6181
Anand Srivatsa , Ananthapadmanabha Thammaiah , Likith Kumar MV , Rajeshwari D , Suma AP
Recent advances in intelligent systems have significantly improved power management, load distribution, and resource management capabilities, far beyond past constraints. Despite these gains, the development of internet-connected technology has brought various vulnerabilities, leading to negative results. The integration of intelligent technology has unintentionally offered chances for hackers to enter networks and modify data sent to central systems for analysis. One of the most serious risks is the false data injection attack (FDIA), which may drastically impair analytical outcomes. Previous research has shown that standard approaches for recovering data affected by FDIA are unreliable and inefficient. This paper investigates the use of the proof of stake (PoS) consensus method in this framework improves data integrity and makes it easier to identify illegal changes. Participating nodes may reject or change block transactions, ensuring the ledger's correctness. Our results show that the PoS consensus method is exceptionally successful in creating and adding transactions to the blockchain. Furthermore, the PoS mechanism's simplicity in block formation enhances both time and energy efficiency, resulting in considerable benefits in operational performance.
Volume: 14
Issue: 6
Page: 6170-6181
Publish at: 2024-12-01

Eligibility rate of applicant’s LinkedIn account: a naïve bayes classification and visualization

10.11591/ijai.v13.i4.pp4334-4343
Khyrina Airin Fariza Abu Samah , Nurul Athirah Ahmad , Anis Amilah Shari , Hana Fakhira Almarzuki , Zuhri Arafah , Lala Septem Riza , Amir Haikal Abdul Halim
In the digital era, social media platforms like LinkedIn have become famous for recruitment, and recruiters widely use them to find potential employees. The recruitment process is crucial in organizations, as it involves selecting qualified applicants from a diverse pool. However, the screening process and manual recruitment process entail significant time, high costs, and potential bias. Consequently, it may cause recruiting unqualified applicants and may affect the organizations. Thus, this study aims to classify and generate a list of potential job applicants by analyzing seven attributes of their LinkedIn accounts: title, location, skills, education, language, certification, and years of experience. Data are collected from LinkedIn profiles and then undergo data pre-processing. The naive Bayes (NB) algorithm is implemented as the classification algorithm and sets the classification as “eligible” or “ineligible”. The NB model achieved an accuracy testing of 89.8%, indicating good performance in classifying potential job applicants. At the same time, we measure the similarity cosine score to set the mean of the eligibility. The classification results are visualized for the suitable applicants in descending rank, allowing users to choose the applicants’ classification status efficiently. For the system usability, we managed to get 90% from the recruitment expert.
Volume: 13
Issue: 4
Page: 4334-4343
Publish at: 2024-12-01

A novel secured open standard framework for internet of things applications integrating elliptic curve cryptography and fog computing

10.11591/ijece.v14i6.pp7224-7235
Krishnapura Srinivasa Ravindra , Malode Vishwanatha Panduranga Rao
The internet of things (IoT) has revolutionized various fields by enabling seamless connectivity and data exchange among numerous devices. However, this interconnectivity introduces significant security challenges, particularly in ensuring data confidentiality, integrity, and authenticity. This study proposes a novel secure open standard framework for IoT applications, addressing these challenges through the integration of elliptic curve cryptography (ECC) and fog computing. The framework consists of three core components: secure device registration, data encryption within the fog gateway, and a robust mechanism for detecting man-in-the-middle (MITM) attacks. The unique aspect of the proposed method lies in its comprehensive approach to IoT security. Utilizing ECC, the framework ensures secure communication among resource constrained IoT devices, balancing encryption strength and efficiency. The integration of fog computing reduces latency and enhances processing efficiency by offloading intensive tasks from IoT devices to the fog layer. The MITM attack detection mechanism continuously monitors cryptographic keys and communication patterns, providing an additional layer of security against advanced cyber threats. The system was implemented and evaluated using the NS-3.26 network simulator and Python for data visualization. The experimental setup included 100 IoT devices, 25 users, a fog gateway, a datacenter, and a cloud server. Results demonstrate the framework's scalability and efficiency, with consistent throughput increases and balanced power consumption across varying IoT device numbers.
Volume: 14
Issue: 6
Page: 7224-7235
Publish at: 2024-12-01

Integrating gait and speech dynamics methodologies for enhanced stuttering detection across diverse datasets

10.11591/ijai.v13.i4.pp4869-4882
Ravikiran Reddappa Reddy , Santhosh Kumar Gangadharaih
Stuttering manifests as involuntary interruptions in the fluency of speech, often involving repetitions, prolongations, or blocks of sounds or syllables. These disruptions can significantly impact effective communication and psychosocial well-being. This research introduces a comprehensive system for speech impairment detection and gait analysis. Speech impairment, with a primary focus on stammer recognition, presents a multifaceted challenge in the field of speech processing. Stammers can manifest in various forms and detecting them accurately is a complex task. Our proposed methodology revolves around the development of StEnsembleNet, a neural network designed to learn spectral features at the frame level, enabling precise and efficient identification of speech impediments. Additionally, we extend our system's capabilities to the domain of gait analysis, leveraging a novel adaptive graph topology convolution network (AGT-ConvNet) for skeletal motion and visually enhanced topological learning to adapt to diverse visual environments and enhance the recognition of gait patterns. This research not only contributes to the field of speech therapy but also offers potential applications in healthcare and motion analysis.
Volume: 13
Issue: 4
Page: 4869-4882
Publish at: 2024-12-01

Grindulu fault cloud radon data for earthquake magnitude prediction using machine learning

10.11591/ijai.v13.i4.pp4572-4582
Thomas Oka Pratama , Sunarno Sunarno , Agus Budhie Wijatna , Eko Haryono
The study investigates the potential of integrating radon gas concentration telemonitoring systems with machine learning techniques to enhance earthquake magnitude prediction. Conducted in Pacitan, East Java, Indonesia, where the stations are near the active Grindulu fault, the research employs random forest (RF), extreme gradient boosting (XGB), neural network (NN), AdaBoost (AB), and support vector machine (SVM) methods. The study aims to refine earthquake magnitude prediction, utilizing real-time radon gas concentration measurements, crucial for disaster preparedness. The evaluation involves multiple metrics like mean absolute error (MAE), mean absolute percentage error (MAPE), root mean square error (RMSE), mean squared error (MSE), symmetric mean absolute percentage error (SMAPE), and conformal normalized mean absolute percentage error (cnSMAPE). XGB and SVM emerge as top performers, showcasing superior predictive accuracy with minimal errors across various metrics. XGB achieved MAE (0.33), MAPE (6.03%), RMSE (0.51), MSE (0.26), SMAPE (0.06), and cnMAPE (0.97), while SVM recorded MAE (0.34), MAPE (6.20%), RMSE (0.51), MSE (0.26), SMAPE (0.06), and cnSMAPE (0.97). The analysis reveals XGB as the most effective method, boasting the lowest error values. The study underscores the importance of expanding data availability to enhance predictive models, ultimately contributing to more precise earthquake magnitude predictions and effective mitigation strategies.
Volume: 13
Issue: 4
Page: 4572-4582
Publish at: 2024-12-01

Effects of environmental art perception on students’ creativity and emotional development

10.11591/ijere.v13i6.29961
Yuanyuan Sun , Shunli Liu
Addressing the gap in understanding how environmental art perception (EAP) and emotional development (ED) influence creativity among fine arts college students, this study focuses on their interrelations in China’s Henan Province. The objective is to investigate the effects of EAP and ED on creativity within this demographic. Employing a survey method, the study gathered data from 585 students across comprehensive art colleges in Henan Province, analyzing the impact of campus art design on creativity and the mediating role of ED. The findings reveal that EAP significantly enhances creativity, with campus art design playing a crucial role. Furthermore, ED serves as an essential mediator, fostering creativity through improved emotional awareness, expression, and management. Based on these insights, the study recommends educational strategies that integrate ED with the cultivation of creative thinking. Such approaches aim to nurture art professionals who not only excel in creativity but also in emotional intelligence, emphasizing the need to optimize environmental art exposure and focus on emotional skills in art education.
Volume: 13
Issue: 6
Page: 3696-3704
Publish at: 2024-12-01

Network reconfiguration for improving performance system in ULP Sungguminasa considering nonlinear loads

10.11591/ijece.v14i6.pp6066-6075
Muhira Dzar Faraby , Yuli Asmi Rahman , Sofyan Sofyan , Sarma Thaha , Musfirah Putri Lukman , Asma Amaliah , Mustika Mustika , Mochammad Apriyadi Hadi Sirad , Anisya Sonita
Network reconfiguration is a very economical technique that can improve electrical system performance. The development of semiconductor electrical equipment technology to meet human needs and work, known as nonlinear loads, has had a negative impact in the form of the spread of harmonic distortion which can accelerate the aging process and even damage equipment. In this paper, the effect of the optimization results of network reconfiguration techniques on the Sungguminasa 165-bus Executive Unit Service or Unit Layanan Pelanggan (ULP) electrical system is contaminated with harmonic distortion due to the use of nonlinear loads. This technique was optimized using the particle swarm optimization (PSO) method with a multi objective function in the form of minimizing %THDv and total losses with several limitations. Simulation results from the optimization process of several study cases are shown by activating the five tie switches from the network reconfiguration process on the Sungguminasa 165-bus ULP system which is able to improve power quality by reducing the average %THDv by 3.89% and total losses by 8.19%.
Volume: 14
Issue: 6
Page: 6066-6075
Publish at: 2024-12-01

Integration of renewable energy into San Andres Island electrical grid

10.11591/ijece.v14i6.pp6160-6169
Keyla Newball Archbold , Alvaro Zambrano , Javier Rosero Garcia
Renewable energy (RE) sources integration in electrical grids is changing the dynamics of planning and operation. Overvoltage, overcurrent, and malfunction of protection schemes are some effects if it is limits are not controlled. This article presents a methodology based on the hosting capacity (HC) concept to estimate performance indexes by considering stochastic methods and systematic simulation taking as study case the grid of San Andres Island. RE is of special interest in islands where diesel generators produce energy with a high footprint and security of supply is low as there is a high dependence on fossil fuels and their transport regime. The simulations are carried out in DigSilent PowerFactory integrated with Python to automate the iterations over different penetration levels. The most limiting factor found is transformer rating. Voltage rise is a factor to be monitored at the end of the circuits. Emissions are reduced with the introduction of renewable energies, but variability needs to be controlled as it could require fast start-up of generators; this modifies monitoring and control schemes to maintain stability. The limit found is higher than the established regulation for non-interconnected zones (NIZ) in Colombia, showing the capability of the grid to integrate RE.
Volume: 14
Issue: 6
Page: 6160-6169
Publish at: 2024-12-01

Structural equation modeling: exploring relationships of body mass index, waist circumference, and hypertension in elderly

10.11591/ijphs.v13i4.24415
Wahyu Tri Sudaryanto , Isnaini Herawati , Wahyuni Wahyuni , Ika Yuli Ayuningrum , Bhisma Murti , Rizki Setiawan , Nuristiqomah Dwi Putri
Waist circumference (WC) measurements in the elderly are uncommon and erratic. Furthermore, its correlation to hypertension has received less attention in Indonesia. This study attempted to investigate direct and indirect risk factors for high blood pressure in the elderly using structural equation modeling (SEM). A cross-sectional study was conducted in Surakarta, Central Java, Indonesia. The 297 elderly aged ≥60 is eager to take part. The path model of factors associated with blood pressure was analyzed using Stata 13. Male (b=-0.43; 95% CI=-5.67 to 4.81; p=0.872), age (b=0.52; 95% CI=0.12 to 0.93; p=0.012), and WC (b=0.35; 95% CI=0.17 to 0.53; p<0.001) directly affected systolic blood pressure (SBP). Male (b=1.83; 95% CI=-1.17 to 4.82; p=0.231), age (b=-0.03; 95% CI=-0.26 to 0.20; p=0.790), BMI (b=0.40; 95% CI=-0.03 to 0.83; p=0.067), and WC (b=0.04; 95% CI=-0.09 to 0.16; p=0.571) directly affected diastolic blood pressure (DBP). Increasing age, body mass index (BMI), and waist circumference (WC) are the important variables that influence blood pressure in older people. This study supports the evidence that body composition and weight control are necessary to prevent and control blood pressure in the elderly.
Volume: 13
Issue: 4
Page: 1599-1608
Publish at: 2024-12-01

Impact of students’ feedback on their engagement towards online classes during COVID-19 pandemic

10.11591/ijere.v13i6.28375
Ildikó Rudnák , Khadija Aya Hamza , Ayman Alshaabani , Judit Garamvölgyi
With the emerge of COVID-19 pandemic in Hungary, Hungarian universities have decided to opt for online teaching methods even for foreign language courses. This sudden change has required a better understanding of students’ behaviors in classes also to view the importance of their feedback to enhance teaching quality and teachers’ effectiveness. The purpose of our study was to focus on students’ feedback and its impact on their engagement in the context of online classes, by considering the mediating role of teaching effectiveness in that relationship. Structural equation modelling was used to examine our primary data, which has been collected from a distributed online questionnaire dedicated to 222 students enrolled in Hungarian language courses at MATE University. The findings reveal that students’ feedback has a direct, significant, and positive effect on students’ engagement in online classes and teaching effectiveness, which itself plays a mediating role in that relationship.
Volume: 13
Issue: 6
Page: 3775-3786
Publish at: 2024-12-01
1 2 3
...
2097 2098
Show 404 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