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Andri Pranolo
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Office 1 ASCEE Secretariat RUMAH KOTAK Jl. Kranginan, Mertosanan Kulon, Potorono, Kec. Banguntapan, Kabupaten Bantul, Daerah Istimewa Yogyakarta 55196, Indonesia Office 2 ASCEE Secretariat Jl. Raya Janti No.130B, Karang Janbe, Karangjambe, Kec. Banguntapan, Kabupaten Bantul, Daerah Istimewa Yogyakarta 55198, Indonesia
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Applied Engineering and Technology
Published by Association for Scientific Computing Electronics and Engineering
ISSN : -     EISSN : 28294998     DOI : http://dx.doi.org/10.31763/aet
Core Subject : Science, Engineering,
Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology
Applied Engineering and Technology provides a forum for information on innovation, research, development, and demonstration in the areas of Engineering and Technology applied to improve the optimization operation of engineering and technology for human life and industries. The journal publishes original papers, review articles, technical notes, and letters to the editor. Authors are encouraged to submit manuscripts that bridge the gaps between research, development, and implementation. The breadth of coverage ranges from innovative technologies and systems of implementation and application development to better human life and industry. The following scope are welcome: Aerospace Engineering, Automobile Engineering, Applied Mathematics, Applied Physics, Bioinformatics, Biophysics, Biotechnology, Chemical Engineering, Chemical Physics, Civil Engineering, Computational Physics, Computer Engineering, Electrical Engineering, Electronic Engineering, Energy Engineering, Environment Engineering, Information Technology, Marine engineering, Mechanical engineering, Medical Engineering, Medical imaging, Medical Physics, Nanotechnology, Ocean Engineering, Optical engineering, Photonics, Robotics, Urban Engineering and Other related engineering topics in general.
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 53 Documents
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Ensemble learning approaches for predicting heart failure outcomes: A comparative analysis of feedforward neural networks, random forest, and XGBoost Ariyanta, Nadindra Dwi; Handayani, Anik Nur; Ardiansah, Jevri Tri; Arai, Kohei
Applied Engineering and Technology Vol 3, No 3 (2024): December 2024
Publisher : ASCEE

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/aet.v3i3.1750

Abstract

Heart failure is a leading cause of morbidity and mortality worldwide, and early prediction of outcomes is critical for timely intervention and improved patient care. Accurate prediction models can help clinicians identify high-risk patients, optimize treatment strategies, and reduce healthcare costs. In this study, we developed and evaluated machine learning models to predict mortality in patients with heart failure using a medical dataset of 299 patients with 13 clinical variables collected in 2015. Four models were tested, including a Feedforward Neural Network (FNN), Random Forest, XGBoost, and an ensemble model combining all three models. The experimental process included data preprocessing, feature scaling, and stratified cross-validation to ensure robust evaluation. The results showed that the ensemble model achieved the best performance with an ROC-AUC of 0.9134 and an F1 score of 0.7439, outperforming individual models such as Random Forest (ROC-AUC: 0.9117) and XGBoost (ROC-AUC: 0.9130). FNN, despite having the highest accuracy (0.8455), showed lower performance in terms of recall and precision, likely due to its sensitivity to overfitting on small datasets. These results highlight the effectiveness of ensemble learning in medical prediction tasks, especially for handling complex, high-dimensional health data. The proposed ensemble model has the potential to be integrated into clinical decision support systems, enabling real-time risk assessment and personalized treatment plans for heart failure patients. Future research should explore larger, multicenter datasets, incorporate advanced feature engineering techniques, and investigate the integration of deep learning architectures such as convolutional neural networks (CNNs) or recurrent neural networks (RNNs) to process sequential data such as ECG signals.
Millimeter-wave microstrip antenna with enhanced gain for dual-band 26 /29 GHz operation Ali, Neksad; Islam, Abu Zafor Muhammad Touhidul
Applied Engineering and Technology Vol 4, No 1 (2025): April 2025
Publisher : ASCEE

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/aet.v4i1.1832

Abstract

This paper suggests a dual-band rectangular patch antenna design and presents analysis of the radiation performance metrics for its operation and application in the 26 and 29 GHz millimeter-wave 5G mobile communication.  To achieve antenna’s hign gain and improved radiation performance, the design includes a rectangular loop and two L-sloted patch and defective ground structure (DSG). The antenna is excited using 50 Ω inset feed line and modeled in CST Studio Suite. The simulation results show that the designed small sized 22.5×18.5 mm2 antenna offers improved super high gain of 9 dB and 11.39 dB and directivity of 9.49 dBi and 12.06 dBi at 26 and 29 GHz mmWave bands.  Moreover, the antenna offers minimum reflection coefficient, acceptable VSWR and very good efficiency of 94.83% at 26 GHz, whilst of 94.44% at 29GHz, respectively. These findings along with its compact design suggest that the projected patch antenna would be a be a good choice for the development of high gain dual-band  antenna for 5G  mmWave mobile systems
Performance optimization of a thermoelectric energy harvesting system utilizing waste heat from an internal combustion engine Gbaarabe, Baribuma; Sodiki, John I.; Lebele-Alawa, Barinaadaa Thaddeus; Nkoi, Barinyima
Applied Engineering and Technology Vol 4, No 2 (2025): August 2025
Publisher : ASCEE

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/aet.v4i2.2093

Abstract

This study presents the performance optimization of a Thermoelectric Energy Harvesting (TEH) system designed to recover waste heat from Internal Combustion Engines (ICEs). It includes optimizing the energy conversion efficiency of the thermoelectric module (TEM), optimizing the design of the Plate Heat Exhcanger (PHE), and simulation-based validation. The optimization process, conducted using Python optimization code developed for the study, yielded an energy conversion efficiency of 7.209%, marking a 56% improvement over the experimentally measured efficiency of 4.63%. The optimized PHE design, incorporate finless triangular-rectangular composite duct. The analysis showed a fully turbulent flow within the PHE, which significantly enhances convective heat transfer coefficients, improve the  heat exchange between the exhaust gas and heat exchanger surfaces, and reduces the risk of fouling and clogging. The exhaust gas contained 1792W of waste heat, with 230W transferred to the hot side of the TEM. This corresponds to a heat exchanger effectiveness of 0.13, indicating that only 13% of the available waste heat in the exhaust gas is utilized by the TEM. The overall TEH system efficiency was determined to be 0.94%, which, despite being relatively modest, yields considerable energy savings in large-scale applications where waste heat is abundant. Computational simulations, using a CAD model in SOLIDWORKS, validated the TEH system’s optimized performance, by ensuring the desired temperature gradient is maintained across the TEM, given that the power output of the TEH is directly proportional to the temperature gradient across the thermoelectric couples in the TEM

Page 6 of 6 | Total Record : 53

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