cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Tole Sutikno
Contact Email
ijece@iaesjournal.com
Phone
-
Journal Mail Official
ijece@iaesjournal.com
Editorial Address
-
Location
Kota yogyakarta,
Daerah istimewa yogyakarta
INDONESIA
International Journal of Electrical and Computer Engineering
Published by Institute of Advanced Engineering and Science
ISSN : 20888708     EISSN : 27222578     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
International Journal of Electrical and Computer Engineering (IJECE, ISSN: 2088-8708, a SCOPUS indexed Journal, SNIP: 1.001; SJR: 0.296; CiteScore: 0.99; SJR & CiteScore Q2 on both of the Electrical & Electronics Engineering, and Computer Science) is the official publication of the Institute of Advanced Engineering and Science (IAES). The journal is open to submission from scholars and experts in the wide areas of electrical, electronics, instrumentation, control, telecommunication and computer engineering from the global world.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 6,486 Documents
 645   646   647   648   649 
Modeling music-induced emotional responses using synthetic electroencephalography data Rashmi Athanikar; Vidyagouri Kulkarni; U. P. Kulkarni; Sumpreetsing Rajaput
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i5.pp2595-2606

Abstract

Music has a significant influence on human emotions; however, understanding its underlying neural mechanisms remains a complex challenge. This study investigates music-induced emotional responses using synthetically generated electroencephalography (EEG) signals. A controlled synthetic EEG dataset is developed to simulate brain activity across diverse demographic groups exposed to self-selected musical preferences. The generated signals undergo preprocessing, feature extraction, and classification using machine learning and deep learning models. Experimental results indicate that synthetic EEG signals can capture distinguishable emotional patterns, achieving a classification accuracy of 58.55% under multi-class conditions. Although the study does not replace real EEG analysis, it provides a scalable and reproducible framework for controlled experimentation. The findings highlight the potential of synthetic data in affective computing and brain–computer interface research, particularly in scenarios where real EEG acquisition is constrained.
Detection and classification of thyroid diseases using ultrasound images through deep learning techniques Kamal Subedi; Akash Kumar Yadav; Lochan Paudel
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i5.pp2806-2818

Abstract

Thyroid diseases remain one of the significant public health issues around the world, with thyroid nodules estimated to be prevalent among approximately 30 to 50% of the adult population through ultrasound imaging screening processes. Determination of the nature of the thyroid nodules in a patient's body is vital for ensuring effective treatment options since early detection of any malignant nodule will result in positive treatment results. The problem with such an analysis is the fact that the visual appearance of both benign and malignant nodules resembles each other in ultrasonic images. Therefore, a two-stage deep learning-based model for automatic detection and classification of thyroid nodules in ultrasound images was developed in this paper. At the first stage, the proposed framework applied Attention U-Net segmentation to identify the region of interest (ROI) for thyroid nodules in ultrasound images. Expert-drawn pixel-level annotations of thyroid nodules were not available; hence, pseudo-labels were used in training. They were obtained by applying CLAHE, Otsu thresholding, and morphological operations. In the second stage, a customized CNN network was applied to classify the segmented thyroid region into either benign or malignant classes. Data augmentation using the Mixup and CutMix techniques helped reduce the risk of overfitting, enhancing the model's generalizability. This experiment was tested on 480 ultrasound images gathered from two teaching hospitals in Nepal. The proposed framework shows that segmentation with weak supervision along with classification using deep learning can provide efficient diagnosis of thyroid nodules even with minimal medical data annotation. Through 3-fold stratified cross-validation, the Attention U-Net achieved a Dice Score of 0.8772 and IoU of 0.7873 for segmentation, while the classification stage obtained an overall accuracy of 96.73%, a specificity rate of 97.89%, and a mean ROC-AUC of 94.60%.
Binary BAT algorithm with greedy repair for the discounted 0-1 knapsack problem Tung Khac Truong
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i5.pp2680-2687

Abstract

The discounted 0-1 knapsack problem (D0-1KP) generalizes the classical knapsack problem by partitioning items into groups of three, in which the third item of every group represents a discounted bundle of the first two and at most one item per group may be loaded. This grouping constraint enlarges the search space and makes the problem markedly harder than its classical counterpart. This paper presents BBAT, a binary bat algorithm for the D0-1KP. Bat velocities are mapped to selection probabilities through a sigmoid transfer function, and a greedy repair-and-optimization operator restores feasibility while raising the profit of every candidate solution. The algorithm keeps the small parameter set of the original bat metaheuristic, which limits the tuning effort required before deployment. BBAT was assessed on 14 benchmark instances drawn from the inverse strongly correlated and strongly correlated families, with 30 independent runs per instance. Against two elite genetic algorithms, BBAT improved the best profit found on 12 of the 14 instances and improved the mean profit on 7 of them. The gain in mean profit is concentrated on the inverse strongly correlated family, whereas on strongly correlated instances BBAT attains better peaks at the cost of higher run-to-run variance. These results identify BBAT as a competitive but variance-sensitive solver for the D0-1KP.
Low-cost educational prototype for IoT-based PV energy monitoring and MPPT control Abdelmalek Mimouni; Youssef Chahet; Aumeur El Amrani; Mohamed Azeroual; Mohamed El Amraoui; Lahcen Bejjit
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i5.pp2282-2293

Abstract

This paper presents a low-cost embedded platform integrating photovoltaic (PV) energy monitoring, maximum power point tracking (MPPT), and Internet of Things (IoT) communication for real-time control and performance evaluation. The proposed system is built around an ESP32 microcontroller with dual INA219 sensors and a custom-designed DC–DC boost converter operating at 20 kHz. Two MPPT strategies, perturb and observe (P&O) and fuzzy logic control (FLC), are implemented and experimentally compared under identical outdoor conditions. Experimental results obtained under natural irradiance demonstrate that the FLC approach achieves faster convergence and reduced steady-state oscillations compared to P&O. Specifically, FLC improves tracking efficiency from 82.5% to 84%, reduces duty cycle ripple, and increases average extracted power by approximately 1.5%. The system also enables real-time acquisition of I–V and P–V characteristics through duty-cycle sweeping. The proposed architecture integrates sensing, control, and IoT telemetry using MQTT and a ThingsBoard dashboard, enabling continuous monitoring with negligible communication latency relative to the MPPT update period. This work contributes a reproducible, low-cost testbed that bridges embedded control, power electronics, and IoT-based monitoring, with potential applications in both education and rapid prototyping of smart PV systems.
Temperature impact analysis on photovoltaic output power using an IoT-based wiper cooling system Ajeng Bening Kusumaningtyas; Ikhsan Kamil; Adnan Afriadi
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i5.pp2272-2281

Abstract

Solar photovoltaic (PV) performance is significantly affected by surface temperature and panel cleanliness, both of which influence energy conversion efficiency. Elevated temperatures reduce power output, while dust accumulation decreases effective solar irradiance. This study proposes an IoT-based integrated cooling and cleaning system as its main contribution, enabling real-time monitoring, intelligent control, and remote operation within a unified framework. The system utilizes an NTC 10K temperature sensor for continuous monitoring, while an ESP8266 microcontroller functions as the central controller for data processing, wireless communication, and web-based interfacing. When the panel temperature exceeds a predefined threshold (40–42 °C), The IoT-enabled system automatically activates a water pump and wiper mechanism to simultaneously reduce temperature and remove surface contaminants. Experimental results show a significant temperature reduction from approximately 41–43 °C to 30–33 °C. This improvement leads to an increase in voltage output of 0.5%–1.2% and current output of 3–6%, depending on load conditions. The cleaning mechanism further enhances irradiance absorption by maintaining panel surface clarity. These findings demonstrate that IoT integration enables adaptive and data-driven optimization of PV performance. The proposed system offers a practical and scalable engineering solution to improve efficiency, reliability, and long-term operation of PV installations in real-world applications.
AI-based benchmarking framework for heterogeneous hospital pharmaceutical demand forecasting Nendi Sunendar; Kursehi Falgenti; Farizal Gunawan
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i5.pp2847-2857

Abstract

Accurate pharmaceutical demand forecasting is challenging because hospital medicine consumption exhibits heterogeneous patterns, including seasonal, volatile, and intermittent demand. This study proposes the regime-aware forecasting framework (RAFF), a reproducible benchmarking approach that combines structural demand diagnostics, leakage-controlled walk-forward validation, statistical comparison, and regime-stratified evaluation. Ten years of monthly demand records covering 300 pharmaceutical items were evaluated using Seasonal Naive, seasonal autoregressive integrated moving average with exogenous factors (SARIMAX), and long short-term memory (LSTM) models. Relative to Seasonal Naive, SARIMAX reduced mean absolute error by 18.20%, and root mean squared error by 20.58%, while LSTM reduced them by 16.48% and 21.75%, respectively. SARIMAX achieved the lowest aggregate mean absolute error, whereas LSTM achieved the lowest root mean squared error and greater stability as volatility increased. The results show that forecasting performance depends on demand structure, supporting regime-aware model selection for hospital inventory and procurement planning.

Page 649 of 649 | Total Record : 6486

 645   646   647   648   649 

Filter by Year

2011 2026


Reset
Filter By Issues
All Issue Vol 16, No 5: October 2026 Vol 16, No 4: August 2026 Vol 16, No 3: June 2026 Vol 16, No 2: April 2026 Vol 16, No 1: February 2026 Vol 15, No 6: December 2025 Vol 15, No 5: October 2025 Vol 15, No 4: August 2025 Vol 15, No 3: June 2025 Vol 15, No 2: April 2025 Vol 15, No 1: February 2025 Vol 14, No 6: December 2024 Vol 14, No 5: October 2024 Vol 14, No 4: August 2024 Vol 14, No 3: June 2024 Vol 14, No 2: April 2024 Vol 14, No 1: February 2024 Vol 13, No 6: December 2023 Vol 13, No 5: October 2023 Vol 13, No 4: August 2023 Vol 13, No 3: June 2023 Vol 13, No 2: April 2023 Vol 13, No 1: February 2023 Vol 12, No 6: December 2022 Vol 12, No 5: October 2022 Vol 12, No 4: August 2022 Vol 12, No 3: June 2022 Vol 12, No 2: April 2022 Vol 12, No 1: February 2022 Vol 11, No 6: December 2021 Vol 11, No 5: October 2021 Vol 11, No 4: August 2021 Vol 11, No 3: June 2021 Vol 11, No 2: April 2021 Vol 11, No 1: February 2021 Vol 10, No 6: December 2020 Vol 10, No 5: October 2020 Vol 10, No 4: August 2020 Vol 10, No 3: June 2020 Vol 10, No 2: April 2020 Vol 10, No 1: February 2020 Vol 9, No 6: December 2019 Vol 9, No 5: October 2019 Vol 9, No 4: August 2019 Vol 9, No 3: June 2019 Vol 9, No 2: April 2019 Vol 9, No 1: February 2019 Vol 8, No 6: December 2018 Vol 8, No 5: October 2018 Vol 8, No 4: August 2018 Vol 8, No 3: June 2018 Vol 8, No 2: April 2018 Vol 8, No 1: February 2018 Vol 7, No 6: December 2017 Vol 7, No 5: October 2017 Vol 7, No 4: August 2017 Vol 7, No 3: June 2017 Vol 7, No 2: April 2017 Vol 7, No 1: February 2017 Vol 6, No 6: December 2016 Vol 6, No 5: October 2016 Vol 6, No 4: August 2016 Vol 6, No 3: June 2016 Vol 6, No 2: April 2016 Vol 6, No 1: February 2016 Vol 5, No 6: December 2015 Vol 5, No 5: October 2015 Vol 5, No 4: August 2015 Vol 5, No 3: June 2015 Vol 5, No 2: April 2015 Vol 5, No 1: February 2015 Vol 4, No 6: December 2014 Vol 4, No 5: October 2014 Vol 4, No 4: August 2014 Vol 4, No 3: June 2014 Vol 4, No 2: April 2014 Vol 4, No 1: February 2014 Vol 3, No 6: December 2013 Vol 3, No 5: October 2013 Vol 3, No 4: August 2013 Vol 3, No 3: June 2013 Vol 3, No 2: April 2013 Vol 3, No 1: February 2013 Vol 2, No 6: December 2012 Vol 2, No 5: October 2012 Vol 2, No 4: August 2012 Vol 2, No 3: June 2012 Vol 2, No 2: April 2012 Vol 2, No 1: February 2012 Vol 1, No 2: December 2011 Vol 1, No 1: September 2011 More Issue