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INDONESIA
Indonesian Journal of Electrical Engineering and Computer Science
Published by Institute of Advanced Engineering and Science
ISSN : 25024752     EISSN : 25024760     DOI : -
Core Subject :
Arjuna Subject : -
Articles 9,138 Documents
 831   832   833   834   835 
Improved direct control of single stage photovoltaic powred system Chouaib, Rahli; Ouada, Mehdi; Ryad, Mebarek Abdesslam; Saad, Salah
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i3.pp1389-1399

Abstract

This paper introduces a novel direct control quasi-Z-source inverter (qZSI) topology as a viable alternative to conventional two-stage converters in photovoltaic (PV) systems. The proposed control strategy effectively merges duty cycle and modulation index within a space vector pulse width modulation (SVPWM) framework for both DC and AC control. To assess the system’s performance under diverse weather conditions, the INC and P&O maximum power point tracking maximum power point tracking (MPPT) algorithms are employed. Rigorous simulations conducted using MATLAB/Simulink demonstrate the proposed method’s ability to achieve multi-objective optimization of PV systems, enhancing overall system efficiency and reliability.
Improvement of horizontal streak on disparity map thru parameter optimization for stereo vision algorithm Yeou Wei, Melvin Gan; Hamzah, Rostam Affendi; Nik Anwar, Nik Syahrim; Herman, Adi Irwan; Jamil Alsayaydeh, Jamil Abedalrahim
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i3.pp1886-1894

Abstract

In this paper, an improved local based stereo vision disparity map (SVDM) algorithm is proposed. The proposed local based SVDM algorithm include four stages and they are matching cost computation, cost aggregation disparity optimization and disparity refinement. The matching cost computation started by combining pixel to pixel matching techniques, which are absolute difference (AD) and gradient matching (GM) in producing the initial disparity map. Next, the cost aggregation uses minimum spanning tree (MST) segmentation, which equipped with edge preserving properties and noise filtering. Then, disparity optimization uses local approach with winner-take-all (WTA) technique. At the final stage, disparity refinement uses bilateral filter (BF) with weighted median (WM), which can improve the disparity map through noise removing and edges preserving. Then, the research continues to optimize the proposed local based SVDM algorithm through parameters optimization in obtaining the final disparity map. Here, multiple parameters from the proposed SVDM algorithm are manipulated and they are constant values for GM and several constant parameters in BF. By selecting the optimum parameter values, the performance of the proposed SVDM algorithm increased, especially robustness towards the horizontal streaks.
Electrocardiogram reconstruction based on Hermite interpolating polynomial with Chebyshev nodes Ray, Shashwati; Chouhan, Vandana
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 2: November 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i2.pp837-845

Abstract

Electrocardiogram (ECG) signals generate massive volume of digital data, so they need to be suitably compressed for efficient transmission and storage. Polynomial approximations and polynomial interpolation have been used for ECG data compression where the data signal is described by polynomial coefficients only. Here, we propose approximation using hermite polynomial interpolation with chebyshev nodes for compressing ECG signals that consequently denoises them too. Recommended algorithm is applied on various ECG signals taken from MIT-BIH arrhythmia database without any additional noise as the signals are already contaminated with noise. Performance of the proposed algorithm is evaluated using various performance metrics and compared with some recent compression techniques. Experimental results prove that the proposed method efficiently compresses the ECG signals while preserving the minute details of important morphological features of ECG signal required for clinical diagnosis.
Dynamic long short-term memory model for enhanced product recommendations in e-commerce Bhogan, Snehal; Rajpurohit, Vijay S.; Sannakki, Sanjeev S.
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i3.pp1866-1875

Abstract

Recommendation systems are pivotal for personalized user experiences, employing algorithms to predict and suggest items aligned with user preferences. Deep learning (DL) models, such as recurrent neural networks (RNNs) and long short-term memory networks (LSTMs), excel in capturing sequential dependencies, enhancing recommendation accuracy. However, challenges persist in session-based recommendation systems, particularly with gradient descent and class imbalances. Addressing these challenges, this work introduces dynamic LSTM (D-LSTM), a novel DL-based recommendation system tailored for dynamic E-commerce environments. The primary objective is to optimize recommendation accuracy by effectively capturing temporal dependencies within user sessions. The methodology involves the integration of D-LSTM with weight matrix optimization and a Bayesian personalized ranking (BPR) adaptable learning rate optimizer to enhance learning efficiency. Experimental results demonstrate the efficacy of D-LSTM, showing significant improvements over existing models. Specifically, comparisons with the hybrid time-centric prediction (HTCP) model reveal a performance enhancement of 19.4%, 17.2%, 35.41%, and 21.99% for hit-rate (HR) and mean reciprocal rank (MRR) in 10k and 20k recommendation sets using the Tmall dataset. These findings underscore the superior performance of D-LSTM, highlighting its potential to advance personalized recommendations in dynamic E-commerce settings.
Optimizing blockchain for healthcare IoT: a practical guide to navigating scalability, privacy, and efficiency trade-offs Alhija, Mwaffaq Abu; Al-Baik, Osama; Hussein, Abdelrahman; Abdeljaber, Hikmat
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i3.pp1773-1785

Abstract

The adoption of blockchain technology provides significant disruptive benefits to internet-of-things (IoT) applications in healthcare in vital aspects like security, integrity, transparency, and efficiency. Nevertheless, in order to fully realize the potential of blockchain-driven solutions, healthcare organizations have to address intricate compromises between essential factors including scalability, privacy and resource utilization considering that the data sensitivity alongside strict regulatory compliance requirements characterize this sector. This research discusses the fundamental aspects of these trade-offs, including the range of consensus protocols (e.g. proof-of-work, proof-of-stake) and cryptographic techniques (e.g. zero-knowledge proofs, homomorphic encryption). A systematic choice matrix is created, which relates specific use cases of the healthcare IoT to the optimal tailored blockchain structures on such critical metrics as transaction volume, frequency, privacy level and resource restrictions. The suggested framework provides solid, actionable recommendations to healthcare organizations in order to help them benefit from the enormous promise of the blockchain for connected IoT healthcare by finding a balance between decentralization advantages and performance, security and compliance requirements.
Enhancing surface water quality prediction efficiency in northeastern thailand using machine learning Uypatchawong, Surasit; Chanamarn, Nipaporn
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 2: November 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i2.pp1189-1198

Abstract

Water is the most vital resource for life and is necessary for most living creatures, including humans, to survive. Three rivers’ surface water quality has been predicted by this study: the Chi river, the Mun river, and the Songkhram river. In the northeastern region of Thailand. The dataset is 881 samples and 13 factors. This study investigated various machine learning methods for predicting water quality, including neural networks (NN), support vector machines (SVM), decision trees (DT), Naive Bayes (NB), and K-nearest neighbors (KNN). Furthermore, this study was conducted to find suitable factors using correlation based feature selection, correlation coefficient, and information gain. And optimize the prediction model using the Bagging Approach. The result is found that the bagging model using the DT technique (BaggingDT) has better performance than all models with an accuracy value equal to 98.64%, precision value equal to 98.70%, recall value equal to 98.60%, F-measure value equal to 98.60% and RMSE value equal to 0.0961. The obtained factors and the most appropriate model can be used to develop a surface water quality standard predicting system.
Spiking neural network with blockchain for tampered image detection using forensic steganography images Basavanyappa, Gurumurthy Shikaripura; Danti, Ajit
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 1: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i1.pp477-485

Abstract

Accurate tools are required to acknowledge misleading images in order to maintain image legitimacy, and these tools must allow for legal operations on images. Additionally, after posting their images to the Internet, image owners lose rights over the images because there are no measures in place to safeguard them from misuse. One of the most well-liked techniques for addressing copyright disputes is the use of steganography technologies. The embedded steganography images can, sadly, be easily altered or deleted. To address this problem, this work presents the spiking neural network (SNN) with blockchain for tampered image detection utilizing forensic steganography images. Forensic steganography images that have been altered can be found with this SNN. Using steganography images from the database, SNN is trained in this model. The blockchain stores the owners’ access policies. The Python platform is used to implement the proposed strategy. F-measure, specificity, accuracy, precision, recall false positive rate (FPR), and false negative rate (FNR) are used to gauge how well the proposed approach performs. When compared to state-of-the-art approaches, the proposed approach obtained an impressive rise of 98.65%, in classification accuracy.
Space vector pulse width modulation realization for three-phase voltage source inverter Palanisamy, Ramasamy; Santhakumari, Valarmathi Thangamani; Venkatarajan, Shanmugasundaram; Hemalatha, Selvaraj; Hepzibah, Albert Alice; Ramkumar, Ravindran; Sugavanam, Vidyasagar
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i3.pp1976-1984

Abstract

This paper presents the implementation of space vector pulse width modulation (SVPWM) for a three-phase voltage source inverter (VSI). SVPWM is a technique used to control the output voltage of VSIs with improved efficiency and precision. The abstract outlines the key steps involved in implementing SVPWM, including reference signal clarification, sector identification, determination of voltage vectors, and switching state calculation. This proposed system provides improved output voltage of the inverter, minimized voltage stress across the switches and reduced total harmonic distortion and electromagnetic interference. The proposed implementation aims to enhance the performance of three-phase VSIs in various applications, such as motor drives, renewable energy systems, and power converters. The simulation results of proposed system are verified using MATLAB Simulink.
Deep transfer learning classification of apple fruit diseases Loutfy, Shaimaa Kamal; Rahouma, Kamel Hussein
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i3.pp1556-1564

Abstract

This paper applies deep convolution neural networks (DCNN) to apple fruit disease classification. Twelve DCNN methods (SqueezeNet, GoogleNet, InceptionV3, DenseNet201, ReaNet50, ResNet101, Xception, InceptionResnetV2, EfficientnetB0, AlexNet, VGG16, and VGG19) have been used. These methods have been trained to classify apples into four categories: normal, blotch, rot, and scab. A dataset of 5179 images, including 3472 for normal, 171 for blotch, 1166 for rot, and 370 for scab, has been used. A practical test on 120 images (30 for each category) has been applied. Seven of these DCNNs—InceptionV3, DenseNet201, ResNet101, ResNet50, GoogleNet, AlexNet, and VGG16—have the best accuracy. InceptionV3 is the highest. It has achieved an accuracy of 100% for all categories. The used dataset is unbalanced and small. So, it's necessary to use data augmentation to overcome any overfitting that may cause. After applying data augmentation, the dataset is balanced and contains 13888 images (3472 for each category). The seven DCNNs are retrained by the balanced dataset and retested by the same 120 images. All DCNN's accuracy has enhanced except InceptionV3, which has decreased. On the other hand, RasNet101 has achieved an accuracy of 100% for all categories. Therefore, ResNet101 has been recommended for apple fruit disease classification.
Enhanced Bengali audio categorization using audio segmentation and deep learning Khan, Niaz Ashraf; Bin Hafiz, Md. Ferdous
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 2: November 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i2.pp952-960

Abstract

This paper presents an enhanced approach for classifying Bengali songs into different genres by leveraging feature importance analysis and deep learning techniques. The research addresses the challenge of limited data points in the Bengali Song Dataset by employing strategies, including audio segmentation and feature importance analysis, to enhance model performance. Multiple machine learning and deep learning architectures are evaluated to identify the most effective models for Bengali song classification. Additionally, this research conducts feature importance analysis to identify significant audio features contributing to classification accuracy. The best-performing deep learning model achieves an impressive validation accuracy of 94.17%, showcasing the project efficacy of the proposed methodology. Our findings highlight the effectiveness of our proposed methodology, demonstrating significant improvements in classification accuracy and contributing to advancements in Bengali music classification research.

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