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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 63 Documents
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Search results for , issue "Vol 33, No 1: January 2024" : 63 Documents clear
Blockchain based drug supply chain for decentralized network Vijaykumar Bidve; Aryan Hamine; Sharwari Akre; Yash Ghan; Pakiriswamy Sarasu; Ganesh Pakle
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 1: January 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i1.pp485-495

Abstract

The concept of supply and demand drives the scales of various markets in today’s world. When it comes to producing a quality product, the right kind of steps need to be taken to ensure that its quality can be supplemented with the process of its making. A supply chain is a business process that delineates the creation of a product. One such supply chain is the drug supply chain, focusing on the manufacturing and distribution of drugs. It is implied that there is an immense importance of traceability in the drug supply chain to ensure transparency amongst various actors and ultimately the end user. Improving on this crucial parameter allows drug supply chains to be carefully monitored and adhere to the various compliances from governing bodies. This work aim is to provide organizations with solutions that allow them to ameliorate the supply chain management. Using the blockchain technology, various transactions recorded in the supply chain can be checked against providing strong traceability and secure record-keeping. The positives that are provided by the blockchain transform the supply chain to a much more efficient and improved operation, impacting various facets of the process for the better.
Design analysis of moth-flame optimized fault tolerant technique for minimally buffered network-on-chip router Subramanian Sumithra; Nagaiyanallur Lakshminarayanan Venkatara; Subramani Suresh Kumar; Ramaiah Purushothaman; Kathiresan Kokulavani; Velankanni Gowri
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 1: January 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i1.pp179-189

Abstract

A network on a chip is a solitary silicon chip utilized to perform the communication characteristics of large-scale (LSI) to very large-scale integration (VLSI) systems. Network-on-chip (NoC) architecture includes links, network interfaces (NI), and routers to unite with external memories or processors. NoC is designed to flow messages from the source module to the destination module through several links involving routing decisions. The design of NoC is complex and the buffer section’s expensiveness creates problems while providing secured data service. Moreover, routers and links in NoC setups are liable to faults. This work introduces a minimal buffered router, and the faults in the network are optimized using moth flame optimized (MFO) fault-tolerant technique. The software named Xilinx ISE design suite 14.5 is employed for the minimum buffered router model. The suggested scheme is operated with less area, low power (0.241 mW), and high speed (965.261 Megahertz (MHz)) when matched with previous works.
Transforming image descriptions as a set of descriptors to construct classification features Volodymyr Gorokhovatskyi; Iryna Tvoroshenko; Olena Yakovleva
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 1: January 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i1.pp113-125

Abstract

The article develops methods to solve a fundamental problem in computer vision: image recognition of visual objects. The results of the research on the construction of modifications for the space of classification features based on the application of the transformation of the structural description through the decomposition in the orthogonal basis and the implementation of the distance matrix model between the components of the description are presented. The application of the system of orthogonal functions as an apparatus for the transformation of the description showed the possibility of a significant gain in the speed of processing while maintaining high indicators of classification accuracy and interference resistance. The synthesized feature systems’ effectiveness has been confirmed in terms of a significant increase in the rate of codes and a sufficient level of efficiency. An experimental example showed that the time spent calculating the relevance of descriptions according to their modified presentation is more than ten times shorter than for traditional metric approaches. The developed classification features can be used in applied tasks where the time of visual objects’ identification is critical.
High-capacity steganography through audio fusion and fission Namitha Mangikuppe Venkateshaiah; Manjula Govinakovi Rudrappa
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 1: January 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i1.pp643-652

Abstract

Information security is required for two reasons, either to conceal the information completely or to prevent the misuse of the information by adding watermarks or metadata. Audio steganography uses audio signals to hide secret information. In the proposed audio steganography technique, cover audio files and secret audio files are transformed from time domain to wavelet domain using discrete wavelet transform, the secret audio file is transformed in two levels, leading to secure and high-capacity data hiding. 1% of the 2-level compressed secret is fused to 99% of the 1-level compressed cover. “Peak signal to noise ratio and mean squared error, Pearson’s correlation coefficient, spearman’s correlation coefficient, perceptual evaluation of speech quality and short-time objective intelligibility” are considered to assess the similarity of cover audio and stego audio and similarity of secret audio embedded, and secret audio retrieved. Results show that the stego audio signal is perceptually indistinguishable from the cover audio signal. The approach also passed the robustness test.
Improving time efficiency in big data through progressive sampling-based classification model Nandita Bangera; Kayarvizhy Kayarvizhy; Shubham Luharuka; Asha S. Manek
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 1: January 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i1.pp248-260

Abstract

The proposed system aims to overcome challenges posed by large databases, data imbalance, heterogeneity, and multidimensionality through progressive sampling as a novel classification model. It leverages sampling techniques to enhance processing performance and overcome memory restrictions. The random forest regressor feature importance technique with the gini significance method is employed to identify important characteristics, reducing the data’s features for classification. The system utilizes diverse classifiers such as random forest, ensemble learning, support vector machine (SVM), k-nearest neighbors’ algorithm (KNN), and logistic regression, allowing flexibility in handling different data types and achieving high accuracy in classification tasks. By iteratively applying progressive sampling to the dataset with the best features, the proposed technique aims to significantly improve performance compared to using the entire dataset. This approach focuses computational resources on the most informative subsets of data, reducing time complexity. Results show that the system can achieve over 85% accuracy even with only 5-10% of the original data size, providing accurate predictions while reducing data processing requirements. In conclusion, the proposed system combines progressive sampling, feature selection using random forest regressor feature importance (RFRFI-PS), and a range of classifiers to address challenges in large databases and improve classification accuracy. It demonstrates promising results in accuracy and time complexity reduction.
Performance analysis of GaN based dual active bridge converter for electric vehicle charging application Snehalika Snehalika; Ranjeeta Patel; Chinmoy Kumar Panigrahi
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 1: January 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i1.pp53-62

Abstract

The research work proposes a Gallium Nitride (GaN) based dual active bridge (DAB) converter for electric vehicle (EV) charging applications. The wide bandgap semiconductor device, GaN is implemented in the DAB topology of an Isolated Bidirectional DC-DC converter (IBDC). GaN-based DC-DC converters lead to higher efficiency, smaller size, faster charging, and reduced heat generation improving the overall performance of the EV charging system. The performance characteristics of GaN based DAB converter is analyzed both in simulation and hardware for EV charging application. The same analysis is extended to a Si based DAB converter and comparative results are presented. A 14.25 kW GaN based DAB and 10.5 kW Si based DAB is designed and evaluated using LTspice XVII software and a scaled-down prototype of 0.612 kW GaN based DAB and 0.25 kW Si based DAB is presented for experimental validation. Result analysis under similar operating conditions indicated an improvement of 36% more output power transfer on the GaN based DAB over the traditional Si based DAB. The developed prototype showcased improved levels of power, voltage, and current under same operating conditions. The power transmission in the developed DAB based IBDC topology is controlled using the single-phase-shift (SPS) control strategy.
Encouraging hygiene permanence in tomato leaf and applying machine learning techniques Saravanan Madderi Sivalingam; Lakshmi Devi Badabagni
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 1: January 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i1.pp343-349

Abstract

Tomatoes are the major ingredient in food preparation, which leads to a huge food production rate. Most countries cultivate huge tomatoes at the same time that crop diseases affect the production rate due to many different types of diseases. The various types of diseases are bacterial spots, septoria leaf spot, left mold, late blight, early blight, arget and spot. Many research studies review these tomato leaf diseases with various statistics. The survey on disease will give a clear idea of reasons and prevention methods, also presenting how to reduce it in the early stages. In another study, tomato leaf images were taken to classify the diseased and non-diseased varieties. Few studies compare the standard model of disease prediction with the machine learning models. Therefore, this research study discusses tomato leaf disease detection and prevention methods used by various researchers in their studies and finally consolidate the observations. This study also deals with encouraging hygiene permanence in tomato leaf using machine learning algorithms. The convolutional neural network (CNN) was used to predict the early nature of the hygiene nature of leafy vegetable plants for the benefit of agriculture people and concluded with better future suggestions.
Predicting progression of Alzheimer’s disease using new survival analysis approach Nour Saad Zawawi; Heba Gamal Saber; Mohamed Hashem
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 1: January 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i1.pp603-611

Abstract

It is critical to determine the risk of Alzheimer’s disease (AD) in people with mild cognitive impairment (MCI) to begin treatment early. Its development is affected by many things, but how each effect and how the disease worsens is unclear. Nevertheless, an in-depth examination of these factors may provide a reasonable estimate of how long it will take for patients at various stages of the disease to develop Alzheimer’s. Alzheimer’s disease neuroimaging initiative (ADNI) database had 900 people with 63 features from magnetic resonance imaging (MRI), genetic, cognitive, demographic, and cerebrospinal fluid data. These characteristics are used to track AD progression. A hybrid approach for dynamic prediction in clinical survival analysis has been developed to track progression to AD. The method uses a random forest cox regression approach to figure out how long it will take for MCI to turn into AD. In order to evaluate the result concordance index is used. The concordance index measures the rank correlation between predicted risk scores and observed time points. The concordance index was statistically considerably higher in the suggested work than in previous approaches with a score of 95.3%, which is higher than others.
Challenges in big data adoption for Malaysian organizations: a review Lee Qi Zian; Nur Zareen Zulkarnain; Yogan Jaya Kumar
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 1: January 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i1.pp507-517

Abstract

Big data has played an ever-increasing role in various sectors of the economy. Despite the availability of big data technologies, many companies and organizations in Malaysia remain reluctant to adopt them. Numerous studies have been published on big data adoption; however, there is a lack of research focusing on identifying the challenges faced by Malaysian organizations. Therefore, this study will implement the technology-organization-environment (TOE) framework to examine the challenges faced by Malaysian organizations with regards to big data adoption. A systematic literature review (SLR) was conducted to examine the challenges. From the result of this study, it was found that the factors from technology context are deemed to be the major challenge faced in big data adoption followed by organization and environment factors. Furthermore, the insights derived from the TOE framework-based information can help address concerns that hinder big data adoption among organizations in Malaysia. Finally, this study concludes with several recommendations.
Hybrid model of convolutional neural network and long short term memory for heart disease prediction Shubham Gupta; Pooja Sharma
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 1: January 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i1.pp389-397

Abstract

Data mining is a process that assists in uncovering meaningful data from large, disorganized datasets. This research is being conducted to predict heart disorders by using available data to make predictions for the future. The approach is carried out in several stages, such as pre-processing the data, extracting relevant features, and classifying the data. all of these steps are essential for predicting heart disease. The deep learning models is already proposed by the researches for the heart disease prediction. This work introduces a hybrid deep learning model that combines convolutional neural network (CNN) and long short-term memory (LSTM) to predict heart disease. The proposed model has been implemented in python, and its accuracy, precision, and recall have been evaluated.

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