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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 9,174 Documents
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Development Paillier's library of fully homomorphic encryption Temirbekova Zhanerke Erlanovna; Tynymbayev Sakhybay; Abdiakhmetova Zukhra Muratovna; Turken Gulzat
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i3.pp1989-1998

Abstract

One of the new areas of cryptography considered-homomorphic cryptography. The article presents the main areas of application of homomorphic encryption. An analysis of existing developments in the field of homomorphic encryption carried out. The analysis showed that existing library implementations only allow processing bits or arrays of bits and do not support division and subtraction operations. However, to solve applied problems, support for performing integer operations are necessary. Because of the analysis, the need to implement the homomorphic division and subtraction operations identified, as well as the relevance of developing our own implementation of a homomorphic encryption library over integers. The ability to perform four operations (addition, difference, multiplication and division) on encrypted data will expand the areas of application of homomorphic encryption. A homomorphic division and subtraction methods proposed that allows the division operation performed on homomorphically encrypted data. An architecture for a library of fully homomorphic operations on integers is proposed. The library supports basic homomorphic operations on integers, as well as homomorphic division method. The article also provides measurements of the time required to perform certain operations on encrypted data and analyzes the efficiency of the developed implementation of the library.
Brain-computer interface-based hand exoskeleton with bidirectional long short-term memory methods Osmalina Nur Rahma; Khusnul Ain; Alfian Pramudita Putra; Riries Rulaningtyas; Khouliya Zalda; Nita Lutfiyah; Nafisa Rahmatul Laili Alami; Rifai Chai
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 1: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i1.pp173-185

Abstract

It takes at least 3 months to restore hand and arm function to 70% of its original value. This condition certainly reduces the quality of life for stroke survivors. The effectiveness in restoring the motor function of stroke survivors can be improved through rehabilitation. Currently, rehabilitation methods for post-stroke patients focus on repetitive movements of the affected hand, but it is often stalled due to the lack of professional rehabilitation personnel. This research aims to design a brain-computer interface (BCI)-based exoskeleton hand motion control for rehabilitation devices. The Bidirectional long short-term memory (Bi-LSTM) method performs motion classification for the ESP32 microcontroller to control the movement of the DC motor on the exoskeleton hand in real-time. The statistical features, such as mean and standard deviation from the sliding windows process of electroencephalograph (EEG) signals, are used as the input for Bi-LSTM. The highest accuracy at the validation stage was obtained in the combination of mean and standard deviation features, with the highest accuracy of 91% at the offline testing stage and reaching an average of 90% in real-time (80%-100%). Overall, the control system design that has been made runs well to perform movements on the hand exoskeleton based on the classification of opening and grasping movements.
DNA computing and meta-heuristic-based algorithm for big data task scheduling in cloud computing Gandhimathinathan, Visalaxi; Alagesan, Muthukumaravel
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 2: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i2.pp1131-1138

Abstract

With the advent of cloud computing, there is a need to enhance both the methods and algorithms of big data workloads for task scheduling. Due to the global spread of services with changing task load circumstances and different cloud client demands, big data task scheduling in cloud systems is a time-consuming process. The proposed approach emphasises the necessity for efficient big data task scheduling in the cloud computing, which exacerbate data processing. Virtual machines frequently utilise all three types of physical resources: CPU, memory, and storage. Big data task scheduling is one of the most important implications of cloud computing application resource management, and this research work meticulously offers a task scheduling technique for advancing cloud computing.
Dynamic base station allocation for 6G wireless networks through narrow neural network Pradnya Kamble; Alam N. Shaikh
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i3.pp1690-1697

Abstract

The 6G wireless communication system will utilize the terahertz (THz) frequency band (0.1-10 THz) to meet customer demand for increased data rates and ultra-high-speed communication in future applications. The exponential surge in data traffic, which is supported by dynamic resource allocation. To mitigate this challenge, the use of artificial intelligence-based methods, such as narrow neural network (NNN), can help to smooth the performance of the network. In this paper, an NNN-based approach for dynamic base station allocation for 6G wireless networks is proposed 14 different 6G parameters used to train the NNN model, initially achieving an accuracy of 89.5% and an F1 score of 0.72 for 200 users. Results demonstrate the efficacy of the proposed NNN approach for dynamic decision-making in 6G networks and its potential for application in other domains where similar problems exist. Moreover, the proposed narrow neural network model shows improved results with an increase in number of users and decrease in fully connected layers and regularization strength (lambda). The validation accuracy received is 98.9% and 99.6% for thousand users with single fully connected layer, none (linear) activation function and regularization strength lambda values of 0.01 and 0.001.
Fabrication and characterization of methylammonium lead iodide-based perovskite solar cells under ambient conditions Dwayne Jensen Reddy; Ian Joseph Lazarus
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i3.pp1410-1419

Abstract

This study investigated the fabrication and characterization of CH3NH3PbI3 based perovskite solar cells (PSCs) using the one-step spin coating technique under ambient conditions, eliminating the need for expensive glovebox and thermal evaporation equipment. The perovskite layer was annealed at 65 °C for 30 seconds and 100 °C for 30 seconds, 1 and 2 minutes. The scanning electron microscope (SEM) images show a smooth and uniform surface coverage for the ETL and CH3NH3PbI3 layers. SEM results also show an average grain size of 397 nm for CH3NH3PbI3 and an average particle size of ~17 nm for TiO2 was confirmed by transmission electron microscopy (TEM). X-ray diffraction (XRD) results confirmed the formation of tetragonal perovskite (CH3NH3PbI3) phase with high crystallinity with a crystallite size of 19.99 nm for the samples annealed for 30 seconds at 65 °C and 1 min at 100 °C. FTIR results also confirmed the presence of anatase TiO2 at wavenumber 438 cm-1 and the formation of the adduct of Pb2 with dimethyl sulfoxide (DMSO) and MAI is confirmed at 1,015 cm-1 . From the Tauc plot the bandgap energy of TiO2 and Perovskite layers was determined to be 3.52 eV and 2.06 eV respectively. An open-circuit voltage was 0.9057 V and short circuit current density was 12.2185 mA/cm2 with a fill factor of 48.05 and power conversion efficiency (PCE) of 5.199%.
Android malware detection using GIST based machine learning and deep learning techniques Udayakumar, Ponnuswamy; Yalamati, Srilatha; Mohan, Lavadiya; Haque, Mohd Junedul; Narkhede, Gaurav; Bhashyam, Krishna Mohan
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 2: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i2.pp1244-1252

Abstract

In today’s digital world, Android phones play a vital part in a variety of facets of both professionals and individuals’ personal and professional lives. Android phones are great for getting things done faster and more organized. The proportionate increase in the number of malicious applications has also been seen to be expanding. Since the play store offers millions of apps, detection of malware apps is challenging task. In this paper, a methodology is introduced for detecting malware in Android applications through the utilization of global image shape transform (GIST) features extracted from grayscale images of the applications. The dataset comprises samples of both malware and benign apps collected from the virus share website. After converting the apps into grayscale images, GIST features are extracted to capture their global spatial layout. Various machine learning (ML) algorithms, such as logistic regression (LR), k-nearest neighbour (KNN), AdaBoost, decision tree (DT), Naïve Bayes (NB), random forest (RF), support vector machine (SVM), extra tree classifier (ETC), and gradient boosting (GB), are employed to classify the applications according to their GIST features. Furthermore, a feed forward neural network (FFNN) is utilized as a deep learning (DL) technique to further improve the accuracy of classification. The performance of each algorithm is evaluated using metrics such as accuracy, precision and recall. The results demonstrated that the FFNN achieves superior accuracy compared to traditional ML classifiers, indicating its effectiveness in detecting malware in Android apps.
Development of a wearable monitor to identify stress levels using internet of things Nurassyl Zholdas; Octavian Postolache; Madina Mansurova; Baurzhan Belgibaev; Murat Kunelbayev; Talshyn Sarsembayeva
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 3: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i3.pp1486-1499

Abstract

Modern life's ubiquitous component of stress has a significant impact on many facets of human existence. This article presents the development of a wearable device integrated with internet of things (IoT) technology, aiming to identify and quantify stress levels in real-time. This technology provides a possible means of improving stress assessment, enabling prompt treatments and individualized stress management techniques. ESP32-PICO computation platform was used as part of wearable stress monitor. The developed wearable monitor also includes a high-sensitivity pulse oximeter and heart-rate sensor (MAX30102) and galvanic skin response (GSR) sensors to acquire physiological signals associated with stress status. The wearable monitor device delivers data to the firebase platform via Wi-Fi. The benefits and prospective uses of the IoT-enabled wearable device are also covered in the article. It demonstrates the mobile wearable monitor adaptability in a variety of scenarios, such as offices, classrooms, and healthcare facilities, where stress management is vita and required for activity optimization. Continuous monitoring capabilities allow users to learn about their stress levels and take proactive self-care measures. During the validation experiments, the accuracy of measurement capabilities of the developed wearable monitor were evaluated reduced errors of heart rate and respiratory rate being observed.
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.
Cloud-based machine learning algorithms for anomalies detection Amarnath, Raveendra N; Gurulakshmanan, Gurumoorthi
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 1: July 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i1.pp156-164

Abstract

Gradient boosting machines harnesses the inherent capabilities of decision trees and meticulously corrects their errors in a sequential fashion, culminating in remarkably precise predictions. Word2Vec, a prominent word embedding technique, occupies a pivotal role in natural language processing (NLP) tasks. Its proficiency lies in capturing intricate semantic relationships among words, thereby facilitating applications such as sentiment analysis, document classification, and machine translation to discern subtle nuances present in textual data. Bayesian networks introduce probabilistic modeling capabilities, predominantly in contexts marked by uncertainty. Their versatile applications encompass risk assessment, fault diagnosis, and recommendation systems. Gated recurrent units (GRU), a variant of recurrent neural networks, emerges as a formidable asset in modeling sequential data. Both training and testing are crucial to the success of an intrusion detection system (IDS). During the training phase, several models are created, each of which can recognize typical from anomalous patterns within a given dataset. To acquire passwords and credit card details, "phishing" usually entails impersonating a trusted company. Predictions of student performance on academic tasks are improved by hyper parameter optimization of the gradient boosting regression tree using the grid search approach.
Maize seed variety identification model using image processing and deep learning Gebeyehu, Seffi; Sintayehu Shibeshi, Zelalem

Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i2.pp990-998

Abstract

Maize is Ethiopia’s dominant cereal crop regarding area coverage and production level. There are different varieties of maize in Ethiopia. Maize varieties are classified based on morphological features such as shape and size. Due to the nature of maize seed and its rotation variant, studies are still needed to identify Ethiopian maize seed varieties. With expert eyes, identification of maize seed varieties is difficult due to their similar morphological features and visual similarities. We proposed a hybrid feature-based maize variety identification model to solve this problem. For training and testing the model, images of each maize variety were collected from the adet agriculture and research center (AARC), Ethiopia. A multi-class support vector machine (MCSVM) classifier was employed on a hybrid of handcrafted (i.e., gabor and histogram of oriented gradients) and convolutional neural network (CNN)-based feature selection techniques and achieved an overall classification accuracy of 99%.

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