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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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Analysis of rank-based latency aware fog scheduling using validating internet of things at large scales J. Geetha; Shaguftha Zuveria Kottur; Riya Ganiga; D. S. Jayalakshmi; Tallapalli Surabhi
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i3.pp1502-1511

Abstract

With the increase in internet of things (IoT) applications' range and scale, it is essential to test the applications before deploying them in the real world. Most common approaches utilize simulations and testbeds; however, these methods lack real-time failure scenarios and the capability to scale, respectively. A virtual environment is a suitable approach that overcomes these drawbacks further, IoT applications using cloud computing have evolved to shift some computing and storage capabilities to the edge networks for ensuring adherence to strict latency constraints for real-time applications. This led to the emergence of fog computing which provides lower latency and better security, among other advantages. As for any processing tasks, scheduling becomes a critical concern for matching the tasks with the devices having appropriate resources. This paper analyzes a hybridized fog scheduling algorithm based on a ranking approach considering latency as the main parameter. It builds a software layer for scheduling on top of the validating internet of things at large scales (VIoLET) infrastructure. The results are compared with the round-robin scheduling algorithm, and it is found that the hybridized algorithm provides closer actual latency values to the expected.
Uplink millimeter-wave multi-cell multi-user massive multi-input multi-output systems Srinivas Kodimyala; Srinivasulu Tadisetty
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 1: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i1.pp261-268

Abstract

In this paper, we delve into the maximized spectral efficiency (SE) of millimeter-wave (mmWave) multicell multiuser massive MIMO Systems for uplink transmission with low-resolution phase shifters (LRPSs). Millimeter-wave massive multiple-input multiple-output (mMIMO) is an important technology for upcoming cellular networks which will provide higher bandwidth and throughput than current wireless systems and networks. LRPSs are commonly used to minimize power consumption, maximize spectral efficiency and diminish the complexity of hybrid precoder and combiner. In this paper, we consider a hybrid analog-digital precoder and combiner design with LRPSs for mmWave multi-cell multiuser mMIMO systems for uplink transmission to spectral efficiency in terms of iterations. The proposed technique outperforms when compared to traditional optimization approaches concerning spectral efficiency and bit error rate (BER). We show through simulation results that our designs with LRPSs outperform standard iteration procedures.
Automatic summarization of YouTube video transcription text using term frequency-inverse document frequency Rand Abdulwahid Albeer; Huda F. AL-Shahad; Hiba J. Aleqabie; Noor D. Al-shakarchy
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i3.pp1512-1519

Abstract

Automatic summarization is a technique for quickly introducing key information by abbreviating large sections of material. Summarization may apply to text and video with a different method to display the abstract of the subject. Natural language processing is employed in automated text summarization in this research, which applies to YouTube videos by transcribing and applying the summary stages in this study. Based on the number of words and sentences in the text, the method term frequency-inverse document frequency (TF-IDF) was used to extract the important keywords for the summary. Some videos are long and boring or take more time to display the information that sometimes finds in a few minutes. Therefore, the essence of the proposed system is to find the way to summarize the long video and introduce the important information to the user as a text with few numbers of lines to benefit the students or the researchers that have no time to spend with long videos for extract the useful data. The results have been evaluated using Rouge method on the convolutional neural network (CNN)-dailymail-master data set.
Adaptation of of March-SS algorithm to word-oriented memory built-in self-test and repair Gobinda Prasad Acharya; Muddapu Asha Rani; Ganjikunta Ganesh Kumar; Lavanya Poluboyina
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 1: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i1.pp96-104

Abstract

The technology shrinkage and the increased demand for high storage memory devices in today’s system on-chips (SoCs) has been the challenges to the designers not only in the design cycle but also to the test engineers in testing these memory devices against the permanent faults, intermittent and soft errors. Around 90% of the chip area in today’s SoCs is being occupied by the embedded memories, and the cost for testing these memory devices contributes a major factor in the overall cost and the time to market. This paperproposes a strategy to develop a word-oriented March SS algorithm-basedmemory built-in self-test (MBIST), which is then applied for memory built-in self-test and repair (MBISTR) strategy. The implementation details for 1 KB of single-port static random-access memory (SRAM) depict that the modified March-SS algorithm based MBISTR-enabled SRAM facilitates self-test and self-repair of embedded memories with a marginal hardware overhead (<1%) in terms of look up tables and slice registers when compared to that of standard SRAM.
Recommendation method based on learner profile and demonstrated knowledge Outmane Bourkoukou; Essaid El Bachari; Mohamed Lachgar
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i3.pp1634-1642

Abstract

The COVID-19 pandemic is increasingly gaining popularity when discussing e-learning in the context of institutional and organizational learning because of its numerous benefits which make it possible for learners to learn regardless of the circumstances and/or the timing. Therefore, the expanding dominion of online learning has caused problem in terms of determining adequate learning activities for the learner in this context, and it relatively becomes a widely used learning technique for learners. Several studies in online learning focused mainly on increasing student achievements based on recommendation systems. An ideal recommender system in e-learning environment should be built with both accurate and pedagogical goals. To address this challenge, we propose a recommendation method based on learner preferences and knowledge level using machine learning technique. The learning approach is designed based on this technology to build a personalized e-learning scenario by selecting the most adequate learning activities for the learner. Moreover, several experiences were conducted in the real environment to evaluate our system. The results show the quality of learning and the learner's satisfaction.
Light weight serverless computing at fog nodes for internet of things systems Mohamed Elkholy; Marwa A. Marzok
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 1: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i1.pp394-403

Abstract

Internet of things (IoT) systems collect large size of data from huge numbers of sensors. A wide rage of IoT systems relies on cloud resources to process and analyze the collected data. However, passing large amount of data to the cloud affects the overall performance and cannot support real-time requirements. Serverless computing is a promising technique that allows developer to write an application code, in any programming language, and specify an event to start its execution. Thus, IoT system can get a good benefit of serverless environment. The proposed work introduces a framework to allow Serverless computing to take place on the Fog nodes near the data collectors. The proposed framework is implemented as an extension to a Kubernetes cluster that manages a set of Docker containers at the fog layer. A prototype of the proposed solution was implemented using Node.Js for coding and YAML files to transfer data. The proposed framework was evaluated against traditional cloud Serverless execution. The experimental results proved the significant enhancement of the framework by dcreasing the respond time especially for data intensive IoT applications.
A bootstrap aggregation approach for adequate crop fertilizer and nutrition recommendation Varshitha D. N.; Savita Choudhary
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i3.pp1773-1780

Abstract

Agriculture is the largest workforce of India and biggest contributor to the Indian economy. Improving agricultural practices with the help of modern computer science technologies have great scope. Helping the farmers to know about their soil fertility, crops which can be grown and fertilizers or nutrients required for their land will be valuable inputs for them. Too much or too little fertilizers may harm the soil, so right amount of fertilization is also important. In this paper we have discussed about the bootstrap aggregation regression method, which is an ensemble machine learning technique to recommend the optimum level of nutrients and fertilizers. Hence customized nutrients recommendation reports could be generated to suggest the fertilizers and nutrients with their adequate quantities. This will be really beneficial for farmers to maintain the soil health and helpful for better crop growth and yield. We consider the features and levels of soil parameters such as nitrogen, phosphorus, potassium (NPK), pH level, organic carbon, electric conductivity, humidity, rainfall and other micro nutrients for predicting the right amount of fertilizers and nutrients. We have also checked other regression methods to compare the results based on the previous work done in the same field.
Performance analysis of intrusion detection for deep learning model based on CSE‑CIC‑IDS2018 dataset Baraa Ismael Farhan; Ammar D. Jasim
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 2: May 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i2.pp1165-1172

Abstract

The evolution of the internet of things as a promising and modern technology has facilitated daily life. Its emergence was accompanied by challenges represented by its frequent exposure to attacks and its being a target for intruders who exploit the gaps in this technology in terms of the nature of its heterogeneous data and its large quantity. This made the study of cyber security an urgent necessity to monitor infrastructures It has network flaw detection and intrusion detection that helps protect the network by detecting attacks early and preventing them. As a result of advances in machine learning techniques, especially deep learning and its ability to self-learning and feature extraction with high accuracy, the research exploits deep learning to analyze the real data set of CSE-CIC-IDS2018 network traffic, which includes normal behavior and attacks, and evaluate our deep model long short-term memory (LSTM), That achieves accuracy of detection up to 99%.
Induction motor drive based on modular-multilevel converter with ripple-power decoupling channels Enaam Abdul-Khaliq Ali; Turki Kahawish Hassan
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 2: May 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i2.pp675-688

Abstract

A driving system for a three-phase variable-speed induction machine-based modular multilevel converter (MMC) with magnetic channels operating at high frequencies -connecting adjacent-arm submodules is displayed in this paper. The primary disadvantage of using MMC in variable-speed motors is a high voltage ripple generated by submodule capacitors at low speeds with constant torque. This study utilises the DHB modules as energy channels, exchanging between the SM capacitors to correct the power imbalance. The ripple power of adjacent-arm SMs may be entirely decoupled, outcomes a virtually fluctuation-set free SM capacitor voltage design. Thus, the typical MMC issue of significant ripple voltage between SM capacitors has been wholly addressed regardless of operating frequency. The design and analysis of Field Oriented Control (FOC) of induction motors is based on an algorithm that ensures the motor's efficiency across a broad speed range. In this paper, we achieved a tiny ripple in the capacitive voltage for some frequencies (50Hz, 25Hz, 10Hz, 5Hz) by (±0.25%) compared with the previous papers that achieved a reduction in ripple within (±5%), and also this system was compared with the traditional system method operating principle was presented analytically and verified using Matlab Simulink.
Development of geo-referenced agricultural map and management information system for Samar Island Charito Dela Cruz Sabate; Mirador Labrador
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i3.pp1718-1724

Abstract

This study focuses on the developed web-based information system which allows local government unit (LGU) in Samar Island and other agricultural sectors to have access to updated farmers’ information and the soil nutrient, soil fertility status, and the crop suitability of soil in the different municipalities of Samar. Geo-referenced agricultural map and management system can be an important tool for both operational and policy levels by accelerating the supply of up-to-date data, supporting the implementation of different types of projects targeting objectives such as municipal development, regional economic development, agricultural development, sustainable resource management, good governance, and many others. The management information system was developed using MySQL. The server-side language used in this study was PHP and the client-side language used was hypertext markup language (HTML). The map was created using Mapbox GL JS, then, geo-referenced map layers are saved to the local server as an array map for direct access via the front-end web application. With the use of the system, LGU in Samar Island and other agricultural sectors can implement project more efficiently because farmers’ information and the nutrient capacity of the soil is readily available.

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