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Bulletin of Electrical Engineering and Informatics
Published by Universitas Ahmad Dahlan
ISSN : -     EISSN : -     DOI : -
Core Subject : Engineering,
Electrical & Electronics Engineering
Bulletin of Electrical Engineering and Informatics (Buletin Teknik Elektro dan Informatika) ISSN: 2089-3191, e-ISSN: 2302-9285 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. The journal publishes original papers in the field of electrical, computer and informatics engineering.
Arjuna Subject : -
Articles 75 Documents
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Search results for , issue "Vol 13, No 5: October 2024" : 75 Documents clear
Analysis of distributed smart grid system on the national grids Mythreyee, M.; Anandan, Nalini
Bulletin of Electrical Engineering and Informatics Vol 13, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i5.6331

Abstract

In the power industry, advanced techniques have furthered the development of the smart grid's power system and management. The world's third-largest country is India, which has a producer and consumer of electricity, is struggling with different power-related problems, as well as distribution losses, transmission, environmental concerns, and electricity theft. The energy sector is investigating innovative technologies to enhance grid efficiency, security, and sustainability to address power-related issues. Recently, smart grid technology has ascribed significance to the energy scenario; the term "smart grid" relates to electric electricity. The study aims to thoroughly evaluate how smart grid technologies might improve the reliability and efficiency of India's electrical system. This article examines the impact of smart grid technologies on national grids and makes some proposals to authorities for switching their traditional grid system to a smart grid system. The results indicate the yearly wind profile, comparative analysis of energy consumption, and cost analysis of the system. Smart grid integration is strengthened by the useful insights provided by the annual wind profile study, which reveals the region's renewable energy potential. Analysis of costs and energy consumption patterns show that switching to a smart grid system is financially feasible in the long term, and studies of impacts on utilization of resources show that it is beneficial.
Windows operating system malware detection using machine learning Hilabi, Rawabi; Abu-Khadrah, Ahmed
Bulletin of Electrical Engineering and Informatics Vol 13, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i5.8018

Abstract

Over the years, cybercriminals have become more sophisticated in manipulating network users. Malware is a popular tool they use to exploit victims, targeting valuable assets such as identities and credit cards in the realm of digital technology. Cybersecurity professionals are consistently innovating to detect malicious activities. Machine learning (ML) algorithms are now a leading method for rapidly identifying unseen malware, offering efficiency and intelligence beyond traditional approaches. In fact, attackers like to see the victims suffer from damage caused by malware. Malware can destroy devices and networks. Additionally, hackers can blackmail individuals and organizations to obtain money through ransomware. Therefore, the aim of this research is developing a new model that has the capability of detecting malwares that are targeting Windows operating systems (OS) through enhancing an existing model by deploying several ML algorithms which are extreme gradient boosting (XGB) and random forest (RF). In addition, the swarm optimization and ML applied to portable executable (SOMLAP) dataset applied in the portable executable (PE) is used for training data and testing these learning algorithms. The result achieved by XGB and RF hybrid technique accuracy was 0.966, precision 0.990 and recall was 0.918.
Implementing trajectory correction strategy through model prediction control for flight vehicle missions Irwanto, Herma Yudhi; Yusgiantoro, Purnomo; Abidin Sahabuddin, Zainal; Oktovianus Bura, Romie; Andiarti, Rika; Eko Putro, Idris; Sudiana, Oka; Hanif, Azizul
Bulletin of Electrical Engineering and Informatics Vol 13, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i5.7798

Abstract

Modeling a high-speed flying vehicle is imperative to ensure the success of vehicle development missions. Moreover, adherence to research protocols mandates a stepwise approach to testing the vehicle model, encompassing simulation trials using software-in-the-loop simulation (SILS), hardware-in-the-loop simulation (HILS), as well as diverse ground and environmental tests prior to flight testing. This study entailed a collaborative effort between MATLAB/Simulink and LabVIEW to seamlessly integrate the model developed in MATLAB/Simulink into LabVIEW for the implementation of model predictive control (MPC) strategy, aimed at trajectory correction (TC) missions for the vehicle. This MPC strategy was directly applied to the onboard flight control system (OBFCS) of the vehicle. Simulation results indicate the successful control of roll and pitch conditions by OBFCS in both SILS and HILS, ensuring the maintenance of flight conditions in accordance with predicted trajectories despite the presence of simulated disturbances. Notably, the simulation demonstrates the independence or absence of interference between each simultaneous MPC control for roll and pitch adjustments.
Effects of DBDS and DBPC antioxidants on the corrosion of copper strips immersed in transformer oil Sutan Chairul, Imran; Ab Ghani, Sharin; Shahril Ahmad Khiar, Mohd; Abu Bakar, Norazhar; Syahrani Johal, Muhammad; Nazri Mohamad Din, Mohamad
Bulletin of Electrical Engineering and Informatics Vol 13, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i5.8196

Abstract

This paper presents the experimental findings on the effects of antioxidants on the corrosion of copper strips immersed in mineral oil (MO)–antioxidant samples. First, the uninhibited MO was mixed thoroughly with dibenzyl disulphide (DBDS) or 2,6-di-tert-butyl-p-cresol (DBPC) at different concentrations (5, 15, 25, 50, and 200 mg/kg) using a hot plate magnetic stirrer set at a temperature of 73 °C and stirring speed of 750 rpm for 15 min. Following this, the MO–antioxidant samples were poured into separate test vessels and copper strips were added into the vessels. Next, each MO–antioxidant sample was blanketed with nitrogen gas, sealed, and placed in a forced convection laboratory oven. The MO–antioxidant samples were then thermally aged at 150 °C for 48 h. The results showed that the acidity of the MO–antioxidant sample decreased with an increase in the antioxidant concentration, regardless whether the antioxidant was DBDS or DBPC. However, the corrosion of the copper strip worsened with an increase in the antioxidant concentration, where DBDS had a higher relative degree of corrositivity to copper compared with DBPC. In addition, the results showed that a DBPC concentration of 25 mg/kg reduced the the acidity of the MO–antioxidant sample and resulted in a moderate tarnish of the copper strip.
Current critical review on prediction stroke using machine learning Byna, Agus; Modi Lakulu, Muhammad; Yusuf Panessai, Ismail
Bulletin of Electrical Engineering and Informatics Vol 13, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i5.7435

Abstract

Strokes are a significant health problem because they often lead to long-term disabilities due to delayed diagnoses and insufficient information about the disease. The use of artificial intelligence (AI), specifically machine learning (ML) and deep learning (DL), has the potential to aid in stroke diagnosis and significantly advance healthcare. This review article critically examines predictive methods for ischemic and hemorrhagic strokes. The preferred reporting items for systematic reviews and meta-analyses (PRISMA) method was used to identify 79 relevant articles from five databases spanning 2012 to 2022, with IEEE having the highest number of articles and citations. China had the most authors, and the random forest (RF) algorithm showed the most accurate results. A taxonomy categorizing the implementation and usage of ML and DL for stroke prediction was created and includes five focus areas: building, system planning, evaluation, comparison, and analysis. Additional research into other disease features related to stroke is warranted. Decentralized federated learning should also be implemented to collect data from remote locations for early diagnosis and create a single training model.
Translation of Mexican sign language into Spanish using machine learning and internet of things devices Caballero-Hernandez, Héctor; Muñoz-Jiménez, Vianney; Ramos-Corchado, Marco Antonio
Bulletin of Electrical Engineering and Informatics Vol 13, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i5.7853

Abstract

This paper describes an application based on internet of things (IoT) and gesture recognition through the MediaPipe library to communicate people with hearing and speech disabilities who use Mexican sign language (MSL) with people who do not have a disability. The system is made up of two modules, on the one hand, there is an Apple watch with the ability to obtain text and voice inputs in Spanish to stand for the translation into sign language through video and images. The system can set up a connection with an application in charge of performing sign recognition by capturing images from a camera, the IoT user can connect so that communication between both modules is carried out bidirectionally through Firebase database. During the development of the experimental tests, the visual recognition module was able to recognize signs belonging to the Spanish alphabet, digits, and greetings, with a 96% of precision, while the IoT device correctly displayed voice translations and text to symbology compared to the MSL.
The new machine learning feature selection method used in fertilizer recommendation D N, Varshitha; Choudhary, Savita
Bulletin of Electrical Engineering and Informatics Vol 13, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i5.7198

Abstract

Fertilizer recommendation is the crucial factor to be considered in automation of agricultural predictions. Fertilizer fill the necessary portion of any farming region. There are some micronutrients and macro nutrients which need to be given to crops for proper growth. If fertilization is not done to an optimum level, it may badly harm the soil quality and crop health ,so optimum fertilization is important. In this paper we discuss fertilizer and nutrient recommender, where we have used a new feature selection methodology. We have shown the difference between two implementation cases considering presence and absence of feature ranking and selection. Feature ranking and selection has clearly increased the efficiency of the fertilizer nutrient recommender in our work from 85% to 98%. Feature selection raking has been introduced with random forest approach.
On the development of an autonomous hexacopter drone for animal detection and collision avoidance system Alshbatat, Abdel Ilah Nour; Awawdeh, Moath
Bulletin of Electrical Engineering and Informatics Vol 13, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i5.7952

Abstract

Traffic accidents caused by collisions with animals are a significant global concern for authorities. The economic impact is substantial, including costs for treating injuries, rehabilitating victims, repairing vehicle damage, and addressing fatalities. As a contribution towards finding a solution, a wireless sensor network (WSN) and an autonomous, power-efficient, and economically feasible drone are presented. WSN is responsible for acquiring relevant information from the farm and communicating the data to the aerial drone denoted as a hexacopter; which is functioning as a mobile roadside device for transferring the warning information to the passing drivers so as to avoid any collision with animals. The proposed system involves the design of a lightweight camel-based sub-system to trigger drones for monitoring herds, a drone-based sub-system for tracking and ensuring safety in agricultural areas, and a vehicle-based sub-system to communicate collision warnings where an alarming protocol has been developed. The whole system has been designed, implemented, tested, and verified in an actual flight test. Experimental results indicate that the system has a unique capability. It can mitigate the number of accidents involving vehicles and animals, especially camels, and thus reduce the economic cost of damages associated with the problem.
Hybrid RNNs and USE for enhanced sequential sentence classification in biomedical paper abstracts Ndama, Oussama; Bensassi, Ismail; En-Naimi, El Mokhtar
Bulletin of Electrical Engineering and Informatics Vol 13, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i5.8240

Abstract

This research evaluates a number of hybrid recurrent neural network (RNN) architectures for classifying sequential sentences in biomedical abstracts. The architectures include long short-term memory (LSTM), bidirectional LSTM (BI-LSTM), gated recurrent unit (GRU), and bidirectional GRU (BI-GRU) models, all of which are combined with the universal sentence encoder (USE). The investigation assesses their efficacy in categorizing sentences into predefined classes: background, objective, method, result, and conclusion. Each RNN variant is used with the pre-trained USE as word embeddings to find complex sequential relationships in biomedical text. Results demonstrate the adaptability and effectiveness of these hybrid architectures in discerning diverse sentence functions. This research addresses the need for improved literature comprehension in biomedicine by employing automated sentence classification techniques, highlighting the significance of advanced hybrid algorithms in enhancing text classification methodologies within biomedical research.
Chaotic ant colony algorithm to control congestion and enhance opportunistic routing in multimedia network Ranganathan, Chitra Sabapathy; Sampathrajan, Rajeshkumar
Bulletin of Electrical Engineering and Informatics Vol 13, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i5.7448

Abstract

The creation of wireless multimedia networks imposed wireless devices that can retrieve multimedia material such as video and audio streams, still photos, and scalar sensor data from the environment is made possible by the availability of low-cost devices. This approach considers the issues of routing packets across a multi-hop network consisting of several traffic sources and links when ensuring bounded delay. The exits of an obstacle create several geographic routing issues, for example, congestion and delay. This article, chaotic ant colony algorithm (CACA) to control congestion and enhance opportunistic routing (CAOR) in multimedia network, is proposed to solve these issues. This mechanism uses the CACA algorithm to detect the obstacle and transmit the data packets on the obstacle edges optimal nodes. Moreover, an opportunistic routing (OR) selects the best forwarder by the forward aware factor (FAF) from the forwarder list (FL). The FAF measures node energy, node received signal strength indication (RSSI), available bandwidth (AB), and packet transmission rate for choosing the best forwarder. Experimental outcomes demonstrate that established delay, energy utilization, and throughput performances are greater than the conventional mechanism.

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