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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 2,901 Documents
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An extreme gradient boost based classification and regression tree for network intrusion detection in IoT Chalichalamala, Silpa; Govindan, Niranjana; Kasarapu, Ramani
Bulletin of Electrical Engineering and Informatics Vol 13, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i3.6843

Abstract

Nowadays, modern technology includes various devices, networks, and apps from the internet of things (IoT), which consist of both positive and negative impacts on social, economic, and industrial effects. To address these issues, IoT applications and networks require lightweight, quick, and adaptable security solutions. In this sense, solutions based on artificial intelligence and big data analytics can yield positive outcomes in the realm of cyber security. This study presents a method called extreme gradient boost (XGBoost) based classification and regression tree to identify network intrusions in the IoT. This model is ideally suited for application in IoT networks with restricted resource availability because of its distributed structure and builtin higher generalization capabilities. This approach is thoroughly tested using botnet internet of things (BoT-IoT) new-generation IoT security datasets. All trials are conducted in a range of different settings, and a number of performance indicators are used to evaluate the effectiveness of the proposed method. The suggested study's findings provide recommendations and insights for situations involving binary classes and numerous classes. The suggested XGBoost model achieved 99.53% of accuracy in attack detection and 99.51% in precision for binary class and multiclass classifications, respectively.
Secure and efficient data storage with Rivest Shamir Adleman algorithm in cloud environment Elumalai, Ezhilarasan; Muruganandam, Dinakaran
Bulletin of Electrical Engineering and Informatics Vol 13, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i4.6421

Abstract

Cloud computing rapidly is a prerequisite and releases resources with minimal management effort. The surfacing of the cloud has significantly distorted the general insight into infrastructure, software services, and development models. In contrast to single-key encryption models based on public or private keys (PKs), hybrid encryption systems combine encryption methods using symmetric or asymmetric methods. Various hybrid algorithms fail to meet users’ expectations regarding data security and cannot prevent all security risks. The secure and efficient data storage and retrieval (SEDSR) algorithm was developed for scalable key management between the content owner, cloud user, and service providers in an un-trusted cloud environment. In the implementation, the SEDSR combines the Rivest Shamir Adleman (RSA) algorithm 4096 key length with a primary symmetric key method to provide adequate and compact security with optimal retrieval systems in the cloud. Based on the experimental evaluation, the SEDSR minimizes 1.7 seconds of encryption times (ET) and 1.5 seconds of decryption time (DT) and improves by 34% throughput (TRP) compared to existing parameters.
A novel high-gain DC-DC converter for photovoltaic applications Thandavarayan, Porselvi; Mouttou, Arounassalame
Bulletin of Electrical Engineering and Informatics Vol 13, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i6.7862

Abstract

This manuscript proposes a novel transformer-less, single-switch direct current (DC)-DC converter for renewable energy systems (RESs). The main aim is to solve the problems of low output voltage generated by photovoltaic (PV) arrays and discontinuous input supply current caused by switching mode power supplies. The new converter is a combination of a single-ended primary inductor, a diode/capacitor circuit, and a conventional quadratic boost converter. The main advantages are the higher rate of voltage conversion (more than 10 times for duty cycle above 50%), the diminished voltage across the active switch with diodes, and diminished gate driver necessity because of the use of a single switch with a continual input current for raising the PV panel life. Furthermore, the new converter produces low switching voltage, which improves system efficiency. The proposed converter operating principle and analysis based on steady-state performance are discussed. The proposed converter’s performance is assessed using simulation in MATLAB/Simulink, and the results were presented. A 100 W prototype model of the designed DC-DC converter is also developed, and finally, the hardware results were compared with the simulated results.
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.
Remote sensing in the analysis of the behavior of CO associated with confinement due to COVID-19, in the city of Manizales Henao-Céspedes, Vladimir; Garcés-Gómez, Yeison Alberto; Cardona-Morales, Oscar
Bulletin of Electrical Engineering and Informatics Vol 13, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i6.7441

Abstract

This article analyzed the behavior of carbon monoxide (CO) levels in Manizales during pre-lockdown, lockdown, and post-lockdown, as a response to the coronavirus disease (COVID-19) pandemic. The analysis focuses on the data of CO levels obtained from the tropospheric monitoring instrument (TROPOMI), precipitation, and temperature (T) recorded by the network of stations of Caldas. The data allowed us to find that during the lockdown, the average value of CO was 9.92% lower than the value registered before the lockdown, and it was 11.75% lower after the lockdown. On the other hand, the correlation between CO levels and population density during the three periods was analyzed, obtaining an ?2 = 0.816 after lockdown. Finally, considering other possible variables that can affect the CO levels, an analysis of the behavior of CO was carried out concerning the temperature and precipitation of the city registered before, during, and after the lockdown. Regarding CO and temperature, the correlation was inverse with Pearson’s ? = −0.599 (Fisher’s ? = −0.692), which also supports the decreasing trend of the value measured, and that the variation of CO levels does not depend only on lockdown but also on other factors. Regarding CO and precipitation, a positive correlation of Pearson’s ? = 0.165 (Fisher’s ? = 0.167) was obtained.
Self-adaptive differential evolution algorithm with dynamic fitness-ranking mutation and pheromone strategy Singsathid, Pirapong; Wetweerapong, Jeerayut; Puphasuk, Pikul
Bulletin of Electrical Engineering and Informatics Vol 13, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i1.6590

Abstract

Differential evolution (DE) is a population-based optimization algorithm widely used to solve a variety of continuous optimization problems. The self-adaptive DE algorithm improves the DE by encoding individual parameters to produce and propagate better solutions. This paper proposes a self-adaptive differential evolution algorithm with dynamic fitness-ranking mutation and pheromone strategy (SDE-FMP). The algorithm introduces the dynamical mutation operation using the fitness rank of the individuals to divide the population into three groups and then select groups and their vectors with adaptive probabilities to create a mutant vector. Mutation and crossover operations use the encoded scaling factor and the crossover rate values in a target vector to generate the corresponding trial vector. The values are changed according to the pheromone when the trial vector is inferior in the selection, whereas the pheromone is increased when the trial vector is superior. In addition, the algorithm also employs the resetting operation to unlearn and relearn the dominant pheromone values in the progressing search. The proposed SDE-FMP algorithm using the suitable resetting periods is compared with the well-known adaptive DE algorithms on several test problems. The results show that SDE-FMP can give high-precision solutions and outperforms the compared methods.
Development of Arduino applications for IoT applications in software engineering education: a systematic literature review Yusop, Noorrezam; Moketar, Nor Aiza; Sadikan, Siti Fairuz Nurr
Bulletin of Electrical Engineering and Informatics Vol 13, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i3.4506

Abstract

The continuous development of software applications is a necessary step in producing high-quality products that will consistently meet end-user expectations and stakeholder needs. Development of Arduino applications, embedded in a product’s hardware, can and should be considered at the software engineering phase itself, even though current practice dictates it be handled by product engineers. The method used in this investigation was based on a systematic literature review (SLR). Therefore, this paper depicts a gap that currently exists within the body of literature surrounding the development of Arduino applications for ‘internet of things’ (IoT) applications in software engineering education in commercial and research fields. The result of this study are two findings investigates: i) relevant Arduino application development used in software engineering and ii) method for applying software engineering for Arduino applications. The limitations and constraints of each technique in respect to Arduino apps were also examined in order to provide a better understanding of each body of study's weaknesses and strengths. We realise that these studies are still insufficient and need to be evaluated and improved further.
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.
Palembang songket fabric motif image detection with data augmentation based on ResNet using dropout Ermatita, Ermatita; Noprisson, Handrie; Abdiansah, Abdiansah
Bulletin of Electrical Engineering and Informatics Vol 13, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i3.6883

Abstract

A good way to spread knowledge about Palembang songket woven cloth patterns is to use information technology, especially artificial intelligence technology. This study's main goal is to develop a ResNet model with dropout regularization methods and find out how dropout regularization affects the ResNet model for detecting Palembang songket fabric motif with more data. Data was collected in places like tujuh saudara songket, Zainal songket, songket PaSH, AMS songket, and batik, Ernawati songket, Nabilah collections, Ilham songket, and Marissa songket. We used eight class of data for this research. A dataset of 7,680 data for training, 960 data for validation, and 960 data for testing is a dataset that has been prepared to be implemented in experiments. In the final results, the experimental results for DResNet demonstrated that accuracy at the training stage was 92.16%, accuracy at the validation stage was 78.60%, and accuracy at the submission stage was 80.3%. The experimental results also show that dropouts are able to increase the accuracy of the ResNet model by adding +1.10% accuracy in the training process, adding +1.80% accuracy in the validation process, and adding +0.40% accuracy in the testing process.
Stereo matching algorithm using deep learning and edge-preserving filter for machine vision Abd Gani, Shamsul Fakhar; Miskon, Muhammad Fahmi; Hamzah, Rostam Affendi; Hamid, Mohd Saad; Kadmin, Ahmad Fauzan; Herman, Adi Irwan
Bulletin of Electrical Engineering and Informatics Vol 13, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i3.5708

Abstract

Machine vision research began with a single-camera system, but these systems had various limitations from having just one point-of-view of the environment and no depth information, therefore stereo cameras were invented. This paper proposes a hybrid method of a stereo matching algorithm with the goal of generating an accurate disparity map critical for applications such as 3D surface reconstruction and robot navigation to name a few. Convolutional neural network (CNN) is utilised to generate the matching cost, which is then input into cost aggregation to increase accuracy with the help of a bilateral filter (BF). Winner-take-all (WTA) is used to generate the preliminary disparity map. An edge-preserving filter (EPF) is applied to that output based on a transform that defines an isometry between curves on the 2D image manifold in 5D and the real line to eliminate these artefacts. The transform warps the input signal adaptively to allow linear 1D filtering. Due to the filter's resistance to high contrast and brightness, it is effective in refining and removing noise from the output image. Based on experimental research employing a Middlebury standard validation benchmark, this approach gives high accuracy with an average non-occluded error of 6.71% comparable to other published methods.

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