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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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3D modelling of the mechanical behaviour of magnetic forming systems Boutana Ilhem; Boussalem Mohamed Elamin; Laouira Ahmed; Bouferroum Salaheddine
Bulletin of Electrical Engineering and Informatics Vol 11, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i4.3628

Abstract

High-speed forming methods become attractive in manufacturing and significantly reduce the cost and energy requirements. Electromagnetic forming is a high-velocity pulse forming technique that applies electromagnetic forces to sheet or tubular workpieces using a pulsed magnetic field. In order to understand the physical behaviours of materials, numerical modeling is highly desired. Therefore, in this study, we investigate the mechanical behaviour of the electromagnetic sheet stamping and magnetic tube expansion and compression systems. For these 3D simulations, COMSOL multiphysics software is used. It provides the possibility to model the electromagnetic aspects of the problem along with the thermal and mechanical aspects in a coupled method. The developed 3D numerical fully coupled models lead to analyze the transient magnetic fields, Lorentz forces acting on workpieces, and the plastic deformations obtained in several magnetic forming systems. The effects of systems parameters are also investigated such as the coil’s form and the number of its turns.
Chest radiographs images retrieval using deep learning networks Sawsan M. Mahmoud; Hanan A. S. Al-Jubouri; Tawfeeq E. Abdoulabbas
Bulletin of Electrical Engineering and Informatics Vol 11, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i3.3478

Abstract

Chest diseases are among the most common diseases today. More than one million people with pneumonia enter the hospital, and about 50,000 people die annually in the U.S. alone. Also, Coronavirus disease (COVID-19) is a risky disease that threatens the health by affecting the lungs of many people around the world. Chest X-ray and CT-scan images are the radiological imaging that can be helpful to detect COVID-19. A radiologist would need to compare a patient's image with the most similar images. Content-based image retrieval in terms of medical images offers such a facility based on visual feature descriptor and similarity measurements. In this paper, a retrieval algorithm was developed to tackle such challenges based on deep convolutional neural networks (e.g., ResNet-50, AlexNet, and GoogleNet) to produce an effective feature descriptor. Also, similarity measures such as City block and Cosine were employed to compare two images. Chest X-ray and CT-scan datasets used to evaluate the proposed algorithms with a highest performance applying ResNet -50 (99% COVID-19 (+) and 98% COVID-19 (–)) and GoogleNet (84% COVID-19 (+) and 81% COVID-19 (–)) for X-ray and CT-scan respectively. The percentage increased about 1-4% when voting was used by a k-nearest neighbor classifier
A novel examination of limonene detection using plastic fiber optic sensors and the tapered approach Thanigai Anbalagan; Hazura Haroon; Hazli Rafis Abdul Rahim; Siti Halma Johari; Siti Khadijah Idris@ Othman; Hanim Abdul Razak; Maisara Othman
Bulletin of Electrical Engineering and Informatics Vol 12, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i2.4379

Abstract

A novel tapered plastic optic fiber (POF) biosensor is proposed and demonstrated for monitoring limonene in different concentrations. The mechanism of this device is based on an increase in the light transmission of a sensor submerged in a higher-concentration limonene solution, which also reflects an increase in the refractive index of the sensor. The tapered POF was fabricated using the chemical etching method to accomplish different waist diameters of 0.6 mm, 0.55 mm, and 0.5 mm, with a fiber length of 10 cm and a 2 cm sensing region. An Arduino integrated development environment (IDE) program was used to drive the voltage values from the photodetectors to obtain the measurements. As the limonene concentration solution varied from 20% to 100%, the output voltage of the sensor increased linearly, showing a sensitivity of 0.295 V/%, 0.33 V/%, and 0.46 V/% for tapered waist diameters of 0.6 mm, 0.55 mm, and 0.5 mm, respectively. The proposed sensor is a low-cost solution measurement option with high sensitivity, while it also involves a simple and easy fabrication technique.
Optical network on chip: design of wavelength routed optical ring architecture Kavitha, Thandapani; Maheswaran, Gopalswamy; Maheswaran, Joly; Pappa, Chandramohan K.
Bulletin of Electrical Engineering and Informatics Vol 12, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i1.4294

Abstract

Network on chip (NoC) technology has now achieved a mature stage of development as a result of their use as a key component in many successful commercial devices. As multiprocessors continue to scale, these ship based electronic networks are more challenging to meet their power budget communication requirements. Innovative technology is emerging with the aim of offering shorter latencies and greater bandwidth with lower power consumption. Ring topology provides superior results among the all wavelength routed topologies in the chip optical network. In this paper, we proposed an optical ring network-on-chip (ORNoC) architecture which is contention free. Communication matrix is used to assign a single waveguide/wavelength pair to implement simultaneous communications. The design constraints for the proposed architecture will be wavelength reused on a single waveguide for multiple communications. We imply automatic wavelength/waveguide assignment for effective design and will prove that the proposed architecture can connect more number of nodes and less wavelengths per waveguide.
Smart evaluation for deep learning model: churn prediction as a product case study Esam Mohamed Elgohary; Mohamed Galal; Ahmed Mosa; Ghada Atef Elshabrawy
Bulletin of Electrical Engineering and Informatics Vol 12, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i2.4180

Abstract

Customer churn prediction recently is one of the vital issues that confronts diverse business industries to sustain the customers base and profits. On the other hand, data scientists employ gigantic customer data to automate the data modelling process to offer these models as a generally portable service. This research has two main contributions: deep learning customer churn prediction model and smart evaluation prediction model service. So, this service harnesses any customer data to automate building, evaluation, and deployment of the churn prediction model. The research consists of three main parts. Firstly, it illustrates the dataset labelling which annotates customers data into churn or non-churn. Secondly, the deep learning churn prediction framework using convolutional neural network (CNN) algorithm. Finally, a case study is presented to show how churn prediction service is automatically trained and generated based on real customer data, where CNN parameters are adapted to achieve the most reliable performance in line with customers' behavior. The applied case study achieves accuracy 0.77, area under the curve (AUC) 0.84 and f1 score 0.83.
Data security in cloud environment using cryptographic mechanism Fairosebanu, Abdul Azis; Jebaseeli, Antony Cruz Nisha
Bulletin of Electrical Engineering and Informatics Vol 12, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i1.4590

Abstract

Virtual computing resources are provided via a cloud system that is both clever and intelligent. Based on the user's request, computing resources are made available. A hybrid cloud is the best option for storing and accessing user data for cloud deployments. Maintaining security in a hybrid cloud environment is time-consuming. This study provides a novel strategy for securing data in the hybrid cloud by ensuring the user's data is protected. Users' data in a hybrid cloud is protected using cryptographic approaches provided in this approach. Using this strategy, users' data may be protected in public and private clouds using various encryption methods. The suggested data security paradigm offers various advantages to both consumers and providers in terms of data security. Three symmetric encryption methods are offered as a service in the cloud. The concept is implemented as a cloud-based application hosted in the cloud, and the effectiveness of three strategies is assessed. They are evaluated in terms of performance and security. Using the recommended encryption methods in a hybrid cloud environment is more efficient than using other methods. The proposed technique can be used for relational data. It can be modified and enhanced to process multimedia data.
Neural network based seizure detection system using statistical package analysis Priyanka Rajendran; Kirupa Ganapathy
Bulletin of Electrical Engineering and Informatics Vol 11, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i5.3771

Abstract

Due to the unpredictable interruptions within the functions of the human brain, disturbance occurs and it affects the behavior of the human and is equally laid low with the frequent occurrence termed as seizures. Therefore, the proposed system detects the seizure using machine learning algorithms. The electroencephalogram (EEG) contains information of the brain to detect the seizure. The objective is to evaluate the performance of machine learning classifiers K-nearest neighbors (KNN), artificial neural network (ANN), support vector machine (SVM) and principal component analysis (PCA) by comparing the accuracy of the classifier. This work uses total of 11,500 EEG samples from the UCI machine learning repository. The seizure detection was done in two ways. First method, features extracted from the EEG signal and classification techniques are done to classify the seizure. The second method uses the principal component analysis algorithm to improve the significant selections of features from the dataset. The outcomes are analyzed using the statistical package for the social science (SPSS) tools. ANN with extracted functions achieved 96% of accuracy and significant efficiency of (p less than 0.05) in comparison with different machine learning classifiers. It would be prudent to conclude that the ANN demonstrated the best accuracy, sensitivity, and specificity.
Metaheuristic based routing incorporated with energy harvesting for enhanced network lifetime in WBAN Ganeson Sathya; Daniel Jasmine Evanjaline
Bulletin of Electrical Engineering and Informatics Vol 12, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i2.4589

Abstract

Wireless body area networks (WBAN) have improved healthcare industries to a large extent by providing contactless measurements and remote data analysis. However, the challenges encountered are mostly in the form of energy depletion scenarios, which results in the reduction of network lifetime to a large extent. This work presents an effective model to provide energy-efficient routing and enhanced energy harvesting mechanisms to improve network lifetime. The ant colony optimization (ACO) method has been extended to include a fitness function that takes into account several factors, and this is the basis for the routing model. These processes ensure effective routing, which conserves energy and, in turn, results in enhanced network lifetime. Performance of the proposed model has been compared with the existing state-of-the-art models in the domain. Comparison with the metaheuristic-based model, cooperative energy efficient and priority based reliable routing protocol with network coding (CEPRAN), indicates the efficiency of the energy harvesting mechanism used in the proposed work. When compared with models using energy harvesting mechanisms, results exhibit higher network lifetime, depicting the efficiency of the proposed routing mechanism.
Finger knuckle pattern person identification system based on LDP-NPE and machine learning methods Ali Mohsin Aljuboori; Mohammed Hamzah Abed
Bulletin of Electrical Engineering and Informatics Vol 11, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i6.4236

Abstract

Biometric-based individual distinguishing proof is a successful strategy for consequently perceiving, with high certainty, an individual's character. The utilization of finger knuckle pictures for individual ID has shown promising outcomes and produced a ton of interest in biometrics. By seeing that the surface example delivered by twisting the finger knuckle is profoundly particular, in this paper we present a new biometric validation framework utilizing finger-knuckle-print (FKP) imaging. In this paper, another methodology in view of neighborhood surface examples is proposed. Local derivative pattern (LDP) histogram is investigated for FKP description. Then based on neighborhood preserving embedding (NPE) is used for dimension reduction to the feature vector. The feature extraction method is computed and evaluated in the identification framework task. The machine learning methods (multiclass support vector machine (MSVM), random forest (RF), k-nearest neighbor (KNN)) are proposed for classification. The system is tested on the PolyU finger knuckle database. The empirical results proved that the proposed model has the best results with RF. Moreover, our proposed LDP-NPE model has been evaluated and the results show remarkable efficiency in comparison with previous work. Experimentally, the proposed model has better accuracy as reflected by 99.65%.
A methodology for transforming BPMN to IFML into MDA Abir Sajji; Yassine Rhazali; Youssef Hadi
Bulletin of Electrical Engineering and Informatics Vol 11, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i5.3973

Abstract

Responding to rising information system complexity and the high expense of technological migration, model driven architecture (MDA) was created. As a result, the OMG advocates raising the abstraction level to overcome technological limitations. MDA seeks to describe the functional and performance requirements of an application on a platform independently. Using the MDA approach, the business process model and notation (BPMN), and interaction flow modeling language (IFML) standards, we represent a methodology that allows transforming semi-automatically from the computation independent model (CIM) level to the platform independent model (PIM) level; to achieve this a collection of unique rules for transforming in a semi-automatic manner from CIM to PIM were developed. At the CIM level, we create models of business process using the notation standard BPMN, and IFML is used to adapt PIM models with web-oriented graphical user interfaces (GUI). To properly demonstrate the transformation procedure from CIM to PIM models a case study of the order management process was presented.

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