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International Journal of Electrical and Computer Engineering

IJECE
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Published by Institute of Advanced Engineering and Science

ISSN : 20888708 EISSN : 27222578 DOI : -

Core Subject : Science, Engineering,

Computer Science & IT Electrical & Electronics Engineering

International Journal of Electrical and Computer Engineering (IJECE, ISSN: 2088-8708, a SCOPUS indexed Journal, SNIP: 1.001; SJR: 0.296; CiteScore: 0.99; SJR & CiteScore Q2 on both of the Electrical & Electronics Engineering, and Computer Science) is the official publication of the Institute of Advanced Engineering and Science (IAES). The journal 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.

Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)

Articles 6,301 Documents

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A Triple Band Bow Tie Array Antenna Using Both-sided MIC Technology Akimun Jannat Alvina; Samia Sabrin; Mohammad Istiaque Reja; Jobaida Akhtar
International Journal of Electrical and Computer Engineering (IJECE) Vol 8, No 5: October 2018
Publisher : Institute of Advanced Engineering and Science

A single-fed linearly polarized 2x2 microstrip bow tie array antenna is proposed. The feed network has microstrip line and slot line where microstrip-slot branch circuit is connected in parallel. The feed network of the array is designed using both-sided MIC Technology to overcome the impedance matching problem of conventional feed networks. The 2x2 half bow tie array antenna is also truncated with spur lines for optimization of antenna performance. The array antenna unit can be realized in very simple and compact structure, as all the antenna elements and the feeding circuit is arranged on a Teflon glass fiber substrate without requiring any external network. The design frequency of the proposed antenna is 5 to 8 GHz (CBand) and the obtained peak gain is 12.41 dBi. The resultant axial ratio indicates that linear polarization is achieved.

Framework for progressive segmentation of chest radiograph for efficient diagnosis of inert regions Savitha S. K.; N. C. Naveen
International Journal of Electrical and Computer Engineering (IJECE) Vol 9, No 2: April 2019
Publisher : Institute of Advanced Engineering and Science

Segmentation is one of the most essential steps required to identify the inert object in the chest x-ray. A review with the existing segmentation techniques towards chest x-ray as well as other vital organs was performed. The main objective was to find whether existing system offers accuracy at the cost of recursive and complex operations. The proposed system contributes to introduce a framework that can offer a good balance between computational performance and segmentation performance. Given an input of chest x-ray, the system offers progressive search for similar image on the basis of similarity score with queried image. Region-based shape descriptor is applied for extracting the feature exclusively for identifying the lung region from the thoracic region followed by contour adjustment. The final segmentation outcome shows accurate identification followed by segmentation of apical and costophrenic region of lung. Comparative analysis proved that proposed system offers better segmentation performance in contrast to existing system.

A Novel Integrated Framework to Ensure Better Data Quality in Big Data Analytics over Cloud Environment C.S. Sindhu; Nagaratna P. Hegde
International Journal of Electrical and Computer Engineering (IJECE) Vol 7, No 5: October 2017
Publisher : Institute of Advanced Engineering and Science

With advent of Big Data Analytics, the healthcare system is increasingly adopting the analytical services that is ultimately found to generate massive load of highly unstructured data. We reviewed the existing system to find that there are lesser number of solutions towards addressing the problems of data variety, data uncertainty, and data speed. It is important that an error-free data should arrive in analytics. Existing system offers single-hand solution towards single platform. Therefore, we introduced an integrated framework that has the capability to address all these three problems in one execution time. Considering the synthetic big data of healthcare, we carried out the investigation to find that our proposed system using deep learning architecture offers better optimization of computational resources. The study outcome is found to offer comparatively better response time and higher accuracy rate as compared to existing optimization technqiues that is found and practiced widely in literature.

An optimized approach for extensive segmentation and classification of brain MRI Harish S; G.F Ali Ahammed
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 3: June 2020
Publisher : Institute of Advanced Engineering and Science

With the significant contribution in medical image processing for an effective diagnosis of critical health condition in human, there has been evolution of various methods and techniques in abnormality detection and classification process. An insight to the existing approaches highlights that potential amount of work is being carried out in detection and segmentation process but less effective modelling towards classification problems. This manuscript discusses about a simple and robust modelling of a technique that offers comprehensive segmentation process as well as classification process using Artificial Neural Network. Different from any existing approach, the study offers more granularities towards foreground/background indexing with its comprehensive segmentation process while introducing a unique morphological operation along with graph-believe network for ensuring approximately 99% of accuracy of proposed system in contrast to existing learning scheme.

Building Fault Tollrence within Clouds at Network Level Sastry Kodanda Rama Jammalamadaka; Kamesh Bala Krishna Duvvuri; Devi Anusha CH; Padmini P; Siva Anjaneyulu G
International Journal of Electrical and Computer Engineering (IJECE) Vol 6, No 4: August 2016
Publisher : Institute of Advanced Engineering and Science

Cloud computing technologies and infrastructure facilities are coming up in a big way making it cost effective for the users to implement their IT based solutions to run business in most cost-effective and economical way. Many intricate issues however, have cropped-up which must be addressed to be able to use clouds the purpose for which they are designed and implemented. Among all, fault tolerance and securing the data stored on the clouds takes most of the importance. Continuous availability of the services is dependent on many factors. Faults bound to happen within a network, software, and platform or within the infrastructure which are all used for establishing the cloud. The network that connects various servers, devices, peripherals etc., have to be fault tolerant to start-with so that intended and un-interrupted services to the user can be made available. A novel network design method that leads to achieve high availability of the network and thereby the cloud itself has been presented in this paper

Multilevel Signal Analyzer Tool for Optical Communication System M.F.L Abdullah; Rahmat Talib
International Journal of Electrical and Computer Engineering (IJECE) Vol 2, No 4: August 2012
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (339.961 KB)

This paper presents an educational software interface tool to analyze multilevel signal at the receiver of an optical communication system. This tool provides visualization in terms of eye-diagram and bit/symbol error rate along the symbol duration using Gaussian approximation method. Besides that, maximum Q-factors, minimum BER and corresponding threshold level are also displayed. This tool is developed using Matlab as an interface programming tool. The developed analyzer has been used to analyze 2, 4 and 8-PAM signal for an optical communication system. A commercial software known as OptiSystem™ has been used to develop and simulate the multilevel optical system. The performance of the develop analyzer has been validated with the build in analyzer of OptiSystem.DOI:http://dx.doi.org/10.11591/ijece.v2i4.1464

Text Mining for Pest and Disease Identification on Rice Farming with Interactive Text Messaging Edio da Costa; Handayani Tjandrasa; Supeno Djanali
International Journal of Electrical and Computer Engineering (IJECE) Vol 8, No 3: June 2018
Publisher : Institute of Advanced Engineering and Science

To overcome pests and diseases of rice farming, farmers always rely on information and knowledge from agricultural experts for decision making. The problem is that experts are not always available when the farmers need and the cost is quite high. Pests and diseases elimination is hard to be done individually since the farmers are lack of knowledge about the pest types that attack the rice fields. The objective of this study is to build a knowledge-based system that can identify pests and diseases interactively based on the information that has been told by the farmers using SMS communication services. The system can provide a convenience way to the farmers in delivering pests and disease problem information using a natural language. The text mining method performs tokenizing, filtering and porter stemming that used to extract important information sent by a SMS service. The method of Jaccard Similarity Coefficient (JSC) was used to calculate similarities of each pest and disease based on symptoms that are sent by the farmers through SMS. The corpus database usedin this study consists of 28.526 root words, 1.309 stop wordsand 180 words list. Pest and disease database reference in this study was obtained from the Ministry of Agriculture and Fisher (MAF) Timor-Leste. The result of the experiment shows that the system is able to identify the symptoms based on the keywords identified with the accuracy of 81%. The result of pest and disease identification has the accuracy of 86%.

Dependability Evaluation and Supervision in Thermal Power Plants Marwa Ben Hamouda; Mohamed Najeh Lakhoua; Lilia El Amraoui
International Journal of Electrical and Computer Engineering (IJECE) Vol 5, No 5: October 2015
Publisher : Institute of Advanced Engineering and Science

In order to improve the productivity and the consistency of its maintenance strategies, the industrial world is based on different techniques and tools developed to ensure safe operation and the supervision of production systems. In fact, dependability evaluation is crucial to controlling the risks associated with system failure, and for this reason, it is one of the fundamental steps in automated system design. In this paper, we present firstly the basic concepts for the study of dependability as well as functional systems analysis. Thus, we present the method SADT (Structured Analysis Design technique). Given the ineffectiveness of methods that are currently exploited are not appropriate because the level of complexity of such industrial systems, we propose in the first the Safe-SADT method which allows the explicit formalization of functional interaction, the identification of the characteristic values affecting complex system dependability, the quantification of RAMS parameters (Reliability, Availability, Maintainability, and Safety) for the system’s operational architecture. Secondly, a methodology for designing supervisory production systems has been presented and has been applied on an example of a SCADA (supervisory control and data acquisition) system of a thermal power plant. Finally, a model of operating safety and supervision of a production system is proposed.

Novel model for boosting security strength and energy efficiency in internet-of-things using multi-staged game Bhagyashree Ambore; Suresh L
International Journal of Electrical and Computer Engineering (IJECE) Vol 9, No 5: October 2019
Publisher : Institute of Advanced Engineering and Science

Security as well as energy efficiency is one of the most inevitable and challenging problems when it comes it large scale network deployment like INternet-of-Things (IoT). After reviewing existing research work on IoT, it was found that there are discrete set of solution for security as well as for energy. However, there is little research work that has jointly investigated both the problems with respect to IoT. Apart from this, there are also various form of attacks that cost energy of sensors that constitutes core physical devices in IoT. Therefore, these manuscripts present a novel idea for identifying and resisting the security breach within an IoT system ensuring energy efficiency too. Harnessing the modelling capability of game-theory, the proposed system offers a joint solution towards these problems. The simulated outcome of the study is found to offer balance performance for better energy efficiency and robust threat mitigation capability when compared with existing approaches.

Approximation Measures for Conditional Functional Dependencies Using Stripped Conditional Partitions Anh Duy Tran; Somjit Arch-int; Ngamnij Arch-int
International Journal of Electrical and Computer Engineering (IJECE) Vol 7, No 3: June 2017
Publisher : Institute of Advanced Engineering and Science

Conditional functional dependencies (CFDs) have been used to improve the quality of data, including detecting and repairing data inconsistencies. Approximation measures have significant importance for data dependencies in data mining. To adapt to exceptions in real data, the measures are used to relax the strictness of CFDs for more generalized dependencies, called approximate conditional functional dependencies (ACFDs). This paper analyzes the weaknesses of dependency degree, confidence and conviction measures for general CFDs (constant and variable CFDs). A new measure for general CFDs based on incomplete knowledge granularity is proposed to measure the approximation of these dependencies as well as the distribution of data tuples into the conditional equivalence classes. Finally, the effectiveness of stripped conditional partitions and this new measure are evaluated on synthetic and real data sets. These results are important to the study of theory of approximation dependencies and improvement of discovery algorithms of CFDs and ACFDs.

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Tole Sutikno

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Kota yogyakarta,

Daerah istimewa yogyakarta

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Home Page

OAI Link

Editorial Team

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Reviewer

Google Scholar

Contact Name Tole Sutikno

Contact Email ijece@iaesjournal.com

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Journal Mail Official ijece@iaesjournal.com

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Location Kota yogyakarta, Daerah istimewa yogyakarta INDONESIA

Abstract

Abstract

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Abstract

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Abstract

Abstract

Abstract

Contact Name
Tole Sutikno

Contact Email
ijece@iaesjournal.com

Phone
-

Journal Mail Official
ijece@iaesjournal.com

Editorial Address
-

Location
Kota yogyakarta,

Daerah istimewa yogyakarta

INDONESIA