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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

74 75 76 77 78

Implementation of deep neural networks (DNN) with batch normalization for batik pattern recognition Ida Nurhaida; Vina Ayumi; Devi Fitrianah; Remmy A. M. Zen; Handrie Noprisson; Hong Wei
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 2: April 2020
Publisher : Institute of Advanced Engineering and Science

One of the most famous cultural heritages in Indonesia is batik. Batik is a specially made drawing cloth by writing Malam (wax) on the cloth, then processed in a certain way. The diversity of motifs both in Indonesia and the allied countries raises new research topics in the field of information technology, both for conservation, storage, publication and the creation of new batik motifs. In computer science research area, studies about Batik pattern have been done by researchers and some algorithms have been successfully applied in Batik pattern recognition. This study was focused on Batik motif recognition using texture fusion feature which is Gabor, Log-Gabor, and GLCM; and using PCA feature reduction to improve the classification accuracy and reduce the computational time. To improve the accuracy, we proposed a Deep Neural Network model to recognise batik pattern and used batch normalisation as a regularises to generalise the model and to reduce time complexity. From the experiments, the feature extraction, selection, and reduction gave better accuracy than the raw dataset. The feature selection and reduction also reduce time complexity. The DNN+BN significantly improve the accuracy of the classification model from 65.36% to 83.15%. BN as a regularization has successfully made the model more general, hence improve the accuracy of the model. The parameters tuning also improved accuracy from 83.15% to 85.57%.

Bin packing algorithms for virtual machine placement in cloud computing: a review Kumaraswamy S; Mydhili K Nair
International Journal of Electrical and Computer Engineering (IJECE) Vol 9, No 1: February 2019
Publisher : Institute of Advanced Engineering and Science

Cloud computing has become more commercial and familiar. The Cloud data centers havhuge challenges to maintain QoS and keep the Cloud performance high. The placing of virtual machines among physical machines in Cloud is signiﬁcant in optimizing Cloud performance. Bin packing based algorithms are most used concept to achieve virtual machine placement(VMP). This paper presents a rigorous survey and comparisons of the bin packing based VMP methods for the Cloud computing environment. Various methods are discussed and the VM placement factors in each methods are analyzed to understand the advantages and drawbacks of each method. The scope of future research and studies are also highlighted.

Human Detection Framework for Automated Surveillance Systems Redwan A.K. Noaman; Mohd Alauddin Mohd Ali; Nasharuddin Zainal; Faisal Saeed
International Journal of Electrical and Computer Engineering (IJECE) Vol 6, No 2: April 2016
Publisher : Institute of Advanced Engineering and Science

Vision-based systems for surveillance applications have been used widely and gained more research attention. Detecting people in an image stream is challenging because of their intra-class variability, the diversity of the backgrounds, and the conditions under which the images were acquired. Existing human detection solutions suffer in their effectiveness and efficiency. In particular, the accuracy of the existing detectors is characterized by their high false positive and negative. In addition, existing detectors are slow for online surveillance systems which lead to large delay that is not suitable for surveillance systems for real-time monitoring. In this paper, a holistic framework is proposed for enhancing the performance of human detection in surveillance system. In general, the framework includes the following stages: environment modeling, motion object detection, and human object recognition. In environment modeling, modal algorithm has been suggested for background initialization and extraction. Then for effectively classifying the motion object, edge detecting and B-spline algorithm have been used for shadow detection and removal. Then, enhanced Lucas–Kanade optical flow has been used to get the area of interest for object segmentation. Finally, to enhance the segmentation, some morphological processes were performed. In the motion object recognition stage, segmentation for each blob is performed and processed to the human detector which is a complete learning-based system for detecting and localizing objects/humans in images using mixtures of deformable part models (PFF detector). Results show enhancement in each phase of the proposed framework. These enhancements are shown in the overall performance of human detection in surveillance system.

Salt and Pepper Noise Removal Using Resizable Window and Gaussian Estimation Function Suhad A. Ali; C. Elaf A. Abbood; Shaymaa Abdu lKadhm
International Journal of Electrical and Computer Engineering (IJECE) Vol 6, No 5: October 2016
Publisher : Institute of Advanced Engineering and Science

Most types of the images are corrupted in many ways that because exposed to different types of noises. The corruptions happen during transmission from space to another, during storing or capturing. Image processing has various techniques to process the image. Before process the image, there is need to remove noise that corrupt the image and enhance it to be as near as to the original image. This paper proposed a new method to process a particular common type of noise. This method removes salt and pepper noise by using many techniques. First, detect the noisy pixel, then increasing the size of the pixel window depending on some criteria to be enough to estimate the results. To estimates the pixels of image, the Gaussian estimation function is used. The resulted image quality is measured by the statistical quantity measures that's Peak Signal-to-Noise Ratio (PSNR) and The Structural Similarity (SSIM) metrics. The results illustrate the quality of the enhanced image compared with the other traditional techniques. The slight gradual of SSIM metric described the performance of the proposed method with high increasing of noise levels.

An Improved Design of Linear Congruential Generator based on Wordlengths Reduction Technique into FPGA Hubbul Walidainy; Zulfikar Zulfikar
International Journal of Electrical and Computer Engineering (IJECE) Vol 5, No 1: February 2015
Publisher : Institute of Advanced Engineering and Science

This paper exposes an improved design of linear congruential generator (LCG) based on wordlengths reduction technique into FPGA. The circuit is derived from LCG algorithm proposed by Lehmer and the previous design. The wordlengths reduction technique has been developed more in order to simplify further circuit. The proposed design based on the fact that in applications only specific input data were used. Some nets connections between blocks of the circuit are ignored or truncated. Simulations either behavior or timing have been done and the results is similar to its algorithm. Four best Xilinx chips have been chosen to extract comparison data of speed and occupied area. Further comparison of occupied area in terms of flip-flop and full adder has been made. In general, the proposed design overcome the previous published LCG circuit.

Path based load balancing for data center networks using SDN V. Deeban Chakravarthy; B. Amutha
International Journal of Electrical and Computer Engineering (IJECE) Vol 9, No 4: August 2019
Publisher : Institute of Advanced Engineering and Science

Due to the increase in the number of users on the internet and the number of applications that is available in the cloud makes Data Center Networking (DCN) has the backbone for computing. These data centre requires high operational cost and also experience the link failures and congestions often. Hence the solution is to use Software Defined Networking (SDN) based load balancer which improves the efficiency of the network by distributing the traffic across multiple paths to optimize the efficiency of the network. Traditional load balancers are very expensive and inflexible. These SDN load balancers do not require costly hardware and can be programmed, which it makes it easier to implement user-defined algorithms and load balancing strategies. In this paper, we have proposed an efficient load balancing technique by considering different parameters to maintain the load efficiently using Open FlowSwitches connected to ONOS controller.

Multi-objective IT Project Selection Model for Improving SME Strategy Deployment Abir El Yamami; Khalifa Mansouri; Mohammed Qbadou; El Hossein Illousamen
International Journal of Electrical and Computer Engineering (IJECE) Vol 8, No 2: April 2018
Publisher : Institute of Advanced Engineering and Science

Due to the limited financial resources of small and Medium-sized enterprises (SMEs), the proven approaches for selecting IT project portfolio for large enterprises may fail to perform in SMEs; SME top management want to make sure that the corporate strategy is carried out effectively by IT project portfolio before investing in such projects. In order to provide automated support to the selection of IT projects, it seems inevitable that a multi-objective approach is required in order to balance possible competing and conflicting objectives. Under such an approach, individual projects would be evaluated not just on their own performance but on the basis of their contribution to balance the overall portfolio. In this paper, we extend and explore the concept of IT project selection to improve SME strategy deployment. In particular, we present a model that assesses an individual project in terms of its contribution to the overall strategic objectives of the portfolio. A simulation using the model illustrates how SME can rapidly achieve maximal business goals by deploying the multi-objective algorithm when selecting IT projects.

Identification of Plant Types by Leaf Textures Based on the Backpropagation Neural Network Taufik Hidayat; Asyaroh Ramadona Nilawati
International Journal of Electrical and Computer Engineering (IJECE) Vol 8, No 6: December 2018
Publisher : Institute of Advanced Engineering and Science

The number of species of plants or flora in Indonesia is abundant. The wealth of Indonesia's flora species is not to be doubted. Almost every region in Indonesia has one or some distinctive plant(s) which may not exist in other countries. In enhancing the potential diversity of tropical plant resources, good management and utilization of biodiversity is required. Based on such diversity, plant classification becomes a challenge to do. The most common way to recognize between one plant and another is to identify the leaf of each plant. Leaf-based classification is an alternative and the most effective way to do because leaves will exist all the time, while fruits and flowers may only exist at any given time. In this study, the researchers will identify plants based on the textures of the leaves. Leaf feature extraction is done by calculating the area value, perimeter, and additional features of leaf images such as shape roundness and slenderness. The results of the extraction will then be selected for training by using the backpropagation neural network. The result of the training (the formation of the training set) will be calculated to produce the value of recognition accuracy with which the feature value of the dataset of the leaf images is then to be matched. The result of the identification of plant species based on leaf texture characteristics is expected to accelerate the process of plant classification based on the characteristics of the leaves.

Design and Analysis System of KNN and ID3 Algorithm for Music Classification based on Mood Feature Extraction Made Sudarma; I Gede Harsemadi
International Journal of Electrical and Computer Engineering (IJECE) Vol 7, No 1: February 2017
Publisher : Institute of Advanced Engineering and Science

Each of music which has been created, has its own mood which is emitted, therefore, there has been many researches in Music Information Retrieval (MIR) field that has been done for recognition of mood to music. This research produced software to classify music to the mood by using K-Nearest Neighbor and ID3 algorithm. In this research accuracy performance comparison and measurement of average classification time is carried out which is obtained based on the value produced from music feature extraction process. For music feature extraction process it uses 9 types of spectral analysis, consists of 400 practicing data and 400 testing data. The system produced outcome as classification label of mood type those are contentment, exuberance, depression and anxious. Classification by using algorithm of KNN is good enough that is 86.55% at k value = 3 and average processing time is 0.01021. Whereas by using ID3 it results accuracy of 59.33% and average of processing time is 0.05091 second.

The transient stability analysis of wind turbines interconected to grid under fault Anass Gourma; Abdelmajid Berdai; Moussa Reddak
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 1: February 2020
Publisher : Institute of Advanced Engineering and Science

Wind farm has been growing in recent years due to its very competitive electricity production cost. Wind generators have gone from a few kilowatts to megawatts. However, the participation of the wind turbine in the stability of the electricity grid is a critical point to check, knowing that the electricity grid is meshed, any change in active and reactive flux at the network level affects its stability. With a rate of 50% wind turbine penetration into the electricity grid, the stability of the rotor angle is a dynamic phenomenon which is only visible by the variation of the active energy. The purpose of this journal is to verify the impact of wind turbine integration on an electrical grid, by exploiting the relationship between the reactive energy produced by the Doubly Fed Induction Generator equipping most wind energy systems, and the stability of the rotor angle of the synchronous generators equipping the conventional power plants in the electrical system.

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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

Contact

Reviewer

Google Scholar

Contact Name Tole Sutikno

Contact Email ijece@iaesjournal.com

Phone -

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