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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 126 Documents

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Search results for , issue "Vol 9, No 4: August 2019" : 126 Documents clear

Performance investigation of WOFDM for 5G wireless networks M. F. Ghanim
International Journal of Electrical and Computer Engineering (IJECE) Vol 9, No 4: August 2019
Publisher : Institute of Advanced Engineering and Science

Nowadays, emerging wireless networks scenarios such as the proposed systems for 5G is discussed widely with diverse requirements. Orthogonal Frequency Division Multiplexing (OFDM) is a conservative proposal which is used to build 5G WOFDM system (Wavelet OFDM system). The simulation of the system is initialized with BPSK then with QAM and 64-QAM the system is improved by increasing the number of levels of Discrete Wavelet Transform to five levels and finally compared with original system to prove that the it is convenient for 5G Wireless networks.

An Improved Differential Evolution Algorithm for Data Stream Clustering Bhaskar Adepu; Jayadev Gyani; G. Narsimha
International Journal of Electrical and Computer Engineering (IJECE) Vol 9, No 4: August 2019
Publisher : Institute of Advanced Engineering and Science

A Few algorithms were actualized by the analysts for performing clustering of data streams. Most of these algorithms require that the number of clusters (K) has to be fixed by the customer based on input data and it can be kept settled all through the clustering process. Stream clustering has faced few difficulties in picking up K. In this paper, we propose an efficient approach for data stream clustering by embracing an Improved Differential Evolution (IDE) algorithm. The IDE algorithm is one of the quick, powerful and productive global optimization approach for programmed clustering. In our proposed approach, we additionally apply an entropy based method for distinguishing the concept drift in the data stream and in this way updating the clustering procedure online. We demonstrated that our proposed method is contrasted with Genetic Algorithm and identified as proficient optimization algorithm. The performance of our proposed technique is assessed and cr eates the accuracy of 92.29%, the precision is 86.96%, recall is 90.30% and F-measure estimate is 88.60%.

Evaluation of Wind Power for Electrical Energy Generation in the Mediterranean Coast of Palestine for 14 Years Ahmed S A Badawi; Nurul Fadzlin Hasbullaha; Siti Yusoff; Sheroz Khan; Aisha Hashim; Alhareth Zyoud; Mohammed Elamassie
International Journal of Electrical and Computer Engineering (IJECE) Vol 9, No 4: August 2019
Publisher : Institute of Advanced Engineering and Science

The generation, distributionand transmission of electricity in Palestine have recently emerged as major issues. This study comprehensively assesses the production of wind energy and the estimation of wind energy potential in Palestine’s south coastal plain. The goal is to evaluate Palestine’s wind energy production by studying wind data and calculating energy and power. This study analyses two actual time series datasets. Findings are elaborated to determine the wind energy conversion per 1 m2. The wind speed data from January 1996 to December 2006 in Gaza and from January 2012 to December 2015 in Ashqelon are selected as the data sample. This study is crucial given the need for clean and renewable energy, the power shortage in the Gaza Strip and the limited number of wind energy studies conducted in the south coastal plain of Palestine, especially Gaza Strip. This study estimates the wind energy potential of the Gaza Strip to determine the wind potential. The annual mean wind speed and power are 4.11 ms-1 and 903.4 Wm-2, respectively. Moreover, the study clarifies the electrical energy generation in the Gaza Strip using small-scale turbines and offers a feasible alternative to existing schemes.

Computer Vision Based 3D Reconstruction : A Review Hanry Ham; Julian Wesley; Hendra Hendra
International Journal of Electrical and Computer Engineering (IJECE) Vol 9, No 4: August 2019
Publisher : Institute of Advanced Engineering and Science

3D reconstruction are used in many fields starts from the object reconstruction such as site, and cultural artifacts in both ground and under the sea levels. The scientist are beneficial for these task in order to learn and keep the environment into 3D data due to the extinction. In this paper explained vision setup that is commonly used such as single camera, stereo camera, Kinect / Structured Light/ Time of Flight camera and fusion approach. The prior works also explained how the 3D reconstruction perform in many fields and using various algorithms.

Notice of Retraction Predicting Heart Ailment in Patients with Varying number of Features using Data Mining Techniques T R Stella Mary; Shoney Sebastian
International Journal of Electrical and Computer Engineering (IJECE) Vol 9, No 4: August 2019
Publisher : Institute of Advanced Engineering and Science

Notice of Retraction-----------------------------------------------------------------------After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IAES's Publication Principles.We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.The presenting author of this paper has the option to appeal this decision by contacting ijece@iaesjournal.com.-----------------------------------------------------------------------Data mining can be defined as a process of extracting unknown, verifiable and possibly helpful data from information. Among the various ailments, heart ailment is one of the primary reason behind death of individuals around the globe, hence in order to curb this, a detailed analysis is done using Data Mining. Many a times we limit ourselves with minimal attributes that are required to predict a patient with heart disease. By doing so we are missing on a lot of important attributes that are main causes for heart diseases. Hence, this research aims at considering almost all the important features affecting heart disease and performs the analysis step by step with minimal to maximum set of attributes using Data Mining techniques to predict heart ailments. The various classification methods used are Naïve Bayes classifier, Random Forest and Random Tree which are applied on three datasets with different number of attributes but with a common class label. From the analysis performed, it shows that there is a gradual increase in prediction accuracies with the increase in the attributes irrespective of the classifiers used and Naïve Bayes and Random Forest algorithms comparatively outperforms with these sets of data.

Analysis and improvement of the efficiency of frequency converters with pulse width modulation Bogdan Vasilev
International Journal of Electrical and Computer Engineering (IJECE) Vol 9, No 4: August 2019
Publisher : Institute of Advanced Engineering and Science

In order to identify the best control algorithm, the effects of modulation control algorithms on the energy characteristics of a two-level autonomous frequency converter inverter were studied. The research was carried out by the methods of mathematical and simulation modeling. The equa-tions of mathematical description were compiled taking into account a number of generally accepted assumptions. An equivalent circuit of a two-level autonomous inverter was created. Comparisons of pulse-width modulation algorithms with carrier signals of various shapes and frequencies were made. Three different forms of carrier signal were used: triangular, sawtooth with a falling edge and sawtooth with a leading edge. Studies were conducted at frequencies of 3,000, 6,000 and 9,000 Hz. Conclusions were made about the identity of the spectral composition of the front and rear edges of the sawtooth signal, it was also noted that with the triangular waveform, being the part of the har-monics, present in the sawtooth form is removed, so, the triangular shape provides the best result of the autonomous inverter. Also, by increasing in the carrier frequency, it was noted that pulse packets appear at different harmonic numbers, shift, and the amplitude and distortion factor decrease, that means, the best performance was obtained at the maximum frequency studied. In the study of the voltage at the output of the chokes at different frequencies of the carrier signal, it was noted that at a higher frequency, the ripple of the output voltage decreases. Throttles do not eliminate harmonics, but only reduce their amplitude. Based on the results, it was concluded that the algorithm with a triangular carrier signal and the maximum frequency provides the best harmonic composition of the output voltage of the frequency converter.

Natural language description of images using hybrid recurrent neural network Md. Asifuzzaman Jishan; Khan Raqib Mahmud; Abul Kalam Al Azad
International Journal of Electrical and Computer Engineering (IJECE) Vol 9, No 4: August 2019
Publisher : Institute of Advanced Engineering and Science

We presented a learning model that generated natural language description of images. The model utilized the connections between natural language and visual data by produced text line based contents from a given image. Our Hybrid Recurrent Neural Network model is based on the intricacies of Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Bi-directional Recurrent Neural Network (BRNN) models. We conducted experiments on three benchmark datasets, e.g., Flickr8K, Flickr30K, and MS COCO. Our hybrid model utilized LSTM model to encode text line or sentences independent of the object location and BRNN for word representation, this reduced the computational complexities without compromising the accuracy of the descriptor. The model produced better accuracy in retrieving natural language based description on the dataset.

Traffic management with elephant flow detection in software defined networks (SDN) Hnin Thiri Zaw; AungHtein Maw
International Journal of Electrical and Computer Engineering (IJECE) Vol 9, No 4: August 2019
Publisher : Institute of Advanced Engineering and Science

Multipath routing is to distribute the incoming traffic load among available paths between source and destination hosts. Instead of using the single best path, multipath scheme can avoid the congested path. Equal Cost Multi-Path (ECMP) performs the static traffic splitting based on some tuples of the packet headers. The limitation of ECMP does not consider the network parameters such as bandwidth and delay. Unlike the traditional networks, Software-Defined Network (SDN) has many advantages to support dynamic multipath forwarding due to its special characteristics, such as separation of control and data planes, global centralized control, and programmability of network behavior. In this paper, we propose a new architecture design for dynamic multipath-based traffic management approach in the SDN, which comprises of five components: detecting long (elephant) flow, computing shortest paths, estimating end-to-end delay and bandwidth utilization, calculating least cost path and rerouting traffic flow from the ongoing path to the best path. The simulation environment is created through the usage of Mininet emulator and ONOS controller. The evaluation outcomes show that the proposed traffic management method outperforms the ECMP and reactive forwarding method for both TCP and UDP traffic.

Ensemble learning for software fault prediction problem with imbalanced data Thanh Tung Khuat; My Hanh Le
International Journal of Electrical and Computer Engineering (IJECE) Vol 9, No 4: August 2019
Publisher : Institute of Advanced Engineering and Science

Fault prediction problem has a crucial role in the software development process because it contributes to reducing defects and assisting the testing process towards fault-free software components. Therefore, there are a lot of efforts aiming to address this type of issues, in which static code characteristics are usually adopted to construct fault classification models. One of the challenging problems influencing the performance of predictive classifiers is the high imbalance among patterns belonging to different classes. This paper aims to integrate the sampling techniques and common classification techniques to form a useful ensemble model for the software defect prediction problem. The empirical results conducted on the benchmark datasets of software projects have shown the promising performance of our proposal in comparison with individual classifiers.

Resource allocation in cloud computing using advanced imperialist competitive algorithm Seyyed-Mohammad Javadi-Moghaddam; Sara Alipour
International Journal of Electrical and Computer Engineering (IJECE) Vol 9, No 4: August 2019
Publisher : Institute of Advanced Engineering and Science

Cloud computing makes possible free access to computing resources and high-level services for performing complex calculations and mass storage of information on the Internet. Resource management is one of the most important tasks of cloud providers, which is known as resource allocation. Heterogeneous resources and diverse requests at different time intervals makes it difficult to solve resources allocation problems and is considered as a NP-hard problem. Providing an efficient algorithm for resources allocation to satisfy the cloud providers and customers has always attracted much attention of researchers. Heuristic methods have always introduced as a good model for problem solving. However, most algorithms suffer from early convergence. This paper proposes a new approach based on imperialist competitive algorithm (ICA) which emphasizes the optimization of resource allocation in reducing time, cost and energy consumption. The proposed approach has been able to improve the early convergence of colonial competition algorithm by combining with the Tabu Search Algorithm to achieve an optimal solution at an acceptable time. The evaluated results show more efficiency performance than several relevant effective algorithms.

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Tole Sutikno

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ijece@iaesjournal.com

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ijece@iaesjournal.com

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Location
Kota yogyakarta,

Daerah istimewa yogyakarta

INDONESIA

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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

Editorial Address -

Location Kota yogyakarta, Daerah istimewa yogyakarta INDONESIA

Abstract

Abstract

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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