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30,411 Article Results

Wave File Features Extraction using Reduced LBP

10.11591/ijece.v8i5.pp2780-2787
Aws Al-Qaisi , Saleh A. Khawatreh , Ahmad A. Sharadqah , Ziad A. Alqadi
In this work, we present a novel approach for extracting features of a digital wave file. This approach will be presented, implemented and tested. A signature or a key to any wave file will be created.  This signature will be reduced to minimize the efforts of digital signal processing applications. Hence, the features array can be used as key to recover a wave file from a database consisting of several wave files using reduced Local binary patterns (RLBP). Experimental results are presented and show that The proposed RLBP method is at least 3 times faster than CSLBP method, which mean that the proposed method is more efficient.
Volume: 8
Issue: 5
Page: 2780-2787
Publish at: 2018-10-01

A Framework for Arabic Concept-Level Sentiment Analysis using SenticNet

10.11591/ijece.v8i5.pp4015-4022
Hend G. Hassan , Hitham M. Abo Bakr , Ibrahim E. Ziedan
Arabic Sentiment analysis research field has been progressing in a slow pace compared to English and other languages. In addition to that most of the contributions are based on using supervised machine learning algorithms while comparing the performance of different classifiers with different selected stylistic and syntactic features. In this paper, we presented a novel framework for using the Concept-level sentiment analysis approach which classifies text based on their semantics rather than syntactic features. Moreover, we provided a lexicon dataset of around 69 k unique concepts that covers multi-domain reviews collected from the internet. We also tested the lexicon on a test sample from the dataset it was collected from and obtained an accuracy of 70%. The lexicon has been made publicly available for scientific purposes.
Volume: 8
Issue: 5
Page: 4015-4022
Publish at: 2018-10-01

A Multi Criteria Recommendation Engine Model for Cloud Renderfarm Services

10.11591/ijece.v8i5.pp3214-3220
Ruby Annette , Aisha Banu W , Subash Chandran P
Cloud services that provide a complete platform for rendering the animation files using the resources in the cloud are known as cloud renderfarm services. This work proposes a multi criteria recommendation engine model for recommending these Cloud renderfarm services to animators. The services are recommended based on the functional requirements of the animation file to be rendered like the rendering software, plug-in required etc and the non functional Quality of Service (QoS) requirements like render node cost, time taken to upload animation files etc. The proposed recommendation engine model uses a domain specific ontology of renderfarm services to identify the right services that could satisfy the functional requirements of the user and ranks the identified services using the popular Multi Criteria Decision Analysis method like Simple Additive Weighting (SAW). The ranked list of services is provided as recommended services to the animators in the ranking order. The Recommendation model was tested to rank and recommend the cloud renderfarm services in multi criteria requirements by assigning different QoS criteria weight for each scenario. The ranking based recommendations were generated for six different scenarios and the results were analyzed. The results show that the services recommended for each scenario were different and were highly dependent on the weights assigned to each criterion.
Volume: 8
Issue: 5
Page: 3214-3220
Publish at: 2018-10-01

Enhancement of On-grid PV System under Irradiance and Temperature Variations Using New Optimized Adaptive Controller

10.11591/ijece.v8i5.pp2650-2660
Othman A. M Omar , Niveen M. Badra , Mahmoud A. Attia
Solar Energy is one of the key solutions to future electrical power generation. Photovoltaic Plants (PV) are fast growing to satisfy electrical power demand. Different maximum power point tracking techniques (MPPT) are used to maximize PV systems generated power. In this paper, on grid PV system model in MATLAB SIMULINK is tested under sudden irradiance and cell temperature variations. Incremental Conductance MPPT is used to maximize generated power from the PV system with the help of new adaptive controller to withstand these heavy disturbances. The new adaptive controller is tuned for optimal operation using two different optimization techniques (Invasive weed and Harmony search).Optimization results for the two techniques are compared. .A robustness test is made to check system stability to withstand different random irradiance and cell temperature patterns without failure to track the maximum power point.Finally, a brief comparison is made with a previous literature and the new adaptive controller gives better results.
Volume: 8
Issue: 5
Page: 2650-2660
Publish at: 2018-10-01

Determining Basis Test Paths Using Genetic Algorithm and J4

10.11591/ijece.v8i5.pp3333-3340
Achmad Arwan , Denny Sagita Rusdianto
Basis test paths is a method that uses a graph contains nodes as a representation of codes and the lines as a sequence of code execution steps. Determination of basis test paths can be generated using a Genetic Algorithm, but the drawback was the number of iterations affect the possibility of visibility of the appropriate basis path. When the iteration is less, there is a possibility the paths do not appear all. Conversely, if the iteration is too much, all the paths have appeared in the middle of iteration. This research aims to optimize the performance of Genetic Algorithms for the generation of Basis Test Paths by determining how many iterations level corresponding to the characteristics of the code. Code metrics Node, Edge, VG, NBD, LOC were used as features to determine the number of iterations. J48 classifier was employed as a method to predict the number of iterations. There were 17 methods have selected as a data training, and 16 methods as a data test. The system was able to predict 84.5% of 58 basis paths. Efficiency test results also show that our system was able to seek Basis Paths 35% faster than the old system.
Volume: 8
Issue: 5
Page: 3333-3340
Publish at: 2018-10-01

Permanent Fault Location in Distribution System Using Phasor Measurement Units (PMU) in Phase Domain

10.11591/ijece.v8i5.pp2709-2720
Ali Khaleghi , Mahmoud oukati Sadegh , Mahdi Ghazizadeh Ahsaee
This paper proposes a new method for locating high impedance fault in distribution systems using phasor measurement units (PMUs) installed at certain locations of the system. To implement this algorithm, at first a new method is suggested for the placement of PMUs. Taking information from the units, voltage and current of the entire distribution system are calculated. Then, the two buses in which the fault has been occurred is determined, and location and type of the fault are identified. The main characteristics of the proposed method are: the use of distributed parameter line model in phase domain, considering the presence of literals, and high precision in calculating the high impedance fault location. The results obtained from simulations in EMTP-RV and MATLAB software indicate high accuracy and independence of the proposed method from the fault type, fault location and fault resistance compared to previous methods, so that the maximum observed error was less than 0.15%
Volume: 8
Issue: 5
Page: 2709-2720
Publish at: 2018-10-01

Adaptive Neuro-Fuzzy Control Approach for a Single Inverted Pendulum System

10.11591/ijece.v8i5.pp3657-3665
Mohammed A. A. Al-Mekhlafi , Herman Wahid , Azian Abd Aziz
The inverted pendulum is an under-actuated and nonlinear system, which is also unstable. It is a single-input double-output system, where only one output is directly actuated. This paper investigates a single intelligent control system using an adaptive neuro-fuzzy inference system (ANFIS) to stabilize the inverted pendulum system while tracking the desired position. The non-linear inverted pendulum system was modelled and built using MATLAB Simulink. An adaptive neuro-fuzzy logic controller was implemented and its performance was compared with a Sugeno-fuzzy inference system in both simulation and real experiment. The ANFIS controller could reach its desired new destination in 1.5 s and could stabilize the entire system in 2.2 s in the simulation, while in the experiment it took 1.7 s to reach stability. Results from the simulation and experiment showed that ANFIS had better performance compared to the Sugeno-fuzzy controller as it provided faster and smoother response and much less steady-state error.
Volume: 8
Issue: 5
Page: 3657-3665
Publish at: 2018-10-01

An Accurate Medium-Term Load Forecasting based on Hybrid Technique

10.11591/ijeecs.v12.i1.pp161-167
Z.M. Yasin , N.F.A. Aziz , N.A. Salim , N.A. Wahab , N.A. Rahmat
An accurate medium term load forecasting is significant for power generation scheduling, economic and reliable operation in power system. Most of classical approach for medium term load forecasting only consider total daily load demand. This approach may not provide accurate results since the load demand is fluctuated in a day. In this paper, a hybrid Ant-Lion Optimizer Least-square Support Vector Machine (ALO-LSSVM) is proposed to forecast 24-hour load demand for the next year. Ant-Lion Optimizer (ALO) is utilized to optimize the RBF Kernel parameters in Least-Square Support Vector Machine (LS-SVM). The objective of the optimization is to minimize the Mean Absolute Percentage Error (MAPE). The performance of ALO-LSSVM technique was compared with those obtained from LS-SVM technique through a 10-fold cross-validation procedure. The historical hourly load data are analyzed and appropriate features are selected for the model. There are 24 inputs and 24 outputs vectors for this model which represents 24-hour load demand for whole year. The results revealed that the high accuracy of prediction could be achieved using ALO-LSSVM.
Volume: 12
Issue: 1
Page: 161-167
Publish at: 2018-10-01

A Study on Big Data Privacy Protection Models using Data Masking Methods

10.11591/ijece.v8i5.pp3976-3983
Archana R. A. , Ravindra S. Hegadi , Manjunath T. N.
In today’s predictive analytics world, data engineering play a vital role, data acquisition is carried out from various source systems and process as per the business applications and domain. Big Data integrates, governs, and secures big data with repeatable, reliable, and maintainable processes. Through volume, speed, and assortment of information characteristics try to reveal business esteem from enormous information. However, with information that is frequently deficient, conflicting, ungoverned, and unprotected, which is hazardous and enormous information being a risk instead of an advantage. What's more, with conventional methodologies that are manual and unpredictable, huge information ventures take too long to acknowledge business esteem. Reasonably and over and again conveying business esteem from enormous information requires another technique. In this connection, raw data has to be moved between onsite and offshore environment during this course of action, data privacy is a major concern and challenge. A Big Data Privacy platform can make it easier to detect, investigate, assess, and remediate threats from intruders. We tried to do complete study of Big Data Privacy using data masking methods on various data loads and different types. This work will help data quality analyst and big data developers while building the big data applications.
Volume: 8
Issue: 5
Page: 3976-3983
Publish at: 2018-10-01

Effect of Feature Selection on Gene Expression Datasets Classification Accurac

10.11591/ijece.v8i5.pp3194-3203
Hicham Omara , Mohamed Lazaar , Youness Tabii
Feature selection attracts researchers who deal with machine learning and data mining. It consists of selecting the variables that have the greatest impact on the dataset classification, and discarding the rest. This dimentionality reduction allows classifiers to be fast and more accurate. This paper traits the effect of feature selection on the accuracy of widely used classifiers in literature. These classifiers are compared with three real datasets which are pre-processed with feature selection methods. More than 9% amelioration in classification accuracy is observed, and k-means appears to be the most sensitive classifier to feature selection.
Volume: 8
Issue: 5
Page: 3194-3203
Publish at: 2018-10-01

Energy-Aware Adaptive Four Thresholds Technique for Optimal Virtual Machine Placement

10.11591/ijece.v8i5.pp3890-3901
A. R. Mohazabiyeh , K. H. Amirizadeh
With the increasing expansion of cloud data centers and the demand for cloud services, one of the major problems facing these data centers is the “increasing growth in energy consumption ". In this paper, we propose a method to balance the burden of virtual machine resources in order to reduce energy consumption. The proposed technique is based on a four-adaptive threshold model to reduce energy consumption in physical servers and minimize SLA violation in cloud data centers. Based on the proposed technique, hosts will be grouped into five clusters: hosts with low load, hosts with a light load, hosts with a middle load, hosts with high load and finally, hosts with a heavy load. Virtual machines are transferred from the host with high load and heavy load to the hosts with light load. Also, the VMs on low hosts will be migrated to the hosts with middle load, while the host with a light load and hosts with middle load remain unchanged. The values of the thresholds are obtained on the basis of the mathematical modeling approach and the 𝐾-Means Clustering Algorithm is used for clustering of hosts. Experimental results show that applying the proposed technique will improve the load balancing and reduce the number of VM migration and reduce energy consumption.
Volume: 8
Issue: 5
Page: 3890-3901
Publish at: 2018-10-01

Design and Optimization of a High Gain Multiband Patch Antenna for Millimeter Wave Application

10.11591/ijece.v8i5.pp2942-2950
A. Zaidi , A. Baghdad , A. Ballouk , A. Badri
This paper presents an enhanced Quadri-band microstrip patch antenna, using defective slots in the ground plane, designed to operate in the millimeter wave band, formulated using cavity model and simulated by an EM-simulator, based on finite element method: HFSSv15 (High Frequency Structure Simulator). The proposed antenna incorporates two symmetric patterns of “U” shaped slots with an “I” shaped slot engraved in the middle of the ground plane. The resulting antenna has four frequency bands; the first resonant frequency is located in the Ka band, at about 27Ghz, the second at nearly 35Ghz, the third at 41Ghz and the last one at 51GHz. Those resonant frequencies could be shifted by tuning the slots dimensions introduced if the ground plane of the proposed antenna . 
Volume: 8
Issue: 5
Page: 2942-2950
Publish at: 2018-10-01

Network Function Modeling and Performance Estimation

10.11591/ijece.v8i5.pp3021-3037
Mario Baldi , Amedeo Sapio
This work introduces a methodology for the modelization of network functions focused on the identification of recurring execution patterns as basic building blocks and aimed at providing a platform independent representation. By mapping each modeling building block on specific hardware, the performance of the network function can be estimated in termsof maximum throughput that the network function can achieve on the specific execution platform. The approach is such that once the basic modeling building blocks have been mapped, the estimate can be computed automatically for any modeled network function. Experimental results on several sample network functions show that although our approach cannot be very accurate without taking in consideration traffic characteristics, it is very valuable for those application where even loose estimates are key. One such example is orchestration in network functions virtualization (NFV) platforms, as well as in general virtualization platforms where virtual machine placement is based also on the performanceof network services offered to them. Being able to automatically estimate the performance of a virtualized network function (VNF) on different execution hardware, enables optimal placement of VNFs themselves as well as the virtual hosts they serve, while efficiently utilizing available resources.
Volume: 8
Issue: 5
Page: 3021-3037
Publish at: 2018-10-01

Breakdown Characteristic of Palm and Coconut Oil with Different Moisture

10.11591/ijeecs.v12.i1.pp363-369
N. A. M.Jamail , N. A. Azali , E. Sulaiman , Q.E. Kamarudin , S.M.N.S. Othman , M.S. Kamarudin
Oils acts as insulation and cooling agent in the transformer. Petroleum-based oils are widely used in transformers due to their qualified properties as good insulation materials. Unfortunately, the used of petroleum-based oils has adverse effects on environment in the event of any failure to transformers such as tank leakage or explosions. Therefore, researchers have been studied and have found environmentally friendly oil that is suitable as substitutes in transformer. Thus, breakdown voltage tests using direct current and alternating current with the addition of different water content were performed to identify the potential of palm oil and coconut oil in the transformer isolation system. Refined, Bleached and Deodorized oil (RBDPO) and coconut oil samples were selected to be test in this study. The oil samples were test by varies the gap distance of test cell electrode and the level of water content. As a conclusion, RBDPO oil has greater breakdown voltage test under DC breakdown voltage test in term of increment of the gap distance.
Volume: 12
Issue: 1
Page: 363-369
Publish at: 2018-10-01

OFCS: Optimized Framework of Compressive Sensing for Medical Images in Bottleneck Network Condition

10.11591/ijece.v8i5.pp2829-2838
Lakshminarayana M , Mrinal Sarvagya
Compressive sensing is one of teh cost effective solution towards performing compression of heavier form of signals. We reviewed the existing research contribution towards compressive sensing to find that existing system doesnt offer any form of optimization for which reason the signal are superiorly compressed but at the cost of enough resources. Therefore, we introduce a framework that optimizes the performance of the compressive sensing by introducing 4 sequential algorithms for performing Random Sampling, Lossless Compression for region-of-interest, Compressive Sensing using transform-based scheme, and optimization. The contribution of proposed paper is a good balance between computational efficiency and quality of reconstructed medical image when transmitted over network with low channel capacity. The study outcome shows that proposed system offers maximum signal quality and lower algorithm processing time in contrast to existing compression techniuqes on medical images.
Volume: 8
Issue: 5
Page: 2829-2838
Publish at: 2018-10-01
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