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A secure image steganography based on burrows wheeler transform and dynamic bit embedding

10.11591/ijece.v9i1.pp460-467
Ahmed Toman Thahab
In modern public communication networks, digital data is massively transmitted through the internet with a high risk of data piracy. Steganography is a technique used to transmit data without arousing suspicion of secret data existence.  In this paper, a color image steganography technique is proposed in spatial domain. The cover image is segmented into non-overlapping blocks which are scattered among image size window using Burrows Wheeler transform before embedding. Secret data is embedded in each block according to its sequence in the Burrows Wheeler transform output. The hiding method is an operation of an exclusive-or between a virtual bit which is generated from the most significant bit and the least significant bits of the cover pixel. Results of the algorithm are analyzed according to its degradation of the output image and embedding capacity. The results are also compared with other existing methods.
Volume: 9
Issue: 1
Page: 460-467
Publish at: 2019-02-01

Granularity analysis of classification and estimation for complex datasets with MOA

10.11591/ijece.v9i1.pp409-416
Chanintorn Jittawiriyanukoon
Dispersed and unstructured datasets are substantial parameters to realize an exact amount of the required space. Depending upon the size and the data distribution, especially, if the classes are significantly associating, the level of granularity to agree a precise classification of the datasets exceeds. The data complexity is one of the major attributes to govern the proper value of the granularity, as it has a direct impact on the performance. Dataset classification exhibits the vital step in complex data analytics and designs to ensure that dataset is prompt to be efficiently scrutinized. Data collections are always causing missing, noisy and out-of-the-range values. Data analytics which has not been wisely classified for problems as such can induce unreliable outcomes. Hence, classifications for complex data sources help comfort the accuracy of gathered datasets by machine learning algorithms. Dataset complexity and pre-processing time reflect the effectiveness of individual algorithm. Once the complexity of datasets is characterized then comparatively simpler datasets can further investigate with parallelism approach. Speedup performance is measured by the execution of MOA simulation. Our proposed classification approach outperforms and improves granularity level of complex datasets.
Volume: 9
Issue: 1
Page: 409-416
Publish at: 2019-02-01

Chicken feed optimization using evolution strategies and firefly algorithm

10.11591/ijece.v9i1.pp585-592
Andreas Nugroho Sihananto , M. Shochibul Burhan , Wayan Firdaus Mahmudy
Mixing broiler chicken and layer hens feed using various feed ingredients is a difficult task. The feed must fulfill the minimum nutrient requirement and must break the constraint. Some classic approach like Pearson’s Square has been already introduced to solve this problem. However, the approaches cannot guarantee to fulfill nutrient requirements and desirable price. The two metaheuristic algorithms Evolution Strategies (ES) and Firefly Algorithms (FA) are being proposed in this paper to know how well they performed this problems. Result show that ES is perform much better compared to classic Pearson’s Square, but ES itself is outperform by FA on both cases.
Volume: 9
Issue: 1
Page: 585-592
Publish at: 2019-02-01

The effect of training set size in authorship attribution: application on short arabic texts

10.11591/ijece.v9i1.pp652-659
Mohammed Al-Sarem , Abdel-Hamid Emara
Authorship attribution (AA) is a subfield of linguistics analysis, aiming to identify the original author among a set of candidate authors. Several research papers were published and several methods and models were developed for many languages. However, the number of related works for Arabic is limited. Moreover, investigating the impact of short words length and training set size is not well addressed. To the best of our knowledge, no published works or researches, in this direction or even in other languages, are available. Therefore, we propose to investigate this effect, taking into account different stylomatric combination. The Mahalanobis distance (MD), Linear Regression (LR), and Multilayer Perceptron (MP) are selected as AA classifiers. During the experiment, the training dataset size is increased and the accuracy of the classifiers is recorded. The results are quite interesting and show different classifiers behaviours. Combining word-based stylomatric features with n-grams provides the best accuracy reached in average 93%.
Volume: 9
Issue: 1
Page: 652-659
Publish at: 2019-02-01

A noble approach to develop dynamically scalable namenode in hadoop distributed file system using secondary storage

10.11591/ijeecs.v13.i2.pp729-736
Tumpa Rani Shaha , Md. Nasim Akhtar , Fatema Tuj Johora , Md. Zakir Hossain , Mostafijur Rahman , R. B. Ahmad
For scalable data storage, Hadoop is widely used nowadays. It provides a distributed file system that stores data on the compute nodes. Basically, it represents a master/slave architecture that consists of a NameNode and copious Data Nodes. Data Nodes contain application data and metadata of application data resides in the Main Memory of NameNode. In cached approach, they fragment the metadata depending on the last access time and move the least frequently used data to secondary memory. If the requested data is not found in main memory then the secondary data will be loaded again on the RAM. So when the secondary data reloads to the primary memory then the NameNode main memory limitation arises again. The focus of this research is to reduce the namespace problem of main memory and to make the system dynamically scalable. A new Metadata Fragmentation Algorithm is proposed that separates the metadata list of NameNode dynamically. The NameNode creates Secondary Memory File in perspective of the threshold value and allocates secondary memory location based on the requirement. According to the proposed algorithm the maximum third, out of fourth of main memory is used at the secondary file caching time. The free space aids in faster operation by Dynamically Scalable NameNode approach. This proposed algorithm shows that the space utilization is increased to 17% and time utilization is increased to 0.0005% with the comparison of the existing fragmentation algorithm.
Volume: 13
Issue: 2
Page: 729-736
Publish at: 2019-02-01

An active technique for power saving in WSN under additive white gaussian noise channel

10.11591/ijece.v9i1.pp386-396
Raad Farhood Chisab
The work with feature of self-power supply by solar cell or by the battery or together at the same time. Therefore, the power dissipation is the big problem in wireless sensor network (WSN) especially when it is works for long time. The efficient method for reducing the power consumption within working is needed. The process of reducing waste power is one of the top priorities of scientists and designers of wireless sensor networks. The aim of this paper is to find the dominant method to reduce the power consumption in the wireless sensor network in order to stay works for long time and maintain the links with other nodes without loss of connection and transfer the information correctly. In this paper, a modified method was invented to minimize power utilization per data bit in a connection. This new method depends on the optimization process for reducing the power consumption as low as possible. All the tests of simulation process were done in additive white Gaussian noise (AWGN) channel. Numerical results demonstrated that the new method reduce the power when different values of noise are present with different types of modulation. Also the distance that the WSN will reach the information to it will be increase with presence of various noise amounts with different types of modulation. As a result, the power was decreased and the signal was reach more distance.
Volume: 9
Issue: 1
Page: 386-396
Publish at: 2019-02-01

Combined heat and power - optimal power flow based on thermodynamic model with associated petroleum and wet gas utilization constraints

10.11591/ijece.v9i1.pp42-54
Priambudi Pujihatma , Sasongko Pramono Hadi , Sarjiya Sarjiya , Tri Agung Rohmat
Oil fields produce associated petroleum and wet gas, which can be mixed with commercial natural gas as fuel. Associated petroleum and wet gas are a low cost, low quality fuel, whereas commercial natural gas is the opposite. Two parameters are affected by this mixture: the fuel cost and the power – steam output of gas turbine – heat recovery steam generators. This research develops a Unit Commitment and Optimal Power Flow model based on Mixed Integer Nonlinear Programming to optimize combined heat and power cost by considering the optimal mixture between associated petroleum - wet gas and commercial natural gas. A thermodynamic model is used to represent the performance of gas turbine – heat recovery steam generators when subjected to different fuel mixtures. The results show that the proposed model can optimize cost by determining the most efficient power – steam dispatch and optimal fuel mixture. Furthermore, the optimization model can analyse the trade-off between power system losses, steam demand and associated - wet gas utilization. 
Volume: 9
Issue: 1
Page: 42-54
Publish at: 2019-02-01

Fault detection in power transformers using random neural networks

10.11591/ijece.v9i1.pp78-84
Amrinder Kaur , Yadwinder Singh Brar , Leena G.
This paper discuss the application of artificial neural network-based algorithms to identify different types of faults in a power transformer, particularly using DGA (Dissolved Gas Analysis) test. The analysis of Random Neural Network (RNN) using Levenberg-Marquardt (LM) and Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithms has been done using the data of dissolved gases of power transformers collected from Punjab State Transmission Corporation Ltd.(PSTCL), Ludhiana, India. Sorting of the preprocessed data have been done using dimensionality reduction technique, i.e., principal component analysis. The sorted data is used as inputs to the Random Neural Networks (RNN) classifier. It has been seen from the results obtained  that BFGS has better performance for the diagnosis of fault in transformer as compared to LM.
Volume: 9
Issue: 1
Page: 78-84
Publish at: 2019-02-01

Review of water-nanofluid based photovoltaic/thermal (PV/T) systems

10.11591/ijece.v9i1.pp134-140
Nur Farhana Mohd Razali , Ahmad Fudholi , Mohd Hafidz Ruslan , Kamaruzzaman Sopian
Solar energy is secure, clean, and available on earth throughout the year. The PV/T system is a device designed to receive solar energy and convert it into electric/thermal energy. Nanofluid is a new generation of heat transfer fluid with promising higher thermal conductivity and improve heat transfer rate compared with conventional fluids. In this review, the recent studies of PV/T using nanofluid is discussed regarding basic concept and theory PV/T, thermal conductivity of nanofluid and experimentally and theoretically study the perfromance of PV/T using nanofluid. A review of the literature shows that many studies have evaluated the potential of nanofluid as heat transfer fluid and optical filter in the PV/T system. The preparations of nanofluid play an essential key for high stability and homogenous nanofluid for a long period. The thermal conductivity of nanofluid is depending on the size of nanoparticles, concentration and preparation of nanofluids.
Volume: 9
Issue: 1
Page: 134-140
Publish at: 2019-02-01

Efficient reduction of PLI in ECG signal using new variable step size least mean fourth adaptive algorithm

10.11591/ijece.v9i1.pp307-313
T. Gowri , Rajesh Kumar P. , D.V.R. Koti Reddy
It is very important in remote cardiac diagnosis to extract pure ECG signal from the contaminated recordings of the signal. When recording the ECG signal in the laboratory, the signal is affected by numerous artifacts. Varies artifacts generally degrades the signal quality are PLI, EM, MA and EM. In addition to these, the channel noise also added when transmitting signal from remote location to diagnosis center for analyzing the signal. There are several approaches are used to reduce the noise present in the ECG signal. From the literature it is proven that compared to non adaptive filters, adaptive filters play vital role to trace the random changes in the corrupted signals. In this paper, we proposed efficient Variable step size leaky least mean fourth algorithm and its sign versions for reducing the complexity. These algorithms shows that it gives low steady state error due to least mean fourth and fast convergence rate that is it tracks the input signal quickly because of its variable step size is high at initial iterations of signal compared to the LMS algorithm. The performance of the algorithm is evaluated using SNR, frequency spectrum, MSE, misadjustment and convergence characteristics.
Volume: 9
Issue: 1
Page: 307-313
Publish at: 2019-02-01

Checking integrity of data and recovery in the cloud environment

10.11591/ijeecs.v13.i2.pp626-633
Neha Narayan Kulkarni , Shitalkumar A. Jain
Cloud computing provides many services to access them dynamically over the internet as per the user's demand. The data is growing in tremendous amount and it should be managed correctly where storage service proves efficient. The Data stored online can be hacked by the third party so to secure this data verifying integrity is essential. Because of the human error or natural disaster, the data may get deleted from the cloud servers. Therefore, there is a requirement of improving the existing algorithm to recover data efficiently. Many algorithms are proposed, but they lack in efficiency, computational cost, and reliability. The proposed system in this paper is having the feature of verifying data integrity using Identity Based Remote Data Integrity Checking and recovery using the XOR operation. When the data is unavailable to the user, the proposed system provides flexibility for the user to regain data from the remote server.
Volume: 13
Issue: 2
Page: 626-633
Publish at: 2019-02-01

Generating similarity cluster of Indonesian languages with semi-supervised clustering

10.11591/ijece.v9i1.pp531-538
Arbi Haza Nasution , Yohei Murakami , Toru Ishida
Lexicostatistic and language similarity clusters are useful for computational linguistic researches that depends on language similarity or cognate recognition. Nevertheless, there are no published lexicostatistic/language similarity cluster of Indonesian ethnic languages available. We formulate an approach of creating language similarity clusters by utilizing ASJP database to generate the language similarity matrix, then generate the hierarchical clusters with complete linkage and mean linkage clustering, and further extract two stable clusters with high language similarities. We introduced an extended k-means clustering semi-supervised learning to evaluate the stability level of the hierarchical stable clusters being grouped together despite of changing the number of cluster. The higher the number of the trial, the more likely we can distinctly find the two hierarchical stable clusters in the generated k-clusters. However, for all five experiments, the stability level of the two hierarchical stable clusters is the highest on 5 clusters. Therefore, we take the 5 clusters as the best clusters of Indonesian ethnic languages. Finally, we plot the generated 5 clusters to a geographical map.
Volume: 9
Issue: 1
Page: 531-538
Publish at: 2019-02-01

Simplified five-level voltage source inverter with level-phase-shifted carriers based modulation technique

10.11591/ijeecs.v13.i2.pp461-468
Suroso Suroso , Daru Tri Nugroho , Abdullah Nur Azis , Toshihiko Noguchi
A simplified circuit topology of the five-level pulse width modulation (PWM) inverter for DC-AC power conversion with no-isolated DC voltage sources and reduced switching device number is presented in this paper. The inverter circuit is based on the three-level H-bridge inverter configuration. The developed five-level inverter needs only five controlled power switches and four isolated gate drive circuits. Furthermore, the proposed topology does not require bidirectional power semiconductor controlled switches, hence a conventional discrete power MOSFETs or IGBTs can be used to build the inverter circuits. To obtain a better quality output voltage waveform, the level-phase-shifted carriers based sinusoidal pulse width modulation control was applied to produce a five-level PWM voltage waveform. The proposed inverter circuit was examined by using computer simulation with Power PSIM software. The basic principle operation of the inverter circuit was verified experimentally in laboratory using two non-isolated DC voltage sources as the inputs of the inverter’s prototype circuit. Some analysis of inverter’s output waveforms are provided and discussed.
Volume: 13
Issue: 2
Page: 461-468
Publish at: 2019-02-01

An improved fitness function for automated cryptanalysis using genetic algorithm

10.11591/ijeecs.v13.i2.pp643-648
Md. Shafiul Alam Forhad , Md. Sabir Hossain , Mohammad Obaidur Rahman , Md. Mostafizur Rahaman , Md. Mokammel Haque , Md. Kamrul Hossain
Genetic Algorithm (GA) is a popular desire for the researchers for creating an automated cryptanalysis system. GA strategy is useful for many problems. Genetic Algorithms try to solve problems by using genetic processes. Different techniques for deciding on fitness function relying on the ciphers have proposed by different researchers. The most necessary component is to set such a fitness function that can evaluate different types of ciphers on the identical scale. In this paper, we have proposed a combined fitness function that is valid for great sorts of ciphers. We use GA to select the fitness function. We have bought the higher result after imposing our proposed method.
Volume: 13
Issue: 2
Page: 643-648
Publish at: 2019-02-01

Wireless water quality monitoring system for high density aquaculture application

10.11591/ijeecs.v13.i2.pp507-513
F. A. Saparudin , T. C. Chee , A. S. Ab Ghafar , H. A. Majid , N. Katiran
Water quality is one of the major factors that greatly affects growth and mortality rate of aquatic livestock especially in high density aquaculture system.  Conventional method requires fish farmer to perform manual water quality test and record on multiple fish tanks in regular basis. This process is meticulous, and may affect aquatic livestock that needs close and immediate attention. In this paper, water quality monitoring system for the high density aquaculture environment is proposed. The monitoring system is composed of multiple sensor nodes and sensor/server node hybrid, which used to collect and manage the water quality parameter data of multiple tanks. The sensor nodes collect and store the water quality parameters in local database and transmit them to the server node through a wireless communication. The server node is used for data analysis, processing and allow public access via web browser through various Wi-Fi enabled smart devices. This paper presents a proof-of concept of clustered wireless monitoring system focused on the use of multiple sensor nodes to determine the water parameter in real time. This system is cost effective, quickly deployable, and user-friendly with wireless capabilities.
Volume: 13
Issue: 2
Page: 507-513
Publish at: 2019-02-01
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