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31,456 Article Results

Optimization of energy consumption in cloud computing datacenters

10.11591/ijece.v11i1.pp686-698
Ahmed Osman , Assim Sagahyroon , Raafat Aburukba , Fadi Aloul
Cloud computing has emerged as a practical paradigm for providing IT resources, infrastructure and services. This has led to the establishment of datacenters that have substantial energy demands for their operation. This work investigates the optimization of energy consumption in cloud datacenter using energy efficient allocation of tasks to resources. The work seeks to develop formal optimization models that minimize the energy consumption of computational resources and evaluates the use of existing optimization solvers in testing these models. Integer linear programming (ILP) techniques are used to model the scheduling problem. The objective is to minimize the total power consumed by the active and idle cores of the servers’ CPUs while meeting a set of constraints. Next, we use these models to carry out a detailed performance comparison between a selected set of Generic ILP and 0-1 Boolean satisfiability based solvers in solving the ILP formulations. Simulation results indicate that in some cases the developed models have saved up to 38% in energy consumption when compared to common techniques such as round robin. Furthermore, results also showed that generic ILP solvers had superior performance when compared to SAT-based ILP solvers especially as the number of tasks and resources grow in size.
Volume: 11
Issue: 1
Page: 686-698
Publish at: 2021-02-01

Knowledge discovery from gene expression dataset using bagging lasso decision tree

10.11591/ijeecs.v21.i2.pp1151-1159
Umu Sa'adah , Masithoh Yessi Rochayani , Ani Budi Astuti
Classifying high-dimensional data are a challenging task in data mining. Gene expression data is a type of high-dimensional data that has thousands of features. The study was proposing a method to extract knowledge from high-dimensional gene expression data by selecting features and classifying. Lasso was used for selecting features and the classification and regression tree (CART) algorithm was used to construct the decision tree model. To examine the stability of the lasso decision tree, we performed bootstrap aggregating (Bagging) with 50 replications. The gene expression data used was an ovarian tumor dataset that has 1,545 observations, 10,935 gene features, and binary class. The findings of this research showed that the lasso decision tree could produce an interpretable model that theoretically correct and had an accuracy of 89.32%. Meanwhile, the model obtained from the majority vote gave an accuracy of 90.29% which showed an increase in accuracy of 1% from the single lasso decision tree model. The slightly increasing accuracy shows that the lasso decision tree classifier is stable.
Volume: 21
Issue: 2
Page: 1151-1159
Publish at: 2021-02-01

An image encryption algorithm with a novel chaotic coupled mapped lattice and chaotic image scrambling technique

10.11591/ijeecs.v21.i2.pp1103-1112
Behrang Chaboki , Ali Shakiba
In this paper, we build a novel chaotic coupled lattice mapping with positive Lyapunov exponent, and introduce a novel chaotic image scrambling mechanism. Then, we propose a chaotic image encryption algorithm which uses the introduced chaotic coupled lattice mapping to apply permutation by iteratively applying the introduced chaotic image scrambling mechanism, and diffusing the pixel values. We use a sorting approach rather than quantizing the chaotic floating-point values to construct the diffusion matrix. We also study the security of the proposed algorithm concerning several security measures including brute-force attack, differential attack, key sensitivity, and statistical attacks. Moreover, the proposed algorithm is robust against data loss and noise attacks.
Volume: 21
Issue: 2
Page: 1103-1112
Publish at: 2021-02-01

A hybrid of CNN and LSTM methods for securing web application against cross-site scripting attack

10.11591/ijeecs.v21.i2.pp1022-1029
Raed Waheed Kadhim , Methaq Talib Gaata
Cross-site scripting (XSS) is today one of the biggest threatthat could targeting the Web application. Based on study published by the open web applications security project (OWASP), XSS vulnerability has been present among the TOP 10 Web application vulnerabilities.Still,an important security-related issue remains how to effectively protect web applications from XSS attacks.In first part of this paper, a method for detecting XSS attack was proposed by combining convolutional neural network (CNN) with long short term memories (LSTM), Initially, pre-processing was applied to XSS Data Set by decoding, generalization and tokanization, and then word2vec was applied to convert words into word vectors in XSS payloads. And then we use the combination CNN with LSTM to train and test word vectors to produce a model that can be used in a web application. Based on the obtaned results, it is observed that the proposed model achevied an excellent result with accuracy of 99.4%.
Volume: 21
Issue: 2
Page: 1022-1029
Publish at: 2021-02-01

Comparative investigation of 15 Level and 17 level cascaded h-bridge MLI with cross h-bridge MLI fed permanent magnet synchronous motor

10.11591/ijeecs.v21.i2.pp723-734
J. Srinivas Rao , Suresh Kumar Tummala , Narasimha Raju Kuthuri
Multilevel inverters offers eminent solutions to high voltage high power applications due to the association of several devices in a series configuration. In this paper, a comparative investigation of both 15 and 17 level cascaded h-bridge multi level inverter with cross h-bridge fed permanent magnet synchronous motor are presented by appropriate simulations and mathematical analysis. Comparative analysis includes Inverter output voltage and current, number of switching devices, stator current and speed of PMSM and total harmonic distortion levels. Limitation of several switching devices, which can afford high voltage in the inverter is the major problems raised in this study. The advantage of this analysis is to figure out the appropriate inverter that can be used for real time application by considering the factors via. Harmonic distortion, output voltage, current, number of switching devices etc. Validation of the analysis is processed through matlab/simulink platform.
Volume: 21
Issue: 2
Page: 723-734
Publish at: 2021-02-01

Features of genetic algorithm for plain text encryption

10.11591/ijece.v11i1.pp434-441
Riyadh Bassil Abduljabbar , Oday Kamil Hamid , Nazar Jabbar Alhyani
The data communication has been growing in present day. Therefore, the data encryption became very essential in secured data transmission and storage and protecting data contents from intruder and unauthorized persons. In this paper, a fast technique for text encryption depending on genetic algorithm is presented. The encryption approach is achieved by the genetic operators Crossover and mutation. The encryption proposal technique based on dividing the plain text characters into pairs, and applying the crossover operation between them, followed by the mutation operation to get the encrypted text. The experimental results show that the proposal provides an important improvement in encryption rate with comparatively high-speed Processing.
Volume: 11
Issue: 1
Page: 434-441
Publish at: 2021-02-01

Sustainable governance in smart cities and use of supervised learning based opinion mining

10.11591/ijece.v11i1.pp489-497
Hena Iqbal , Sujni Paul , Khaliquzzaman Khan
Evaluation is an analytical and organized process to figure out the present positive influences, favourable future prospects, existing shortcomings and ulterior complexities of any plan, program, practice or a policy. Evaluation of policy is an essential and vital process required to measure the performance or progression of the scheme. The main purpose of policy evaluation is to empower various stakeholders and enhance their socio-economic environment. A large number of policies or schemes in different areas are launched by government in view of citizen welfare. Although, the governmental policies intend to better shape up the life quality of people but may also impact their every day’s life. A latest governmental scheme Saubhagya launched by Indian government in 2017 has been selected for evaluation by applying opinion mining techniques. The data set of public opinion associated with this scheme has been captured by Twitter. The primary intent is to offer opinion mining as a smart city technology that harness the user-generated big data and analyse it to offer a sustainable governance model.
Volume: 11
Issue: 1
Page: 489-497
Publish at: 2021-02-01

Differential evolution detection models for SMS spam

10.11591/ijece.v11i1.pp596-601
Sarab M. Hameed
With the growth of mobile phones, short message service (SMS) became an essential text communication service. However, the low cost and ease use of SMS led to an increase in SMS Spam. In this paper, the characteristics of SMS spam has studied and a set of features has introduced to get rid of SMS spam. In addition, the problem of SMS spam detection was addressed as a clustering analysis that requires a metaheuristic algorithm to find the clustering structures. Three differential evolution variants viz DE/rand/1, jDE/rand/1, jDE/best/1, are adopted for solving the SMS spam problem. Experimental results illustrate that the jDE/best/1 produces best results over other variants in terms of accuracy, false-positive rate and false-negative rate. Moreover, it surpasses the baseline methods.
Volume: 11
Issue: 1
Page: 596-601
Publish at: 2021-02-01

Improved feature exctraction process to detect seizure using CHBMIT-dataset

10.11591/ijece.v11i1.pp827-843
Raveendra Kumar T. H. , C. K. Narayanappa
One of the most dangerous neurological disease, which is occupying worldwide, is epilepsy. Fraction of second nerves in the brain starts impulsion i.e. electrical discharge, which is higher than the normal pulsing. So many researches have done the investigation and proposed the numerous methodology. However, our methodology will give effective result in feature extraction. Moreover, we used numerous number of statistical moments features. Existing approaches are implemented on few statistical moments with respect to time and frequency. Our proposed system will give the way to find out the seizure-effected part of the brain very easily using TDS, FDS, Correlation and Graph presentation. The resultant value will give the huge difference between normal and seizure effected brain. It also explore the hidden features of the brain.
Volume: 11
Issue: 1
Page: 827-843
Publish at: 2021-02-01

Dialogue state tracking accuracy improvement by distinguishing slot-value pairs and dialogue behaviour

10.11591/ijeecs.v21.i2.pp1057-1064
Khaldoon H. Alhussayni , Alexander Zamyatin , S. Eman Alshamery
Dialog state tracking (DST) plays a critical role in cycle life of a task-oriented dialogue system. DST represents the goals of the consumer at each step by dialogue and describes such objectives as a conceptual structure comprising slot-value pairs and dialogue actions that specifically improve the performance and effectiveness of dialogue systems. DST faces several challenges: diversity of linguistics, dynamic social context and the dissemination of the state of dialogue over candidate values both in slot values and in dialogue acts determined in ontology. In many turns during the dialogue, users indirectly refer to the previous utterances, and that produce a challenge to distinguishing and use of related dialogue history, Recent methods used and popular for that are ineffective. In this paper, we propose a dialogue historical context self-Attention framework for DST that recognizes relevant historical context by including previous user utterance beside current user utterances and previous system actions where specific slot-value piers variations and uses that together with weighted system utterance to outperform existing models by recognizing the related context and the relevance of a system utterance. For the evaluation of the proposed model the WoZ dataset was used. The implementation was attempted with the prior user utterance as a dialogue encoder and second by the additional score combined with all the candidate slot-value pairs in the context of previous user utterances and current utterances. The proposed model obtained 0.8 per cent better results than all state-of-the-art methods in the combined precision of the target, but this is not the turnaround challenge for the submission.
Volume: 21
Issue: 2
Page: 1057-1064
Publish at: 2021-02-01

Relationship of drain induced barrier lowering and top/bottom gate oxide thickness in asymmetric junctionless double gate MOSFET

10.11591/ijece.v11i1.pp232-239
Hakkee Jung
The relationship of drain induced barrier lowering (DIBL) phenomenon and channel length, silicon thickness, and thicknesses of top and bottom gate oxide films is derived for asymmetric junctionless double gate (JLDG) MOSFETs. The characteristics between the drain current and the gate voltage is derived by using the potential distribution model to propose in this paper. In this case, the threshold voltage is defined as the corresponding gate voltage when the drain current is (W/L) × 10-7 A, and the DIBL representing the change in the threshold voltage with respect to the drain voltage is obtained. As a result, we observe the DIBL is proportional to the negative third power of the channel length and the second power of the silicon thickness and linearly proportional to the geometric mean of the top and bottom gate oxide thicknesses, and derive a relation such as DIBL =25.15ηL_g^(-3) t_si^2 √(t_ox1∙t_ox2 ), where η is a static feedback coefficients between 0 and 1. The η is found to be between 0.5 and 1.0 in this model. The DIBL model of this paper has been observed to be in good agreement with the result of other paper, so it can be used in circuit simulation such as SPICE.
Volume: 11
Issue: 1
Page: 232-239
Publish at: 2021-02-01

Categorizing and measurement satellite image processing of fire in the forest greece using remote sensing

10.11591/ijeecs.v21.i2.pp846-853
Ali Abdul Wahhab Mohammed , Hussein Thary Khamees
This paper has been utilized satellite Sentinel-2A imagery, this satellite is a polar-orbiting, multispectral high-resolution to cover Athens city, Greece that located at latitude (37° 58′ 46″) N, (23° 42′ 58″) E.,the work aims to measurement and study the wildfires natural resourcesbefore and after fire break out that happenedin forests of Athens city in Greece for a year (2007, 2018) and analysis the damage caused by these wildfiresand their impact on environment  and soil  by categorize the satellite images for the interested region before and after wildfires for a year (2007) and  a year (2018) and Discuss techniques that compute the area covered of each class and lessen  or limit the rapidly spreading wildfires damage.The categorizing utilizing the moments with (K-Means) grouping algorithm in RS (remote sensing). And the categorizing results show five unique classes (water, trees, buildings without tree, buildings with tree, bare lands) where, it can be notice that the region secured by each class before and after wildfires and the changed pixels for all classes.The experimental resulted of categorizing technique shows that the good performance exactness with a good categorizing and result analysisa bout the harms resulted from the fires in the forest Greece for a years (2007 and 2018).
Volume: 21
Issue: 2
Page: 846-853
Publish at: 2021-02-01

Performance evaluation of different structures of power system stabilizers

10.11591/ijece.v11i1.pp114-123
M. Balasubbareddy , G. V. K. Murthy , K. Sowjan Kumar
The electric power from the system should be reliable and economical for consumer’s equipment satisfaction. An electric power system consists of many generators, transformers, transmission lines, loads, etc. For the power system network, dynamic performance and stability are important. The system is lost its stability by some disturbances i.e., load variations, generator failure, prime mover failure, transmission line outage, etc. Whenever load variations in the system, generator rotor speed will vary, means oscillations in the rotor speed, which is deviating from rated speed. The excitation system will control the generator rated line voltage. When fault occurs at any equipment in the system, the system will unstable. If fault occurs at generator, the generator oscillates. To reduce the oscillations and to make the system stable used power system stabilizers (PSS’s). Here, three types of PSS’s are used i.e., PSS1B, PSS2B, PSS4B. Comparisons of three PSS’s are on the multi machine system under some disturbance. From the observations, concluding that PSS4B is quickly control the oscillations in the physical parameters of machine in the system than other power system stabilizers.
Volume: 11
Issue: 1
Page: 114-123
Publish at: 2021-02-01

Evaluation of load balancing algorithms on overlappiing wireless accesspoints

10.11591/ijeecs.v21.i2.pp895-902
Marion Olubunmi Adebiyi , Egbe Egbe Adeka , Florence A. Oladeji , Roseline Oluwaseun Ogundokun , Micheal Olaolu Arowolo , Ayodele Ariyo Adebiyi
Wireless networks came into the computing world replacing the costlier and more complex wired method of connecting numerous equipment in the same or different location via the use of cables. It provides the user devices a connection to one another and the greater internet via connections to access points. Generally, 802.11 access point products follow a default strongest signal first approach in selecting user devices or nodes to connect to the access point or overlapping access points. This standard does not make provisions for even distribution of load and hence the quality of service and the throughput in areas of congestion would be reduced. This article brings forward two algorithms used in load balancing and they include round-robin technique and the weighted round-robin technique to be used in the simulation of the distribution of the load amongst the access points with the results collated and compared to clearly show which algorithm is best suited to be used as a standard for access point load distribution.
Volume: 21
Issue: 2
Page: 895-902
Publish at: 2021-02-01

Improving signal detection accuracy at FC of a CRN using machine learning and fuzzy rules

10.11591/ijeecs.v21.i2.pp1140-1150
Md Abul Kalam Azad , Anup Majumder , Jugal Krishna Das , Md Imdadul Islam
The performance of a cognitive radio network (CRN) mainly depends on the faithful signal detection at fusion center (FC). In this paper, the concept of weighted Fuzzy rule in Iris data classification, as well as, four machine learning techniques named fuzzy inference system (FIS), fuzzy c-means clustering (FCMC), support vector machine (SVM) and convolutional neural network (CNN) are applied in signal detection at FC taking signal-to-interference plus noise ratio of secondary users as parameter. The weighted Fuzzy rule gave the detection accuracy of 86.6%, which resembles the energy detection model of majority rule of FC; however, CNN gave an accuracy of 91.3% at the expense of more decision time. The FIS, FCMC and SVM gave some intermediate results; however, the combined method gave the best result compared to that of any individual technique.
Volume: 21
Issue: 2
Page: 1140-1150
Publish at: 2021-02-01
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