International Journal of Intelligent Systems and Applications in Engineering

International Journal of Intelligent Systems and Applications in Engineering

Basic info

  • Publisher: IJISAE
  • Country of publisher: turkey
  • Date added to EuroPub: 2017/Apr/13

Subject and more

  • LCC Subject Category: Engineering, Nanotechnology
  • Publisher's keywords: engineering
  • Language of fulltext: english

Publication charges

  • Article Processing Charges (APCs): No
  • Submission charges: No
  • Waiver policy for charges? Yes

Editorial information

Open access & licensing

  • Type of License: CC BY
  • License terms
  • Open Access Statement: Yes
  • Year open access content began: 2013
  • Does the author retain unrestricted copyright? True
  • Does the author retain publishing rights? True

Best practice polices

  • Permanent article identifier: None
  • Content digitally archived in: Nopolicy
  • Deposit policy registered in: None

This journal has '83' articles

Improving Intrusion Detection using Genetic Linear Discriminant Analysis

Improving Intrusion Detection using Genetic Linear Discriminant Analysis

Authors: Azween Abdullah *| School of Computing and IT, Taylors University, Subang Jaya, Selangor, Malaysia, Cai Long Zheng| Unitar International University, P...
( 24 downloads)
Abstract

The objective of this research is to propose an efficient soft computing approach with high detection rates and low false alarms while maintaining low cost and shorter detection time for intrusion detection. Our results were promising as they showed the new proposed system, hybrid feature selection approach of Linear Discriminant Analysis and Genetic Algorithm (GA) called Genetic Linear Discriminant Analysis (GLDA) and Support Vector Machines (SVM) Kernels as classifiers with different combinations of NSL-KDD data sets is an improved and effective solution for intrusion detection system (IDS).

Keywords: IDS, Features selection, Features transformation, NSL-KDD, GLDA, SVM Kernels
Fuzzy Multicriterial Methods for the Selection of IT-Professionals

Fuzzy Multicriterial Methods for the Selection of IT-Professionals

Authors: Masuma Mammadova*| Institute of Information Technology of National Academy Science of Azerbaijan, Zarifa Jabrayilova| Institute of Information Technol...
( 24 downloads)
Abstract

This paper presents the solution of issues related to selection based on evaluation of demand set forth to IT specialists, to develop appropriate decision support system. In this case problem is reduced to multicriterial task of decision making, functioning in a fuzzy environment.We propose criteria estimation method allowing regulation and selection of the best alternative according to the scenario appropriate to the requirements of the decision making person, at a current time. For realization of abovementioned task on the basis of fuzzy logic methods we propose methods of expert knowledge processing of the importance criteria and their characterizing factors.

Keywords: Decision Support System, Fuzzy Environment, Personnel Selection Problem, Fuzzy Multicriterial Model, Multiscenario Approach, Importance Factor of the Criteria
Particle Swarm Optimization Based Approach for Location Area Planning in Cellular Networks

Particle Swarm Optimization Based Approach for Location Area Planning in Cellular Networks

Authors: Mays S. Algebary*| Tripoli University, Computer Engineering Department, Tripoli, Libya, Issmail M. Ellabib| Tripoli University, Computer Engineering D...
( 22 downloads)
Abstract

Location area planning problem plays an important role in cellular networks because of the trade-off caused by paging and registration signalling (i.e., location update). Compromising between the location update and the paging costs is essential in order to improve the performance of the network. The trade-off between these two factors can be optimized in such a way that the total cost of paging and location update can be minimized along with the link cost. Due to the complexity of this problem, meta-heuristic techniques are often used for analysing and solving practical sized instances. In this paper, we propose an approach to solve the LA planning problem based on the Particle Swarm Optimization (PSO) algorithm. The performance of the approach is investigated and evaluated with respect to the solution quality on a range of problem instances. Moreover, experimental work demonstrated the performance comparison in terms of different degree of mobility, paging load, call traffic load, and TRX load. The performance of the proposed approach outperform other existing meta-heuristic based approaches for the most problem instances.

Keywords: Particle Swarm Optimization, Simulated Annealing Optimization, Ant Colony Optimization, Location Management in Cellular Networks, Swarm Intelligence
New Approach in E-mail Based Text Steganography

New Approach in E-mail Based Text Steganography

Authors: Kemal Tutuncu *| Faculty of Technology, Selcuk University Campus, Konya, Turkey, Abdikarim Abi Hassan| Affiliation not present
( 23 downloads)
Abstract

In this study combination of lossless compression techniques and Vigenere cipher was used in text steganography that makes use of email addresses to be the keys to reconstruct the secret message which has been embedded into the email text. After selecting the cover text that has highest repetition pattern regarding to the secret message the distance matrix was formed. The members of distance matrix were compressed by following lossless compression algorithms as in written sequence; Run Length Encoding (RLE) + Burrows Wheeler Transform (BWT) + Move to Forward (MTF) + Run Length Encoding + Arithmetic Encoding (AE). Later on Latin Square was used to form stego key 1and then Vigenere table was used to increase complexity of extracting stego key 1. Final step was to choose e-mail addresses by using stego key 1 and stego key 2to embed secret message into forward e-mail platform. The experimental results showed that proposed method has reasonable performance with high complexity.

Keywords: Text steganography, Latin square, Vigenere chipher, Stego key, BWT, MTF
Grade prediction improved by regular and maximal association rules

Grade prediction improved by regular and maximal association rules

Authors: Anca Loredana Udristoiu *| University of Craiova, Department of Computers and Information Technology blvd. Decebal, n. 107,Craiova, Romania, Stefan Ud...
( 44 downloads)
Abstract

In this paper we propose a method of predicting student scholar performance using the power of regular and maximal association rules. Due to the large number of generated rules, traditional data mining algorithms can become difficult and inappropriate to educational systems. Thus, we use some methods to overcome this problem, discovering rules useful in educational process. These methods are applied to the e-learning system Moodle, for “Database” course.

Keywords: Education data mining, Regular association rule, Maximal association rule, Learning management system
Comparison among Feature Encoding Techniques for HIV-1 Protease Cleavage Specificity

Comparison among Feature Encoding Techniques for HIV-1 Protease Cleavage Specificity

Authors: Uğur Turhal| University of Balıkesir – 10100, Turkey, Murat Gök*| University of Yalova – 77100, Turkey, Aykut Durgut| University of Balıkesir – 10100,...
( 23 downloads)
Abstract

HIV-1 protease which is responsible for the generation of infectious viral particles by cleaving the virus polypeptides, play an indispensable role in the life cycle of HIV-1. Knowledge of the substrate specificity of HIV-1 protease will pave the way of development of efficacious HIV-1 protease inhibitors. In the prediction of HIV-1 protease cleavage site techniques, many efforts have been devoted. Last decade, several works have approached the prediction of HIV-1 protease cleavage site problem by applying a number of methods from the field of machine learning. However, it is still difficult for researchers to choose the best method due to the lack of an effective and up-to-date comparison.

Keywords: HIV-1 protease specificity, Feature extraction, Peptide classification, Machine learning algorithms, Amino acids
Atmospheric and light-induced effects in nanostructured silicon deposited by capacitively and inductively-coupled plasma

Atmospheric and light-induced effects in nanostructured silicon deposited by capacitively and inductively-coupled plasma

Authors: Zaki Mohammad Saleh*| Arab American University-Jenin, Palestinian Territory, Occupied, Gizem Nogay| Middle East Technical University, Turkey, Engin Oz...
( 22 downloads)
Abstract

Renewable sources of energy have demonstrated the potential to replace much of the conventional sources but the cost continues to pose a challenge. Efforts to reduce cost involve highly efficient and less expensive materials as well as enhanced light management. Nanostructured materials consisting of silicon quantum dots in a matrix of amorphous silicon (a-Si) are promising for higher efficiency and better stability. Quantum confinement offers a tunable band gap, relaxes momentum conservation rule, and may permit multi exciton generation, MEG. We employ electron spin resonance (ESR), the temperature dependence of dark and photoconductivity to compare the stability of amorphous and nanostructured silicon films deposited by inductively- and capacitively-coupled plasma against atmospheric and light exposure. Distinctly different behaviors are observed for amorphous and nanostructured films suggesting that nanostructured films are more permeable to oxygen infusion but more resistant to light induced effect.

Keywords: Photoconductivity, amorphous, nanostructure, atmospheric aging, electron spin resonance
Performance Evaluation of Different Feature Extractors and Classifiers for Recognition of Human Faces with Low Resolution Images

Performance Evaluation of Different Feature Extractors and Classifiers for Recognition of Human Faces with Low Resolution Images

Authors: Soodeh Nikan*| ECE, University of Windsor, Windsor, ON – N9B 3P4, Canada, Majid Ahmadi| ECE, University of Windsor, Windsor, ON – N9B 3P4, Canada
( 22 downloads)
Abstract

Face recognition is an effective biometric identification technique used in many applications such as law enforcement, document validation and video surveillance. In this paper the effect of low resolution images which are captured in real world applications, on the performance of different feature extraction techniques combined with a variety of classification approaches is evaluated. Gabor features and its combination with local phase quantization histogram (GLPQH) are dimensionality reduced by principal component analysis (PCA), linear discriminant analysis (LDA), locally sensitive discriminant analysis (LSDA) and neighbourhood preserving embedding (NPE) to extract discriminant image characteristics and the class label is attributed using the extreme learning machine (ELM), sparse classifier (SC), fuzzy nearest neighbour (FNN) or regularized discriminant classifier (RDC). ORL and AR databases are utilized and the results show that ELM and RDC have better performance and stability against resolution reduction, especially on Gabor-PCA and Gabor-LDA techniques. Among the interpolation approaches that we employed to enhance the image resolution, nearest neighbour outperforms other methods.

Keywords: Face recognition, Feature Extraction, Classification, Interpolation, Dimensionality Reduction
Rainfall estimation for the south shore of the Mediterranean Sea using MSG infrared images

Rainfall estimation for the south shore of the Mediterranean Sea using MSG infrared images

Authors: Mohsene A. TEBBI*| Laboratory of image processing and radiation, University of sciences and technology Houari Boumediene, 32, El Alia, Bab Ezzouar, 16...
( 26 downloads)
Abstract

The objective of this paper is the estimation of rainfall over the Algerian territory using MSG (Meteosat Second Generation) infrared data. To achieve this aim, we applied a calibrated GPI (GOES Precipitation Index) approach. This technique is tested to the complex situation of the Mediterranean climate. The rainfall estimated is obtained by using an affectation of adapted rain rates for a brightness temperature. These rain rates are determinate by analysing two years of in situ measures. The tests have been concluded during the rainy months January, February, and September of 2006/2007. The results show a good correlation between measured and estimated rainfall in winter and summer where stratiform and convective cells are present.

Keywords: Meteorological satellite, Mediterranean climate, IR MSG images data, Convective and Stratiform clouds, GPI approach
BAT algorithm for Cryptanalysis of Feistel cryptosystems

BAT algorithm for Cryptanalysis of Feistel cryptosystems

Authors: T. Mekhaznia*| LAMIS Laboratory, University of Tebessa, Algeria, A. Zidani| Department of Computer Sciences, University of Batna, Algeria
( 25 downloads)
Abstract

Recent cryptosystems constitute an effective task for cryptanalysis algorithms due to their internal structure based on nonlinearity. This problem can be formulated as NP-Hard. It has long been subject to various attacks; available results, emerged many years ago remain insufficient when handling large instances due to resources requirement which increase with the amount of processed data. On another side, optimization techniques inspired by swarm intelligence represents a set of approaches used to solve complex problems. This is mainly due to their fast convergence with a consumption of reduced resources. The purpose of this paper is to provide, and for a first time, a more detailed study about the performance of BAT algorithm in cryptanalysis of some variant of Data encryption standard algorithms. Experiments were performed to study the effectiveness of the used algorithm in solving the considered problem and underline the difficulties encountered.

Keywords: Cryptanalysis, Feistel ciphers, bat algorithm
Classification of Siirt and Long Type Pistachios (Pistacia vera L.) by Artificial Neural Networks

Classification of Siirt and Long Type Pistachios (Pistacia vera L.) by Artificial Neural Networks

Authors: Kadir SABANCI| Karamanoglu Mehmetbey University, Faculty of Engineering, Electrical- Electronic Engineering Department, Karaman, Turkey, Murat KOKLU*|...
( 22 downloads)
Abstract

Quality is one of the important factors in agricultural products marketing. Grading machines have great role in quality control systems. The most efficient method used in grading machines today is image processing. This study aims to do the grading of high valued agricultural product of our land called pistachio that has two different types namely Siirt and Long type of pistachios by image processing methods and artificial neural networks. Photos of Siirt and long type of pistachios are taken by a Webcam with CCD sensor. These photos were converted to gray scale in Matlab. Afterwards, these photos were converted to binary photo format using Otsu’s Method. Then this data was used to train multi-layered neural network to complete grading. Matlab was used for both image processing and artificial neural networks. Successes of the grading with image processing and artificial neural networks for mixed type pistachios Siirt and Long were researched.

Keywords: Long type of pistachios, Siirt pistachios, Classification, Image processing, Artificial neural networks
Dependability Assessment of the Railway Signalling Systems Based on the Stochastic Petri Nets Analysis

Dependability Assessment of the Railway Signalling Systems Based on the Stochastic Petri Nets Analysis

Authors: Jaouad Boudnaya| Moulay Ismail University, ENSAM Meknes, Laboratory of Mechanics,Mechatronics and Control (L2MC), Marjane 2, PO Box 15290, Al-Mansour...
( 23 downloads)
Abstract

In this article, we propose a methodology to evaluate the performances of the railway signalling systems in terms of the availability. Firstly, level crossings in Morocco are presented. Secondly, a railway signalling system ERTMS level 2 modelling is proposed .The human factor and network failures are also taken into account. Finally, this system performance evaluation is proposed in every state (nominal way of functioning, degraded mode, and failure mode).

Keywords: Railway Signalling System, Level Crossing, Modelling, Petri Nets, Risks, Accident
About a discussion ‘‘Development a new mutation operator to solve the Traveling Salesman Problem by aid of genetic algorithms’’, by Murat Albayrak and Novruz Allahverdi, 2011. Expert System with Applications, 38; 3, pp. 1313–1320.

About a discussion ‘‘Development a new mutation operator to solve the Traveling Salesman Problem by aid of genetic algorithms’’, by Murat Albayrak and Novruz Allahverdi, 2011. Expert System with Applications, 38; 3, pp. 1313–1320.

Authors: Novruz Allahverdi*| Selcuk University, Faculty of Technology, Department of Computer Engineering, Konya, Turkey
( 22 downloads)
Abstract

In the Short Communication published in “Expert Systems with Application” in volume 41 2014, (Comments on "Albayrak, M., & Allahverdi N. (2011). Development a new mutation operator to solve the Traveling Salesman Problem by aid of Genetic Algorithms. Expert Systems with Applications, 38(3), 1313-1320": A Proposal of Good Practice; E. Osaba, E. Onieva, F. Diaz, R. Carballedo, Volume: 41, Issue: 4, Pages: 1530-1531, Part: 1, MARCH 2014) the Osoba E. et al have discussed our method to solve the Traveling Salesman Problem pointing that we use our developed new algorithm to compare different versions of a classical genetic algorithm, each of one with a different mutation operator and they write that this can generate some controversy. Here we shortly analyze the comment of Osaba E. et al to show that our comparing method has a chance of existence.

Keywords: Genetic algorithms, Traveling Salesman Problem, algorithm Greedy Sub Tour Mutation (GSTM)
The Principal Component Analysis Method Based Descriptor for Visual Object Classification

The Principal Component Analysis Method Based Descriptor for Visual Object Classification

Authors: Zühal Kurt| Mathematics - Computer Sciences Department, Eskişehir Osmangazi University, Eskişehir/Turkey, Kemal Özkan| Computer Engineering Department...
( 21 downloads)
Abstract

In the field of machine learning, which values / data labeling or recognition is done by pattern recognition. Visual object classification is an example of pattern recognition, which attempts prompt to assign each object to one of a given set of object classes.

Keywords: Feature Descriptor, Feature Extraction, Visual Object Classification, Principal Component Analysis, Bag Of Words Model
Neural Boundary Conditions in Optic Guides

Neural Boundary Conditions in Optic Guides

Authors: Pınar ÖZKAN BAKBAK| Yildiz Technical University, Department of Electronic and Communication Engineering, Davutpasa, 34220, Istanbul, Turkey
( 23 downloads)
Abstract

In this study, the boundary coefficients of Transverse Electric (TE) and Transverse Magnetic (TM) modes at a planar slab optic guides are modeled by Neural Networks (NN). After modal analysis, train and test files are prepared for NN. Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF) neural networks are performed and compared with each other. NNs are expected to be capable of modeling optical fiber technology in industry based on the same approaches as a result of this study.

Keywords: Boundary Conditions, Optic Guides, Neural Network

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