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
  • Time from submission to publication: 4 weeks

Publication charges

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

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

Lossless Text Compression Technique with LSB Technique to Hide Secret Message inside an Image (CLSB)

Lossless Text Compression Technique with LSB Technique to Hide Secret Message inside an Image (CLSB)

Authors: Hanan Ali Al Zaloutee| Faculty of Information Technology at university of Tripoli/Libya, Libya, Hanan Ettaher Dagez*| Faculty of Information Technolog...
(24 downloads)
Abstract

This paper presents CLSB algorithm to improve and increase the security ofhiding message inside an image by using Least Significant Bit (LSB) method. This research attempts to improve the way has been introduced in [1], where they use digital images to hide Secret Text files. This method is working by distributing data (BMP image format) randomly without use any table to store the path of hiding data. CLSB propose new method to reduce size of dictionary index. Therefore, the main objective of this research is to develop a new method to increase the security by adding a secret key to decode and improve the quality by using LZW method to reduce the size of text file before hiding. Results proved and has also presented in this paper.

Keywords: Security system, Cryptography, Watermarking, Steganography, LSB, LZW
Application of global thresholding in bread porosity evaluation

Application of global thresholding in bread porosity evaluation

Authors: Atanaska D. Bosakova-Ardenska| University of Food Technologies, Plovdiv, Bulgaria.
(21 downloads)
Abstract

The white bread is one of most popular food in Bulgaria. Its quality is defined by standards and control is also standardized. The white bread has four groups of quality parameters - organoleptic, physicochemical, chemical contaminants and microbiological. This paper presents one research over white bread porosity which is one of physicochemical parameters. By standard evaluation of bread porosity is time expensive procedure. Current research proposes one fast computer based approach for white bread porosity evaluation. In experiments are used three brands white breads. The images of breads are binarized with four known algorithms and coefficient of diversity (ratio of white pixels and all image pixels) for resulting binary images is calculated. This coefficient corresponds with bread porosity. Experimental results show that one of these algorithms – Vector Median Thresholding, is appropriate for bread porosity evaluation.

Keywords: Global Thresholding, Vector Median Thresholding, White Bread, Porosity Evaluation
A Modified Flower Pollination Algorithm forFractional Programming Problems

A Modified Flower Pollination Algorithm forFractional Programming Problems

Authors: Mohamed Abdel-Baset*| Department of Operations Research, faculty of Computers and Informatics, Zagazig University, El-ZeraSquare, Zagazig,Sharqiyah, E...
(22 downloads)
Abstract

Flower pollination algorithm is a new nature-inspired algorithm, based on the characteristics of flowering plants. In this paper, a new method is developed chaos-based Flower Pollination Algorithm (CFPA) to solve Fractional Programming Problems (FPPs). The proposed algorithm is tested using several ROP benchmarks. The test aims to prove the capability of the CFPA to solve any type of FPPs. The solution results employing the CFPA algorithm are compared with a number of exact and metaheuristic solution methods used for handling FPPs. Numerical examples are given to show the feasibility, effectiveness, and robustness of the proposed algorithm. The results obtained using CFPA indicated the superiority of the proposed technique among others in computational time.

Keywords: Flower Pollination Algorithm, Nature-Inspired Algorithm, Optimization, Chaos, Fractional Programming Problems
Cloud Computing Environments Which Can Be Used in Health Education

Cloud Computing Environments Which Can Be Used in Health Education

Authors: Mustafa Buber*| Doganhisar Vocational School, University of Selcuk, Konya, Turkey, Fadime Sucu| Computer Education and Instructional Technology Depart...
(24 downloads)
Abstract

At the present time, it is known that cloud computing technologies began to be used widely in information technology. The purpose of this study is to provide information about cloud technologies that can be used in health education. For this purpose,firstly as sample of the learning content management system, Edmodo has been introduced. Hapyak Interactive Video Creation Platform which can be used for creating interactive video to enrich the learning environment that will be submitted with Edmodo, Bubbl.Us which can be benefited from summarizing the discussed and Socrative platforms which enable concept maps application and online test creation have been introduced.

Keywords: Cloud Computing, Edmodo, Health Education
The Classification of Eye State by Using kNN and MLP Classification Models According to the EEG Signals

The Classification of Eye State by Using kNN and MLP Classification Models According to the EEG Signals

Authors: Kadir Sabancı| Karamanoglu Mehmetbey University, Faculty of Engineering, ElectricalElectronic Engineering Department, Karaman, Turkey, Murat Koklu*| S...
(44 downloads)
Abstract

What is widely used for classification of eye state to detect human’s cognition state is electroencephalography (EEG). In this study, the usage of EEG signals for online eye state detection method was proposed. In this study, EEG eye state dataset that is obtained from UCI machine learning repository database was used. Continuous 14 EEG measurements forms the basic of the dataset. The duration of the measurement is 117 seconds (each measurement has14980 sample). Weka (Waikato Environment for Knowledge Analysis) program is used for classification of eye state. Classification success was calculated by using k-Nearest Neighbors algorithm and multilayer perceptron neural networks models. The obtained success of classification methods were compared. The classification success rates were calculated for various number of neurons in the hidden layer of a multilayer perceptron neural network model. The highest classification success rate have been obtained when the number of neurons in the hidden layer was equal to 7. And it was 56.45%. The classification success rates were calculated with k-nearest neighbors algorithm for different neighbourhood values. The highest success was achieved in the classification made with kNN algorithm. In kNN models, the success rate for 3 nearest neighbor were calculated as 84.05%.

Keywords: EEG signals, eye state,, weka, multilayer perceptron, kNN classifier
An Artificial Neural Network Model for Wastewater Treatment Plant of Konya

An Artificial Neural Network Model for Wastewater Treatment Plant of Konya

Authors: Abdullah Erdal TÜMER*| Computer Engineering Department, University of Necmettin Erbakan, Konya, Turkey, Serpil EDEBALİ| Chemical Engineering Departmen...
(23 downloads)
Abstract

In this study, modelling of Konya wastewater treatment plant was studied by using artificial neural network with different architectures in Matlab software. All data were obtained from wastewater treatment plant of Konya during daily records over four month. Treatment efficiency of the plant was determined by taking into account of input values of pH, temperature, COD, TSS and BOD with output values TSS. Performance of the model was compared via the parameters of Mean Squared Error (MSE), and correlation coefficient (R). The suitable architecture of the neural network model is determined after several trial and error steps. According to the modelling study, the ANN can predict the plant performance with correlation coefficient (R) between the observed and predicted output variable reached up to 0.96.

Keywords: Artificial Neural Network, Modelling, Wastewater Treatment Plant, Performance
Classification of Leaf Type Using Artificial Neural Networks

Classification of Leaf Type Using Artificial Neural Networks

Authors: Ali Yasar| Guneysinir Vocational School, University of Selcuk, Konya, Turkey, Ismail Saritas| Faculty of Technology, University of Selcuk, Konya, Turk...
(23 downloads)
Abstract

A number of shape features for automatic plant recognition based on digital image processing have been proposed by Pauwels et al. in 2009. Then Silva et al in 2014 have presented database comprises 40 different plant species. We performed in our study a classification process using dataset and artificial neural networks which have been prepared by Silva and et al. It has been determined that classification accuracy is over 92%.

Keywords: Artificial Neural Network, Leaf Dataset, ANN, Levenberg-Marquardt
Fuzzy approach to estimate the demand and supply quantitative imbalance at the labor market of information technology specialists

Fuzzy approach to estimate the demand and supply quantitative imbalance at the labor market of information technology specialists

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

This document considers the processes of modelling supply and demand interactions in the labour market for information technology experts (IT professionals) and management of their quantitative disparity at the macro level. The types of supply and demand imbalance for IT professionals are marked out. The methods are proposed for estimating the structural mismatch in the labour market for IT professionals, the degree of supply and demand imbalance for IT professionals based on fuzzy unbalance scale. The algorithm of fuzzy classification of states of imbalance is proposed.This document considers the processes of modelling supply and demand interactions in the labour market for information technology experts (IT professionals) and management of their quantitative disparity at the macro level. The types of supply and demand imbalance for IT professionals are marked out. The methods are proposed for estimating the structural mismatch in the labour market for IT professionals, the degree of supply and demand imbalance for IT professionals based on fuzzy unbalance scale. The algorithm of fuzzy classification of states of imbalance is proposed.

Keywords: labour market for IT professionals, supply-demand balancing, quantitative imbalance, fuzzy mismatch scale, fuzzy classification of states of imbalance
SVM-Based Sleep Apnea Identification Using Optimal RR-Interval Features of the ECG Signal

SVM-Based Sleep Apnea Identification Using Optimal RR-Interval Features of the ECG Signal

Authors: Laiali Almazaydeh| Department of Software Engineering, Al Hussein Bin Talal University, Jordan, Khaled Elleithy| Department of Computer Science and En...
(21 downloads)
Abstract

Sleep apnea (SA) is the most commonly known sleeping disorder characterized by pauses of airflow to the lungs and often results in day and night time symptoms such as impaired concentration, depression, memory loss, snoring, nocturnal arousals, sweating and restless sleep. Obstructive Sleep Apnea (OSA), the most common SA, is a result of a collapsed upper respiratory airway, which is majorly undiagnosed due to the inconvenient Polysomnography (PSG) testing procedure at sleep labs. This paper introduces an automated approach towards identifying sleep apnea. The idea is based on efficient feature extraction of the electrocardiogram (ECG) signal by employing a hybrid of signal processing techniques and classification using a linear-kernel Support Vector Machine (SVM). The optimum set of RR-interval features of the ECG signal yields a high classification accuracy of 97.1% when tested on the Physionet Apnea-ECG recordings. The results provide motivating insights towards future developments of convenient and effective OSA screening setups.

Keywords: sleep apnea, PSG, ECG, RR interval, features extraction, SVMs
An Analysis of Archive Update for Vector Evaluated Particle Swarm Optimization

An Analysis of Archive Update for Vector Evaluated Particle Swarm Optimization

Authors: Zuwairie Ibrahim*| Universiti Malaysia Pahang, 26600 Pekan, Pahang, Malaysia, Lim Kian Sheng, Faradila Naim, Mohd Falfazli Mat Jusof, Nurul Wahidah Ar...
(23 downloads)
Abstract

Multi-objective optimization problem is commonly found in many real world problems. In computational intelligence, Particle Swarm Optimization (PSO) algorithm is a popular method in solving optimization problems. An extended PSO algorithm called Vector Evaluated Particle Swarm Optimization (VEPSO) has been introduced to solve multi-objective optimization problems. VEPSO algorithm requires an archive, which is used to record the solutions found. However, the outcome may be differ depending on how the archive is used. Hence, in this study, the performance of VEPSO algorithm when updates the archive at different instance is investigated by measuring the convergence and diversity by using standard test functions. The results show that the VEPSO algorithm performs better when update the archive during the search process, in the iterations.

Keywords: Multi-objective, Optimization, Particle Swarm Optimization, Vector-Evaluated, Archive
Classification of Wheat Types by Artificial Neural Network

Classification of Wheat Types by Artificial Neural Network

Authors: Ali YASAR| Computer Programming, Guneysinir Vocational School of Higher Education Selcuk University Guneysinir, Konya, 42190,Turkey, Esra KAYA| Facult...
(23 downloads)
Abstract

In this study, the types of wheat seeds are classified using present data with artificial neural network (ANN) approach. Seven inputs, one hidden layer with 10 neurons and one output has been used for the ANN in our system. All of these parameters were real-valued continuous. The wheat varieties, Kama, Rosa and Canadian, characterized by measurement of main grain geometric features obtained by X-ray technique, have been analyzed. Results indicate that the proposed method is expected to be an effective method for recognizing wheat varieties. These seven input parameters reaches the 10-neurons hidden layer of the network and they are processed and then classified with an output. The classification process of 210 units of data using ANN is determined to make a successful classification as much as the actual data set. The regression results of the classification process is quite high. It is determined that the training regression R is 0,9999, testing regression is 0,99785 and the validation regression is 0,9947, respectively. Based on these results, classification process using ANN has been seen to achieve outstanding success.

Keywords: ANN, Seed, Classification, Artificial Neural Network, Kama, Rosa Canadian
Banknote Classification Using Artificial Neural Network Approach

Banknote Classification Using Artificial Neural Network Approach

Authors: Esra Kaya *| Selcuk University,Konya – 42075, Turkey, Ali Yasar| Selcuk University,Konya – 42075, Turkey, Ismail Saritas| Gneysinir Vocational School...
(23 downloads)
Abstract

In this study, clustering process has been performed using artificial neural network (ANN) approach on the pictures belonging to our dataset to determine if the banknotes are genuine or counterfeit. Four input parameters, one hidden layer with 10 neurons and one output has been used for the ANN. All of these parameters were real-valued continuous. Data were extracted from images that were taken from genuine and forged banknote-like specimens. For digitization, an industrial camera usually used for print inspection was used. The final images have 400x 400 pixels. Due to the object lens and distance to the investigated object gray-scale pictures with a resolution of about 660 dpi were gained. Wavelet Transform tool were used to extractfeatures from images. Four input parameters are processed in the hidden layer with 10 neurons and the output realizes the clustering process. The classification process of 1372 unit data by using ANN approach is sure to be a success as much as the actual data set. The regression results of the clustering process is considerably well. It is determined that the training regression is 0,99914, testing regression is 0,99786 and the validation regression is 0,9953, respectively. Based on the results obtained, it is seen that classification process using ANN is capable of achieving outstanding success.

Keywords: ANN, Banknote, Classification, Machine Learning Database
PID Parameters Prediction Using Neural Network for A Linear Quarter Car Suspension Control

PID Parameters Prediction Using Neural Network for A Linear Quarter Car Suspension Control

Authors: Kenan Muderrisoglu| Yıldız Technical University – Mechatronic Engineering Department, Dogan Onur Arisoy| Boğaziçi University – Institute of Biomedical...
(27 downloads)
Abstract

Providing control for suspension systems in vehicles is an enhancing factor for comfort and safety. With the improvement of control conditions, it is possible to design a cost-efficient controller which will maintain optimum comfort within harsher environmental conditions. The aim of this study is to design an adaptive PID controller with a predictive neural network model, which will be referred as NPID (NeuralPID), to control a suspension system. For this purpose, a NN (Neural Network) model is designed to produce outputs for PID’s Proportional (P) parameter to provide optimum responses for different road inputs. Also, reliability of the system outputs, which is using adaptive Proportional parameter, is tested. PID parameters for linear quarter vehicle model are decided through Zeigler-Nichols method. An ideal PID model, where Integral (I) and Derivative (D) parameters are bound to Proportional parameter, is used in the system. When the outputs of different controlled and not controlled systems, which are free, PID and NPID, are compared; it has been seen that NPID outputs are more convenient. In addition, it is possible to design controllers, with adaptively adjusting P parameter, which are operating cost-effective.

Keywords: Neural Network, PID, Quarter Car Model, 2-DOF, Suspension Control, MATLAB
The Classification of Diseased Trees by Using kNN and MLP Classification Models According to the Satellite Imagery

The Classification of Diseased Trees by Using kNN and MLP Classification Models According to the Satellite Imagery

Authors: Muhammed Fahri Unlersen *| Selcuk University Doğanhisar Vocational School Konya Turkey, Kadir Sabanci| Karamanoglu Mehmetbey University Electrical and...
(23 downloads)
Abstract

In this study, the Japanese Oak and Pine Wilt in forested areas of Japan was classified into two group as diseased trees and all other land cover area according to the 6 attributes in the spectral data set of the forest. The Wilt Data Set which was obtained from UCI machine learning repository database was used. Weka (Waikato Environment for Knowledge Analysis) software was used for classification of areas in the forests. The classification success rates and error values were calculated and presented for classification data mining algorithms just as Multilayer Perceptron (MLP) and k-Nearest Neighbor (kNN). In MLP neural networks the classification performance for various numbers of neurons in the hidden layer was presented. The highest success rate was obtained as 86.4% when the number of neurons in the hidden layer was 10. The classification performance of kNN method was calculated for various counts of neighborhood. The highest success rate was obtained as 72% when the count of neighborhood number was 2.

Keywords: k - Nearest Neighbor, Multilayer Perceptron Neural Network, Weka, Classification, Remote Sensing
GA Based Selective Harmonic Elimination for Five-Level Inverter Using Cascaded H-bridge Modules

GA Based Selective Harmonic Elimination for Five-Level Inverter Using Cascaded H-bridge Modules

Authors: Enes Bektas| Engineering Faculty, Cankiri Karatekin University – Cankiri, Turkey, Hulusi Karaca *| Technology Faculty, Selcuk University – Konya, Turk...
(24 downloads)
Abstract

Multilevel inverters (MLI) have been commonly used in industry especially to get quality output voltage in terms of total harmonic distortion (THD). In addition, development in semiconductor technology and advanced modulation techniques make MLI implementation more attractive. Selective Harmonic Elimination (SHE) that can be applied MLI at desired switching frequency offers elimination of harmonics in the output voltage. Also, by using SHE technique with cascaded multilevel inverters, the necessity of using filter in the output can be minimized. In this paper, SHE equations have been solved by using of Genetic Algorithm (GA) Toobox&Matlab and it has been aimed to eliminate desired harmonic orders at fundamental output voltage. Simulation results have clearly demonstrated that GA based SHE techniques can eliminate the demanded harmonic orders.

Keywords: Cascaded multilevel inverter, Genetic algorithm, H-bridge modules, Matlab&Optimization Tooolbox, Selective harmonic elimination

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