International Journal of Intelligent Systems and Applications in Engineering
International Journal of Intelligent Systems and Applications in Engineering (IJISAE) is an international and interdisciplinary journal for both invited and contributed peer reviewed articles that intelligent systems and applications in engineering at all levels. The journal publishes a broad range of papers covering theory and practice in order to facilitate future efforts of individuals and groups involved in the field. IJISAE, a peer-reviewed double-blind refereed journal, publishes original papers featuring innovative and practical technologies related to the design and development of intelligent systems in engineering. Its coverage also includes papers on intelligent systems applications in areas such as nanotechnology, renewable energy, medicine engineering, Aeronautics and Astronautics, mechatronics, industrial manufacturing, bioengineering, agriculture, services, intelligence based automation and appliances, medical robots and robotic rehabilitations, space exploration and etc.
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<![CDATA[The Effect of Feature Extraction Based on Dictionary Learning on ECG Signal Classification ]]>
<![CDATA[Ceylan, Rahime]]>
<![CDATA[ International Journal of Intelligent Systems and Applications in Engineering Vol 6, No 1 (2018) ]]>
Publisher : <![CDATA[Advanced Technology and Science (ATScience) ]]>
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DOI: 10.18201/ijisae.2018637929
<![CDATA[The detection of effective features or data reduction is one of the phases of signal classification. In feature extraction phase, the detection of features which increase performance of classification is very important in terms of diagnosis of disease. Due to this reason, the using of an effective algorithm for feature extraction increases classification accuracy and also it decreases processing time of classifier.           In this study, two well-known dictionary learning algorithms are used to extract features of ECG signals. The features of ECG signals are extracted by using Method of Optimal Direction (MOD) and K-Singular Value Decomposition (K-SVD) and the extracted features are classified by Artificial Neural Network (ANN). Twelve different ECG signal classes which taken from MIT-BIH ECG Arrhythmia Database are used. When the obtained results are examined, it is seen that performance of classifier increases in usage of K-SVD for feature extraction. The highest classification accuracy is obtained as %98.74 with 5 nonzero elements in [20 1] feature vector, when K-SVD is used in feature extraction phase. This is the first time in literature that feature extraction based on dictionary learning is performed on 12 ECG signal classes and the extracted features are classified by ANN.]]>
<![CDATA[Feature Selection using FFS and PCA in Biomedical Data Classification with AdaBoost-SVM ]]>
<![CDATA[Ceylan, Rahime]]>;
<![CDATA[Barstugan, Mucahid]]>
<![CDATA[ International Journal of Intelligent Systems and Applications in Engineering Vol 6, No 1 (2018) ]]>
Publisher : <![CDATA[Advanced Technology and Science (ATScience) ]]>
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DOI: 10.18201/ijisae.2018637928
<![CDATA[: Recently, there has been an increasing trend to propose computer aided diagnosis systems for biomedical pattern recognition. A computer aided diagnosis method, which aims higher classification accuracy, is developed to classify the biomedical dataset. This new method includes two types of machine learning algorithms: feature selection and classification. In this method, firstly, features were extracted from biomedical dataset, then the extracted features were classified by hybrid AdaBoost-Support Vector Machines (SVM) classifier structure. For feature selection, Forward Feature Selection (FFS) and Principal Component Analysis (PCA) algorithms were used. Following it, advantages and disadvantages of these algorithms were evaluated. The proposed two different hybrid structures and other studies in literature were compared with our findings. Wisconsin Breast Cancer (WBC), Pima Diabetes (PD), Heart (Statlog) biomedical datasets and Electrocardiogram (ECG) signals were taken from UCI database and these datasets were used to test the proposed hybrid structure. The obtained results show that the proposed hybrid structure has high classification accuracy for biomedical data classification.]]>
<![CDATA[An Efficient Image Encryption Algorithm for the Period of Arnolds CAT Map ]]>
<![CDATA[Elmacı, Deniz]]>;
<![CDATA[Bas Catak, Nursin]]>
<![CDATA[ International Journal of Intelligent Systems and Applications in Engineering Vol 6, No 1 (2018) ]]>
Publisher : <![CDATA[Advanced Technology and Science (ATScience) ]]>
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DOI: 10.18201/ijisae.2018637935
<![CDATA[Arnolds CAT Map (ACM) is a chaotic transformation the 2-dimensional toral automorphism T^2 defined by the mapping /Gamma:T^2 to T^2. There are many applications of ACM in various research areas such as: steganography, encryption of images, texts and watermarks. The transformation of an image is achieved by the randomized order of pixels. After a finite number of repetitions of the transformation, the original image reappears. In this study, encryption of two images is demonstrated together with a proposed algorithm. Moreover, the periodicity of ACM is discussed and an algorithm to change the period of ACM is suggested. The resultant period obtained from the new algorithm is compared with the period obtained from the usual ACM. The results show that the period of the proposed algorithm grows exponentially while the period of ACM has an upper bound.]]>
<![CDATA[Operating Frequency Estimation of Slot Antenna by Using Adapted kNN Algorithm ]]>
<![CDATA[Yigit, Enes]]>
<![CDATA[ International Journal of Intelligent Systems and Applications in Engineering Vol 6, No 1 (2018) ]]>
Publisher : <![CDATA[Advanced Technology and Science (ATScience) ]]>
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DOI: 10.18201/ijisae.2018637927
<![CDATA[In this study ultra-high frequency slot antennaâs operating frequency is estimated by using adapted k-nearest neighbor (kNN) algorithm. kNN doesnât use the training data points to do any generalization and it can be usually used for many classification. However, kNN can be adapted to estimate slot antennaâs operating frequency by assessing the best k-nearest value. To find the optimal k for operating frequency estimation, 96 slot antennas with seven antenna parameters are simulated with respect to the operating frequency by using a computational electromagnetic software. Antenna parameters includes the patch dimensions, height and relative permittivity of the substrate. The simulated 81 antennas are used to construct feature data pool and the residual 15 antennas are used to test kNN algorithm. The performance of the kNN is evaluated by comparing the output of operating frequency to the simulated one. Then the proposed model is corroborated with simulated antennas and validating with prototyped antenna data. The results shows that the kNN based model simply and fast computes the operating frequency of the slot antennas much close to real one without performing any simulations or measurement.]]>
<![CDATA[A Novel Feature Selection Method for the Dynamic Security Assessment of Power Systems Based on Multi-Layer Perceptrons ]]>
<![CDATA[Beyranvand, Peyman]]>;
<![CDATA[Kucuktezcan, Cavit Fatih]]>;
<![CDATA[Cataltepe, Zehra]]>;
<![CDATA[Genc, Veysel Murat Istemihan]]>
<![CDATA[ International Journal of Intelligent Systems and Applications in Engineering Vol 6, No 1 (2018) ]]>
Publisher : <![CDATA[Advanced Technology and Science (ATScience) ]]>
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DOI: 10.18201/ijisae.2018637931
<![CDATA[In this study, the effect of feature selection methods on the performance of multi-layer perceptrons used for the dynamic security assessment of electric power systems is investigated. The existence of many measurable parameters (features) characterizing the power system security status complicates the use of multi-layer perceptron both in terms of prediction accuracy and training time. In this paper, the dynamic security of a power system subject to a number of critical contingencies is assessed as the critical clearing time of any credible fault is predicted by a multi-layer perceptron. In addition to the study of two different feature selection methods, which are Minimum Redundancy Maximum Relevance (mRMR), and Regressional ReliefF (RReliefF), a novel multi-layer perceptron based feature selection method is proposed to be applied in the prediction of security indices. The performance of the feature selection methods on the dynamic security assessment is investigated on a 16-generator, 68-bus test system.]]>
<![CDATA[Analysis and Validation of medical Application through Electrical Impedance based System ]]>
<![CDATA[Kumar, Ramesh]]>;
<![CDATA[Kumar, Sharwan]]>;
<![CDATA[Gupta, A.]]>
<![CDATA[ International Journal of Intelligent Systems and Applications in Engineering Vol 6, No 1 (2018) ]]>
Publisher : <![CDATA[Advanced Technology and Science (ATScience) ]]>
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DOI: 10.18201/ijisae.2018637925
<![CDATA[This paper discussed the Design and implementation of the electrical impedance-based system, which covers both techniques (Impedance Plethysmography (IPG) and Electrical Impedance Tomography (EIT)). The electrical impedance distribution image of the cross section of a phantom based on current excitation and voltage measurement using electrodes pair is reconstructs image according to the electrical property of the medical phantom. The electrical impedance based image provides the significant morphological information. Image quality depends upon many other iterative process and image processing algorithms in addition to. The hardware designing of the system is a most important part for all impedance-based techniques. In measurement section, the current source of the electrical impedance-based system should supply multi-frequency signal for measurement because it provides more useful information about the phantom. Therefore, a signal source that provides accurate excitation and a reference signal for measurement are more useful. Matlab is used for image reconstruction and processing of the impedance data.]]>
<![CDATA[An Application of ANN Trained by ABC Algorithm for Classification of Wheat Grains ]]>
<![CDATA[Kayabasi, Ahmet]]>
<![CDATA[ International Journal of Intelligent Systems and Applications in Engineering Vol 6, No 1 (2018) ]]>
Publisher : <![CDATA[Advanced Technology and Science (ATScience) ]]>
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DOI: 10.18201/ijisae.2018637936
<![CDATA[Artificial Neural Networks (ANNs) have emerged as an important tool for classification problem. This paper presents an application of ANN model trained by artificial bee colony (ABC) optimization algorithm for classification the wheat grains into bread and durum. ABC algorithm is used to optimize the weights and biases of three-layer multilayer perceptron (MLP) based ANN. The classification is carried out through data of wheat grains (#200) acquired using image-processing techniques (IPTs). The data set includes five grainâs geometric parameters: length, width, area, perimeter and fullness. The ANN-ABC model input with the geometric parameters are trained through 170 wheat grain data and their accuracies are tested via 30 data. The ANN-ABC model numerically calculate the outputs with mean absolute error (MAE) of 0.0034 and classify the grains with accuracy of 100% for the testing process. The results of ANN-ABC model are compared with other ANN models trained by 4 different learning algorithms. These results point out that the ANN trained by ABC optimization algorithm can be successfully applied to classification of wheat grains. ]]>
<![CDATA[Multi objective design optimization of plate fin heat sinks using improved differential search algorithm ]]>
<![CDATA[Turgut, Oguz Emrah]]>
<![CDATA[ International Journal of Intelligent Systems and Applications in Engineering Vol 6, No 1 (2018) ]]>
Publisher : <![CDATA[Advanced Technology and Science (ATScience) ]]>
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DOI: 10.18201/ijisae.2018637924
<![CDATA[This study provides the multi-objective optimization of plate fin heat sinks equipped with flow â through and impingement-flow air-cooling system by using Improved Differential Search algorithm. Differential Search algorithm mimics the subsistence characteristics of the living beings through the migration process. Convergence speed of the algorithm is enhanced with the local search based perturbation schemes and this improvement yields favorable solution outputs according to the results obtained from the widely quoted optimization test problems. Improved algorithm is employed on multi-objective design optimization of plate fins heat sink considering the objective functions of entropy generation rate and total material cost. Total of seven decision variables such as oncoming stream velocity, number of fins on the plate, gap between consecutive fins, base thickness of the plate, width, length and height of the plate fin heat sink are selected to be optimized. Pareto frontiers are constructed for both flow-through and impingement flow air-cooling system design and best solutions are obtained  by means of widely reputed decision-making theories of LINMAP, TOPSIS, and Shannonâs entropy theory. Results retrieved from the case studies show that reliable outcomes could be achieved in terms of solution accuracy through  Improved Differential Search optimizer.  ]]>
<![CDATA[The Impact of Feature Selection on Urban Land Cover Classification ]]>
<![CDATA[Dogan, Turgut]]>;
<![CDATA[Uysal, Alper Kursat]]>
<![CDATA[ International Journal of Intelligent Systems and Applications in Engineering Vol 6, No 1 (2018) ]]>
Publisher : <![CDATA[Advanced Technology and Science (ATScience) ]]>
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DOI: 10.18201/ijisae.2018637933
<![CDATA[Many of the studies in the literature about land cover classification are focused on the feature extraction and classification rather than feature selection. In this paper, the impact of feature selection on urban land cover classification is extensively analyzed. Three types of features namely spectral, texture, and size/shape features are used for this analysis. This analysis is carried out using three variations of a filter based feature selection method and three widely-known classification algorithms. The feature selection method used for the comparison is a multivariate filter method namely correlation-based feature subset selection where a feature subset evaluator and a search method are integrated. Best first search, genetic search, and greedy stepwise search are three different search methods used for this integration. The classification algorithms employed are Bayesian network, random forest, and support vector machine. The experimental results explicitly indicate that feature selection improves classification accuracy in all cases. Besides, according to the experimental results, random forest classifier is the most successful one among these three classifiers while both feature selection is applied and not applied. Largest improvement in the classification performance is obtained when greedy stepwise search based feature selection method and support vector machine classifier is applied together. Also, the contribution of spectral features to the performance of classification is more than size/shape and texture features.]]>
<![CDATA[Application of hybrid of Fuzzy Set, Trust and Genetic Algorithm in query log mining for effective Information Retrieval ]]>
<![CDATA[Chawla, Suruchi]]>
<![CDATA[ International Journal of Intelligent Systems and Applications in Engineering Vol 6, No 1 (2018) ]]>
Publisher : <![CDATA[Advanced Technology and Science (ATScience) ]]>
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DOI: 10.18201/ijisae.2018637930
<![CDATA[The precision of Information Retrieval (IR) System is low due to imprecise user queries as well as because of information overload on web. The Fuzzy set infers the userâs information need from vague and imprecise queries and web recommender systems are used to overcome information overload problem. The performance of recommender system is still low due to data sparsity. The concept of trust is used to deal with data sparseness problem and improves the performance of recommender system. Optimization techniques like Genetic Algorithm(GA) have been applied in domain of information retrieval for effective web search. In this research hybrid of Fuzzy set, GA and Trust has been used together in query log mining for personalized web search based on using fuzzy queries for recommendation of optimal set of trusted documents. Thus the use of hybrid of Fuzzy set, trust and GA together infer the userâs information need from vague and imprecise userâs queries and optimize the web page ranking of trusted web pages for effective personalized web search. The experimental results were analyzed statistically as well as compared with GA IR, and Fuzzy Trust based IR. Hence based on comparative analysis of results, thus hybrid of Fuzzy Set, Trust and GA shows the improvement in average precision of search results and confirms the effective personalization of web search. ]]>
