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.
Articles
53 Documents
Search results for
, issue
" 2016: Special Issue"
:
53 Documents
clear
<![CDATA[Classification of Heuristic Information by Using Machine Learning Algorithms ]]>
<![CDATA[KOKLU, Murat]]>;
<![CDATA[SABANCI, Kadir]]>;
<![CDATA[UNLERSEN, Muhammed Fahri]]>
<![CDATA[ International Journal of Intelligent Systems and Applications in Engineering 2016: Special Issue ]]>
Publisher : <![CDATA[Prof. Dr. Ismail SARITAS ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.18201/ijisae.2016Special Issue-146984
<![CDATA[The User Knowledge Modelling dataset in the UCI machine learning repository was used in this study. The students were classified into 4 class (very low, low, middle, and high) due to the 5 performance data in the dataset. 258 data of 403 data in the dataset were used for training and 145 of them were used for tests. The Weka (Waikato Environment for Knowledge Analysis) software was used for classification. In classification Multilayer Perceptron (MLP), k Nearest Neighbors (kNN), J48, NativeBayes, BayesNet, KStar, RBFNetwork and RBFClassifier machine learning algorithms were used and success rates and error rates were calculated. In this study 8 different data mining algorithm were used and the best classification success rate was obtained by MLP. With Multilayer perceptron neural network model the classification success rates was calculated when there are different number of neurons in the hidden layer of MLP. The best classification success rate was achieved as 97.2414% when there was 8 neurons in the hidden layer. MAE and RMSE values were obtained for this classification success rate as 0.0242 and 0.1094 respectively.]]>
<![CDATA[A Note on Background Subtraction by Utilizing a New Tensor Approach ]]>
<![CDATA[Işık, Şahin]]>;
<![CDATA[Özkan, Kemal]]>;
<![CDATA[Doğan, Muzaffer]]>;
<![CDATA[Gerek, Ömer Nezih]]>
<![CDATA[ International Journal of Intelligent Systems and Applications in Engineering 2016: Special Issue ]]>
Publisher : <![CDATA[Prof. Dr. Ismail SARITAS ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.18201/ijisae.267154
<![CDATA[This study deals with determining the foreground region by background subtraction based on a new tensor decomposition method. With this aim, the concept of Common Matrix Approach (CMA) is utilized with a purpose of background modelling. The performance of proposed method is validated by making experiments on real videos provided by Wallflower dataset. The obtained results are compared with well-known methods based on subjective on objective evaluation measures. The obtained good results indicate that using the CMA algorithm for background modelling is a simple and effective technique in terms computational cost and implementation. As an eventual result, we have observed that the superior results are determined on complex backgrounds including dynamic objects and illumination variation in image sets.]]>
<![CDATA[B-Spline Curve Fitting with Intelligent Water Drops (IWD) ]]>
<![CDATA[Uyar, Kübra]]>;
<![CDATA[Ülker, Erkan]]>;
<![CDATA[Arslan, Ahmet]]>
<![CDATA[ International Journal of Intelligent Systems and Applications in Engineering 2016: Special Issue ]]>
Publisher : <![CDATA[Prof. Dr. Ismail SARITAS ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.18201/ijisae.2016Special Issue-146975
<![CDATA[The use of B-spline curves has spreaded too many fields such as computer aided design (CAD), data visualization, surface modelling, signal processing and statistics. The flexible and powerful mathematical properties of B-spline are the cause of being one of the most preferred curve in literature. They can represent a large variety of shapes efficiently. The curve behind of the model can be obtained by doing approximation of control points, approximation of knot points or parameterization. It is obvious that the selection of knot points in B-spline curve approximation has an important and considerable effect on the behaviour of final approximation. In addition to this, an unreasonable knot vector may introduce unpredictable and unacceptable shape. Recently, in literature, there has been a considerable attention on the algorithms inspired from natural processes or events to solve optimization problems such as simulated annealing, ant colony optimization, particle swarm optimization, artificial bee colony optimization, and genetic algorithms. This paper implements and analyses a solution to approximate B-spline curves using Intelligent Water Drops (IWD) algorithm. This algorithm is a swarm based optimization algorithm inspired from the processes that happen in the natural river systems. The algorithm is based on the actions and reactions that take place between water drops in the river and the changes that happen in the environment. Some basic properties of natural water drops are adopted in the algorithm here to solve B-spline curve fitting problem. Optimal knots are selected through IWD algorithm. The proposed algorithm convergences optimal solutions and finds good and promising results.]]>
<![CDATA[A Hybrid Approach for Indoor Positioning ]]>
<![CDATA[Keser, Sinem Bozkurt]]>;
<![CDATA[yayan, uğur]]>
<![CDATA[ International Journal of Intelligent Systems and Applications in Engineering 2016: Special Issue ]]>
Publisher : <![CDATA[Prof. Dr. Ismail SARITAS ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.18201/ijisae.2016Special Issue-146966
<![CDATA[Positioning systems have wide range of applications with the developing technology. Global Positioning System (GPS) is an efficient solution for outdoor applications but it gives poor accuracy in indoor environment. And, various methods are proposed in the literature such as geometric-based, fingerprint-based, etc. In this study, a hybrid approach that uses both clustering and classification is developed for fingerprint-based method. Information gain based feature selection method is used for selection of the most appropriate features from the WiFi fingerprint dataset in the initial step of this approach. Then, Expectation Maximization (EM) algorithm is applied for clustering purpose. Then, decision tree algorithm is used as a classification task for each cluster. Experimental results indicate that applied algorithms lead to a substantial improvement on localization accuracy. Since, cluster specific decision tree models reduce the size of the tree significantly; computational time of position phase is also reduced.]]>
<![CDATA[Wind Power Forecasting For The Province Of Osmaniye Using Artificial Neural Network Method ]]>
<![CDATA[YANIKTEPE, BULENT]]>;
<![CDATA[TASDEMIR, SAKIR]]>;
<![CDATA[GUHER, A. BURAK]]>;
<![CDATA[AKCAN, Sultan]]>
<![CDATA[ International Journal of Intelligent Systems and Applications in Engineering 2016: Special Issue ]]>
Publisher : <![CDATA[Prof. Dr. Ismail SARITAS ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.18201/ijisae.2016Special Issue-146957
<![CDATA[Although wind energy at certain intervals and random in nature, today it is one of the commonly utilized alternative energy source in the world. Because of sustainability and environmentally-friendly energy source, countries increasingly benefit from wind energy. Several estimation methods are applied in the determination of a regions wind energy potential. Today, one of the most commonly used prediction methods is artificial neural network (ANN) method. In this study, Estimation of wind power in Osmaniye district was investigated in method with artificial neural network (ANN) using data from meteorological measurement stations from the meteorological measurement device at the campus of Osmaniye Korkut ATA University. In order to give the best values of prediction results, several methods increasing the impact on output of different models for the input variables were investigated. ]]>
<![CDATA[An Application in SPSS Clementine Based on the Comparison of Association Algorithms in Data Mining ]]>
<![CDATA[Karalök, Seren Sezen]]>;
<![CDATA[Ersöz, Süleyman]]>;
<![CDATA[Aktepe, Adnan]]>
<![CDATA[ International Journal of Intelligent Systems and Applications in Engineering 2016: Special Issue ]]>
Publisher : <![CDATA[Prof. Dr. Ismail SARITAS ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.18201/ijisae.2016Special Issue-146980
<![CDATA[Data mining is the process of acquiring information form large data pools. In this study, associate analysis method is used. The application and comparisons are found by using 3 different algorithms from SPSS Clementine which is a data mining software. In this study, the results are varied because different associate methods are applied on. Therefore, new findings are obtained. Consequent to this, it will lead us to new strategies to develop for customers in Market Basket Analysis. This study is done by using a big supermarket data. Results are compared and reported for every each of 3 different algorithms. ]]>
<![CDATA[Big Bang-Big Crunch Optimization Algorithm for Solving the Uncapacitated Facility Location Problem ]]>
<![CDATA[KOC, Ismail]]>
<![CDATA[ International Journal of Intelligent Systems and Applications in Engineering 2016: Special Issue ]]>
Publisher : <![CDATA[Prof. Dr. Ismail SARITAS ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.18201/ijisae.2016Special Issue-146971
<![CDATA[The big bang–big crunch (BB–BC) algorithm has been proposed as a new optimization method based on the big bang and big crunch theory, one of the theories of the evolution of the universe. The BB-BC algorithm has been firstly presented to solve the optimization problems with continuous solutions space. If the solution space of the problem is binary-structural, the algorithm must be modified to solve this kind of the problems. Therefore, in this study, the BB-BC method, one of the population-based optimization algorithms, is modified to deal with binary optimization problems. The performance of the proposed methods is analyzed on uncapacitated facility location problems (UFLPs) which are one of the binary problems used in literature. The well-known small and medium twelve instances of UFLPs are used to analyze the performances and the effects of the control parameter of the BB-BC algorithm. The obtained results are comparatively presented. According to the experimental results, the binary version of the BB-BC method achieves successful results in solving UFLP in terms of solution quality.]]>
<![CDATA[Knowledge Mining Approach For Healthy Monitoring From Pregnancy Data With Big Volumes ]]>
<![CDATA[Santur, Yunus]]>;
<![CDATA[Santur, Sinem Güven]]>
<![CDATA[ International Journal of Intelligent Systems and Applications in Engineering 2016: Special Issue ]]>
Publisher : <![CDATA[Prof. Dr. Ismail SARITAS ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.18201/ijisae.270369
<![CDATA[The process for obtaining information that will create value on a large-scale data stack is called data mining by its general name. Data mining is commonly used in sales and marketing departments, in determining strategies and making critical decisions for the future in many sectors. Similarly, data mining is used in the determination of health policies, more effective implementation of health services and in the management of resources and institutions in the health sector. In this study, it was aimed to create a software architecture of data mining that will help the personal monitoring of the pregnancy process in a more effective way in the health sector. Many different types of data such as age, gender, location, education, physical characteristics, lifestyle habits and medical history of the people that could be used for this purpose are stored online by health institutions. The machine learning algorithms have been created to determine classification, clustering and association rule on these data. ]]>
<![CDATA[A Model of Automatic Block Reallocation in the Land Consolidation Projects Using Artificial Bee Colony Algorithm ]]>
<![CDATA[İnceyol, Yaşar]]>;
<![CDATA[Özbeyaz, Abdurrahman]]>
<![CDATA[ International Journal of Intelligent Systems and Applications in Engineering 2016: Special Issue ]]>
Publisher : <![CDATA[Prof. Dr. Ismail SARITAS ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.18201/ijisae.2016Special Issue-146985
<![CDATA[Equitably reallocating of blocks among land owners has been one of the most important tasks in Land Consolidation studies. This task has to be fairly solved among landholdings for a land. This complicated problem is difficult to solve using linear methods. Therefore, a method is needed to solve this non-linear problem among land owners impartially. There are many applications employing optimization algorithms for solving the complicated and non-linear problems in literature. When we examine the literature, it is seen that Genetic Algorithm has been only used to overcome the block reallocation problem. Artificial Bee Colony (ABC) algorithm is one of the optimization algorithms that have been used to solve the non-linear and complicated problems in literature. Furthermore, this method has better performance when it is compared with the other optimization algorithms. In this study, we have aimed to fairly reallocate the landholding areas to blocks in a land by developing an algorithm using Artificial Bee Colony optimization method. When we develop the steps of the algorithm, we give priority to landholdings preferences and places of fixed installations. Data tables have been arranged by taking land consolidation data of DOT Village in Adiyaman, Turkey that into consideration. DOT Village land consolidation project includes 143 blocks and 225 landholders. Consequently, we have introduced the steps of an algorithm solving the block reallocation problem automatically using ABC for a sample land. Also, we have observed the applicability of the proposed method for automatic block reallocation problem in this study. This study is a preliminary study helping us to develop software providing to automatically solve complicated block reallocation problem in real time land consolidation process.]]>
<![CDATA[Improvement of Facility Layout by Using Data Mining Algorithms and an Application ]]>
<![CDATA[KOKOÇ, Melda]]>;
<![CDATA[ERSÖZ, Süleyman]]>;
<![CDATA[AKTEPE, Adnan]]>;
<![CDATA[TÜRKER, Ahmet Kürşad]]>
<![CDATA[ International Journal of Intelligent Systems and Applications in Engineering 2016: Special Issue ]]>
Publisher : <![CDATA[Prof. Dr. Ismail SARITAS ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.18201/ijisae.267175
<![CDATA[The facility layout problems are always important in the production or service industry system. For many years, it has been a common research field in active research. It seen that various methods were used in literature review for improving facility layout. We used association analysis which is one of the techniques of data mining in this study. The primary aim of this study is to improving the emergency departments efficiency, increasing patient satisfaction and employee satisfaction, decreasing travelled distance in emergency department. In this study, data acquired from information system of an emergency service was examined by means of data mining techniques (association rules such as GRI and Apriori) and relations between departments were found out. Association rules were analyzed via data mining techniques applied and then departments having more flow density with each other were determined. In consideration of this information an alternative facility layout planning was planned in regard to analysis results of departments’ closeness situations, advices of emergency service doctor and observations made.]]>
