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[A new subspace based solution to background modelling and change detection ]]>
<![CDATA[Işık, Şahin]]>;
<![CDATA[Özkan, Kemal]]>;
<![CDATA[Gerek, Ömer Nezih]]>;
<![CDATA[Doğan, Muzaffer]]>
<![CDATA[ International Journal of Intelligent Systems and Applications in Engineering 2016: Special Issue ]]>
Publisher : <![CDATA[Prof. Dr. Ismail SARITAS ]]>
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DOI: 10.18201/ijisae.267148
<![CDATA[For surveillance system, the background subtraction plays an important role for moving object detection with an algorithm embedded in the camera. Since the existence algorithms cannot satisfy the good accuracy on complex backgrounds including illumination change and dynamic objects, we have put forward the concept of Common Vector Approach (CVA) as a new idea for background modelling. Effectiveness of proposed method is presented through the experiments on popular Wallflower dataset. The obtained visual outputs are compared with well-known methods based on the subjective and objective criteria. From the overall evaluation, we can note the proposed method is not only exhibit successful foreground detection results, but also promises an effective and efficient system for background modelling.]]>
<![CDATA[Short-term Load Forecasting based on ABC and ANN for Smart Grids ]]>
<![CDATA[Cevik, Hasan Huseyin]]>;
<![CDATA[Harmancı, Hüseyin]]>;
<![CDATA[Çunkaş, Mehmet]]>
<![CDATA[ International Journal of Intelligent Systems and Applications in Engineering 2016: Special Issue ]]>
Publisher : <![CDATA[Prof. Dr. Ismail SARITAS ]]>
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DOI: 10.18201/ijisae.266014
<![CDATA[Short term load forecasting is a subject about estimating future electricity consumption for a time interval from one hour to one week and it has a vital importance for the operation of a power system and smart grids. This process is mandatory for distribution companies and big electricity consumers, especially in liberalized energy markets. Electricity generation plans are made according to the amount of electricity consumption forecasts. If the forecast is overestimated, it leads to the start-up of too many units supplying an unnecessary level of reserve, therefore the production cost is increased. On the contrary if the forecast is underestimated, it may result in a risky operation and consequently power outages can occur at the power system. In this study, a hybrid method based on the combination of Artificial Bee Colony (ABC) and Artificial Neural Network (ANN) is developed for short term load forecasting. ABC algorithm is used in ANN learning process and it optimizes the neuron connections weights of ANN. Historical load, temperature difference and season are selected as model inputs. While three years hourly data is selected as training data, one year hourly data is selected as testing data. The results show that the application of this hybrid system produce forecast values close to the actual values.]]>
<![CDATA[Customer Satisfaction Using Data Mining Approach ]]>
<![CDATA[ORALHAN, Burcu]]>;
<![CDATA[UYAR, Kumru]]>;
<![CDATA[ORALHAN, Zeki]]>
<![CDATA[ International Journal of Intelligent Systems and Applications in Engineering 2016: Special Issue ]]>
Publisher : <![CDATA[Prof. Dr. Ismail SARITAS ]]>
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DOI: 10.18201/ijisae.266801
<![CDATA[Customers and products are the main assets for every business. Companies make their best to satisfy customers because of coming back to their companies. After sales service related to different steps that make customers are satisfied with the company service and products. After sales service covers different many activities to investigate whether the customer is satisfied with the service, products or not? Hence, after sales service is acting very crucial role for customer satisfaction, retention and loyalty. If the after sales service customer and services data is saved by companies, this data is the key for growing companies. Companies can add value their brand value with the managing of this data. In this study, we aim to investigate effect of 6 factors on customer churn prediction via data mining methods. After sale service software database is the source of our data. Our data source variables are Customer Type, Usage Type, Churn Reason, Subscriber Period and Tariff The data is examined by data mining program. Data are compared 8 classification algorithm and clustered by simple K means method. We will determine the most effective variables on customer churn prediction. As a result of this research we can extract knowledge from international firms marketing data.]]>
<![CDATA[Experimental Investigation and Fuzzy Logic Modelling of Performance Hydroxy Dry Cell with Different Plate Combination ]]>
<![CDATA[Işıktaş, Abdullah]]>;
<![CDATA[Dincer, Kevser]]>;
<![CDATA[Verim, Ali]]>;
<![CDATA[Türkmen, Osman]]>;
<![CDATA[Ata, Sadık]]>
<![CDATA[ International Journal of Intelligent Systems and Applications in Engineering 2016: Special Issue ]]>
Publisher : <![CDATA[Prof. Dr. Ismail SARITAS ]]>
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DOI: 10.18201/ijisae.265480
<![CDATA[In this study, hydroxy (HHO) dry cell with different plate combination performances in terms of current and temperature were experimentally investigated and modeled with Rule-Based Mamdani-Type Fuzzy (RBMTF) modeling technique. Input parameters plate number and time; output parameters current, temperature were described by RBMTF if-the rules. The dimensions of the plates were          9x9 cm2, 10x10 cm2 and 11x11 cm2. Current and temperature were measured for the different plate combination. Tap water was used in the experiments and the system was set to 5 minutes. For each combination, new cells were prepared. Experimental data which obtained for current and temperature according to combination and time were used in the training step. Numerical parameters of input and output variables were fuzzificated as linguistic variables: very very low (L1), very low (L2), low (L3), negative medium (L4), medium (L5), positive medium (L6), high (L7), very high (L8) and very very high (L9) linguistic classes. With the linguistic variables used, rules were obtained for this system. The comparison between experimental data and RBMTF is done by using statistical methods like the coefficient of multiple determinations (R2). The actual values and RBMTF results indicated that RBMTF can be successfully used in HHO dry cell. ]]>
<![CDATA[Training ANFIS Using Genetic Algorithm for Dynamic Systems Identification ]]>
<![CDATA[HAZNEDAR, Bülent]]>;
<![CDATA[KALINLI, Adem]]>
<![CDATA[ International Journal of Intelligent Systems and Applications in Engineering 2016: Special Issue ]]>
Publisher : <![CDATA[Prof. Dr. Ismail SARITAS ]]>
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DOI: 10.18201/ijisae.266053
<![CDATA[In this study, the premise and consequent parameters of ANFIS are optimized using Genetic Algorithm (GA) based on a population algorithm. The proposed approach is applied to the nonlinear dynamic system identification problem. The simulation results of the method are compared with the Backpropagation (BP) algorithm and the results of other methods that are available in the literature. With this study it was observed that the optimisation of ANFIS parameters using GA is more successful than the other methods.]]>
<![CDATA[Only One Neuron either N-bit Parity Rule Based Modified Translated Multiplicative or McCulloch-Pitts Models for Some Machine Learning Problems ]]>
<![CDATA[Özdemir, Ali]]>;
<![CDATA[Ä°nal, Mehmet Melih]]>
<![CDATA[ International Journal of Intelligent Systems and Applications in Engineering 2016: Special Issue ]]>
Publisher : <![CDATA[Prof. Dr. Ismail SARITAS ]]>
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DOI: 10.18201/ijisae.267039
<![CDATA[In this study, solutions to machine learning problems such as Monk’s 2 (M2), Balloon and Tic-Tac-Toe problems employing a single neuron dependent on rules which use either modified translated multiplicative (Àm) neuron or McCulloch-Pitts neuron model is proposed. Since M2 problem is similar to N-bit parity problem, translated multiplicative (Àt) neuron model is modified for M2 problem. Also, McCulloch-Pitts neuron model is used to increase classification performance. Then either Àm or McCulloch-Pitts neuron model is applied to Balloon and Tic-Tac-Toe problems. When the result of proposed only one Àm neuron model that is not required any training stage and hidden layer is compared with the other approaches, it shows satisfactory performance.]]>
<![CDATA[The Classification of White Wine and Red Wine According to Their Physicochemical Qualities ]]>
<![CDATA[Er, Yeşim]]>;
<![CDATA[ATASOY, Ayten]]>
<![CDATA[ International Journal of Intelligent Systems and Applications in Engineering 2016: Special Issue ]]>
Publisher : <![CDATA[Prof. Dr. Ismail SARITAS ]]>
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DOI: 10.18201/ijisae.265954
<![CDATA[The main purpose of this study is to predict wine quality based on physicochemical data. In this study, two large separate data sets which were taken from UC Irvine Machine Learning Repository were used. These data sets contain 1599 instances for red wine and 4898 instances for white wine with 11 features of physicochemical data such as alcohol, chlorides, density, total sulfur dioxide, free sulfur dioxide, residual sugar, and pH. First, the instances were successfully classified as red wine and white wine with the accuracy of 99.5229% by using Random Forests Algorithm. Then, the following three different data mining algorithms were used to classify the quality of both red wine and white wine: k-nearest-neighbourhood, random forests and support vector machines. There are 6 quality classes of red wine and 7 quality classes of white wine. The most successful classification was obtained by using Random Forests Algorithm. In this study, it is also observed that the use of principal component analysis in the feature selection increases the success rate of classification in Random Forests Algorithm. ]]>
<![CDATA[Simulation Study on Power Factor Correction Controlling Excitation Current of Synchronous Motor with Fuzzy Logic Controller ]]>
<![CDATA[GANÄ°, Ahmet]]>;
<![CDATA[KEÇECİOĞLU, Ökkeş Fatih]]>;
<![CDATA[AÇIKGÖZ, Hakan]]>;
<![CDATA[YILDIZ, Ceyhun]]>;
<![CDATA[ŞEKKELİ, Mustafa]]>
<![CDATA[ International Journal of Intelligent Systems and Applications in Engineering 2016: Special Issue ]]>
Publisher : <![CDATA[Prof. Dr. Ismail SARITAS ]]>
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DOI: 10.18201/ijisae.2016Special Issue-146979
<![CDATA[The correction of power factor in electric power systems is called reactive power compensation. A synchronous motor is used as a capacitive reactive power generator in compensation systems. It is less costly for an enterprise to use a synchronous motor as both mechanical power generator and power factor corrector, which increases their efficiency. There are various studies on increasing the efficiency, capacity and stability of a power system using power factor correction under different operating conditions. This study focuses on the power factor correction of the system by controlling the excitation current of the synchronous motor via fuzzy logic thanks to the asynchronous motor connected to the system.]]>
<![CDATA[Separation of Wheat Seeds from Junk in a Dynamic System Using Morphological Properties ]]>
<![CDATA[KAYA, ESRA]]>;
<![CDATA[SARITAŞ, İSMAİL]]>;
<![CDATA[ÖZKAN, İLKER ALİ]]>
<![CDATA[ International Journal of Intelligent Systems and Applications in Engineering 2016: Special Issue ]]>
Publisher : <![CDATA[Prof. Dr. Ismail SARITAS ]]>
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DOI: 10.18201/ijisae.270632
<![CDATA[Wheat is the main food source of the humankind. After its harvest, it goes through many procedures from its separation from chaff to its packaging. With the development in technology, many of these procedures are realized with automatic systems which saves the manufacturer the cost of labour, time and provides the customer with more quality food. One of the main concerns of quality food production is to provide a customer with the product in its purest form which means the product must be separated from all foreign matters. In this study, type-1252 durum wheat seeds have been separated from junk using the morphological properties of wheat seeds through the uncompressed video image taken with the camera Prosilica GT2000c. The main references for the quality measurement of wheat seeds are the shape and the dimensions of a wheat seed. Aiming for high quality wheat grain storage with no junk, this article has adopted various image processing techniques from image preprocessing to feature extraction. The image processing has been realized in a computer environment and the results show that the image processing is successful and the detection of wheat seeds from junk was accurate.]]>
<![CDATA[The Assessment of Time-Domain Features for Detecting Symptoms of Diabetic Retinopathy ]]>
<![CDATA[Elibol, Gülin]]>;
<![CDATA[Ergin, Semih]]>
<![CDATA[ International Journal of Intelligent Systems and Applications in Engineering 2016: Special Issue ]]>
Publisher : <![CDATA[Prof. Dr. Ismail SARITAS ]]>
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DOI: 10.18201/ijisae.270351
<![CDATA[ Diabetes affects the capillary vessels in retina and causes vision loss. This disorder of retina due to diabetes is named as Diabetic Retinopathy (DR). Diagnosing the stages of DR is performed on a publicly available database (DiaraetDB1) via detecting the symptoms of this disease. Time-domain features are extracted and selected to classify a fundus image. Fisher’s Linear Discriminant Analysis (FLDA), Linear Bayes Normal Classifier (LDC), Decision Tree (DT) and k-Nearest Neighbor (k-NN) are used as the classification methods in the experimental benchmarking. The recognition accuracies are obtained using all features (68 features) and selected features separately. k-NN is observed as the best classification method for without feature selection case and it gives averagely 92.22% accuracy. For feature selection case, LDC gives the best average accuracy as 92.45% with maximum 7 carefully chosen features.]]>
