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
200 Documents
<![CDATA[Adaptive Control Solution for a Class of MIMO Uncertain Underactuated Systems with Saturating Inputs ]]>
<![CDATA[Kulkarni, Ajay]]>;
<![CDATA[Kumar, Abhay]]>
<![CDATA[ International Journal of Intelligent Systems and Applications in Engineering Vol 4, No 4 (2016) ]]>
Publisher : <![CDATA[Advanced Technology and Science (ATScience) ]]>
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DOI: 10.18201/ijisae.2016426385
<![CDATA[This paper addresses the issue of controller design for aclass of multi-input multi-output (MIMO) uncertain underactuatedsystems with saturating inputs. A systematic controller framework,composed of a hierarchically generated control term, meant toensure the stabilization of a particular portion of system dynamicsand some dedicated control terms designed to solve the trackingproblem of the remaining system dynamics is presented. Waveletneural networks are used as adaptive tuners to approximate thesystem uncertainties also to reshape the control terms so as to dealwith the saturation nonlinearity in an antiwindup paradigm.Gradient based tuning laws are developed for the online tuning ofadjustable parameters of the wavelet network. A Lyapunov basedstability analysis is carried out to ensure the uniformly ultimatelybounded (UUB) stability of the closed loop system. Finally, asimulation is carried out which supports the theoreticaldevelopment.]]>
<![CDATA[Neural Boundary Conditions in Optic Guides ]]>
<![CDATA[Özkan-Bakbak, Pınar]]>
<![CDATA[ International Journal of Intelligent Systems and Applications in Engineering Vol 3, No 3 (2015) ]]>
Publisher : <![CDATA[Advanced Technology and Science (ATScience) ]]>
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DOI: 10.18201/ijisae.04354
<![CDATA[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.]]>
<![CDATA[A new integrated fuzzy MCDM approach and its application to wastewater management ]]>
<![CDATA[Dursun, Mehtap]]>
<![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.2018634723
<![CDATA[This paper proposes a fuzzy multi-criteria group decision making methodology that combines 2-tuple fuzzy linguistic representation model, linguistic hierarchies, Decision Making Trial and Evaluation Laboratory (DEMATEL) method and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The multigranular linguistic information obtained from decision-makers are unified and aggregated employing linguistic hierarchies and 2-tuple fuzzy linguistic representation model. The weights of the criteria are calculated employing DEMATEL method, which enables to consider inner dependencies among criteria. Then, fuzzy TOPSIS method is utilized to rank the alternatives. The developed methodology is able to handle information in a decision making problem with multiple information sources. Furthermore, it enables managers to deal with heterogeneous information without loss of information. The developed methodology is used to determine the most suitable wastewater treatment (WWT) alternative for Istanbul, the largest city of Turkey that is also listed among the worlds most crowded cities.]]>
<![CDATA[Development of an Automatic Grading System Based on Energy Circular Hough Transform and Causal Median Filter ]]>
<![CDATA[Bayar, Gokhan]]>
<![CDATA[ International Journal of Intelligent Systems and Applications in Engineering Vol 5, No 3 (2017) ]]>
Publisher : <![CDATA[Advanced Technology and Science (ATScience) ]]>
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DOI: 10.18201/ijisae.2017531422
<![CDATA[Optical mark recognition machines are used for performing automatic grading of the exam papers that have multiple choice answers. They use some mathematical operations to achieve recognizing the answers marked by the ones who take the exam. In this study, an automatic grading system developed by the use of Hough transform and a filtering system is proposed. The system introduced brings a new perspective for grading the multiple choice exam papers. It focuses on adapting the energy based circular Hough transform for identifying the marked answer bubbles. The procedure is also combined with a data filtering method known as casual median filter. The filtering system, which targets for detecting the outliers and removing them, is commonly used by the robotics and mechatronics researchers for cleaning the unwanted data. The whole system is verified by testing more than 2500 exam answer sheets of the Technical English course offered to the second year Mechanical Engineering students of the Bulent Ecevit University located in Zonguldak, Turkey. The system performance is also tested by observing the results obtained in three different case studies designed and conducted for different goals.]]>
<![CDATA[Improved Artificial Cooperative Search Algorithm for Solving Non-convex Economic Dispatch Problems with Valve-point Effects ]]>
<![CDATA[Turgut, Oguz Emrah]]>
<![CDATA[ International Journal of Intelligent Systems and Applications in Engineering Vol 6, No 3 (2018) ]]>
Publisher : <![CDATA[Prof. Dr. Ismail SARITAS ]]>
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DOI: 10.18201/ijisae.2018644782
<![CDATA[This paper presents Improved Artificial Cooperative Search (IACS) algorithm for solving economic dispatch problems considering the valve point effects, ramp rate limits, transmission losses and prohibited operation zones.  In order to improve the solution quality and increase the search efficiency, a novel perturbation scheme called âGlobal best guided chaotic local searchâ is proposed and incorporated into ACS algorithm.  The effectiveness of the proposed IACS algorithm has been benchmarked with twelve widely known optimization test problems. In order to assess the performance of the proposed algorithm on non-convex optimization problems, four case studies related to highly nonlinear economic dispatch problems have been solved . Results retrieved from IACS algorithm have been compared with literature approaches in terms of minimum, maximum and average generation cost values. Comparison results indicate that IACS produces more economical power load than those of other optimizers available in the literature]]>
<![CDATA[OPTIMAL POWER DISTRIBUTION PLANNING USING IMPROVED PARTICLE SWARM OPTIMIZATION ]]>
<![CDATA[Kumari, Meena]]>;
<![CDATA[Ranjan, Rakesh]]>;
<![CDATA[Singh, VR]]>;
<![CDATA[Swapnil, Shubham]]>
<![CDATA[ International Journal of Intelligent Systems and Applications in Engineering Vol 6, No 3 (2018) ]]>
Publisher : <![CDATA[Prof. Dr. Ismail SARITAS ]]>
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DOI: 10.18201/ijisae.2018644773
<![CDATA[In planning of radial power distribution system, optimal feeder routing and optimal branch conductor selection plays an important role. The highly economical distribution system requires effective planning method which involves optimization procedure to connect the given load to the substations. In this paper optimal power distribution system is presented with minimum energy loss cost for paths and optimal conductors. The proposed optimal power distribution is procedure is described in following stages, initially read the input system data and identify all the possible paths. Then for each identified path forward/backward sweep load flow technique is applied to calculate the energy loss costs and select the minimum energy loss cost path for the power distribution. Finally, the optimal branch conductor selection of radial distribution system is performed by using particle swarm optimization (PSO). Here, the optimization is improved by using the power loss and depreciation on capital investment parameters. This results the optimal conductor and then the location of optimal conductor is chosen as the optimal substation and then through the optimal substation power is distributed optimally. ]]>
<![CDATA[An Aspect-Sentiment Pair Extraction Approach Based on Latent Dirichlet Allocation ]]>
<![CDATA[Ekinci, Ekin]]>;
<![CDATA[Ilhan Omurca, Sevinc]]>
<![CDATA[ International Journal of Intelligent Systems and Applications in Engineering Vol 6, No 3 (2018) ]]>
Publisher : <![CDATA[Prof. Dr. Ismail SARITAS ]]>
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DOI: 10.18201/ijisae.2018644779
<![CDATA[Online user reviews have a great influence on decision-making process of customers and product sales of companies. However, it is very difficult to obtain user sentiments among huge volume of data on the web consequently; sentiment analysis has gained great importance in terms of analyzing data automatically. On the other hand, sentiment analysis divides itself into branches and can be performed better with aspect level analysis. In this paper, we proposed to extract aspect-sentiment pairs from a Turkish reviews dataset. The proposed task is the fundamental and indeed the critical step of the aspect level sentiment analysis. While extracting aspect-sentiment pairs, an unsupervised topic model Latent Dirichlet Allocation (LDA) is used. With LDA, aspect-sentiment pairs from user reviews are extracted with 0.86 average precision based on ranked list. The aspect-sentiment pair extraction problem is first time realized with LDA on a real-world Turkish user reviews dataset. The experimental results show that LDA is effective and robust in aspect-sentiment pair extraction from user reviews.]]>
<![CDATA[An Empirical Study of the Extreme Learning Machine for Twitter Sentiment Analysis ]]>
<![CDATA[Coban, Onder]]>;
<![CDATA[Ozyildirim, Buse Melis]]>;
<![CDATA[Ozel, Selma Ayse]]>
<![CDATA[ International Journal of Intelligent Systems and Applications in Engineering Vol 6, No 3 (2018) ]]>
Publisher : <![CDATA[Prof. Dr. Ismail SARITAS ]]>
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DOI: 10.18201/ijisae.2018644774
<![CDATA[Extreme Learning Machine (ELM) method is proposed for single hidden layer feed-forward networks (SLFNs). The ELMemploys feed-forward neural network architecture and works with randomly determined input weights. In this aspect, ELM depends onprinciple that enables to determine weights and biases in the network. In the first phase of ELM that can be named as feature mapping,the usage of random values differs the ELM from other methods that employ a kernel function for feature mapping such as SupportVector Machines (SVM) and Deep Neural Networks. After the feature mapping, the main goal of the ELM is to learn weights betweenhidden and output layers by minimizing the error. The ELM has gained much more popularity recently; and can be utilized forclassification, regression, and dimension reduction. In literature, Twitter sentiment analysis is generally considered as a classificationtask. Therefore, in this study, the basic ELM is utilized for Twitter sentiment analysis and compared with the SVM which is one of themost successful machine learning algorithms used for sentiment analysis. Experiments are conducted on two different Turkish datasets.Experimental results show that the performance of the two methods are slightly different, but SVM outperforms basic ELM.]]>
<![CDATA[Urban Traffic Optimization with Real Time Intelligence Intersection Traffic Light System ]]>
<![CDATA[Celik, Yuksel]]>;
<![CDATA[Karadeniz, Alper Talha]]>
<![CDATA[ International Journal of Intelligent Systems and Applications in Engineering Vol 6, No 3 (2018) ]]>
Publisher : <![CDATA[Prof. Dr. Ismail SARITAS ]]>
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DOI: 10.18201/ijisae.2018644780
<![CDATA[Traffic system is a complex system where a lot of smart components which include signals, vehicles, sensor and pedestrian have communication skills with together on local level and act in a particular manner on high level. Insufficient traffic light control system on intersections brings about unnecessary delays and waste of time, extremely oil firing of engine which run idle mode on lights and increasing greenhouse gas emission. Various systems have been developed in order to overcome these traffic problems. In this paper we proposed an Intelligence Traffic Light System. In this system, the traffic flow at intersections is optimized using instant traffic information. These are the primary developed methods for the traffic optimization systems: fixed time period systems of lighting where time is pre-determined, green wave lighting system and real time optimization system of traffic light. Real data of traffic has been gathered on Karabuk-Safranbolu route to test above systems for different data density. The results of these tests on data show that real time traffic light optimization systems get better results than fixed time period and green wave lighting systems.]]>
<![CDATA[Reservoir Sampling Based Streaming Method for Large Scale Collaborative Filtering ]]>
<![CDATA[Aytekin, Tevfik]]>
<![CDATA[ International Journal of Intelligent Systems and Applications in Engineering Vol 6, No 3 (2018) ]]>
Publisher : <![CDATA[Prof. Dr. Ismail SARITAS ]]>
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DOI: 10.18201/ijisae.2018644776
<![CDATA[Collaborative filtering algorithms work on user feedback data (such as purchases, clicks, or ratings.) in order to build models of users and items. User feedback data in real life e-commerce sites can be very large which incurs high costs on maintenance and model building. Parallelization of computation might help but it results in additional costs for extra computing power and maintenance problems of very large datasets still persist. Sampling at this point can be an effective approach for reducing the amount of data. In this work we propose a novel sampling technique for collaborative filtering which can be used to reduce the amount of data considerably. Experimental results on three real life datasets show that the proposed method leads to a significant reduction in the amount of data with little harm to the accuracy of the models. The method works in a streaming fashion which makes it suitable for being used in real time at large-scale e-commerce applications where there is a large flow of continuous user feedback. ]]>
