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[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. ]]>
<![CDATA[A Novel Multi-Swarm Approach for Numeric Optimization ]]>
<![CDATA[Babalik, Ahmet]]>
<![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.2018644781
<![CDATA[In order to solve the numeric optimization problems, swarm-based meta-heuristic algorithms can be used as an alternative to solve optimization problems. Meta-heuristic algorithms do not guarantee finding the optimal solution but they produce acceptable solutions in a reasonable computation time. By depending on the nature of the problems and the structure of the meta-heuristic algorithms, different results are obtained by different algorithms, and none of the meta-heuristic algorithm could guarantee to find the optimal solution. Particle swarm optimization (PSO) and artificial bee colony (ABC) algorithms are well known meta-heuristic algorithms often used for solving numeric optimization problems. In this study, a novel multi-swarm approach based on PSO and ABC algorithms is suggested. The proposed multi-swarm approach includes PSO and ABC algorithms together and replacing the swarm which achieves better solutions than the other algorithm in a pre-defined migration period. By this migration, swarm always include better solutions concerned to the algorithm which achieves better results. While running PSO and ABC algorithms competitively, this migration ensures to utilize better solutions of both the solutions of PSO or ABC algorithms, and the convergence characteristic of each algorithm provides different approximation to the solution space. Thus, it is expected to obtain successful solutions and increasing the success rate at each migration cycle. The suggested approach has been tested on 14 well-known benchmark functions, and the results of the study are compared with the results in literature. The experimental results and comparisons show that the proposed approach is better than the other algorithms.]]>
<![CDATA[Speed Control of DC Motor Using Interval Type-2 Fuzzy Logic Controller ]]>
<![CDATA[Acikgoz, Hakan]]>
<![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.2018644777
<![CDATA[Direct current (DC) motors are widely used for speed control and position control in industry. The simplicity of DC motor speed control is also the main reason for its widespread use. Recently, in parallel with rapid developments in power electronics, microprocessors and semiconductor materials, many control structures are designed for DC motors. In this study, the speed control of the DC motor is carried out using the Matlab/Simulink package program. Type-2 Fuzzy Logic controller (T2FLC) which has efficient performance in modelling uncertainties is proposed for the speed control of DC motor. The classical PI controller and Type-2 Fuzzy Logic controller (T2FLC) are applied to the speed control unit of the DC motor. Simulation studies have also been realized for the designed DC motor model under the same conditions. The results obtained from the classical PI controller and T2FLC have been examined and compared to disturbances such as tracking reference speeds and load changes. According to the obtained simulation results, it has been observed that T2FLC has better results than the classical PI controller in whole conditions. ]]>
<![CDATA[Development of a Prototype Using the Internet of Things for Kinetic Gait Analysis ]]>
<![CDATA[Caliskan, Muhammet]]>;
<![CDATA[Tumer, Abdullah Erdal]]>;
<![CDATA[Sengul, Sumeyra Busra]]>
<![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.2018644783
<![CDATA[The proliferation of mobile devices and the gradual development of technology have led to the emergence of the concept of Internet of Things. The Internet of Things has led to an increase in the work done especially on the medical field. At the beginning of the reasons for using the Internet of Things in medical studies is to be able to detect and display instantaneous changes that physicians can not even observe. Another important cause is that it allows the patient to be observed in the natural world. This is especially true in gait analysis, where a natural gait must be measured for accurate diagnosis. This study is concerned with the use of Internet of Things technology in gait analysis. To follow the foot pressure distribution in the study, a prototype was installed in shoes using the Internet of Things. The prototype consists of a thin, flexible base that collects analog data from the 32 sensors and transmits it wirelessly to mobile or PC computers via Bluetooth technology. The software developed by the prototype shows the pressure on every small sensor in the room and draws a walking graph. The accuracy and reliability of the prototype were evaluated by preliminary experimental measures. The results show that the system has the capacity to reliably measure pressure distributions of the sole of the foot.]]>
<![CDATA[Novel approach to locate region of interest in mammograms for Breast cancer ]]>
<![CDATA[Divyashree, B V]]>;
<![CDATA[Amarnath, R]]>;
<![CDATA[Naveen, M]]>;
<![CDATA[Hemantha Kumar, G]]>
<![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.2018644775
<![CDATA[Locating region of interest for breast cancer in the mammographic image is a challenging problem in medical image processing. In this research work, we perform breast region segmentation followed by identification of the region of interest. Initially red, green and blue bands of the image are sliced into multiple segments based on intensity values and threshold. Masking operation is performed on each segment for understanding background and foreground(breast). Intersection of these segments would provide the breast segmentation. Next, we calculate the entropy of the segmented breast region. Here, quad division is performed based on the entropy. More entropy would result in additional quad division in the region of interest. This approach is tested on a DDSM datasets comprising of positive and negative mammographic images for breast location. Illustration is provided for the model. However, breast cancer detection needs radiologistâs attention in classifying normal and malignant cases. This leads to reduce false positive and negative rates when post processing is required for the detection of breast cancer.]]>
