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[COTTAPP: An Online University Timetable Application based on a Goal Programming Model ]]>
<![CDATA[Dursun, Tugce]]>;
<![CDATA[Su, Yasemin]]>;
<![CDATA[Cosgun, Rana]]>;
<![CDATA[Durak, Ayse Sevde]]>;
<![CDATA[Yet, Barbaros]]>
<![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.2017531426
<![CDATA[Preparing university course timetables is a challenging task as many constraints and requirements from the university and lecturers must be satisfied without overlapping courses for different student groups. Although many mathematical optimization models have been proposed to automate this task, a wider use of these models have been limited as deep technical understanding of mathematical and computer programming are required in order to use and implement them. This paper proposes a simple and flexible course timetabling application that is based on a weighted binary goal programming model with a powerful solver. Our application enables the users to modify and run this model by using a simple web and spreadsheet interface. Consequently, the model does not require deep technical understanding of the underlying models from its users even though it is based on a complex mathematical model. The web application and the underlying optimization model is illustrated by using a case study of an undergraduate program of industrial engineering.  ]]>
<![CDATA[A Comprehensive Analysis of Web-based frequency in Multiword Expression Detection ]]>
<![CDATA[Aka Uymaz, Hande]]>;
<![CDATA[Kumova Metin, Senem]]>
<![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.2017531431
<![CDATA[Multiword expressions (MWEs) are syntactic and/or semantic units in language, where the meaning of whole is limitedly connected to the meanings of the constituting units. The most prominent property that distinguishes MWEs from random word combinations is the recurrence. The recurrence is commonly measured by the occurrence frequencies of the MWE and the constituting words. Though occurrence frequency measures are known to be best in distinguishing MWEs from random combinations, the performance of those measures depend mainly on the quality and size of the data source where frequencies are obtained. The main goal of this study is to provide a detailed analysis on the change in performance of frequency based measures when the traditional frequency source, corpus, is swapped with a massive and dynamic data source, the World Wide Web. In order to use the web as a frequency source, the constituting words and word combinations are queried among a popular search engine, and the number of results for each query is accepted to be web-based frequency for the regarding word/word combination.  In this study, the web-based frequencies are employed in three different MWE detection-related experiments utilizing a Turkish data set. In first group of experiments, the individual performances of 20 well-known frequency metrics in ranking/sorting MWE candidates based on their tendency to be a MWE is examined. Secondly, the most successful frequency metrics are determined by a feature selection method: filtering. Lastly, MWE detection is accepted to be a classification problem. Eight supervised methods are applied in order to show the combined performance of frequency metrics when the frequency is obtained from web.  In all experiments, the performance of web-based frequencies in identification of MWEs is compared to the performance of traditional corpus based frequencies. The experimental results showed that the use of web-based frequency in identification of MWEs reveals promising results.]]>
<![CDATA[Use of NLP Techniques for an Enhanced Mobile Personal Assistant: The Case of Turkish ]]>
<![CDATA[Eryigit, Gulsen]]>;
<![CDATA[Celikkaya, 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.2017531424
<![CDATA[This article introduces a Turkish mobile assistant application which produces state-of-the art results for the Turkish language by using natural language processing (NLP) techniques. The voice-enabled mobile assistant application allows users to enter queries for nine pre-defined tasks; namely, making calls, sending sms messages and emails, getting directions, querying exchange rates, weather forecast and traffic information, searching on the internet and launching applications on the phone. Usersâ queries are processed in a multi-stage approach (viz., NLP, query classification and parameter extraction). Either the requested task is performed or the requested information is displayed as the response of the application. The article presents the architecture of the introduced system, its comparison with some prominent mobile assistants as well as the newly created data resources (viz., two query datasets annotated for classification and parameter extraction, two specific datasets for domain adaptation of named entity recognition and syntactic parsing NLP modules) to be used in further research. The evaluations on the impact of NLP preprocessing layers to the query classification performances reveal that the added value by NLP may range from 0.2 to 10.7 percentage points depending on the preferred machine learning algorithm for the query classification stage. The impact of NLP for the parameter extraction stage is also crucial since the outputs of NLP modules are used systematically by the extraction rules. The overall performance of the introduced approach is measured as 70.8% which is very promising under the fact that the system is trained with very limited-size of annotated data. The technology introduced in this article is basically designed for the case of a mobile assistant but it can also be used for every voice-enabled control system to improve the user experience, such as smart homes or smart televisions.   ]]>
<![CDATA[A Novel Hybrid Multi Criteria Decision Making Model: Application to Turning Operations ]]>
<![CDATA[Sofuoglu, Mehmet Alper]]>;
<![CDATA[Orak, Sezan]]>
<![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.2017531427
<![CDATA[Multi criteria decision making models (MCDM) are extensively used in material and process selection in engineering. In this study, a novel hybrid decision making model is developed. Best-Worst method (BWM) is hybridized with TOPSIS, Grey Relational Analysis (GRA) and Weighted Sum Approach (WSA). Developed hybrid models produce similar results in different weight value of decision makers so they are combined. The model is tested in a turning operation and an optimization study is conducted by using Taguchi experimental design. The developed model can be used by engineers and operators in manufacturing environment.]]>
<![CDATA[MLP and KNN Algorithm Model Applications for Determining the Operating Frequency of A-Shaped Patch Antennas ]]>
<![CDATA[Kayabasi, Ahmet]]>
<![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.2017531432
<![CDATA[In this study, two machine learning methods, namely multilayer perceptron (MLP) and K-nearest neighbors (KNN) algorithm models are used for determining the operating frequency of A-shaped patch antennas (APAs) at UHF band applications. Firstly, data set is obtained from the 144 antenna simulations using IE3D⢠software based on method of moment (MoM). Weka (Waikato Environment for Knowledge Analysis) program was then used to build models by considering 144 simulation data. The models input with the various dimensions and electrical parameters of 124 APAs are trained and their accuracies are tested via 20 APAs. The mean absolute error (MAE) values are calculated for different number of hidden layer neurons and different neighbourhood values in MLP and KNN models, respectively. The performance of the MLP and KNN models are compared in the training and testing process. The lowest MAEs are obtained with 6 hidden layer neurons for MLP and 2 neighbourhood values for KNN. These results point out that this models can be successfully applied to computation operating frequencies of APAs.]]>
<![CDATA[Fraud Detection on Financial Statements Using Data Mining Techniques ]]>
<![CDATA[Sorkun, Murat Cihan]]>;
<![CDATA[Toraman, Taner]]>
<![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.2017531428
<![CDATA[This study explores the use of data mining methods to detect fraud for on e-ledgers through financial statements. For this purpose, data set were produced by rule-based control application using 72 sample e-ledger and error percentages were calculated and labeled. The financial statements created from the labeled e-ledgers were trained by different data mining methods on 9 distinguishing features. In the training process, Linear Regression, Artificial Neural Networks, K-Nearest Neighbor algorithm, Support Vector Machine, Decision Stump, M5P Tree, J48 Tree, Random Forest and Decision Table were used. The results obtained are compared and interpreted.]]>
<![CDATA[Determination of Wind Potential of a Specific Region using Artificial Neural Networks ]]>
<![CDATA[Tasdemir, Sakir]]>;
<![CDATA[Yaniktepe, Bulent]]>;
<![CDATA[Guher, A.Burak]]>
<![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.2017531433
<![CDATA[There is a widespread trend in alternative energy sources in todays world. Achieving energy without harming the environment has been the most important target of the countries in recent years. For this reason, it is necessary to make utmost use of natural energy sources such as wind, sun and water. Among these sources, wind energy is the most utilized. Because it was cheap and quickly return to investment it is carried out many studies in this area. However, the most important problem is the continuity when the wind energy is obtained. The first thing to do before a wind power plant is installed in a region is to calculate the wind potential of the area concerned. This process is long-term under normal conditions. Artificial Neural Networks (ANN) is one of the most frequently used methods for determining a wind power potential in a short time period. In this study, it is aimed to estimate the wind potential of a certain region within the boundaries of Osmaniye province. ANN was used to estimate the wind power potential. As a result of comparing the statistical values of the forecast values with the measured actual values, the performance of the method applied is indicated. The meteorology station at Osmaniye Korkut Ata University using data has been successfully estimated wind potential.]]>
<![CDATA[Simple and Novel Approach for Image Representation with Application to Face Recognition ]]>
<![CDATA[Eleyan, Alaa]]>
<![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.2017531423
<![CDATA[In this paper a new statistical image descriptor for the face recognition problem is proposed. To the best of our knowledge, no one has attempted to implement this approach before. The idea is simple and straight forward. For each face image, a feature descriptor is formed by concatenating 4 vectors together. These four vectors are formed by taking the sum of pixels in four different directions, namely; row-wise sum (0), column-wise sum (90 ), diagonal-wise sum (45 ) and antidiagonal-wise sum (-45 ). For test purposes, the generated feature descriptor is used in face recognition problem. The experiments are carried out on two different face databases namely; ORL and PUT databases. Simulation results show that the proposed approach gave a comparative performance to the well-known feature extraction algorithms in face recognition.]]>
<![CDATA[Real-Time Fuzzy Logic Control of Switched Reluctance Motor ]]>
<![CDATA[Uysal, Ali]]>
<![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.2017531429
<![CDATA[In this study, 8/6 switched reluctance motor (SRM) is controlled by fuzzy logic. For driving SRM, four phase asymmetric bridge converter is chosen. STM32F4 Discovery processor and MATLAB Simulink software fuzzy logic controller (FLC) are used. SRMâs speed and current are transferred to the computer in real-time. Measured speeds and currents are plotted. It is shown here that, the SRM for different reference speeds and loads is controlled by a STM32F4 Discovery card with MATLAB Simulink FLC.]]>
<![CDATA[Hybrid Artificial Cooperative Search â Crow Search Algorithm for Optimization of a Counter Flow Wet Cooling Tower ]]>
<![CDATA[Turgut, Mert Sinan]]>;
<![CDATA[Turgut, Oguz Emrah]]>
<![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.2017531425
<![CDATA[In this paper, an improved version of Artificial Cooperative Search (ACS) algorithm is applied on a counter flow wet-cooling tower design problem. The Merkelâs method is used to determine the characteristic dimensions of cooling tower, along with empirical correlations for the loss and overall mass transfer coefficients in the packing region of the tower. Basic perturbation schemes of the Crow Search Algorithm, a recent developed metaheuristic algorithm inspired by the food searching behaviors intelligent crows, are incorporated into ACS to enhance the convergence speed and increase the solution diversity of the algorithm. In order to assess the solution performance of the proposed method, fourteen widely known optimization test function have been solved and corresponding convergence histories has been depicted. .Then the improved ACS algorithm (IACS) is applied on six different examples of counter flow wet-cooling tower optimization problem. The results obtained by applying the proposed algorithm are compared with the results of some other algorithms in the literature. Optimization results show that IACS is an effective algorithm with rapid convergence performance for the optimization of counter flow wet-cooling towers. ]]>
