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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200 Documents
<![CDATA[Particle Swarm Optimization Design of Optical Directional Coupler Based on Power Loss Analysis ]]>
<![CDATA[Özkan-Bakbak, Pınar]]>
<![CDATA[ International Journal of Intelligent Systems and Applications in Engineering Vol 1, No 2 (2013) ]]>
Publisher : <![CDATA[Advanced Technology and Science (ATScience) ]]>
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<![CDATA[In this work, feasible design is presented as an optimization problem for an optical directional coupler and designed by using particle swarm optimization (PSO). Principally, identical, weakly guiding, slab and lossless optical waveguides are supposed to be weakly coupled to each other. The power loss and the propagation constant change of TE and TM modes in mutual coupling of two cladded and uncladded optical waveguides are analyzed by the modal analysis and PSO. PSO design of an optical directional coupler is an optimization problem consisting of input variables and design parameters within a fitness function (FF). FF is the power loss of TE and TM modes. PSO should minimize the FF and obtain design criteria. The analysis shows that the results, by using PSO are compatible with modal analysis results. The availability of the optical coupler design by PSO has been tested successfully.]]>
<![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[BAT algorithm for Cryptanalysis of Feistel cryptosystems ]]>
<![CDATA[Tahar, Mekhaznia]]>
<![CDATA[ International Journal of Intelligent Systems and Applications in Engineering Vol 3, No 2 (2015) ]]>
Publisher : <![CDATA[Advanced Technology and Science (ATScience) ]]>
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DOI: 10.18201/ijisae.82426
<![CDATA[Recent cryptosystems constitute an effective task for cryptanalysis algorithms due to their internal structure based on nonlinearity. This problem can be formulated as NP-Hard. It has long been subject to various attacks; available results, emerged many years ago remain insufficient when handling large instances due to resources requirement which increase with the amount of processed data. On another side, optimization techniques inspired by swarm intelligence represents a set of approaches used to solve complex problems. This is mainly due to their fast convergence with a consumption of reduced resources. The purpose of this paper is to provide, and for a first time, a more detailed study about the performance of BAT algorithm in cryptanalysis of some variant of Data encryption standard algorithms. Experiments were performed to study the effectiveness of the used algorithm in solving the considered problem and underline the difficulties encountered.]]>
<![CDATA[An Analysis of Archive Update for Vector Evaluated Particle Swarm Optimization ]]>
<![CDATA[Naim, Faradila]]>;
<![CDATA[Zuwairie, Ibrahim]]>;
<![CDATA[Sheng, Lim Kian]]>;
<![CDATA[Jusof, Mohd Falfazli Mat]]>;
<![CDATA[Arshad, Nurul Wahidah]]>
<![CDATA[ International Journal of Intelligent Systems and Applications in Engineering Vol 4, No 1 (2016) ]]>
Publisher : <![CDATA[Advanced Technology and Science (ATScience) ]]>
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DOI: 10.18201/ijisae.48588
<![CDATA[Multi-objective optimization problem is commonly found in many real world problems. In computational intelligence, Particle Swarm Optimization (PSO) algorithm is a popular method in solving optimization problems. An extended PSO algorithm called Vector Evaluated Particle Swarm Optimization (VEPSO) has been introduced to solve multi-objective optimization problems. VEPSO algorithm requires an archive, which is used to record the solutions found. However, the outcome may be differ depending on how the archive is used. Hence, in this study, the performance of VEPSO algorithm when updates the archive at different instance is investigated by measuring the convergence and diversity by using standard test functions. The results show that the VEPSO algorithm performs better when update the archive during the search process, in the iterations.]]>
<![CDATA[The Control of A Non-Linear Chaotic System Using Genetic and Particle Swarm Based On Optimization Algorithms ]]>
<![CDATA[Kose, Ercan]]>;
<![CDATA[Muhurcu, Aydin]]>
<![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.2016426386
<![CDATA[In this study, the control of a non-linear system was realized by using a linear system control strategy. According to the strategy and by using the controller coefficients, system outputs were controlled for all reference points with the same coefficients via focused references. In the framework of this study, the Lorenz chaotic system as non-linear structure, and the discrete-time PI algorithm as the control algorithm has selected. The genetic algorithm and particle swarm optimization methods have used in the optimization process, and the success of both methods has been discussed among themselves. Closed-loop control system has run simultaneously under the Matlab / Simulink programmer. The results have discussed by using the ISE, IAE, ITAE error criteria, and improved dTISDSE purpose functions.]]>
<![CDATA[Lossless Text Compression Technique with LSB Technique to Hide Secret Message inside an Image (CLSB) ]]>
<![CDATA[Dagez, Hanan Ettaher]]>
<![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.61154
<![CDATA[Abstractâ This paper presents CLSB algorithm to improve and increase the security ofhiding message inside an image by using Least Significant Bit (LSB) method. This research attempts to improve the way has been introduced in [1], where they use digital images to hide Secret Text files. This method is working by distributing data (BMP image format) randomly without use any table to store the path of hiding data. CLSB propose new method to reduce size of dictionary index. Therefore, the main objective of this research is to develop a new method to increase the security by adding a secret key to decode and improve the quality by using LZW method to reduce the size of text file before hiding. Results proved and has also presented in this paper.  Abstractâ This paper presents CLSB algorithm to improve and increase the security ofhiding message inside an image by using Least Significant Bit (LSB) method. This research attempts to improve the way has been introduced in [1], where they use digital images to hide Secret Text files. This method is working by distributing data (BMP image format) randomly without use any table to store the path of hiding data. CLSB propose new method to reduce size of dictionary index. Therefore, the main objective of this research is to develop a new method to increase the security by adding a secret key to decode and improve the quality by using LZW method to reduce the size of text file before hiding. Results proved and has also presented in this paper.  ]]>
<![CDATA[Feature Selection using FFS and PCA in Biomedical Data Classification with AdaBoost-SVM ]]>
<![CDATA[Ceylan, Rahime]]>;
<![CDATA[Barstugan, Mucahid]]>
<![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.2018637928
<![CDATA[: Recently, there has been an increasing trend to propose computer aided diagnosis systems for biomedical pattern recognition. A computer aided diagnosis method, which aims higher classification accuracy, is developed to classify the biomedical dataset. This new method includes two types of machine learning algorithms: feature selection and classification. In this method, firstly, features were extracted from biomedical dataset, then the extracted features were classified by hybrid AdaBoost-Support Vector Machines (SVM) classifier structure. For feature selection, Forward Feature Selection (FFS) and Principal Component Analysis (PCA) algorithms were used. Following it, advantages and disadvantages of these algorithms were evaluated. The proposed two different hybrid structures and other studies in literature were compared with our findings. Wisconsin Breast Cancer (WBC), Pima Diabetes (PD), Heart (Statlog) biomedical datasets and Electrocardiogram (ECG) signals were taken from UCI database and these datasets were used to test the proposed hybrid structure. The obtained results show that the proposed hybrid structure has high classification accuracy for biomedical data classification.]]>
<![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 Fuzzy Logic Controller with Tuning Output Scaling Factor for Induction Motor Control Taking Core Loss into Account ]]>
<![CDATA[Mannan, Mohammad Abdul]]>
<![CDATA[ International Journal of Intelligent Systems and Applications in Engineering Vol 2, No 3 (2014) ]]>
Publisher : <![CDATA[Advanced Technology and Science (ATScience) ]]>
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<![CDATA[This paper presents a design of a fuzzy logic controller (FLC) with tuning output scaling factor for speed control of indirect field oriented induction motor (IM) taking core loss into account. The variation of output scaling factor of FLC depends on the normalized output of FLC. Firstly the speed control of IM taking core loss into account is presented by using FLC with fixed scaling factors (FLC-FSF). Secondly the speed controller based on suggested FLC with tuning output scaling factor (FLC-TOSF) is proposed. The performance of the proposed FLC-TOSF for speed control of IM are investigated and compared to those obtained using FLC-SFS at different operating conditions and variation of parameters. A comparison of simulation results shows that the convergence of actual speed to reference speed is faster by using the proposed FLC-TOSF.]]>
<![CDATA[Training Product-Unit Neural Networks with Cuckoo Optimization Algorithm for Classification ]]>
<![CDATA[Kahramanli, Humar]]>
<![CDATA[ International Journal of Intelligent Systems and Applications in Engineering Vol 5, No 4 (2017) ]]>
Publisher : <![CDATA[Advanced Technology and Science (ATScience) ]]>
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DOI: 10.18201/ijisae.2017533900
<![CDATA[In this study Product-Unit Neural Networks (PUNN) which is the special class of feed-forward neural network, has been trained using Cuckoo Optimization algorithm. The trained model has been applied to two classification problem. BUPA liver disorders and Habermans Survival Data have been used for application. The both data have been obtained from UCI machine Learning Repository. For comparison Backpropagation (BP) and LevenbergâMarquardt (LM) algorithms have been used. The application results show that the PUNN trained with Cuckoo Optimization algorithm is achieved better classification accuracy.]]>
