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[The Effect of Feature Extraction Based on Dictionary Learning on ECG Signal Classification ]]>
<![CDATA[Ceylan, Rahime]]>
<![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.2018637929
<![CDATA[The detection of effective features or data reduction is one of the phases of signal classification. In feature extraction phase, the detection of features which increase performance of classification is very important in terms of diagnosis of disease. Due to this reason, the using of an effective algorithm for feature extraction increases classification accuracy and also it decreases processing time of classifier.           In this study, two well-known dictionary learning algorithms are used to extract features of ECG signals. The features of ECG signals are extracted by using Method of Optimal Direction (MOD) and K-Singular Value Decomposition (K-SVD) and the extracted features are classified by Artificial Neural Network (ANN). Twelve different ECG signal classes which taken from MIT-BIH ECG Arrhythmia Database are used. When the obtained results are examined, it is seen that performance of classifier increases in usage of K-SVD for feature extraction. The highest classification accuracy is obtained as %98.74 with 5 nonzero elements in [20 1] feature vector, when K-SVD is used in feature extraction phase. This is the first time in literature that feature extraction based on dictionary learning is performed on 12 ECG signal classes and the extracted features are classified by ANN.]]>
<![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[Rainfall Runoff Modelling Using Generalized Neural Network and Radial Basis Network ]]>
<![CDATA[Gowda, C Chandre]]>;
<![CDATA[S. G., Mayya]]>
<![CDATA[ International Journal of Intelligent Systems and Applications in Engineering Vol 2, No 4 (2014) ]]>
Publisher : <![CDATA[Advanced Technology and Science (ATScience) ]]>
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<![CDATA[Rainfall runoff study has a wide scope in water resource management. To provide a reliable prediction model is of paramount importance. Runoff prediction is carried out using generalized regression neural network and radial basis neural network. Daily Rainfall runoff model was developed for Nethravathi river basin located at the west coast of Karnataka, India. The comparative study showed Radial basis neural network performed better than generalized neural network during its evaluation by performance indicators]]>
<![CDATA[Detection of PCB Soldering Defects using Template Based Image Processing Method ]]>
<![CDATA[Ozturk, Saban]]>;
<![CDATA[Akdemir, Bayram]]>
<![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.2017534388
<![CDATA[In this study, a predefined template-based image processing system is proposed to automatically detect of PCB soldering defects that negatively affect circuit operation. The proposed system consists of a prototype, a camera, an image processing method and inspect process. The prototype is produced using a plastic material, depending on the focal length of the camera and the PCB size. Image processing step comprises two steps. Firstly, solder joints are determined using Fuzzy C-means clustering algorithm. Then, the center of each joint is determined. In the next step, a joint template is created that contains solder joints information. This joint template contains information about the effects of touching other joints for each joint. In this way, the inspection of soldering defects is getting shorter. Finally, each joint is only inspected for the joints specified in the template. The proposed method is evaluated on 85 real PCB image with 4250 soldering joints.]]>
<![CDATA[A New Approach Based on Image Processing for Measuring Compressive Strength of Structures ]]>
<![CDATA[Baygin, Mehmet]]>;
<![CDATA[Ozkaya, Suat Gokhan]]>;
<![CDATA[Ozdemir, Muhammed Alperen]]>;
<![CDATA[Kazaz, Ilker]]>
<![CDATA[ International Journal of Intelligent Systems and Applications in Engineering 2017: Special Issue ]]>
Publisher : <![CDATA[Advanced Technology and Science (ATScience) ]]>
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DOI: 10.18201/ijisae.2018SpecialIssue31419
<![CDATA[The compressive strength factor in civil engineering is a very important parameter used to determine the performance of structures. The stability of structures can be tested with this parameter which is used to measure the performance of concrete under different loads. This parameter, which should be determined for the safety of the structures, is usually based on experimental analyses performed in the laboratory environment. In this study, a new approach to compressive strength measurement in civil engineering is proposed. With this approach, which is based on image processing, measurement of compressive strength parameter of concrete samples taken from structures is performed. For this purpose, images of concrete specimens with different strengths are taken and these images are divided into two groups as training and test set. Then, image processing algorithms are applied to these images and the compressive strength of concrete specimens is calculated. It has been determined that the approach suggested in the test runs performed with an error rate of about 1-2%]]>
<![CDATA[Structure-Texture Decomposition of RGB-D Images ]]>
<![CDATA[Erdem, Aykut]]>
<![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.2016426381
<![CDATA[In this paper, we study the problem of separating texture from structure in RGB-D images. Our structure preserving image smoothing operator is based on the region covariance smoothing (RCS) method in [16] that we present a number of modifications to this framework to make it depth-aware and increase its effectiveness. In particular, we propose to incorporate three geometric depth features, namely height above ground, angle with gravity and horizontal disparity to the pool of image features used in that study. We also suggest to use a new kernel function based on KL-divergence between the distributions of extracted features. We demonstrate our approach on challenges images from NYU-Depth v2 Dataset [24], achieving more accurate decompositions than the state-of-the-art approaches which do not utilize any depth information.]]>
<![CDATA[User Profile Based Paper Recommendation System ]]>
<![CDATA[Kaya, Buket]]>
<![CDATA[ International Journal of Intelligent Systems and Applications in Engineering Vol 6, No 2 (2018) ]]>
Publisher : <![CDATA[Advanced Technology and Science (ATScience) ]]>
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DOI: 10.18201/ijisae.2018642079
<![CDATA[As the spread of science and the number of researchers working in academic fields increase, there is also a considerable increase in the number of academic studies. Researchers always follow new works published for keeping their knowledge up to date. However, with many different sources and thousands of academic publications published every day, academics are not always able to find publications about their subjects. Today, almost all of online academic databases employ a recommendation module which only considers the studies similar to the paper that the user looked at. However, a recommendation system based on the information of a single article is often not enough. In this study, the proposed method recommends by considering users publications, userâs co-authors and co-authorsâ papers. Therefore, meta-data of the articles published by the researcher in the past are scored on a time-base basis with the method we propose. With the help of the sum of scores, there is a score of the user profile in the subject matter. It aims to find the closest studies to the profile of the user by searching with the method propsoed in the data pool which we created from the exact contents of hundreds of thousands of academic works. In the proposed method, TF-IDF is used from frequency-based similarity analysis methods. In the evaluation phase, the performance of the proposed method was examined. The success test of the method was measured by several different methods. These are to be evaluated by presenting them to real users and the other is to compare with existing data. The results are very promising and demonstrate that the method can produce accurate and quality results.]]>
<![CDATA[Performance Comparison of Tetrolet Transform and Wavelet-Based Transforms for Medical Image Denoising ]]>
<![CDATA[Ceylan, Murat]]>;
<![CDATA[Canbilen, Ayse Elif]]>
<![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.2017533895
<![CDATA[Noise reduces the quality of medical images and raise the difficulties of diagnosis. Although the wavelet transform has already been used in medical noise removal applications extensively, there are many other multi-resolution analysis methods proposed in recent years for denoising. The main goal of this study is comparing the image denoising abilities of some of these methods with wavelet transform. In this paper, image denoising is implemented by a three-stage methodology. Effectiveness of the multiresolution analysis methodologies has been investigated for standard test images beside magnetic resonans, mammography and fundus images. Performances of the transforms are compared by using peak signal to noise ratio, mean square error, mean structural similarity index and feature similarity index. The best results are obtained by tetrolet transform for random and rician noise with the benchmark images. Medical image denoising performance of Tetrolet transform is compared to other multiresolution analysis methods for the first time in the literature with this study. It surpassed ridgelet and haar wavelet transforms while the noise ratio was low. On the other hand, it is seen that curvelet transforms are effectively produce the best results for all rates of noise on medical images.]]>
<![CDATA[A Multi-Criteria Decision-Making Method Based Upon Type-2 Interval Fuzzy Sets For Auxiliary Systems Of A Shipâs Main Diesel Engine ]]>
<![CDATA[BALIN, Abit]]>
<![CDATA[ International Journal of Intelligent Systems and Applications in Engineering Vol 5, No 2 (2017) ]]>
Publisher : <![CDATA[Advanced Technology and Science (ATScience) ]]>
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DOI: 10.18201/ijisae.2017528727
<![CDATA[Abstract:Â A well-qualified ship engine conductor having an effective error detection system is required to find failure as a result of which action are of immediate to be taken to prevent any possible engine impairments. Otherwise failures cumulatively can end up with crippling and irreversible profit loss. This paper proposes a fuzzy MADM methodology can help determine the most effective system for a shipâs main diesel engine. A novel interval type-2 fuzzy MADM method is chosen for the study, resting on VIKOR, to assess and employ the failure detection of auxiliary systems of a marine diesel engine. The evaluation is conducted by various groups of experts. It has been presumed that this study will also work out as a useful future maintenance process reference for marine engineering operators. All the same, the importance of the using time effectively to determine and respond to such failures is also underlined within the study. The results reveal that a fuel system is categorized as the most effective alternative followed subsequently by governor system, air supply system, and lastly cooling system. The results are grounded on the opinions expressed by three decision-making groups who put the MDEAS alternatives according to twenty ably selected criteria]]>
<![CDATA[Using Word Embeddings for Ontology Enrichment ]]>
<![CDATA[Pembeci, Ä°zzet]]>
<![CDATA[ International Journal of Intelligent Systems and Applications in Engineering Vol 4, No 3 (2016) ]]>
Publisher : <![CDATA[Advanced Technology and Science (ATScience) ]]>
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DOI: 10.18201/ijisae.58806
<![CDATA[Word embeddings, distributed word representations in a reduced linear space, show a lot of promise for accomplishing Natural Language Processing (NLP) tasks in an unsupervised manner. In this study, we investigate if the success of word2vec, a Neural Networks based word embeddings algorithm, can be replicated in an aggluginative language like Turkish. Turkish is more challenging than languages like English for complex NLP tasks because of her rich morphology. We picked ontology enrichment, again a relatively harder NLP task, as our test application. Firstly, we show how ontological relations can be extracted automaticaly from Turkish Wikipedia to construct a gold standard. Then by running experiments we show that the word vector representations produced by word2vec are useful to detect ontological relations encoded in Wikipedia. We propose a simple but yet effective weakly supervised ontology enrichment algorithm where for a given word a few know ontologically related concepts coupled with similarity scores computed via word2vec models can result in discovery of other related concepts. We argue how our algorithm can be improved and augmented to make it a viable component of an ontoloy learning and population framework.]]>
