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[A New Approach to Determine Eps Parameter of DBSCAN Algorithm ]]>
<![CDATA[Ozkok, Fatma Ozge]]>;
<![CDATA[Celik, Mete]]>
<![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.2017533899
<![CDATA[In recent years, data analysis has become important with increasing data volume. Clustering, which groups objects according to their similarity, has an important role in data analysis. DBSCAN is one of the most effective and popular density-based clustering algorithm and has been successfully implemented in many areas. However, it is a challenging task to determine the input parameter values of DBSCAN algorithm, which are neighborhood radius, Eps, and minimum number of points, MinPts. The values of these parameters significantly affect clustering performance of the algorithm. In this study, we propose AE-DBSCAN algorithm, which includes a new method to determine the value of neighborhood radius Eps automatically. The experimental evaluations showed that the proposed method outperformed the analytical DBSCAN.]]>
<![CDATA[Improving an Expert-Supported Dynamic Programming Algorithm and Adaptive-Neuro Fuzzy Inference System for Long-Term Load Forecasting ]]>
<![CDATA[Cetinkaya, Nurettin]]>
<![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.2017533858
<![CDATA[Load forecasting is very important to manage the electrical power systems. Load forecasting can be analyzed in three different ways as short-term, medium-term and long-term. Long-term load forecasting (LTLF) is in need to plan and carry on future energy demand and investment such as size of energy plant. LTLF is affected by energy consumption, national incoming per year, rates of civilization, increasing population rates and moreover economical parameters. Some of the forecasting models use mathematical formulas and statistical models such as correlation and regression analysis. In this study, a new effective expert-supported dynamic programming algorithm (ESDP) has been improved. Additionally, adaptive neuro-fuzzy inference system (ANFIS) and mathematical modeling (MM) are used to forecast long term energy demand. ANFIS is one of the famous artificial intelligence and has widely used to solve forecasting problems in literature. In addition to numerical inputs, ANFIS has linguistics inputs. The results obtained from ESDP, ANFIS and MM are compared to show availability. In order to show error levels mean absolute percentage error (MAPE) and (MAE) are used. The obtained results show that the proposed algorithms are available.]]>
<![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[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[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.]]>
<![CDATA[Sleep Stage Classification via Ensemble and Conventional Machine Learning Methods using Single Channel EEG Signals ]]>
<![CDATA[Ilhan, Hamza Osman]]>;
<![CDATA[Bilgin, Gokhan]]>
<![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.2017533859
<![CDATA[Sleep-stages play important roles in the diagnosis of the sleep disorders and the sleep-related illnesses. In this sense, accurate identification of the sleep-stages is a necessity for more robust and e client diagnosis systems. Several traditional machine-learning and pattern recognition algorithms are deployed on modern computer aided diagnosis systems. However, current results are not as satisfactory as expected. In the last two decade, a new concept has emerged with âensemble learningâ title. It has attracted the attention of many researchers from various disciplines. In this study, several ensemble-learning methods are utilized and inspected on EEG signals for sleep-stage classification. Conventional machine-learning methods are also performed in same testing phase to report comparative results. Additionally, methods are evaluated in two different scenarios; subject specific and independent. Study proves that combination of DTs and SVMs in Bagging theorem surpasses all of the conventional methods used in the experiments. Moreover, test trials reveal that both conventional and ensemble models need to be improved for subject independent scenario which is more essential case in the development of independent computer based diagnosis systems.]]>
<![CDATA[Prototype Design and Application of a Semi-circular Automatic Parking System ]]>
<![CDATA[Atacak, Ismail]]>;
<![CDATA[Erdogdu, Ertugrul]]>
<![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.2017534390
<![CDATA[Nowadays, with the increasing population in urban areas, the number of vehicles used in traffic has also increased in these areas. This has brought with it major problems that are caused by insufficient parking areas, in terms of traffic congestion, drivers and environment. In this study, in order to overcome these problems, a multi-storey automatic parking system that automatically performs vehicle recognition, vehicle parking, vehicle delivery and pricing processes has been designed and the practical application of this system has been realized on a prototype. The vehicle recognition process in the designed system has been fulfilled through a software prepared on the personal computer connected to the webcam. A multi-storey semi-circular structure has been used as a parking area to resolve parking area deficiencies. Therefore, the carrying system that carries out the parking process in the system has been designed as a cylindrical coordinated robot that can move horizontally, vertically and in the forward-back direction. The control of whole system has been realized by PIC16F877A microcontroller. The results obtained from the prepared prototype have showed that the proposed system can provide significant contributions to the solution of problems resulting from parking area deficiencies.]]>
<![CDATA[Comparison of Multi-Label Classification Methods for Prediagnosis of Cervical Cancer ]]>
<![CDATA[Ceylan, Zeynep]]>;
<![CDATA[Pekel, Ebru]]>
<![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.2017533896
<![CDATA[Cervical cancer is one of the most common causes of cancer death of women. Prediagnosis of cervical cancer at early stages is critical to reduce mortality ratios. Additionally, early prediction of cervical cancer can help both the patients and the physicians depending on easiness of treatment. Cervical cancer results from various risk factors such as family history, education level, having multiple full-term pregnancies, smoking, and sexually transmitted diseases and etc. Recently, different types of advanced methods were developed for risk prediction analysis based on machine learning techniques. The purpose of this study is to investigate the efficacy of using multi-label classification techniques for diagnosing cervical cancer at early stage. Four common learning algorithms such as Naïve Bayes, J48 Decision Tree, Sequential Minimal Optimization, and Random Forest were compared in terms of their accuracy, hamming loss, exact match (subset accuracy) and ranking loss performance evaluation metrics. Thus, this study can help to physicians, academics and cancer researchers to make fast and accurate diagnosis.]]>
<![CDATA[Classification of Cervical Disc Herniation Disease using Muscle Fatigue Based Surface EMG Signals by Artificial Neural Networks ]]>
<![CDATA[Ozmen, Guzin]]>;
<![CDATA[Ekmekci, Ahmet Hakan]]>
<![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.2017533901
<![CDATA[This study presents the classification of cervical disc herniation patient and healthy persons by using muscle fatigue information. Cervical disc herniation patients suffer from neck pain and muscle fatigue in the neck increases these aches. Neck pain is the most common pain type encountered after back pain. The discomforts that occur in the neck region affect the daily quality of life, so the number of researches done in this area is increasing. In this study surface Electromyography (EMG) signals were used to examine muscle fatigue. EMG signals were obtained from Trapezius and Sternocleidomastoid (SCM) muscles in the cervical region of 10 control subject and 10 cervical disc herniation patients. Surface EMG was preferred because it is a noninvasive method. In the first step of this study, EMG signals were filtered and adapted for analysis. In the second step, muscle fatigue was determined using Median and Mean frequency values obtained by Fourier Transform and Welch methods. Feature extraction was the third step which was performed by Short Time Fourier Transform (STFT), Discrete Wavelet Transform (DWT) and Autoregressive method (AR). Finally, Artificial Neural Network (ANN) was used for classification. Training and test data were created by using feature vectors to classify patients with ANN. According to the results, the superior feature extraction method was investigated on patient classification using muscle fatigue information. The best results were obtained by AR method with %99 classification accuracy. Also, the best results were obtained by DWT with %100 classification accuracy for SCM muscle. This study has contributed that AR and DWT are a suitable feature extraction methods for surface EMG signals by providing high accuracy classification with artificial intelligence methods for cervical disc herniation disease. Besides, it is shown that muscle fatigue distinguishes cervical disc herniation patients from healthy people.]]>
<![CDATA[Comparison of Artifıcial Neural Networks and Response Surface Methodology in Stone Mastic Asphalt Using Waste Granite Filler ]]>
<![CDATA[Caner, Murat]]>
<![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.2017533860
<![CDATA[This study examined the modeling performance of Artificial Neural Networks (ANN) and Response Surface Methodology (RSM) using experimental data of mechanical and volumetric properties of stone mastic asphalt (SMA) samples. These samples were produced with Marshall Design method using different ratios of granite sludge filler (11-12%) and limestone filler (10%). The impact of percentage of bitumen, mineral filler rates and unit volume weights of samples were used as input parameters and Marshall Stability (MS) values were used as output parameter. Mechanical immersion tests were performed to examine moisture susceptibility on SMA samples that have different filler rates (10-11-12%). In order to examine the reliability of the obtained models error and regression analysis results were shown comparing model responses with the experimental results. ]]>
