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[Classification of Different Wheat Varieties by Using Data Mining Algorithms ]]>
<![CDATA[Sabancı, Kadir]]>;
<![CDATA[Akkaya, Mustafa]]>
<![CDATA[ International Journal of Intelligent Systems and Applications in Engineering Vol 4, No 2 (2016) ]]>
Publisher : <![CDATA[Advanced Technology and Science (ATScience) ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.18201/ijisae.62843
<![CDATA[There are various applications using computer-aided quality controlling system. In this study, seed data set acquired from UCI machine learning database was used. The purpose of the study is to perform the operations for separation of seed species from each other in the seed data set. Three different seed whose data was acquired from the UCI machine learning database was used. Later it was classified by applying the methods of KNN, Naive Bayes, J48 and multilayer perceptron to the dataset. While wheat seed data received from the UCI machine learning database was classified, WEKA program was used. Depending on the number of neurons the highest classification success came in 7-layer neurons. Our success rate for the number of 7-layer neurons came to 97.17% When the classification success rate was calculated according to KNN for the values of different neighbour, the highest success rate for neighbour was set at 95.71% for 4. Neighbour. With this method, classification of seeds depending on their properties was provided more quickly and effectively. ]]>
<![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) ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
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[Performance Evaluation of Different Feature Extractors and Classifiers for Recognition of Human Faces with Low Resolution Images ]]>
<![CDATA[Nikan, Soodeh]]>;
<![CDATA[Ahmadi, Majid]]>
<![CDATA[ International Journal of Intelligent Systems and Applications in Engineering Vol 3, No 2 (2015) ]]>
Publisher : <![CDATA[Advanced Technology and Science (ATScience) ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.18201/ijisae.28949
<![CDATA[Face recognition is an effective biometric identification technique used in many applications such as law enforcement, document validation and video surveillance. In this paper the effect of low resolution images which are captured in real world applications, on the performance of different feature extraction techniques combined with a variety of classification approaches is evaluated. Gabor features and its combination with local phase quantization histogram (GLPQH) are dimensionality reduced by principal component analysis (PCA), linear discriminant analysis (LDA), locally sensitive discriminant analysis (LSDA) and neighbourhood preserving embedding (NPE) to extract discriminant image characteristics and the class label is attributed using the extreme learning machine (ELM), sparse classifier (SC), fuzzy nearest neighbour (FNN) or regularized discriminant classifier (RDC). ORL and AR databases are utilized and the results show that ELM and RDC have better performance and stability against resolution reduction, especially on Gabor-PCA and Gabor-LDA techniques. Among the interpolation approaches that we employed to enhance the image resolution, nearest neighbour outperforms other methods.]]>
<![CDATA[Fuzzy approach to estimate the demand and supply quantitative imbalance at the labor market of information technology specialists ]]>
<![CDATA[Jabrayilova, Zarifa]]>;
<![CDATA[Mammadova, Masuma]]>;
<![CDATA[Mammadzade, Faig]]>
<![CDATA[ International Journal of Intelligent Systems and Applications in Engineering Vol 3, No 4 (2015) ]]>
Publisher : <![CDATA[Advanced Technology and Science (ATScience) ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.18201/ijisae.24856
<![CDATA[This document considers the processes of modelling supply and demand interactions in the labour market for information technology experts (IT professionals) and management of their quantitative disparity at the macro level. The types of supply and demand imbalance for IT professionals are marked out. The methods are proposed for estimating the structural mismatch in the labour market for IT professionals, the degree of supply and demand imbalance for IT professionals based on fuzzy unbalance scale. The algorithm of fuzzy classification of states of imbalance is proposed.This document considers the processes of modelling supply and demand interactions in the labour market for information technology experts (IT professionals) and management of their quantitative disparity at the macro level. The types of supply and demand imbalance for IT professionals are marked out. The methods are proposed for estimating the structural mismatch in the labour market for IT professionals, the degree of supply and demand imbalance for IT professionals based on fuzzy unbalance scale. The algorithm of fuzzy classification of states of imbalance is proposed.]]>
<![CDATA[The Impact of Feature Selection on Urban Land Cover Classification ]]>
<![CDATA[Dogan, Turgut]]>;
<![CDATA[Uysal, Alper Kursat]]>
<![CDATA[ International Journal of Intelligent Systems and Applications in Engineering Vol 6, No 1 (2018) ]]>
Publisher : <![CDATA[Advanced Technology and Science (ATScience) ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.18201/ijisae.2018637933
<![CDATA[Many of the studies in the literature about land cover classification are focused on the feature extraction and classification rather than feature selection. In this paper, the impact of feature selection on urban land cover classification is extensively analyzed. Three types of features namely spectral, texture, and size/shape features are used for this analysis. This analysis is carried out using three variations of a filter based feature selection method and three widely-known classification algorithms. The feature selection method used for the comparison is a multivariate filter method namely correlation-based feature subset selection where a feature subset evaluator and a search method are integrated. Best first search, genetic search, and greedy stepwise search are three different search methods used for this integration. The classification algorithms employed are Bayesian network, random forest, and support vector machine. The experimental results explicitly indicate that feature selection improves classification accuracy in all cases. Besides, according to the experimental results, random forest classifier is the most successful one among these three classifiers while both feature selection is applied and not applied. Largest improvement in the classification performance is obtained when greedy stepwise search based feature selection method and support vector machine classifier is applied together. Also, the contribution of spectral features to the performance of classification is more than size/shape and texture features.]]>
<![CDATA[A Region Covariances-based Visual Attention Model for RGB-D Images ]]>
<![CDATA[Erdem, Erkut]]>
<![CDATA[ International Journal of Intelligent Systems and Applications in Engineering Vol 4, No 4 (2016) ]]>
Publisher : <![CDATA[Advanced Technology and Science (ATScience) ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.18201/ijisae.2016426384
<![CDATA[Existing computational models of visual attention generally employ simple image features such as color, intensity or orientation to generate a saliency map which highlights the image parts that attract human attention. Interestingly, most of these models do not process any depth information and operate only on standard two-dimensional RGB images. On the other hand, depth processing through stereo vision is a key characteristics of the human visual system. In line with this observation, in this study, we propose to extend two state-of-the-art static saliency models that depend on region covariances to process additional depth information available in RGB-D images. We evaluate our proposed models on NUS-3D benchmark dataset by taking into account different evaluation metrics. Our results reveal that using the additional depth information improves the saliency prediction in a statistically significant manner, giving more accurate saliency maps.]]>
<![CDATA[Skin Lesion Classification using Machine Learning Algorithms ]]>
<![CDATA[OZKAN, Ilker Ali]]>;
<![CDATA[KOKLU, Murat]]>
<![CDATA[ International Journal of Intelligent Systems and Applications in Engineering Vol 5, No 4 (2017) ]]>
Publisher : <![CDATA[Advanced Technology and Science (ATScience) ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
<![CDATA[Melanoma is a deadly skin cancer that breaks out in the skinâs pigment cells on the skin surface. Melanoma causes 75% of the skin cancer-related deaths. This disease can be diagnosed by a dermatology specialist through the interpretation of the dermoscopy images in accordance with ABCD rule. Even if dermatology experts use dermatological images for diagnosis, the rate of the correct diagnosis of experts is estimated to be 75-84%. The purpose of this study is to pre-classify the skin lesions in three groups as normal, abnormal and melanoma by machine learning methods and to develop a decision support system that should make the decision easier for a doctor. The objective of this study is skin lesions based on dermoscopic images PH2 datasets using 4 different machine learning methods namely; ANN, SVM, KNN and Decision Tree. Correctly classified instances were found as 92.50%, 89.50%, 82.00% and 90.00% for ANN, SVM, KNN and DT respectively. The findings show that the system developed in this study has the feature of a medical decision support system which can help dermatologists in diagnosing of the skin lesions.]]>
<![CDATA[Diagnosis of Mesothelioma Disease Using Different Classification Techniques ]]>
<![CDATA[Tutuncu, Kemal]]>;
<![CDATA[Cataltas, Ozcan]]>
<![CDATA[ International Journal of Intelligent Systems and Applications in Engineering 2017: Special Issue ]]>
Publisher : <![CDATA[Advanced Technology and Science (ATScience) ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.18201/ijisae.2017SpecialIssue31416
<![CDATA[Mesothelioma, which is a disease of the pleura and peritoneum, is an asbestos-related environmental disease in undeveloped countries. Although the incidence of this disease is lower than that of lung cancer, the reaction it creates in society is very high. In this study, 9 different classification algorithms of data mining were applied to the Mesethelioma data set obtained from real patients in Dicle University, Faculty of Medicine and loaded into UCI Machine Learning Repository, and the results were compared. When the obtained results were examined, it has been seen that Artificial Neural Network (ANN) had %99.0740 correct classification ratio. ]]>
<![CDATA[A simple Mathematical Fuzzy Model of Brain Emotional Learning to Predict Kp Geomagnetic Index ]]>
<![CDATA[Lotfi, Ehsan]]>;
<![CDATA[Keshavarz, A.]]>
<![CDATA[ International Journal of Intelligent Systems and Applications in Engineering Vol 2, No 2 (2014) ]]>
Publisher : <![CDATA[Advanced Technology and Science (ATScience) ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
<![CDATA[In this paper, we propose fuzzy mathematical model of brain limbic system (LS) which is responsible for emotional stimuli. Here the proposed model is utilized to predict the chaotic activity of the earthâs magnetosphere. Numerical results show that the correlation of the results obtained from the proposed fuzzy model is higher than non-fuzzy models. Hence, the proposed model can be applied in real time chaotic time series prediction.]]>
<![CDATA[Hybridizing a Multi Response Taguchi Algorithm with Reference Ideal Method to Solve Machining Problems ]]>
<![CDATA[SOFUOGLU, Mehmet Alper]]>
<![CDATA[ International Journal of Intelligent Systems and Applications in Engineering Vol 5, No 2 (2017) ]]>
Publisher : <![CDATA[Advanced Technology and Science (ATScience) ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.18201/ijisae.2017528730
<![CDATA[Multi criteria decision making models (MCDM) are extensively used in material-process selection, and optimization in machining problems in engineering. In this study, a novel hybrid optimization model is developed. Taguchi method is hybridized with Reference Ideal Method. The model is tested in case studies taken from literature. The developed model produced similar results with literature. The proposed model can be used by engineers and operators in manufacturing environment.]]>
