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INDONESIA
International Journal of Intelligent Systems and Applications in Engineering
Published by Ismail SARITAS
ISSN : 21476799     EISSN : -     DOI : -
Core Subject : Science,
Computer Science & IT
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.
Arjuna Subject : -
Articles 200 Documents
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Classification of Wheat Types by Artificial Neural Network Yasar, Ali; Kaya, Esra; Saritas, Ismail
International Journal of Intelligent Systems and Applications in Engineering Vol 4, No 1 (2016)
Publisher : Advanced Technology and Science (ATScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.64198

Abstract

In this study, the types of wheat seeds are classified using present data with artificial neural network (ANN) approach.      Seven inputs, one hidden layer with 10 neurons and one output has been used for the ANN in our system. All of these parameters were real-valued continuous. The wheat varieties, Kama, Rosa and Canadian, characterized by measurement of main grain geometric features obtained by X-ray technique, have been analyzed. Results indicate that the proposed method is expected to be an effective method for recognizing wheat varieties. These seven input parameters reaches the 10-neurons hidden layer of the network and they are processed and then classified with an output. The classification process of 210 units of data using ANN is determined to make a successful classification as much as the actual data set. The regression results of the classification process is quite high. It is determined that the training regression R is 0,9999, testing regression is 0,99785 and the validation regression is 0,9947, respectively. Based on these results, classification process using ANN has been seen to achieve outstanding success.
An Efficient Image Encryption Algorithm for the Period of Arnolds CAT Map Elmacı, Deniz; Bas Catak, Nursin
International Journal of Intelligent Systems and Applications in Engineering Vol 6, No 1 (2018)
Publisher : Advanced Technology and Science (ATScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.2018637935

Abstract

Arnolds CAT Map (ACM) is a chaotic transformation the 2-dimensional toral automorphism T^2 defined by the mapping /Gamma:T^2 to T^2. There are many applications of ACM in various research areas such as: steganography, encryption of images, texts and watermarks. The transformation of an image is achieved by the randomized order of pixels. After a finite number of repetitions of the transformation, the original image reappears. In this study, encryption of two images is demonstrated together with a proposed algorithm. Moreover, the periodicity of ACM is discussed and an algorithm to change the period of ACM is suggested. The resultant period obtained from the new algorithm is compared with the period obtained from the usual ACM. The results show that the period of the proposed algorithm grows exponentially while the period of ACM has an upper bound.
Fuzzy multicriterial methods for the selection of IT-professionals Jabrayilova, Zarifa Gasim
International Journal of Intelligent Systems and Applications in Engineering Vol 3, No 2 (2015)
Publisher : Advanced Technology and Science (ATScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.08607

Abstract

This paper presents the solution of issues related to selection based on evaluation of demand set forth to IT specialists, to develop appropriate decision support system. In this case problem is reduced to multicriterial task of decision making, functioning in a fuzzy environment.We propose criteria estimation method allowing regulation and selection of the best alternative according to the scenario appropriate to the requirements of the decision making person, at a current time. For realization of abovementioned task on the basis of fuzzy logic methods we propose methods of expert knowledge processing of the importance criteria and their characterizing factors.
Application of global thresholding in bread porosity evaluation Bosakova-Ardenska, Atanaska
International Journal of Intelligent Systems and Applications in Engineering Vol 3, No 3 (2015)
Publisher : Advanced Technology and Science (ATScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.30637

Abstract

The white bread is one of most popular food in Bulgaria. Its quality is defined by standards and control is also standardized. The white bread has four groups of quality parameters - organoleptic, physicochemical, chemical contaminants and microbiological. This paper presents one research over white bread porosity which is one of physicochemical parameters. By standard evaluation of bread porosity is time expensive procedure. Current research proposes one fast computer based approach for white bread porosity evaluation. In experiments are used three brands white breads. The images of breads are binarized with four known algorithms and coefficient of diversity (ratio of white pixels and all image pixels) for resulting binary images is calculated. This coefficient corresponds with bread porosity. Experimental results show that one of these algorithms – Vector Median Thresholding, is appropriate for bread porosity evaluation.
Operating Frequency Estimation of Slot Antenna by Using Adapted kNN Algorithm Yigit, Enes
International Journal of Intelligent Systems and Applications in Engineering Vol 6, No 1 (2018)
Publisher : Advanced Technology and Science (ATScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.2018637927

Abstract

In this study ultra-high frequency slot antenna’s operating frequency is estimated by using adapted k-nearest neighbor (kNN) algorithm. kNN doesn’t use the training data points to do any generalization and it can be usually used for many classification. However, kNN can be adapted to estimate slot antenna’s operating frequency by assessing the best k-nearest value. To find the optimal k for operating frequency estimation, 96 slot antennas with seven antenna parameters are simulated with respect to the operating frequency by using a computational electromagnetic software. Antenna parameters includes the patch dimensions, height and relative permittivity of the substrate. The simulated 81 antennas are used to construct feature data pool and the residual 15 antennas are used to test kNN algorithm. The performance of the kNN is evaluated by comparing the output of operating frequency to the simulated one.  Then the proposed model is corroborated with simulated antennas and validating with prototyped antenna data. The results shows that the kNN based model simply and fast computes the operating frequency of the slot antennas much close to real one without performing any simulations or measurement.
Comparison of Classification Techniques on Energy Efficiency Dataset TOPRAK, Ahmet; KOKLU, Nigmet; TOPRAK, Aysegul; OZCAN, Recai
International Journal of Intelligent Systems and Applications in Engineering Vol 5, No 2 (2017)
Publisher : Advanced Technology and Science (ATScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.2017534722

Abstract

The definition of the data mining can be told as to extract information or knowledge from large volumes of data. Statistical and machine learning techniques are used for the determination of the models to be used for data mining predictions. Today, data mining is used in many different areas such as science and engineering, health, commerce, shopping, banking and finance, education and internet. This study make use of WEKA (Waikato Environment for Knowledge Analysis) to compare the different classification techniques on energy efficiency datasets. In this study 10 different Data Mining methods namely Bagging, Decorate, Rotation Forest, J48, NNge, K-Star, Naïve Bayes, Dagging, Bayes Net and JRip classification methods were applied on energy efficiency dataset that were taken from UCI Machine Learning Repository. When comparing the performances of algorithms it’s been found that Rotation Forest has highest accuracy whereas Dagging had the worst accuracy.
Speed Control of Direct Torque Controlled Induction Motor By using PI, Anti-Windup PI And Fuzzy Logic Controller AÇIKGÖZ, Hakan; KECECIOGLU, O. Fatih; GANI, Ahmet; SEKKELI, Mustafa
International Journal of Intelligent Systems and Applications in Engineering Vol 2, No 3 (2014)
Publisher : Advanced Technology and Science (ATScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

In this study, comparison between PI controller, fuzzy logic controller (FLC) and an anti-windup PI (PI+AW) controller used for speed control with direct torque controlled induction motor is presented. Direct torque controlled induction motor drive system is implemented in MATLAB/Simulink environment and the FLC is developed using MATLAB/Fuzzy-Logic toolbox. The proposed control strategy is performed different operating conditions. Simulation results, obtained from PI controller, FLC and PI+AW controller showing the performance of the closed loop control systems, are illustrated in the paper. Simulation results show that FLC is more robust than PI and PI+AW controller against parameter variations and FLC gives better performance in terms of rise time, maximum peak overshoot and settling time.
Classification of Cervical Disc Herniation Disease using Muscle Fatigue Based Surface EMG Signals by Artificial Neural Networks Ozmen, Guzin; Ekmekci, Ahmet Hakan
International Journal of Intelligent Systems and Applications in Engineering Vol 5, No 4 (2017)
Publisher : Advanced Technology and Science (ATScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.2017533901

Abstract

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.
Feature Selection from 3D Brain Model for Some Dementia Subtypes Using Genetic Algorithm Okyay, Savas; Adar, Nihat; Ozkan, Kemal; Adapinar, Baki
International Journal of Intelligent Systems and Applications in Engineering 2017: Special Issue
Publisher : Advanced Technology and Science (ATScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.2017SpecialIssue31415

Abstract

Brain scans that are appropriate to the medical standards are obtained from magnetic resonance imaging devices. Through image processing techniques, 3D brain models can be constructed by mapping medical brain imaging files structurally. Physical characteristics of patient brains can be extracted from those 3D brain models. Characteristics of some specific brain regions are more efficacious in predicting the type of the disease. For that reason, researches are made for finding the worthwhile features out using cortical volumes, gray volumes, surface areas, and thickness averages for left and right brain parts separately or together. The main objective of this work is determining more influential sections throughout the entire brain in establishing the clinical diagnosis. To that end, among all the measurements exported from 3D models, the significant brain features that are effective in identifying some dementia subtypes are sought. The dataset has 3D brain models generated from magnetic resonance scans of 63 samples. Each sample is labeled with one of the following three disease types: Alzheimer’s disease (19), frontotemporal dementia (19), and vascular dementia (25). The genetic algorithm based wrapper feature selection method with various classifiers is proposed to select the features that state the aforementioned dementia subtypes best. The tests are performed by applying cross validation technique and confusion matrices are shown. At the end, the best features are listed, and the accuracy results up to 95.2% are achieved.
Comparison of Artifıcial Neural Networks and Response Surface Methodology in Stone Mastic Asphalt Using Waste Granite Filler Caner, Murat
International Journal of Intelligent Systems and Applications in Engineering Vol 5, No 4 (2017)
Publisher : Advanced Technology and Science (ATScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.2017533860

Abstract

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. 

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