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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 5 Documents
 1 
Search results for , issue " Vol 4, No 2 (2016)" : 5 Documents clear
The Classification of Diseased Trees by Using kNN and MLP Classification Models According to the Satellite Imagery Unlersen, Muhammed Fahri; Sabanci, Kadir
International Journal of Intelligent Systems and Applications in Engineering Vol 4, No 2 (2016)
Publisher : Advanced Technology and Science (ATScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.05552

Abstract

In this study, the Japanese Oak and Pine Wilt in forested areas of Japan was classified into two group as diseased trees and all other land cover area according to the 6 attributes in the spectral data set of the forest. The Wilt Data Set which was obtained from UCI machine learning repository database was used. Weka (Waikato Environment for Knowledge Analysis) software was used for classification of areas in the forests. The classification success rates and error values were calculated and presented for classification data mining algorithms just as Multilayer Perceptron (MLP) and k-Nearest Neighbor (kNN). In MLP neural networks the classification performance for various numbers of neurons in the hidden layer was presented. The highest success rate was obtained as 86.4% when the number of neurons in the hidden layer was 10. The classification performance of kNN method was calculated for various counts of neighborhood. The highest success rate was obtained as 72% when the count of neighborhood number was 2.
GA Based Selective Harmonic Elimination for Five-Level Inverter Using Cascaded H-bridge Modules Bektas, Enes; Karaca, Hulusi
International Journal of Intelligent Systems and Applications in Engineering Vol 4, No 2 (2016)
Publisher : Advanced Technology and Science (ATScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.97681

Abstract

Multilevel inverters (MLI) have been commonly used in industry especially to get quality output voltage in terms of total harmonic distortion (THD). In addition, development in semiconductor technology and advanced modulation techniques make MLI implementation more attractive. Selective Harmonic Elimination (SHE) that can be applied MLI at desired switching frequency offers elimination of harmonics in the output voltage. Also, by using SHE technique with cascaded multilevel inverters, the necessity of using filter in the output can be minimized. In this paper, SHE equations have been solved by using of Genetic Algorithm (GA) Toobox&Matlab and it has been aimed to eliminate desired harmonic orders at fundamental output voltage. Simulation results have clearly demonstrated that GA based SHE techniques can eliminate the demanded harmonic orders.
Design and Implementation of High Speed Artificial Neural Network Based Sprott 94 S System on FPGA Koyuncu, Ismail
International Journal of Intelligent Systems and Applications in Engineering Vol 4, No 2 (2016)
Publisher : Advanced Technology and Science (ATScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.97824

Abstract

FPGA-based embedding system designs have been preferred for industrial applications and prototyping because of the advantages of parallel processing, reconfigurability and low cost. Due to having characteristic structure of the parallel processing of Artificial Neural Networks (ANNs), these systems provide the advantage of speed and performance when they are implemented with FPGA-based hardware. The hardware implementation of transfer functions used for modeling non-linear systems is a challenging problem. Therefore, this problem creates convergence problems. In this paper, non-linear Sprott 94 S system has been modeled using ANNs running on FPGA. All related parameter values and processes are defined with IEEE-754-1985 32-bit floating point number format. ANN-based Sprott 94 S system design has been developed using VHDL synthesized using Xilinx ISE Design Tools. In test stage, ANN-based Sprott 94 S system has been tested using 3X100 data set and obtained error analysis results have been presented.  The constructed design has been performed for Xilinx VIRTEX-6 family XC6VHX255T-3FF1923 FPGA chip using Place&Route process and chip usage statistics have been given. The clock frequency of ANN-based Sprott 94 S system which has pipeline processing scheme has been obtained with the value of 304.534 MHz. Accordingly, the proposed FPGA-based ANN system has produced 3X3.284 billion outputs in 1 second.
Classification of Different Wheat Varieties by Using Data Mining Algorithms Sabancı, Kadir; Akkaya, Mustafa
International Journal of Intelligent Systems and Applications in Engineering Vol 4, No 2 (2016)
Publisher : Advanced Technology and Science (ATScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.62843

Abstract

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. 
Application of ANN Modelling of Fire Door Resistance Altin, Mustafa; Tasdemir, Sakir
International Journal of Intelligent Systems and Applications in Engineering Vol 4, No 2 (2016)
Publisher : Advanced Technology and Science (ATScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.90445

Abstract

Fire doors are compulsorily used in every kind of building nowadays. The determination of fire doors’ resistance in which kind of buildings is also essential. This determination is needed to be watched through the experimental works done. Computer technologies and applications are commonly used in many fields in industry. In this study, by using the data obtained as a result of experiments made in order to determine the resistance of fire doors, artificial neural network (ANN) model was developed. With this model, it is aimed to evaluate the inner temperature of fire room having an important role in resistance of the fire door. In the developed system, temperature values belonging to thermocouples on the door (Top Left, Top  Right, Middle Left, Middle Right, Bottom Left, Bottom Right (oC) and time (minute) were taken as input parameters and in-room temperature (oC) was taken as output parameters. When the results obtained from ANN and experimental data are compared, it is determined that two groups of data were coherent. It is shown that ANN can be safely used in the determination of fire door resistance. 

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