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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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Different Apple Varieties Classification Using kNN and MLP Algorithms Sabancı, Kadir
International Journal of Intelligent Systems and Applications in Engineering 2016: Special Issue
Publisher : Prof. Dr. Ismail SARITAS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.2016Special Issue-146967

Abstract

In this study, three different apple varieties grown in Karaman province are classified using kNN and MLP algorithms. 90 apples in total, 30 Golden Delicious, 30 Granny Smith and 30 Starking Delicious have been used in the study. DFK 23U445 USB 3.0 (with Fujinon C Mount Lens) industrial camera has been used to capture apple images. 4 size properties (diameter, area, perimeter and fullness) and 3 color properties (red, green, blue) have been decided using image processing techniques through analyzing each apple image.  A data set which contains 7 physical features for each apple has been obtained. Classification success rates and error rates have been decided changing the neuron numbers in the hidden layers in the classification using MLP model and in different neighbor values in the classification made using kNN algorithm. It is seen that the classification using MLP model is much higher. While the success rate of classification made according to apple type is 98.8889%.
Application of Fuzzy Logic in Land Consolidation-Classification Studies Ertunç, Ela; Çay, Tayfun
International Journal of Intelligent Systems and Applications in Engineering 2016: Special Issue
Publisher : Prof. Dr. Ismail SARITAS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.267961

Abstract

Land classification is one of the most important stages of consolidation projects. The success and timely completion of this project depends on that this classification is useful and fair and are accepted by landowners. Different methods have been developed for the classification. Effects on the success of the land consolidation of the results of these methods are being investigated. In this study, fuzzy logic method has been used for land classification according to Law No. 5403. In Mamdani Type Fuzzy Logic, Values of soil index, productivity index and the location index, which are used to determine the value of the parcel index, have been defined as input, whereas  the value of parcel index have been defined as the output. Inputs and outputs have been converted to the linguistic terms (such as very efficient, inefficient, somewhat efficient, remote, near) by creating membership functions. Rule base has been created for calculating of the parcel index. As a result of fuzzy inference and defuzzification process, the model formed by Mamdani Type Fuzzy Logic gives the value of parcel index. By giving random input values to test generated model, results has been compared with results obtained manually. 
An Investigation of the Effect of Meteorological Parameters on Wind Speed Estimation Using Bagging Algorithm Emeksiz, Cem; Demir, Gülden
International Journal of Intelligent Systems and Applications in Engineering Vol 6, No 4 (2018)
Publisher : Prof. Dr. Ismail SARITAS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.2018448459

Abstract

Wind speed is the most important parameter of the wind energy conversion system. Therefore temperature, humiditiy and pressure data, which has significant effect on the wind speed, have become extremely important. In the literature, various models have been used to realize the wind speed estimation. In this study; Six different data mining algorithms were used to determine the effect of meteorological parameters on wind speed estimation. The data were collected from the measurement station established on the campus of Gaziosmanpaşa University. We focused on the bagging algorithm to determine the appropriate combination of wind speed estimates.  The bagging algorithm was used for the first time in estimation of wind speed by taking into account meteorological parameters. To find the most efficiency method on such problem 10-fold cross validation technique was used for comparision. From results, It is concluded that bagging algorithm and temperature-humiditiy-pressure combination showed the best performance. Additionaly, temperature and pressure data are more effective in the wind speed estimation.
A Simple and Efficient Approach to Compute the Operating Frequency of Annular Ring Patch Antennas by Using ANN with Bayesian Regularization Learning Algorithm Kayabaşı, Ahmet; Akdağlı, Ali
International Journal of Intelligent Systems and Applications in Engineering 2016: Special Issue
Publisher : Prof. Dr. Ismail SARITAS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.2016Special Issue-146981

Abstract

An annular ring patch antenna (ARPA) constructed by loading a circular slot in the center of the circular patch antenna is a popular microstrip antenna due to its favourable properties. In this paper, an application of artificial neural network (ANN) using bayesian regularization (BR) learning algorithm based on multilayer perceptron (MLP) model is presented for computing the operating frequency of annular ring ARPAs in UHF band.  Firstly, the operating frequencies of 80 ARPAs having varied dimensions and electrical parameters were simulated with IE3DTM packaged software based on method of moment (MoM) in order to generate the data set for training and testing processes of the ANN model. Then ANN model was built with data set and while 70 simulated ARPAs and remaining 10 simulated ARPAs were employed for ANN model training and testing respectively. The proposed ANN model were confirmed by comparing with the suggestions reported elsewhere via measurement data published earlier in the literature. These results show that ANN model with BR learning algorithm can be successfully used to compute the operating frequency of ARPAs. 
Surface Roughness Estimation for Turning Operation Based on Different Regression Models Using Vibration Signals Neseli, Suleyman; Yalcin, Gokhan; Yaldiz, suleyman
International Journal of Intelligent Systems and Applications in Engineering Vol 6, No 4 (2018)
Publisher : Prof. Dr. Ismail SARITAS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.2018648454

Abstract

On machined parts, major indication of surface quality is surface roughness and also surface quality is one of the most specified customer requirements. In the turning process, the importance of machining parameter choice is enhancing, as it controls the required surface quality. To obtain the better surface quality, the most essential control parameters are tool overhang and tool geometry in turning operations. The goal of this study was to develop an empirical multiple regression models for prediction of surface roughness (Ra) from the input variables in finishing turning of 42CrMo4 steel. The main input parameters of this model are tool overhang and tool geometry such as tool nose radius, approaching angle, and rake angle in negative direction. Regression analysis with linear, quadratic and exponential data transformation is applied so as to find the best suitable model. The best results according to comparison of models considering determination coefficient (R2) are achieved with quadratic regression model. In addition, tool nose radius was determined as the most effective parameter on turning by variance analysis (ANOVA). Cutting experiments and statistical analysis demonstrate that the model developed in this work produces smaller errors than those from some of the existing models and have a satisfactory goodness in all three models construction and verification.
A modified cuckoo search using different search strategies HAKLI, Hüseyin
International Journal of Intelligent Systems and Applications in Engineering 2016: Special Issue
Publisher : Prof. Dr. Ismail SARITAS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.2016Special Issue-146972

Abstract

Cuckoo search (CS) is one of the recent population-based algorithms used for solving continuous optimization problems. The most known problem for optimization techniques is balancing between exploration and exploitation. CS uses two search strategies to updating the nest: local and global search.  Although cuckoo search are adequate for the exploration, it is not well enough the exploitation. Only one search equation is used for local search, this equation remains incapable and causes some deficiencies about the exploitation.  To enhance the ability of exploitation and to balance between global search and local search, different search strategies were implemented in CS algorithm. The proposed method was compared with basic CS on well-known unimodal and multimodal benchmark functions. Experimental results show that the proposed method is more successful than the basic CS in terms of solution quality.
Implementation of Fuzzy Logic Based Speed Control of Brushless Direct Current Motors via Industrial PC ÇEVEN, Süleyman; BAYIR, Raif
International Journal of Intelligent Systems and Applications in Engineering 2016: Special Issue
Publisher : Prof. Dr. Ismail SARITAS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.270370

Abstract

The brushless direct current motors are often preferred in the industry due to their high development torques, efficiencies, speed and position controls. Especially, they are used with robotic, numeric-controlled machines, electrical vehicles, etc. One of the biggest difficulties of these motors is the closed loop operation of these motors with driver circuits and a controller. In this study, the speed control of the brushless direct current motors was made with PLC-based industrial computer by using the methods of PID and Fuzzy Logic. PLC based industrial computer of Beckhoff firm CX9020 was preferred as a controller. In this industrial computer, the software of the controller was developed by using Structured Text programming language of Twincat 2.11 program. In experimental studies, the speed control of the brushless direct current motors is made with PID and fuzzy logic controller, according to the requested reference. The performances of the controllers were tested by using step, ramp and ladder functions. While PID controller gave better results in reference speed areas whose parameters were determined, fuzzy logic controller gave better results in variable references. Although PID is given as a ready block in PLC and PLC based controllers, fuzzy logic is under development in many of them. In this study, classical PLCs and PLC based industrial PCs, which did not have any fuzzy logic controller module, were transformed into intelligent controllers with Structured Text programming language. As a result of this, classical PLCs and PLC based industrial PCs can be used in intelligent control, which is very important for industry 4.0.
PAPR Reduction in OFDM Systems using Partial Transmit Sequence combined with Cuckoo Search Optimization Algorithm (ICAT16-0286) BOZKURT, Yüksel TOKUR; TASPINAR, Necmi
International Journal of Intelligent Systems and Applications in Engineering 2016: Special Issue
Publisher : Prof. Dr. Ismail SARITAS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.2016Special Issue-146986

Abstract

Partial transmit sequence (PTS) scheme is one of the efficacious tool for lessening peak-to-average power ratio (PAPR) in orthogonal frequency division multiplexing (OFDM) system. However, computational cost for the optimum phase factors searches of PTS scheme entails huge computational requirements and limits its applicability to practical applications especially for high-speed data transmissions. This study proposes a PAPR reduction method with a low computational complexity based on a combination of cuckoo search optimization algorithm with PTS scheme in OFDM system. In terms of PAPR and computational cost reductions, the performance of the cuckoo-PTS scheme is comparatively investigated by performing a set of simulations with different PTS schemes.
COMPARISON OF SIMULATED ANNEALING AND GENETIC ALGORITHM APPROACHES ON INTEGRATED PROCESS ROUTING AND SCHEDULING PROBLEM Botsalı, Ahmet Reha
International Journal of Intelligent Systems and Applications in Engineering 2016: Special Issue
Publisher : Prof. Dr. Ismail SARITAS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.267358

Abstract

Today flexible manufacturing systems are highly popular due to their capability of quick response to customer needs. Although the advantages of flexible manufacturing systems cannot be denied, these systems also bring new issues on production planning side. Especially assigning machines to production operations and scheduling these operations with respect to machine constraints turn out to be an NP-Hard problem. In this study, the integrated process routing and scheduling problem is explained, and the performance of two different meta-heuristic techniques, which are genetic algorithms and simulated annealing, are compared in terms of solution time and quality.
A New Supervised Epidemic Model for Intelligent Viral Content Classification Şenoğlu, Abdulkerim; Yavanoğlu, Uraz; Özdemir, Suat
International Journal of Intelligent Systems and Applications in Engineering 2016: Special Issue
Publisher : Prof. Dr. Ismail SARITAS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.2016Special Issue-146977

Abstract

In this study, we propose an information diffusion model which is based on neural networks, artificial intelligence and supervised epidemic approach. We collected epidemically diffused data from Twitter with supervision to create a ranking system that forms the base of our diffusion model. The collected data is also used to train the proposed model. The outputs of the proposed model are shown to be useful for the provenance problem and the diffusion prediction systems in both physical and social networks. Knowing the viral content beforehand can be used in advertisement, industry, politics or any other end user that wants to reach a large number of people. Our performance analysis show that the proposed model can achieve over 90% training success rate and 78% test success rate of classifying viral content which is better than some of the existing models.

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