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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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A Performance Comparison of Graph Coloring Algorithms Aslan, Murat; Baykan, Nurdan Akhan
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.273053

Abstract

Graph coloring problem (GCP) is getting more popular to solve the problem of coloring the adjacent regions in a map with minimum different number of colors. It is used to solve a variety of real-world problems like map coloring, timetabling and scheduling. Graph coloring is associated with two types of coloring as vertex and edge coloring. The goal of the both types of coloring is to color the whole graph without conflicts. Therefore, adjacent vertices or adjacent edges must be colored with different colors.  The number of the least possible colors to be used for GCP is called chromatic number. As the number of vertices or edges in a graph increases, the complexity of the problem also increases. Because of this, each algorithm can not find the chromatic number of the problems and may also be different in their executing times. Due to these constructions, GCP is known an NP-hard problem. Various heuristic and metaheuristic methods have been developed in order to solve the GCP. In this study, we described First Fit (FF), Largest Degree Ordering (LDO), Welsh and Powell (WP), Incidence Degree Ordering (IDO), Degree of Saturation (DSATUR) and Recursive Largest First (RLF) algorithms which have been proposed in the literature for the vertex coloring problem and these algorithms were tested on benchmark graphs provided by DIMACS. The performances of the algorithms were compared as their solution qualities and executing times. Experimental results show that while RLF and DSATUR algorithms are sufficient for the GCP, FF algorithm is generally deficient. WP algorithm finds out the best solution in the shortest time on Register Allocation, CAR, Mycielski, Stanford Miles, Book and Game graphs. On the other hand, RLF algorithm is quite better than the other algorithms on Leighton, Flat, Random (DSJC) and Stanford Queen graphs. 
A Comparative Study of Statistical and Artificial Intelligence based Classification Algorithms on Central Nervous System Cancer Microarray Gene Expression Data Arslan, Mustafa Turan; Kalinli, Adem
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.267094

Abstract

A variety of methods are used in order to classify cancer gene expression profiles based on microarray data. Especially, statistical methods such as Support Vector Machines (SVM), Decision Trees (DT) and Bayes are widely preferred to classify on microarray cancer data. However, the statistical methods can often be inadequate to solve problems which are based on particularly large-scale data such as DNA microarray data. Therefore, artificial intelligence-based methods have been used to classify on microarray data lately. We are interested in classifying microarray cancer gene expression by using both artificial intelligence based methods and statistical methods. In this study, Multi-Layer Perceptron (MLP), Radial basis Function Network (RBFNetwork) and Ant Colony Optimization Algorithm (ACO) have been used including statistical methods. The performances of these classification methods have been tested with validation methods such as v-fold validation. To reduce dimension of DNA microarray gene expression has been used Correlation-based Feature Selection (CFS) technique. According to the results obtained from experimental study, artificial intelligence-based classification methods exhibit better results than the statistical methods.
A PSO Tuned Fractional-Order PID Controlled Non-inverting Buck-Boost Converter for a Wave/UC Energy System SAHIN, Erdinc; ALTAS, Ä°smail Hakki
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.265971

Abstract

In this study, a fractional order PID (FOPID) controller is designed and used to control a DC-DC non-inverting buck-boost converter (NIBBC) for a wave/ultra-capacitor (UC) energy system. Because of the energy discontinuities encountered in wave energy conversion systems (WECS), an UC is integrated to the WECS. In order to obtain the best controller performance, particle swarm optimization (PSO) is employed to find the optimum controller parameters. Integral of time weighted absolute error (ITAE) criteria is used as an objective function. Also, an optimized PID controller is designed to test the performance of the FOPID controller. The whole system is developed in Matlab/Simulink/SimPower environment. The simulation results show that the FOPID controller provides lower value performance indices than the PID controller in terms of reducing the output voltage sags and swells.
Preparing Diet List Suggestion with Fuzzy Expert System UYAR, OKAN
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.266528

Abstract

Proportion of disease is growing due to the malnutrition and sedentary life. In this work, a diet list is proposed to the user by calculating the necessary amount of calorie according to gender, weight, height, age and activity level. Diet list are prepared for seven days of a week as divided by six meals. Parameter assessment of users and offering recommendations are made via fuzzy expert system. Prepared diet list are constituted considering calories of nutrients and based on the dieticians’ general diet list proposals. Developed software also includes some functions such as nutrition advices, calculation of ideal weight, information about benefits of several nutrients and calorie evaluation of some daily activities. Thus, nutrition suggestion software carried out against for growing obesity and healthy eating problems in order that people would educate themselves about wellness.
An Analysis on the Comparison of the Performance and Configuration Features of Big Data Tools Solr and Elasticsearch AKCA, Mustafa Ali; Aydoğan, Tuncay; İlkuçar, Muhammer
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: 10.18201/10.18201/ijisae.271328

Abstract

Today, every kind of text, audio and visual data, which are thought to be transformed into pieces of information, are stored for long periods of time for processing. The concept of Bid Data is not only associated with the data stored, but also with the system involving hardware and software that collects, processes, stores, and analyzes the data. As the data grows bigger, their physical storage options must be provided in a distributed architecture. Solr and Elasticsearch are among the most preferred tools which makes this storage process easier. As a part of Apache Lucene project, Solr is a software which was started to be developed in 2004 with the searching features of full text, multiple search, dynamic clustering, database-integrated, open source and elasticity. Similarly, Elasticsearch is a new open-source tool for real-time, full-text and distributed search, which was launched in 2010 using the Lucene library. Although Solr and Elasticsearch have similar features, there are many parameters that differentiates one from the other such as intended use, type of use, and query and indexing performances. This study researches and analyzes the differences between Solr and Elasticsearch with regards to their query and indexing speeds, ease and difficulties of use, configuration forms, and architectures in light of the literature, and the results are discussed regarding these tools’ performances. 
A new subspace based solution to background modelling and change detection Işık, Şahin; Özkan, Kemal; Gerek, Ömer Nezih; Doğan, Muzaffer
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.267148

Abstract

For surveillance system, the background subtraction plays an important role for moving object detection with an algorithm embedded in the camera. Since the existence algorithms cannot satisfy the good accuracy on complex backgrounds including illumination change and dynamic objects, we have put forward the concept of Common Vector Approach (CVA) as a new idea for background modelling. Effectiveness of proposed method is presented through the experiments on popular Wallflower dataset. The obtained visual outputs are compared with well-known methods based on the subjective and objective criteria. From the overall evaluation, we can note the proposed method is not only exhibit successful foreground detection results, but also promises an effective and efficient system for background modelling.
Short-term Load Forecasting based on ABC and ANN for Smart Grids Cevik, Hasan Huseyin; Harmancı, Hüseyin; Çunkaş, Mehmet
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.266014

Abstract

Short term load forecasting is a subject about estimating future electricity consumption for a time interval from one hour to one week and it has a vital importance for the operation of a power system and smart grids. This process is mandatory for distribution companies and big electricity consumers, especially in liberalized energy markets. Electricity generation plans are made according to the amount of electricity consumption forecasts. If the forecast is overestimated, it leads to the start-up of too many units supplying an unnecessary level of reserve, therefore the production cost is increased. On the contrary if the forecast is underestimated, it may result in a risky operation and consequently power outages can occur at the power system. In this study, a hybrid method based on the combination of Artificial Bee Colony (ABC) and Artificial Neural Network (ANN) is developed for short term load forecasting. ABC algorithm is used in ANN learning process and it optimizes the neuron connections weights of ANN. Historical load, temperature difference and season are selected as model inputs. While three years hourly data is selected as training data, one year hourly data is selected as testing data. The results show that the application of this hybrid system produce forecast values close to the actual values.
Customer Satisfaction Using Data Mining Approach ORALHAN, Burcu; UYAR, Kumru; ORALHAN, Zeki
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.266801

Abstract

Customers and products are the main assets for every business. Companies make their best to satisfy customers because of coming back to their companies. After sales service related to different steps that make customers are satisfied with the company service and products. After sales service covers different many activities to investigate whether the customer is satisfied with the service, products or not? Hence, after sales service is acting very crucial role for customer satisfaction, retention and loyalty. If the after sales service customer and services data is saved by companies, this data is the key for growing companies.  Companies can add value their brand value with the managing of this data. In this study, we aim to investigate effect of 6 factors on customer churn prediction via data mining methods. After sale service software database is the source of our data. Our data source variables are Customer Type, Usage Type, Churn Reason, Subscriber Period and Tariff  The data is examined by data mining program. Data are compared 8 classification algorithm and clustered by simple K means method. We will determine the most effective variables on customer churn prediction. As a result of this research we can extract knowledge from international firms marketing data.
Experimental Investigation and Fuzzy Logic Modelling of Performance Hydroxy Dry Cell with Different Plate Combination Işıktaş, Abdullah; Dincer, Kevser; Verim, Ali; Türkmen, Osman; Ata, Sadık
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.265480

Abstract

In this study, hydroxy (HHO) dry cell with different plate combination performances in terms of current and temperature were experimentally investigated and modeled with Rule-Based Mamdani-Type Fuzzy (RBMTF) modeling technique. Input parameters plate number and time; output parameters current, temperature were described by RBMTF if-the rules. The dimensions of the plates were          9x9 cm2, 10x10 cm2 and 11x11 cm2. Current and temperature were measured for the different plate combination. Tap water was used in the experiments and the system was set to 5 minutes. For each combination, new cells were prepared. Experimental data which obtained for current and temperature according to combination and time were used in the training step. Numerical parameters of input and output variables were fuzzificated as linguistic variables: very very low (L1), very low (L2), low (L3), negative medium (L4), medium (L5), positive medium (L6), high (L7), very high (L8) and very very high (L9) linguistic classes. With the linguistic variables used, rules were obtained for this system. The comparison between experimental data and RBMTF is done by using statistical methods like the coefficient of multiple determinations (R2). The actual values and RBMTF results indicated that RBMTF can be successfully used in HHO dry cell. 
Training ANFIS Using Genetic Algorithm for Dynamic Systems Identification HAZNEDAR, Bülent; KALINLI, Adem
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.266053

Abstract

In this study, the premise and consequent parameters of ANFIS are optimized using Genetic Algorithm (GA) based on a population algorithm. The proposed approach is applied to the nonlinear dynamic system identification problem. The simulation results of the method are compared with the Backpropagation (BP) algorithm and the results of other methods that are available in the literature. With this study it was observed that the optimisation of ANFIS parameters using GA is more successful than the other methods.

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