cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
-
Contact Email
-
Phone
-
Journal Mail Official
-
Editorial Address
-
Location
,
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 53 Documents
 1   2   3   4   5 
Search results for , issue " 2016: Special Issue" : 53 Documents clear
Estimation of Turkey Electric Energy Demand until Year 2035 Using TLBO Algorithm TEFEK, Mehmet Fatih; UGUZ, Harun
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.266082

Abstract

In this study, the estimation of Turkey primary electric energy demand until 2035 is tried to estimate by using Teaching-Learning Based Optimization (TLBO) Algorithm. Two models are proposed which are based on economic indicators TLBO algorithm linear energy demand (TLBOEDL) and TLBO algorithm quadratic energy demand (TLBOEDQ). In both of these two models the indicators used are Gross Domestic Product (GDP), population, importation and exportation. After a comparison of these two models with real values between 1979 and 2005 years, it is applied to the estimation of Turkey electric energy demand until 2035 by three different scenario. The estimation results are suitable with the estimation of Turkey total primary energy supply of 2013 Energy Report of World Energy Council Turkish National Committee (WEC-TNC ).   
RSSI and Flower Pollination Algorithm Based Location Estimation for Wireless Sensor Networks Sesli, Erhan; Hacıoğlu, Gökçe
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.265424

Abstract

Wireless Sensor Networks (WSN’s) have been finding to itself new applications continuously. Many of these applications need location information of nodes. The localization of nodes can be made by range based or range free localization methods conventionally. Angle-of-Arrival (AoA), Time-Difference-of-Arrival (TDoA), Received Signal Strength Indicator (RSSI), Time-of-Arrival (ToA) are well known range based methods. Therefore AoA, ToA and TDoA have some hardware and software difficulties for nodes which have limited processing and power sources. However RSSI based localization doesn’t cost high processing resources or complex hardware modifications. Most of the WSN nodes already have RSSI measurement capability. However RSSI measurements is vulnerable to noise and environmental effects. Therefore error of RSSI based localization can be over to an acceptable level. Centroid, APIT, DV-Hop and Amorphous are some of the range free localization methods. Range free methods can only give location information approximately but they don’t need any extra hardware or high processing capability. In this study WSN nodes are assumed randomly or regularly distributed on a certain area. Some of the nodes are beacon nodes. The beacon nodes are assumed as having higher power resources and GPS receivers. The locations of nodes are assumed as fixed. The beacon nodes send their location information sequentially. Localization of nodes are made through RSSI and location information of beacon nodes. The mean of RSSI is calculated to reduce effect of noise on it. A rough location estimation made by weighted centroid. A probabilistic based location estimation and flower pollination algorithm (FPA) are used separately to make final decision about the location. Rough estimates are used to limit search area of flower pollination algorithm in order to reduce convergence time.
Stiffness Analysis of Above Knee Prosthesis Ege, Mücahit; Küçük, Serdar
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.267047

Abstract

While a healthy human walks, his or her legs mutually perform good repeatability with high accuracy. This provides an esthetical movement and balance. People with above knee prosthesis want to perform walking as esthetical as a healthy human. Therefore, to achieve a healthy walking, the above knee prosthesis must provide a good stiffness performance. Especially stiffness values are required when adding a second axis movement to the ankle for eversion and inversion. In this paper, stiffness analysis of above-knee prosthesis is presented. The translational displacement of above knee prosthesis is obtained when the prosthesis is subjected to the external forces. Knowing stiffness values of the above knee prosthesis, designers can compute prosthesis parameters such as ergonomic structure, height, and weight and energy consumption.
The Diagnosis and Estimate of Chronic Kidney Disease Using the Machine Learning Methods ÇELİK, Enes; Atalay, Muhammet; Kondiloglu, Adil
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.265967

Abstract

Chronic kidney disease is a prolonged disease that damages the kidneys and prevents the normal duties of the kidneys. This disease is diagnosed with an increase of urinary albumin excretion lasting more than three months or with significant reduction in a kidney functions. Chronic kidney disease can lead to complications such as high blood pressure, anemia, bone disease and cardiovascular disease. In this study we have been investigated to determine the factors that decisive for early detection of chronic kidney disease, launching early patients treatment processes, prevent complications resulting from the disease and predict of disease.  The study aimed diagnosis and prediction of disease using the data set that composed of data of 250 patients with chronic kidney disease and 150 healthy people. First, the chronic kidney disease data was classified with machine learning algorithms and then training and test results were analysed.  The estimation results of chronic kidney disease were compared with similar data and studies.
Biogeography-Based Optimization Algorithm for Designing of Planar Steel Frames TUNCA, OSMAN; ÇARBAŞ, SERDAR
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.266128

Abstract

The optimization can be defined as a solution of problem under specific conditions to achieve a specific purpose. Optimization strategies commonly used for solving of various problems and have gained great importance in recent years especially in engineering.  Evolving optimization methods over the years has many varieties such as shape optimization, topology optimization, size optimization etc. The latest trend of optimization methods is metaheuristics which are more useful with easy applicable to complex problems regarding to traditional optimization methods. So that metaheuristics have supplanted the traditional methods particularly in engineering by the time. In this study, a planar steel frame which is designed according to the requirements comprised by AISC-LRFD (American Institute of Steel Construction-Load and Resistance Factor Design) has been optimized by aid of biogeography-based optimization (BBO) algorithm.
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. 

Page 1 of 6 | Total Record : 53

 1   2   3   4   5 

Filter by Year

2016 2016


Reset
Filter By Issues
All Issue Vol 6, No 4 (2018) Vol 6, No 3 (2018) Vol 6, No 2 (2018) Vol 6, No 1 (2018) Vol 5, No 4 (2017) Vol 5, No 3 (2017) Vol 5, No 2 (2017) Vol 5, No 1 (2017) 2017: Special Issue Vol 4, No 4 (2016) Vol 4, No 3 (2016) Vol 4, No 2 (2016) Vol 4, No 1 (2016) 2016: Special Issue Vol 3, No 4 (2015) Vol 3, No 3 (2015) Vol 3, No 2 (2015) Vol 3, No 1 (2015) Vol 2, No 4 (2014) Vol 2, No 3 (2014) Vol 2, No 2 (2014) Vol 1, No 4 (2013) Vol 1, No 3 (2013) Vol 1, No 2 (2013) More Issue