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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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Wind Power Forecasting For The Province Of Osmaniye Using Artificial Neural Network Method YANIKTEPE, BULENT; TASDEMIR, SAKIR; GUHER, A. BURAK; AKCAN, Sultan
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-146957

Abstract

Although wind energy at certain intervals and random in nature, today it is one of the commonly utilized alternative energy source in the world. Because of sustainability and environmentally-friendly energy source, countries increasingly benefit from wind energy. Several estimation methods are applied in the determination of a regions wind energy potential. Today, one of the most commonly used prediction methods is artificial neural network (ANN) method. In this study, Estimation of wind power in Osmaniye district was investigated in method with artificial neural network (ANN) using data from meteorological measurement stations from the meteorological measurement device at the campus of Osmaniye Korkut ATA University. In order to give the best values of prediction results, several methods increasing the impact on output of different models for the input variables were investigated. 
Psychological Stress Detection from Social Media Data using a Novel Hybrid Model Ali, Mohammed Mahmood; Hajera, Shaikha
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.2018448457

Abstract

Psychological stress is considered as the biggest threat to individual’s health. Hence, it is vital to detect and manage stress before it turns into severe problem. However, conventional stress detection strategies rely on psychological scales and physiological devices, which require active individual participation making it labor-consuming, complex and expensive. With the rapid growth of social networks, people are willing to share moods via social media platforms making it practicable to leverage online social interaction data for stress detection. The developed novel hybrid model Psychological Stress Detection (PSD), automatically detect the individual’s psychological stress from social media. It comprises of three modules Probabilistic Naïve Bayes Classifier, Visual (Hue, Saturation, Value) and Social, to leverage text, image post and social interaction information we have defined the set of stress-related textual ‘F = {f1, f2, f3, f4}’, visual ‘vF = {vf1, vf2}’, social ‘sf’ to detect and predict stress from social media content. Experimental results show that the proposed PSD model improves the detection performance when compared to TensiStrength and Teenchat framework, PSD achieves 95% of Precision rate. PSD model would be useful in developing stress detection tools for mental health agencies and individuals.
An Application in SPSS Clementine Based on the Comparison of Association Algorithms in Data Mining Karalök, Seren Sezen; Ersöz, Süleyman; Aktepe, Adnan
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-146980

Abstract

Data mining is the process of acquiring information form large data pools. In this study, associate analysis method is used.  The application and comparisons are found by using 3 different algorithms from SPSS Clementine which is a data mining software. In this study, the results are varied because different associate methods are applied on. Therefore, new findings are obtained.  Consequent to this, it will lead us to new strategies to develop for customers in Market Basket Analysis. This study is done by using a big supermarket data. Results are compared and reported for every each of 3 different algorithms. 
A fuzzy-genetic based design of permanent magnet synchronous motor Mutluer, Mümtaz
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.2018648453

Abstract

This paper presents a fuzzy-genetic based design of permanent magnet synchronous motor. The selected motor structure with surface magnet and double layer winding is for high torque and low speed applications. The design approach involves combining fuzzy logic and genetic algorithm in a powerful combination. While the genetic algorithm is used in scanning of the solution space, the fuzzy logic approach has been utilized in selecting the most appropriate solutions. While choosing geometric parameters as input for optimization, design equations are obtained by using geometrical, electrical and magnetic properties of the motor. The output results are evaluated with motor efficiency, motor weight and weight of magnets as the objective function. Furthermore, the multiobjective design optimization results are compared with the results obtained for each single objective and tested with finite element method. The results are finally remarkable and quite compatible with the finite element method results.
Big Bang-Big Crunch Optimization Algorithm for Solving the Uncapacitated Facility Location Problem KOC, Ismail
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-146971

Abstract

The big bang–big crunch (BB–BC) algorithm has been proposed as a new optimization method based on the big bang and big crunch theory, one of the theories of the evolution of the universe. The BB-BC algorithm has been firstly presented to solve the optimization problems with continuous solutions space. If the solution space of the problem is binary-structural, the algorithm must be modified to solve this kind of the problems. Therefore, in this study, the BB-BC method, one of the population-based optimization algorithms, is modified to deal with binary optimization problems. The performance of the proposed methods is analyzed on uncapacitated facility location problems (UFLPs) which are one of the binary problems used in literature. The well-known small and medium twelve instances of UFLPs are used to analyze the performances and the effects of the control parameter of the BB-BC algorithm. The obtained results are comparatively presented. According to the experimental results, the binary version of the BB-BC method achieves successful results in solving UFLP in terms of solution quality.
Knowledge Mining Approach For Healthy Monitoring From Pregnancy Data With Big Volumes Santur, Yunus; Santur, Sinem Güven
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.270369

Abstract

The process for obtaining information that will create value on a large-scale data stack is called data mining by its general name. Data mining is commonly used in sales and marketing departments, in determining strategies and making critical decisions for the future in many sectors. Similarly, data mining is used in the determination of health policies, more effective implementation of health services and in the management of resources and institutions in the health sector. In this study, it was aimed to create a software architecture of data mining that will help the personal monitoring of the pregnancy process in a more effective way in the health sector. Many different types of data such as age, gender, location, education, physical characteristics, lifestyle habits and medical history of the people that could be used for this purpose are stored online by health institutions. The machine learning algorithms have been created to determine classification, clustering and association rule on these data. 
A Model of Automatic Block Reallocation in the Land Consolidation Projects Using Artificial Bee Colony Algorithm İnceyol, Yaşar; Özbeyaz, Abdurrahman
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-146985

Abstract

Equitably reallocating of blocks among land owners has been one of the most important tasks in Land Consolidation studies. This task has to be fairly solved among landholdings for a land. This complicated problem is difficult to solve using linear methods. Therefore, a method is needed to solve this non-linear problem among land owners impartially. There are many applications employing optimization algorithms for solving the complicated and non-linear problems in literature. When we examine the literature, it is seen that Genetic Algorithm has been only used to overcome the block reallocation problem. Artificial Bee Colony (ABC) algorithm is one of the optimization algorithms that have been used to solve the non-linear and complicated problems in literature. Furthermore, this method has better performance when it is compared with the other optimization algorithms. In this study, we have aimed to fairly reallocate the landholding areas to blocks in a land by developing an algorithm using Artificial Bee Colony optimization method. When we develop the steps of the algorithm, we give priority to landholdings preferences and places of fixed installations. Data tables have been arranged by taking land consolidation data of DOT Village in Adiyaman, Turkey that into consideration. DOT Village land consolidation project includes 143 blocks and 225 landholders. Consequently, we have introduced the steps of an algorithm solving the block reallocation problem automatically using ABC for a sample land. Also, we have observed the applicability of the proposed method for automatic block reallocation problem in this study. This study is a preliminary study helping us to develop software providing to automatically solve complicated block reallocation problem in real time land consolidation process.
Improvement of Facility Layout by Using Data Mining Algorithms and an Application KOKOÇ, Melda; ERSÖZ, Süleyman; AKTEPE, Adnan; TÜRKER, Ahmet Kürşad
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.267175

Abstract

The facility layout problems are always important in the production or service industry system. For many years, it has been a common research field in active research. It seen that various methods were used in literature review for improving facility layout. We used association analysis which is one of the techniques of data mining in this study. The primary aim of this study is to improving the emergency departments efficiency, increasing patient satisfaction and employee satisfaction, decreasing travelled distance in emergency department. In this study, data acquired from information system of an emergency service was examined by means of data mining techniques (association rules such as GRI and Apriori) and relations between departments were found out. Association rules were analyzed via data mining techniques applied and then departments having more flow density with each other were determined. In consideration of this information an alternative facility layout planning was planned in regard to analysis results of departments’ closeness situations, advices of emergency service doctor and observations made.
A New Real Time Control Approach for Time Efficiency in Group Elevator Control System BAYĞIN, Mehmet; ORHAN, Dilbirin; YAMAN, Orhan; KARAKÖSE, 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.2016Special Issue-146976

Abstract

In parallel with the increase seen in the number of high-rise buildings, vertical transport systems are progressing. One of the results of this progress is the emergence of group elevator systems and their primary aim is to transport its passengers to the target floor the fastest way possible. Studies on this field are generally simulation and optimization based and they have an aim of minimizing the passengers’ waiting and traveling periods. In this study, a real time group elevator experimental setup was created and an optimization algorithm was applied on the setup. Genetic algorithm was chosen as optimization algorithm and this method was tested in an elevator prototype of 10 floors and 5 cabins. The results obtained revealed efficiency, performance and accuracy of proposed method.
A Review of Smart Parking System based on Internet of Things. Kaur, Harkiran; Malhotra, Jyoteesh
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.2018448450

Abstract

: The Internet of Things expands the wireless paradigm to the edge of the network, which enables to develop a different kind of applications or services. By using IoT the application becomes more reliable, flexible or portable. It has a large amount of nodes on the various geographic positions, to increase the awareness of location or reduce the latency. There are numerous IoT applications like smart grid, smart hospital, smart industry, smart traffic management etc. In this paper, we describe the smart traffic management and parking system using the internet to control the chaos and also discuss the various researches on this concept.

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