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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 7 Documents
 1 
Search results for , issue " 2017: Special Issue" : 7 Documents clear
A New Approach Based on Image Processing for Measuring Compressive Strength of Structures Baygin, Mehmet; Ozkaya, Suat Gokhan; Ozdemir, Muhammed Alperen; Kazaz, Ilker
International Journal of Intelligent Systems and Applications in Engineering 2017: Special Issue
Publisher : Advanced Technology and Science (ATScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.2018SpecialIssue31419

Abstract

The compressive strength factor in civil engineering is a very important parameter used to determine the performance of structures. The stability of structures can be tested with this parameter which is used to measure the performance of concrete under different loads. This parameter, which should be determined for the safety of the structures, is usually based on experimental analyses performed in the laboratory environment. In this study, a new approach to compressive strength measurement in civil engineering is proposed. With this approach, which is based on image processing, measurement of compressive strength parameter of concrete samples taken from structures is performed. For this purpose, images of concrete specimens with different strengths are taken and these images are divided into two groups as training and test set. Then, image processing algorithms are applied to these images and the compressive strength of concrete specimens is calculated. It has been determined that the approach suggested in the test runs performed with an error rate of about 1-2%
Nonstationary Fuzzy Systems for Modelling and Control in Cyber Physical Systems under Uncertainty Yetis, Hasan; Karakose, Mehmet
International Journal of Intelligent Systems and Applications in Engineering 2017: Special Issue
Publisher : Advanced Technology and Science (ATScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.2017SpecialIssue31420

Abstract

The applications of cyber-physical systems (CPS), which have a wide range from industrial to medical, are increasing day by day thanks to its reliable, scalable and flexible structure. In a CPS, the consistency and reliability of system are much more important, because they are generally used in large-scale and critical tasks. Uncertainties are unexpected situations and no matter how well a system designed they are a threat to a system always. Fuzzy logic is one of the algorithms that can be utilized in cyber layer easily. But because of its insufficiency in handling uncertainties new fuzzy types are emerged. Nonstationary fuzzy system is a type of fuzzy logic which is able to handle uncertainty in reasonable time. In this study a new inference system for nonstationary fuzzy systems is developed to enhance nonstationary fuzzy systems. The system is based on two main steps, first adding some random uncertainties to nonstationary inputs, and second obtaining single output value for the inputs. Thus, the fuzzy system always has uncertainty and the behavior of system is prepared for the uncertainties. The proposed method is verified by simulation results which demonstrate the effectiveness of system especially for noisy data compared to the type-1, and nonstationary fuzzy systems. The proposed method can be used in CPS which need consistency and robustness.   
Feature Selection from 3D Brain Model for Some Dementia Subtypes Using Genetic Algorithm Okyay, Savas; Adar, Nihat; Ozkan, Kemal; Adapinar, Baki
International Journal of Intelligent Systems and Applications in Engineering 2017: Special Issue
Publisher : Advanced Technology and Science (ATScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.2017SpecialIssue31415

Abstract

Brain scans that are appropriate to the medical standards are obtained from magnetic resonance imaging devices. Through image processing techniques, 3D brain models can be constructed by mapping medical brain imaging files structurally. Physical characteristics of patient brains can be extracted from those 3D brain models. Characteristics of some specific brain regions are more efficacious in predicting the type of the disease. For that reason, researches are made for finding the worthwhile features out using cortical volumes, gray volumes, surface areas, and thickness averages for left and right brain parts separately or together. The main objective of this work is determining more influential sections throughout the entire brain in establishing the clinical diagnosis. To that end, among all the measurements exported from 3D models, the significant brain features that are effective in identifying some dementia subtypes are sought. The dataset has 3D brain models generated from magnetic resonance scans of 63 samples. Each sample is labeled with one of the following three disease types: Alzheimer’s disease (19), frontotemporal dementia (19), and vascular dementia (25). The genetic algorithm based wrapper feature selection method with various classifiers is proposed to select the features that state the aforementioned dementia subtypes best. The tests are performed by applying cross validation technique and confusion matrices are shown. At the end, the best features are listed, and the accuracy results up to 95.2% are achieved.
A Deep Learning Approach for Optimization of Systematic Signal Detection in Financial Trading Systems with Big Data Karaoglu, Sercan; Arpaci, Ugur; Ayvaz, Serkan
International Journal of Intelligent Systems and Applications in Engineering 2017: Special Issue
Publisher : Advanced Technology and Science (ATScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.2017SpecialIssue31421

Abstract

Expert systems for trading signal detection have received considerable attention in recent years. In financial trading systems, investors’ main concern is determining the best time to buy or sell a stock. The trading decisions are often influenced by the emotions and feelings of the investors. Therefore, investors and researchers have aimed to develop systematic models to reduce the impact of emotions on trading decisions. Nevertheless, the use of algorithmic systems face another problem called “lack of dynamism”. Due to dynamic nature of financial markets, trading robots should quickly learn and adapt as human traders. Recently, a solution for detecting trading signals based on a dynamic threshold selection was proposed. In this study, we extend this approach by adopting several different rule based systems and enhancing it by using the Recurrent Neural Network algorithm. Recurrent Neural Networks learn the connection weights of subsystems with arbitrary sequences of inputs that make them a great fit for time series data. Our model is based on Piecewise Linear Representation and Recurrent Neural Network with the goal of detecting potential excessive movements in noisy stream of time series data. We use an exponential smoothing technique to detect abnormalities. Trading signals are produced using fixed time interval data from Istanbul Stock Exchange. The evaluations indicated that our model produces successful results in trading data. Future work will focus on further improvements and scalability of the model.
Training Of Artificial Neural Network Using Metaheuristic Algorithm Alwaisi, Shaimaa Safaa Ahmed; Baykan, Omer Kaan
International Journal of Intelligent Systems and Applications in Engineering 2017: Special Issue
Publisher : Advanced Technology and Science (ATScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.2017SpecialIssue31417

Abstract

This article clarify enhancing classification accuracy of Artificial Neural Network (ANN) by using metaheuristic optimization algorithm. Classification accuracy of ANN depends on the well-designed ANN model. Well-designed ANN model Based on the structure, activation function that are utilized for ANN nodes, and the training algorithm which are used to detect the correct weight for each node. In our paper we are focused on improving the set of synaptic weights by using Shuffled Frog Leaping metaheuristic optimization algorithm which are determine the correct weight for each node in ANN model. We used 10 well known datasets from UCI machine learning repository. In order to investigate the performance of ANN model we used datasets with different properties. These datasets have categorical, numerical and mixed properties. Then we compared the classification accuracy of proposed method with the classification accuracy of back propagation training algorithm. The results showed that the proposed algorithm performed better performance in the most used datasets.
Diagnosis of Mesothelioma Disease Using Different Classification Techniques Tutuncu, Kemal; Cataltas, Ozcan
International Journal of Intelligent Systems and Applications in Engineering 2017: Special Issue
Publisher : Advanced Technology and Science (ATScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.2017SpecialIssue31416

Abstract

Mesothelioma, which is a disease of the pleura and peritoneum, is an asbestos-related environmental disease in undeveloped countries. Although the incidence of this disease is lower than that of lung cancer, the reaction it creates in society is very high. In this study, 9 different classification algorithms of data mining were applied to the Mesethelioma data set obtained from real patients in Dicle University, Faculty of Medicine and loaded into UCI Machine Learning Repository, and the results were compared. When the obtained results were examined, it has been seen that Artificial Neural Network (ANN) had %99.0740 correct classification ratio. 
Implementation and Evaluation of Face Recognition Based Identification System Elbizim, Faruk Can; Kasapbasi, Mustafa Cem
International Journal of Intelligent Systems and Applications in Engineering 2017: Special Issue
Publisher : Advanced Technology and Science (ATScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.2017SpecialIssue31418

Abstract

Face recognition has been widely used and implemented to many systems for the purpose of authentication, identification, finding faces, etc. In this study Yale face database [1] is used which consist of 15 different people. For each of person there are 11 different images with different face expressions. In this study images are categorized as normal, normal and center light, normal and happy, normal with left light and right light. In order to recognize these faces 4 different face recognition methods namely Eigenface, Fisherface, LBPHface and SURF are utilized in the developed environment. In order to test the mentioned face recognition algorithms a software is developed using EmguCV in .NET environment. After evaluating and comparing the obtained confusion matrix amongst other the LBPHface  method was found to be superior method with an average accuracy of 99%, it was ~98% SURF, ~97% for EigenFace and FisherFace. FicherFace was slightly better then the Eigenface method.

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