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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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Prototype Design and Application of a Semi-circular Automatic Parking System Atacak, Ismail; Erdogdu, Ertugrul
International Journal of Intelligent Systems and Applications in Engineering Vol 5, No 4 (2017)
Publisher : Advanced Technology and Science (ATScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.2017534390

Abstract

Nowadays, with the increasing population in urban areas, the number of vehicles used in traffic has also increased in these areas. This has brought with it major problems that are caused by insufficient parking areas, in terms of traffic congestion, drivers and environment. In this study, in order to overcome these problems, a multi-storey automatic parking system that automatically performs vehicle recognition, vehicle parking, vehicle delivery and pricing processes has been designed and the practical application of this system has been realized on a prototype. The vehicle recognition process in the designed system has been fulfilled through a software prepared on the personal computer connected to the webcam. A multi-storey semi-circular structure has been used as a parking area to resolve parking area deficiencies. Therefore, the carrying system that carries out the parking process in the system has been designed as a cylindrical coordinated robot that can move horizontally, vertically and in the forward-back direction. The control of whole system has been realized by PIC16F877A microcontroller. The results obtained from the prepared prototype have showed that the proposed system can provide significant contributions to the solution of problems resulting from parking area deficiencies.
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.   
The Usage Of Artificial Neural Networks Method In The Diagnosis Of Rheumatoid Arthritis Tok, Kadir; Saritas, Ismail
International Journal of Intelligent Systems and Applications in Engineering Vol 4, No 4 (2016)
Publisher : Advanced Technology and Science (ATScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.2016426382

Abstract

In this study, artificial neural networks (ANN) method is used for the diagnosis of rheumatoid arthritis in order to support medical diagnostics. For the diagnosis of rheumatoid arthritis, backpropagation algorithm was examined in Matlab R2015b environment in artificial neural networks. With the system, the data in a data set, which are received from the patients with rheumatoid arthritis and from the people who are not suffering from rheumatoid arthritis, are classified successfully. Also, ANN backpropagation algorithm results and the results found by Perceptron algorithm are compared in terms of performance. Whereas %82 accuracy percentage is obtained with the Backpropagation method in performance tests in the data set, the accuracy percentage is calculated %71 with Perceptron method.
SDF: psychological Stress Detection Framework from Microblogs using Pre-defined rules and Ontologies Ali, Mohammed Mahmood; Tajuddin, Mohd; Kabeer, M.
International Journal of Intelligent Systems and Applications in Engineering Vol 6, No 2 (2018)
Publisher : Advanced Technology and Science (ATScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.2018642080

Abstract

Spreading of Unwanted microblogs from Social Networking Sites (SNS) is pervasive  in social media that leads to unaccountable disturbances such as Mental disorders, Wastage of precious time, Break-up of relationships, Stressness giving birth to psychological health problems and manymore.  To overcome these problems, the immense necessity is to ignore those unwanted microblogs in SNS, which is uncontrollable by humans due to addiction towards social media. Even the literate people fall prey to psychological stress from SNS. This seriousness of stress related issues is very rarely attended by researchers, to tackle such vicious microblogs. The prediction strategy is proposed named as Stress Detection Framework (SDF) to analyze the stress in microblog. SDF is developed using Ontology based Information Extraction technique using Probabilistic Model (GSHL & TreeAlignment Algorithm), set of pre-defined knowledge based logical rules that constitutes of low-level attributes (simple textual, linguistic words) and visual features (emoticons & Images) and social Interaction (Likes and Dislikes) to detect and predict stress in microblog messages.SDF is compared with TeniStrength that has shown an increase of 94.2% of stress detection rate. The experimental results obtained will aid to take precise decision for blocking/eradicating/ segregating stress related microblogs from Social media (especially SNS).
Comparison of Multi-Label Classification Methods for Prediagnosis of Cervical Cancer Ceylan, Zeynep; Pekel, Ebru
International Journal of Intelligent Systems and Applications in Engineering Vol 5, No 4 (2017)
Publisher : Advanced Technology and Science (ATScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.2017533896

Abstract

Cervical cancer is one of the most common causes of cancer death of women. Prediagnosis of cervical cancer at early stages is critical to reduce mortality ratios.  Additionally, early prediction of cervical cancer can help both the patients and the physicians depending on easiness of treatment. Cervical cancer results from various risk factors such as family history, education level, having multiple full-term pregnancies, smoking, and sexually transmitted diseases and etc. Recently, different types of advanced methods were developed for risk prediction analysis based on machine learning techniques. The purpose of this study is to investigate the efficacy of using multi-label classification techniques for diagnosing cervical cancer at early stage. Four common learning algorithms such as Naïve Bayes, J48 Decision Tree, Sequential Minimal Optimization, and Random Forest were compared in terms of their accuracy, hamming loss, exact match (subset accuracy) and ranking loss performance evaluation metrics. Thus, this study can help to physicians, academics and cancer researchers to make fast and accurate diagnosis.
Effects of Zero Velocity Update on Total Displacement for Indoor Inertial Positioning Systems ULAMIS, Faruk; LUY, Murat; CAM, Ertugrul; UZUN, Ibrahim
International Journal of Intelligent Systems and Applications in Engineering Vol 5, No 2 (2017)
Publisher : Advanced Technology and Science (ATScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.2017528729

Abstract

In this paper; the effects of Zero Velocity Update method, which is one of the most important components of indoor inertial positioning systems, on total displacement is studied. For this purpose, acceleration and angular velocity measurements on three axes are obtained by a low cost foot mounted inertial measurement unit while walking. The obtained acceleration values are processed and velocity and total displacement are estimated by using double integration. Velocity and displacement estimations done at the end of the each step have been calculated with and without ZUPT algorithm and the results have been compared. Furthermore, in order to understand ZUPT algorithm well, a rectangular shape is plotted with the system containing IMU and microprocessor by stopping at every corner. ZUPT algorithm is implemented at each stop on the corners of the rectangular shape. The results are plotted in MATLAB. Effects of the errors on total displacement are pointed out.
Validation of Registration for Renal Dynamic Contrast Enhanced MRI Imaging Yuksel, Seniha Esen
International Journal of Intelligent Systems and Applications in Engineering Vol 4, No 3 (2016)
Publisher : Advanced Technology and Science (ATScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.45496

Abstract

In Dynamic Contrast Enhanced Resonance Imaging (DCE-MRI), abdomen is scanned repeatedly and rapidly after injection of a contrast agent. During data acquisition, collected images suffer from the motion induced by the patient if he/she moves or breathes heavily during the scan. Therefore, these images should be aligned accurately to correct the motion. Recently, mutual information (MI) registration has become the first tool to register renal DCE-MRI images before any further processing. However, MI registration is sensitive to initial conditions and optimization methods, and it is bound to fail under certain conditions such as extreme movement or noise in the image. Therefore, if automated image analysis for renal DCE-MRI is to enter the clinical settings, it is necessary to have validation strategies that show the limitations of registration models on known datasets. In this study, two methods are introduced for the validation of registration of renal DCE-MRI images. The first method demonstrates how to use the inverse transform to generate realistic looking DCE-MRI kidney images and use them in validation. The second method shows how to generate checkerboard images and how to evaluate the goodness of registration for real DCE-MRI images. These validation methods can be incorporated into the registration studies to quantitatively and qualitatively demonstrate the success and the limitations of registration models.
Store data from experiments with microorganisms used in food industry Bosakova-Ardenska, Atanaska
International Journal of Intelligent Systems and Applications in Engineering Vol 1, No 3 (2013)
Publisher : Advanced Technology and Science (ATScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The aim of this paper is to present results from collaboration of computer engineers and experimenters in microbiology working with molecular-genetic methods. The experimenters in microbiological laboratory at the University of Food Technologies use ARDRA (Amplified Ribosomal DNA Restriction Analysis) analyses and DNA sequencing processed with BLAST (Basic Local Alignment Search Tool) algorithm to identify some microorganisms. Their results have been accumulated in designed database. This will improve the effectiveness and productivity of the molecular-genetic analyses in department of microbiology.
MLP and KNN Algorithm Model Applications for Determining the Operating Frequency of A-Shaped Patch Antennas Kayabasi, Ahmet
International Journal of Intelligent Systems and Applications in Engineering Vol 5, No 3 (2017)
Publisher : Advanced Technology and Science (ATScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.2017531432

Abstract

In this study, two machine learning methods, namely multilayer perceptron (MLP) and K-nearest neighbors (KNN) algorithm models are used for determining the operating frequency of A-shaped patch antennas (APAs) at UHF band applications. Firstly, data set is obtained from the 144 antenna simulations using IE3D™ software based on method of moment (MoM). Weka (Waikato Environment for Knowledge Analysis) program was then used to build models by considering 144 simulation data. The models input with the various dimensions and electrical parameters of 124 APAs are trained and their accuracies are tested via 20 APAs. The mean absolute error (MAE) values are calculated for different number of hidden layer neurons and different neighbourhood values in MLP and KNN models, respectively.  The performance of the MLP and KNN models are compared in the training and testing process. The lowest MAEs are obtained with 6 hidden layer neurons for MLP and 2 neighbourhood values for KNN. These results point out that this models can be successfully applied to computation operating frequencies of APAs.
Classification of Siirt and Long Type Pistachios (Pistacia vera L.) by Artificial Neural Networks Sabanci, Kadir; Koklu, Murat; Unlersen, Muhammed Fahri
International Journal of Intelligent Systems and Applications in Engineering Vol 3, No 2 (2015)
Publisher : Advanced Technology and Science (ATScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.74573

Abstract

Quality is one of the important factors in agricultural products marketing. Grading machines have great role in quality control systems. The most efficient method used in grading machines today is image processing. This study aims to do the grading of high valued agricultural product of our land called pistachio that has two different types namely Siirt and Long type of pistachios by image processing methods and artificial neural networks. Photos of Siirt and long type of pistachios are taken by a Webcam with CCD sensor. These photos were converted to gray scale in Matlab. Afterwards, these photos were converted to binary photo format using Otsu’s Method. Then this data was used to train multi-layered neural network to complete grading.  Matlab was used for both image processing and artificial neural networks. Successes of the grading with image processing and artificial neural networks for mixed type pistachios Siirt and Long were researched.

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