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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 5 Documents
 1 
Search results for , issue " Vol 4, No 1 (2016)" : 5 Documents clear
An Analysis of Archive Update for Vector Evaluated Particle Swarm Optimization Naim, Faradila; Zuwairie, Ibrahim; Sheng, Lim Kian; Jusof, Mohd Falfazli Mat; Arshad, Nurul Wahidah
International Journal of Intelligent Systems and Applications in Engineering Vol 4, No 1 (2016)
Publisher : Advanced Technology and Science (ATScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.48588

Abstract

Multi-objective optimization problem is commonly found in many real world problems. In computational intelligence, Particle Swarm Optimization (PSO) algorithm is a popular method in solving optimization problems. An extended PSO algorithm called Vector Evaluated Particle Swarm Optimization (VEPSO) has been introduced to solve multi-objective optimization problems. VEPSO algorithm requires an archive, which is used to record the solutions found. However, the outcome may be differ depending on how the archive is used. Hence, in this study, the performance of VEPSO algorithm when updates the archive at different instance is investigated by measuring the convergence and diversity by using standard test functions. The results show that the VEPSO algorithm performs better when update the archive during the search process, in the iterations.
Classification of Wheat Types by Artificial Neural Network Yasar, Ali; Kaya, Esra; Saritas, Ismail
International Journal of Intelligent Systems and Applications in Engineering Vol 4, No 1 (2016)
Publisher : Advanced Technology and Science (ATScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.64198

Abstract

In this study, the types of wheat seeds are classified using present data with artificial neural network (ANN) approach.      Seven inputs, one hidden layer with 10 neurons and one output has been used for the ANN in our system. All of these parameters were real-valued continuous. The wheat varieties, Kama, Rosa and Canadian, characterized by measurement of main grain geometric features obtained by X-ray technique, have been analyzed. Results indicate that the proposed method is expected to be an effective method for recognizing wheat varieties. These seven input parameters reaches the 10-neurons hidden layer of the network and they are processed and then classified with an output. The classification process of 210 units of data using ANN is determined to make a successful classification as much as the actual data set. The regression results of the classification process is quite high. It is determined that the training regression R is 0,9999, testing regression is 0,99785 and the validation regression is 0,9947, respectively. Based on these results, classification process using ANN has been seen to achieve outstanding success.
Banknote Classification Using Artificial Neural Network Approach Kaya, Esra; Yasar, Ali; Saritas, Ismail
International Journal of Intelligent Systems and Applications in Engineering Vol 4, No 1 (2016)
Publisher : Advanced Technology and Science (ATScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.55250

Abstract

In this study, clustering process has been performed using artificial neural network (ANN) approach on the pictures belonging to our dataset to determine if the banknotes are genuine or counterfeit.  Four input parameters, one hidden layer with 10 neurons and one output has been used for the ANN. All of these parameters were real-valued continuous. Data were extracted from images that were taken from genuine and forged banknote-like specimens. For digitization, an industrial camera usually used for print inspection was used. The final images have 400x 400 pixels. Due to the object lens and distance to the investigated object gray-scale pictures with a resolution of about 660 dpi were gained. Wavelet Transform tool were used to extractfeatures from images.  Four input parameters are processed in the hidden layer with 10 neurons and the output realizes the clustering process. The classification process of 1372 unit data by using ANN approach is sure to be a success as much as the actual data set. The regression results of the clustering process is considerably well. It is determined that the training regression is 0,99914, testing regression is 0,99786 and the validation regression is 0,9953, respectively. Based on the results obtained, it is seen that classification process using ANN is capable of achieving outstanding success.
PID Parameters Prediction Using Neural Network for A Linear Quarter Car Suspension Control Muderrisoğlu, Kenan; Arisoy, Dogan Onur; Ahan, A. Oguzhan; Akdogan, Erhan
International Journal of Intelligent Systems and Applications in Engineering Vol 4, No 1 (2016)
Publisher : Advanced Technology and Science (ATScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.75361

Abstract

Providing control for suspension systems in vehicles is an enhancing factor for comfort and safety. With the improvement of control conditions, it is possible to design a cost-efficient controller which will maintain optimum comfort within harsher environmental conditions. The aim of this study is to design an adaptive PID controller with a predictive neural network model, which will be referred as NPID (NeuralPID), to control a suspension system. For this purpose, a NN (Neural Network) model is designed to produce outputs for PID’s Proportional (P) parameter to provide optimum responses for different road inputs. Also, reliability of the system outputs, which is using adaptive Proportional parameter, is tested. PID parameters for linear quarter vehicle model are decided through Zeigler-Nichols method. An ideal PID model, where Integral (I) and Derivative (D) parameters are bound to Proportional parameter, is used in the system. When the outputs of different controlled and not controlled systems, which are free, PID and NPID, are compared; it has been seen that NPID outputs are more convenient. In addition, it is possible to design controllers, with adaptively adjusting P parameter, which are operating cost-effective.
SVM-Based Sleep Apnea Identification Using Optimal RR-Interval Features of the ECG Signal almazaydeh, laiali; Elleithy, Khaled; Faezipour, Miad; Ocbagabir, Helen
International Journal of Intelligent Systems and Applications in Engineering Vol 4, No 1 (2016)
Publisher : Advanced Technology and Science (ATScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.79075

Abstract

Sleep apnea (SA) is the most commonly known sleeping disorder characterized by pauses of airflow to the lungs and often results in day and night time symptoms such as impaired concentration, depression, memory loss, snoring, nocturnal arousals, sweating and restless sleep. Obstructive Sleep Apnea (OSA), the most common SA, is a result of a collapsed upper respiratory airway, which is majorly undiagnosed due to the inconvenient Polysomnography (PSG)  testing procedure at sleep labs. This paper introduces an automated approach towards identifying sleep apnea. The idea is based on efficient feature extraction of the electrocardiogram (ECG) signal by employing a hybrid of signal processing techniques and classification using a linear-kernel Support Vector Machine (SVM). The optimum set of RR-interval features of the ECG signal yields a high classification accuracy of  97.1% when tested on the Physionet Apnea-ECG recordings. The results provide motivating insights towards future developments of convenient and effective OSA screening setups.  

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