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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 3 (2016)" : 5 Documents clear
Estimating of Compressive Strength of Concrete with Artificial Neural Network According to Concrete Mixture Ratio and Age Ozkan, Ilker Ali; ALTIN, Mustafa
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.263977

Abstract

Compressive strength of concrete is one of the most important elements for an existing building and a new structure to be built. While obtaining the desired compressive strength of concrete with an appropriate mix and curing conditions for a new structure, with non-destructive testing methods for an existing structure or by taking core samples the concrete compressive strength are determined. One of the most important factors that affects the concrete compressive strength is age of concrete. In this study, it is attempted to estimate compressive strength, modelling Artificial Neural Networks (ANN) and using different mixture ratios and compressive strength of concrete samples at different ages. In accordance with obtained data’s in the estimation of concrete compressive strength, ANN could be used safely.
Using Word Embeddings for Ontology Enrichment Pembeci, Ä°zzet
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.58806

Abstract

Word embeddings, distributed word representations in a reduced linear space, show a lot of promise for accomplishing Natural Language Processing (NLP) tasks in an unsupervised manner. In this study, we investigate if the success of word2vec, a Neural Networks based word embeddings algorithm, can be replicated in an aggluginative language like Turkish. Turkish is more challenging than languages like English for complex NLP tasks because of her rich morphology. We picked ontology enrichment, again a relatively harder NLP task, as our test application. Firstly, we show how ontological relations can be extracted automaticaly from Turkish Wikipedia to construct a gold standard. Then by running experiments we show that the word vector representations produced by word2vec are useful to detect ontological relations encoded in Wikipedia. We propose a simple but yet effective weakly supervised ontology enrichment algorithm where for a given word a few know ontologically related concepts coupled with similarity scores computed via word2vec models can result in discovery of other related concepts. We argue how our algorithm can be improved and augmented to make it a viable component of an ontoloy learning and population framework.
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.
A highly Reliable and Fully Automated Classification System for Sleep Apnea Detection Almazaydeh, Laiali; Elleithy, Khaled; Faezipour, Miad
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.47487

Abstract

Sleep apnea (SA) in the form of Obstructive sleep apnea (OSA) is becoming the most common respiratory disorder during sleep, which is characterized by cessations of airflow to the lungs. These cessations in breathing must last more than 10 seconds to be considered an apnea event. Apnea events may occur 5 to 30 times an hour and may occur up to four hundred times per night in those with severe SA [1]. Nowadays, polysomnography (PSG) is a standard testing procedure to diagnose OSA which includes the monitoring of the breath airflow, respiratory movement, and oxygen saturation (SpO2), body position, electroencephalography (EEG), electromyography (EMG), electrooculography (EOG), and electrocardiography (ECG). Therefore, a final diagnosis decision is obtained by means of medical examination of these recordings [2]. However, new simplified diagnostic methods and continuous screening of OSA is needed in order to have a major benefit of the treatment on OSA outcomes. In this regard, a portable monitoring system is developed to facilitate the self-administered sleep tests in familiar surroundings environment closer to the patients’ normal sleep habits. With only three data channels: tracheal breathing sounds, ECG and SpO2 signals, a patient does not need hospitalization and can be diagnosed and receive feedback at home, which eases follow-up and retesting after treatment.
An Efficient Approach for Ground Echoes Suppression Based on Textural Features and SVM Hedir, Mehdia
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.94394

Abstract

The use of the Support Vector Machine (SVM) technique for the clutter identification in the context of meteorological data is presented. The clutter is due to ground echoes and anomalous propagation. The SVM is combined with textural approach which is based on the Grey Level Co-occurrence Matrix (GLCM) that is the most used in the textural analysis image. An incoherent radar site is considered for this study. The results reveal that over than 91.1% of ground echoes are identified and 90.3% of precipitations are preserved. In addition 95.99% of anomalous propagation are removed. The use of our approach is lasts than 1mn for the treatment of each image. We can then filter the radar image in real time

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