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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 16 Documents
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Search results for , issue " Vol 5, No 4 (2017)" : 16 Documents clear
Development a New Intelligent System for Monitoring Environment Information using Wireless Sensor Networks Dener, Murat
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.2017533897

Abstract

Wireless Sensor Networks are a new technology that has been on the agenda lately and can be applied to many areas. By using Wireless Sensor Networks, information can be gathered interactively and this information can be collectively evaluated and can be changed on the basis of information when necessary. In this work, a sensor node and a gateway node are designed and developed. With designed new nodes, a new intelligent system is developed. In the new system, Temperature, humidity, sound and water level data are perceived and monitored. This system can be used in all environments that need these four information. It is estimated that our work will benefit sensor network users. 
Modelling and Evaluating Air Quality with Fuzzy Logic Algorithm-Ankara-Cebeci Sample Atacak, Ismail; Arici, Nursal; Guner, Dilem
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.2017533902

Abstract

Air is one of the most important life sources for all living things. Gases that are present and absent in the composition of clean air also considered as pollutants in the atmosphere. If the pollutants rise above a certain concentration level, air pollution occurs. Air pollution damages all living things, especially human health. Accurate estimation of pollutant concentrations through air pollution modeling has an important effect in reducing the adverse effects of pollution and taking necessary precautions. Conventional statistical models are widely used in air pollution forecasting and modeling. As a different approach, in this study, fuzzy logic algorithm, which has been increasingly successful in many field applications, has been used to model air quality and air pollution analyzes were made based on this model. Ankara -Cebeci province data was used in the sample of the research.
A Review on Business Intelligence and Big Data Sirin, Erkan; Karacan, Hacer
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.2017533861

Abstract

Improvement of data generating, processing, storing and networking technologies has made storing, capturing and sharing of data easier and cheaper than before and has enabled organizations to handle huge volume of data at high velocity and variety, named as big data. Big data offers many opportunities when the associated difficulties are addressed properly. Business Intelligence (BI) basically focuses on transforming raw data into usable, valuable and actionable information for decision-making. It can be classified as a kind of data-driven decision support system. Although big data related papers have increased for last fifteen years, there are not sufficient papers that directly overviews big data impact on BI. As data is growing exponentially, storage, process and analytics tools and technologies become more important for BI solutions. With the advent of big data, BI’s concept, architecture and capabilities are meant to be changed. Unlike a decades before, BI now is to be extract value from huge data ocean by using big data tools as well as classical ones. So, an interclusion has emerged between big data and BI. This paper overviews the current state of the art of BI and big data, and discuss how big data era affects BI solutions in general context.
Skin Lesion Classification using Machine Learning Algorithms OZKAN, Ilker Ali; KOKLU, Murat
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

Abstract

Melanoma is a deadly skin cancer that breaks out in the skin’s pigment cells on the skin surface. Melanoma causes 75% of the skin cancer-related deaths. This disease can be diagnosed by a dermatology specialist through the interpretation of the dermoscopy images in accordance with ABCD rule. Even if dermatology experts use dermatological images for diagnosis, the rate of the correct diagnosis of experts is estimated to be 75-84%. The purpose of this study is to pre-classify the skin lesions in three groups as normal, abnormal and melanoma by machine learning methods and to develop a decision support system that should make the decision easier for a doctor. The objective of this study is skin lesions based on dermoscopic images PH2 datasets using 4 different machine learning methods namely; ANN, SVM, KNN and Decision Tree. Correctly classified instances were found as 92.50%, 89.50%, 82.00% and 90.00% for ANN, SVM, KNN and DT respectively. The findings show that the system developed in this study has the feature of a medical decision support system which can help dermatologists in diagnosing of the skin lesions.
Global Best Algorithm Based Parameter Identification of Solar Cell Models Turgut, Oguz Emrah
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.2017533892

Abstract

Effectivity of the solar energy systems is thoroughly dependent of successful modeling of the I-V characteristic curves. However, due to the lack of information about the precise model parameters those are profoundly involved in characterizing governing equations; an efficient design has not been accurately accomplished by researchers yet. This article proposes Global Best Algorithm (GBEST) in order to extract unknown parameters of solar cell models accurately. In order to test the performance of the proposed optimizer, nine different unconstrained optimization test functions are evaluated and their statistical results are compared with the recently developed metaheuristic algorithms. GBEST is applied on PV module, single and double diode models which are mathematically formulated as multi-dimensional and highly nonlinear in their nature.   Results reveal that GBEST is superior to the other methods in terms of solution accuracy and efficiency.
Equivalent Circuit Modelling of an L-shaped Patch Antenna by Optimizing the Lumped Elements Using Differential Evolution Algorithm Toktas, Abdurrahim
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.2017533894

Abstract

L-shaped patch antenna (LPA) is formed by combining two monopole patch radiators. Proper modelling of a LPA using lumped elements is crucial in antenna design and analysis. In this study, a novel equivalent circuit (EC) modeling of an LPA using differential evolution (DE) optimization algorithm is presented. Two parallel branches each represents the monopole patch radiator compose the EC topology. In each branch, a serial resistance and inductance pair stands for patch conductor, a parallel resistance and capacitance pair symbolizes the dielectric substrate. The expressions of these eight lumped elements enclosing the antenna’s physical and electrical parameters accompanying with optimization variables are constituted considering the element definitions of microstrip transmission line (MTL). Return loss equation is derived through input impedance equation of the EC model. The variables are then optimally found by fitting the calculated return loss to the simulated results by DE algorithm. The proposed EC model is then verified through results of simulated and measured LPA. Moreover, real and imaginary parts of the EC input impedance are comparatively calculated. These show that the proposed EC model gives almost the same results in terms of important antenna parameters.

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