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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 5, No 1 (2017)" : 5 Documents clear
Predicting Student Success in Courses via Collaborative Filtering Cakmak, Ali
International Journal of Intelligent Systems and Applications in Engineering Vol 5, No 1 (2017)
Publisher : Advanced Technology and Science (ATScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.2017526690

Abstract

Based on their skills and interests, students’ success in courses may differ greatly. Predicting student success in courses before they take them may be important. For instance, students may choose elective courses that they are likely to pass with good grades. Besides, instructors may have an idea about the expected success of students in a class, and may restructure the course organization accordingly. In this paper, we propose a collaborative filtering-based method to estimate the future course grades of students. Besides, we further enhance the standard collaborative filtering by incorporating automated outlier elimination and GPA-based similarity filtering. We evaluate the proposed technique on a real dataset of course grades. The results indicate that we can estimate the student course grades with an average error rate of 0.26, and the proposed enhancements improve the error value by 16%. 
Artificial Neural Network Models for Predicting The Energy Consumption of The Process of Crystallization Syrup in Konya Sugar Factory Tumer, Abdullah Erdal; Koc, Bilgen Ayan; Kocer, Sabri
International Journal of Intelligent Systems and Applications in Engineering Vol 5, No 1 (2017)
Publisher : Advanced Technology and Science (ATScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.2017526691

Abstract

In this study, artificial neural network models have been developed from the sugar production process stages in Konya Sugar Factory using artificial neural networks to estimate the energy consumption of the process of crystallization syrup. Models developing specific enthalpy, mass and pressure as input layer parameters and consumption energy as output layer  were used.124 different data are taken from Konya Sugar Factory during January 2016. Feedforward back propagation algorithm was used in the training phase of the network. Learning function LEARNGDM and the number of hidden layer kept constant as 2 and transfer functions are modified. In the developed 27 ANN model, 2-5-1 network architecture was determined as the best suitable network architecture and transfer function is determined logsig function as the optimal transfer function. Optimum results of the model taken in the coefficient of determination was found R = 0.98 neural network training, testing and validate was also found to be R = 0.98, the performance of the network for not shown data to network was found R=0,99.
A Robust Adaptive Control of Interleaved Boost Converter with Power Factor Correction in Wind Energy Systems Karik, Fatih; Yildiz, Ceyhun; Kaytez, Fazil
International Journal of Intelligent Systems and Applications in Engineering Vol 5, No 1 (2017)
Publisher : Advanced Technology and Science (ATScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.2017526692

Abstract

Power converters are generally utilized to convert the power from the wind sources to match the load demand and grid requirement to improve the dynamic and steady-state characteristics of wind generation systems and to integrate the energy storage system to solve the challenge of the discontinuous character of the renewable energy. In the low-voltage wind energy systems, interleaved boost converters (IBC) are often used to operate high currents in the system. IBCs are extremely sensitive to the constantly changing loading conditions. These situations require a robust control operation which can ensure a sufficient performance of the IBC over a large-scale changing load. Neural networks (NN) have emerged over the years and have found applications in many engineering fields, including control. In this paper, the adaptive control of interleaved boost converter with power factor correction (PFC) is investigated for grid-connected synchronous generator of wind energy system. For this purpose, a model reference adaptive control (MRAC) based on NN is proposed. Analysis results show that the proposed control strategy for the IBCs achieves near unity power factor (PF) and low total harmonic distortion (THD) in a wide operating range.
Vulnerability Analysis of Multiple Critical Fault Outages and Adaptive Under Voltage Load Shedding Scenarios in Marmara Region Electrical Power Grid Pamuk, Nihat
International Journal of Intelligent Systems and Applications in Engineering Vol 5, No 1 (2017)
Publisher : Advanced Technology and Science (ATScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.2017526693

Abstract

The utilization of electrical power system has been rising frequently from past to now and there is a need of dependable electrical transmission and distribution networks so as to ensure continuous and balanced energy. Besides, conventional energy governance systems have been forced to change as a result of rises in the usage of renewable energy resources and the efficiency of demand-side on the market. In this regard, electrical power systems should be planned and operated, appropriately and the balance of production and consumption demand should be provided within the nominal voltage limits. In this study, firstly, the current status of Marmara region interconnected power grid in Turkey is evaluated. Afterwards, the multiple cascading failure outages scenarios are modeled by “DIgSILENT Power Factory V14” software. The critical transmission line scenarios are implemented on the high voltage power grid model improved. These scenarios are based on the period of maximum and minimum production and consumption demand and the effects of demand response in this period. As a result of grid vulnerability analyses performed, several findings has been obtained about the impacts of different line scenarios on the high voltage transmission system, the optimization of power grid voltage profile and the role of production and consumption demand response on voltage regulation.
Classification of Neurodegenerative Diseases using Machine Learning Methods Aydin, Fatih; Aslan, Zafer
International Journal of Intelligent Systems and Applications in Engineering Vol 5, No 1 (2017)
Publisher : Advanced Technology and Science (ATScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.2017526689

Abstract

In this study, neurodegenerative diseases (Amyotrophic Lateral Sclerosis, Huntington’s disease, and Parkinson’s disease) were diagnosed and classified using force signals.  In the classification, five machine learning algorithms (Averaged 2-Dependence Estimators (A2DE), K* (K star), Multilayer Perceptron (MLP), Diverse Ensemble Creation by Oppositional Relabeling of Artificial Training Examples (DECORATE), Random Forest) were compared by the 10-fold Cross Validation method. K* classifier gave the best outcome among these algorithms. As a result of quad classification of the K* classifier, the best classification accuracy was 99.17%. According to the first three and five principal component qualifications which are created from these 19 features, the best classification accuracies of K* classifier were 95.44% and 96.68% respectively.

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