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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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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
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.
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.
Proposal of Machine Learning Approach for Identification of Instant Messaging Applications in Raw Network Traffic Pektaş, Abdurrahman
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.2018642060

Abstract

Identification of Internet protocol from either raw network traffic or either network flows plays a crucial role at maintaining and improving the security of computer systems. A significant amount of research is carried out while exploiting a variety of identification techniques.  Although certain level in success at detection of network protocols for unencrypted traffic has been achieved, accuracy and performance is rather poor for encrypted traffic.  Considering technological trends, new and existing applications have been adopted to use encryption mechanism to protect information and privacy. Therefore, classification of encrypted network traffic is mandatory for ensuring security. Moreover, while performing network forensic investigation, labelling of network protocols/applications is a must to accomplish. In this study, we propose a method to automatically identify instant messaging applications from raw network traffic. To this end, we first extract flow based static features from network capture and then apply machine learning algorithms. The proposed method is evaluated with fairly large dataset. The dataset compromise of publicly available NISM dataset and the network traffic of 9 popular instant messaging applications collected in a controlled environment. The dataset overall contains 716607network flows belonging to 20 application categories. The proposed method classifies network flows of instant messaging applications into their corresponding application categories with the accuracy over 0.99 and F1-score of 0.99.
New Approach in E-mail Based Text Steganography Tutuncu, Kemal; Hassan, Abdikarim Abi
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.05687

Abstract

In this study combination of lossless compression techniques and Vigenere cipher was used in text steganography that makes use of email addresses to be the keys to reconstruct the secret message which has been embedded into the email text. After selecting the cover text that has highest repetition pattern regarding to the secret message the distance matrix was formed. The members of distance matrix were compressed by following lossless compression algorithms as in written sequence; Run Length Encoding (RLE) + Burrows Wheeler Transform (BWT) + Move to Forward (MTF) + Run Length Encoding + Arithmetic Encoding (AE).  Later on  Latin Square was used to form stego key 1and then Vigenere table was used to increase complexity of extracting stego key 1. Final step was to choose e-mail addresses by using stego key 1 and stego key 2to embed secret message into forward e-mail platform. The experimental results showed that proposed method has reasonable performance with high complexity.
Cloud Computing Environments Which Can Be Used in Health Education Buber, Mustafa; Sucu, Fadime; Bulut, Ismail; Kursun, Ramazan
International Journal of Intelligent Systems and Applications in Engineering Vol 3, No 4 (2015)
Publisher : Advanced Technology and Science (ATScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.92756

Abstract

At the present time, it is known that cloud computing technologies began to be used widely in information technology. The purpose of this study is to provide information about cloud technologies that can be used in health education. For this purpose,firstly as sample of the learning content management system, Edmodo has been introduced. Hapyak Interactive Video Creation Platform which can be used for creating interactive video to enrich the learning environment that will be submitted with Edmodo, Bubbl.Us which can be benefited from summarizing the discussed and Socrative platforms which enable concept maps application and online test creation have been introduced.
Application of hybrid of Fuzzy Set, Trust and Genetic Algorithm in query log mining for effective Information Retrieval Chawla, Suruchi
International Journal of Intelligent Systems and Applications in Engineering Vol 6, No 1 (2018)
Publisher : Advanced Technology and Science (ATScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.2018637930

Abstract

The precision of Information Retrieval (IR) System is low due to imprecise user queries as well as because of information overload on web.  The Fuzzy set infers the user’s information need from vague and imprecise queries and web recommender systems are used to overcome information overload problem. The performance of recommender system is still low due to data sparsity. The concept of trust is used to deal with data sparseness problem and improves the performance of recommender system.  Optimization techniques like Genetic Algorithm(GA) have been applied in domain of information retrieval for effective web search. In this research hybrid of Fuzzy set, GA and Trust has been used together in query log mining for personalized web search based on using fuzzy queries for recommendation of optimal set of trusted documents. Thus the use of hybrid of Fuzzy set, trust and GA together infer the user’s information need from vague and imprecise user’s queries and optimize the web page ranking of trusted web pages for effective personalized web search. The experimental results were analyzed statistically as well as compared with GA IR, and Fuzzy Trust based IR. Hence based on comparative analysis of results, thus hybrid of Fuzzy Set, Trust and GA shows the improvement in average precision of search results and confirms the effective personalization of web search. 
Hybrid Artificial Cooperative Search – Crow Search Algorithm for Optimization of a Counter Flow Wet Cooling Tower Turgut, Mert Sinan; Turgut, Oguz Emrah
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.2017531425

Abstract

In this paper, an improved version of Artificial Cooperative Search (ACS) algorithm is applied on a counter flow wet-cooling tower design problem. The Merkel’s method is used to determine the characteristic dimensions of cooling tower, along with empirical correlations for the loss and overall mass transfer coefficients in the packing region of the tower.  Basic perturbation schemes of the Crow Search Algorithm, a recent developed metaheuristic algorithm inspired by the food searching behaviors intelligent crows, are incorporated into ACS to enhance the convergence speed and increase the solution diversity of the algorithm. In order to assess the solution performance of the proposed method, fourteen widely known optimization test function have been solved and corresponding convergence histories has been depicted.  .Then the improved ACS algorithm (IACS) is applied on six different examples of counter flow wet-cooling tower optimization problem. The results obtained by applying the proposed algorithm are compared with the results of some other algorithms in the literature. Optimization results show that IACS is an effective algorithm with rapid convergence performance for the optimization of counter flow wet-cooling towers. 
Optimal Energy Management System for PV/Wind/Diesel-Battery Power Systems for Rural Health Clinic Ani, Vincent Anayochukwu
International Journal of Intelligent Systems and Applications in Engineering Vol 2, No 4 (2014)
Publisher : Advanced Technology and Science (ATScience)

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

Abstract

Good operation of a hybrid system can be achieved only by a suitable control of the interaction in the operation of the different devices. This paper proposed a supervisory control system that will be used to control and supervise the operations of PV/Wind-Diesel hybrid power generation system. The controller was developed in such a way that it coordinates when power should be generated by renewable energy (PV panels and Wind turbine) and when it should be generated by diesel generator and is intended to maximize the use of renewable system while limiting the use of diesel generator. Diesel generator is allocated only when the demand cannot be met by the renewable energy sources including battery bank. The structural analysis of the supervisory control is described in details through data flow diagrams. The developed control system was used to study the operations of the hybrid PV/Wind-Diesel energy system for the three hypothetical off-grid remote health clinics at various geographical locations in Nigeria. It was observed that the hybrid controller allocates the sources optimally according to the demand and availability. From the control simulation, we were able to see the performance of the system over the course of the year to see which mode(s) the system spends most time in, the power supplied by each of the energy sources over the year, and the power required by the load over the year. This is a very useful manner to check how the system is being supplied and which source of energy is the most proficient in supplying the load.
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.

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