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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 9 Documents
 1 
Search results for , issue " Vol 6, No 2 (2018)" : 9 Documents clear
Crow Search based Multi-objective Optimization of Irreversible Air Refrigerators Turgut, Oguz Emrah
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.2018642064

Abstract

This study proposes the optimum performance of the irreversible air refrigerators through recently developed metaheuristic algorithm called Crow search algorithm by means of finite time thermodynamics. Finite time thermodynamics is based on choosing the optimum pathways for any kind of thermodynamic system in order to reach the maximum efficiency of the thermodynamic cycle. Handful of objectives for assessing the performance of the irreversible air refrigerators such as coefficient of performance (COP), exergetic efficiency (ηII)  , ecological coefficient of  performance (ECOP), thermoeconomic optimization (F), and thermoecologic optimization functions (ECF) have been  successfully applied on the system. Three optimization scenarios have been studied for the multi objective optimization of irreversible air refrigerators. First scenario evaluates the concurrent optimization of objectives including exergetic efficiency (ηII), coefficient of performance (COP), and ecological coefficient of performance (ECOP). In second scenario, coefficient of performance (COP), thermoeconomic parameter (F), and  thermoecological coefficient of  performance (ECOP) have been simultaneously maximized to retain optimum working point of the cycle. Third case studies the simultaneous optimization of the imposed objectives such as second law efficiency (ηII), coefficient of performance (COP), and thermoecological function (ECF).  Widely known decision-making theorems of LINMAP, TOPSIS, and Shannon’s entropy theorem have been applied on the Pareto curve constructed by the non-dominated solutions to decide the most favorable solution on the frontier. 
User Profile Based Paper Recommendation System Kaya, Buket
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.2018642079

Abstract

As the spread of science and the number of researchers working in academic fields increase, there is also a considerable increase in the number of academic studies. Researchers always follow new works published for keeping their knowledge up to date. However, with many different sources and thousands of academic publications published every day, academics are not always able to find publications about their subjects.  Today, almost all of online academic databases employ a recommendation module which only considers the studies similar to the paper that the user looked at. However, a recommendation system based on the information of a single article is often not enough. In this study, the proposed method recommends by considering users publications, user’s co-authors and co-authors’ papers. Therefore, meta-data of the articles published by the researcher in the past are scored on a time-base basis with the method we propose. With the help of the sum of scores, there is a score of the user profile in the subject matter. It aims to find the closest studies to the profile of the user by searching with the method propsoed in the data pool which we created from the exact contents of hundreds of thousands of academic works. In the proposed method, TF-IDF is used from frequency-based similarity analysis methods. In the evaluation phase, the performance of the proposed method was examined. The success test of the method was measured by several different methods. These are to be evaluated by presenting them to real users and the other is to compare with existing data. The results are very promising and demonstrate that the method can produce accurate and quality results.
A Two Stage Hybrid Ensemble Classifier Based Diagnostic Tool for Chronic Kidney Disease Diagnosis Using Optimally Selected Reduced Feature Set Sharma, Sahil
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.2018642067

Abstract

Objective: This paper presents an idea of applying a two stage hybrid ensemble classifier for improving the prediction accuracy of Machine Learning based automated diagnosis of chronic kidney disease on the basis of values of an optimally selected subset of clinical and physiological parameters fed to it.Methodology: Chronic kidney disease is a generalized term for various heterogeneous disorders affecting the structure and function of the kidney. It is a disease with high mortality rate. In this paper the authors have proposed a two stage hybrid ensemble technique with very high efficiency. In two stage hybrid ensemble classifier the potential of individual classification algorithms are combined together. In addition to this the authors optimally selected 8 parameters of prime importance from the set of 24 parameters of the dataset used for the study .The parameters (features) selected represent the intersection of the two sets; one containing medically essential parameters arranged in decreasing contribution to the diagnosis and other set containing parameters ranked in decreasing order of their contribution in the Machine Learning classification process. Results: The results depict that the two stage hybrid ensemble is a very efficient method for classification of chronic kidney disease. The results of this ensemble classifier on the optimally selected reduced feature set (with 8 parameters) as well as the complete feature set (with 24 parameters)  in terms of various performance metrics are predictive accuracy of (2-class) 100%, sensitivity of 1, precision of 1, specificity of 1 and F-value of 1.Conclusion: The GUI based diagnostic tool developed on the basis of the proposed ensemble can act as a tool for assisting doctors for cross-validating their findings of initial screening of chronic kidney disease using fewer clinical parameters thus helping them to attend to the needs of more patients in less time.
SDF: psychological Stress Detection Framework from Microblogs using Pre-defined rules and Ontologies Ali, Mohammed Mahmood; Tajuddin, Mohd; Kabeer, M.
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.2018642080

Abstract

Spreading of Unwanted microblogs from Social Networking Sites (SNS) is pervasive  in social media that leads to unaccountable disturbances such as Mental disorders, Wastage of precious time, Break-up of relationships, Stressness giving birth to psychological health problems and manymore.  To overcome these problems, the immense necessity is to ignore those unwanted microblogs in SNS, which is uncontrollable by humans due to addiction towards social media. Even the literate people fall prey to psychological stress from SNS. This seriousness of stress related issues is very rarely attended by researchers, to tackle such vicious microblogs. The prediction strategy is proposed named as Stress Detection Framework (SDF) to analyze the stress in microblog. SDF is developed using Ontology based Information Extraction technique using Probabilistic Model (GSHL & TreeAlignment Algorithm), set of pre-defined knowledge based logical rules that constitutes of low-level attributes (simple textual, linguistic words) and visual features (emoticons & Images) and social Interaction (Likes and Dislikes) to detect and predict stress in microblog messages.SDF is compared with TeniStrength that has shown an increase of 94.2% of stress detection rate. The experimental results obtained will aid to take precise decision for blocking/eradicating/ segregating stress related microblogs from Social media (especially SNS).
DATA ANALYTICS OF BUILDING AUTOMATION SYSTEMS: A CASE STUDY DOGAN, GULUSTAN
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.2018642071

Abstract

In today’s technology, when costs of time, energy and human resources are considered, efficient use of resources provides significant advantages over many aspects. In light of this, role of building automation systems, which are a part of smart cities, become even more important. At the very core of building automation systems there lies the efficient use of resources and systems for providing comfortable living situations. With the advancement in network technology, systems can be programmed smartly and any malfunctions on the systems can be detected and fixed remotely. In addition to that, all data gathered during this process can be analyzed to create machine learning solutions for a system to control and program itself. In this work, we pulled the sensor data and developed an interface to do analysis. Our aim is to understand how the system behaves. This interface will be the basis of our work on developing machine learning algorithms to predict system behaviour for programming the system for energy.
Determining the Carrot Volume via Radius and Length Using ANN Örnek, Mustafa Nevzat; Kahramanli, Humar
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.2018642081

Abstract

In this study a total of 464 carrots were taken from Kaşınhanı, where the most carrots are produces in Turkey. The length and radiuses with an interval of 5 cm and volume were measured and recorded. Three different Artificial Neural Network models: BP, LM and PUNN were designed for predicting the carrot volume. To assess the success of the system, statistical measures such as Root Mean Squared Error, Mean Absolute Error and R2 were used. The results were showed that all three methods are successful in this problem, while LM and PUNN seems bit.
The Impact of Enhanced Space Forests with Classifier Ensembles on Biomedical Dataset Classification Kilimci, Zeynep Hilal; Ilhan Omurca, Sevinc
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/Ilhan

Abstract

In this paper, we propose to improve the classification success of classifier ensembles by investigating the contribution of enhanced space forests on biomedical datasets. For this purpose, this study especially is focused on enhanced feature spaces by implementing the most popular feature selection techniques, namely information gain (IG), and chi-square (CHI). After performing these methods on the feature space, training phase is evaluated with all the original and the most significant features. That is, the new training dataset is constructed by combining the original features and the new ones. Then, the training is done with the well-known classification algorithm namely decision tree, using the enhanced feature space. Finally, three types of ensemble algorithms, namely bagging, random subspace, and random forest are carried out. A wide range of comparative experiments are conducted on publicly available and widely-used 36 datasets from the UCI machine learning repository to observe the impact of the enhanced space forests with classifier ensembles. Experiment results demonstrate that the proposed enhanced space forests perform better classification accuracy than the state of the art studies. Approximately, 1% - 3% improvement of the classification success is an indicator that our proposed technique is efficient.
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.
Fuzzy and Taguchi based Fuzzy Optimization of Performance Criteria of the Process Control Systems Kara, Fatih; Kucuk, Arda; Simsek, Baris
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.2018642073

Abstract

This paper proposes a Taguchi based Fuzzy and Fuzzy PID application using MATLAB® version 2015a to assess and optimize of process control performance criteria of liquid level and flow rate control system. When the main effect graphs for the liquid level and flow rate control system are evaluated, it was seen that the change in the membership function is the most effective factor on the process control performance. It can be said that the Gaussian membership function provides the lowest mean and standard deviation in the offset value. Improvement rates for “overshoot”, “rise time”, “first peak time”, “%95 setting time, “%99 setting time”, “mean” and “the standard deviation of the offset values” are %50, %50, %55, %77, %64, %5, %63 for flow rate control system; %50, %49, %55, %43, %48, %4, %63 for liquid level control system in order. In comparison with the classical PID method, in the Fuzzy PID method, the improvement is calculated as 54% in the average of the offset value and 99% in the standard deviation.

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