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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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A Multistage Deep Belief Networks Application on Arrhythmia Classification ALTAN, Gokhan; KUTLU, Yakup; ALLAHVERDI, Novruz
International Journal of Intelligent Systems and Applications in Engineering 2016: Special Issue
Publisher : Prof. Dr. Ismail SARITAS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.2016Special Issue-146978

Abstract

An electrocardiogram (ECG) is a biomedical signal type that determines the normality and abnormality of heart beats using the electrical activity of the heart and has a great importance for cardiac disorders. The computer-aided analysis of biomedical signals has become a fabulous utilization method over the last years. This study introduces a multistage deep learning classification model for automatic arrhythmia classification. The proposed model includes a multi-stage classification system that uses ECG waveforms and the Second Order Difference Plot (SODP) features using a Deep Belief Network (DBN) classifier which has a greedy layer wise training with Restricted Boltzmann Machines algorithm. The multistage DBN model classified the MIT-BIH Arrhythmia Database heartbeats into 5 main groups defined by ANSI/AAMI standards. All ECG signals are filtered with median filters to remove the baseline wander. ECG waveforms were segmented from long-term ECG signals using a window with a length of 501 data points (R wave centered). The extracted waveforms and elliptical features from the SODP are utilized as the input of the model.  The proposed DBN-based multistage arrhythmia classification model has discriminated five types of heartbeats with a high accuracy rate of 96.10%.
Knowledge Discovery On Investment Fund Transaction Histories and Socio-Demographic Characteristics for Customer Churn Cil, Fatih; Cetinyokus, Tahsin; Gokcen, Hadi
International Journal of Intelligent Systems and Applications in Engineering Vol 6, No 4 (2018)
Publisher : Prof. Dr. Ismail SARITAS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.2018448452

Abstract

The need of turning huge amounts of data into useful information indicates the importance of data mining. Thanks to latest improvement in information technologies, storing huge data in computer systems becomes easier. Thus, “knowledge discovery” concept becomes more important. Data mining is the process of finding hidden and unknown patterns in huge amounts of data. It has a wide application area such as marketing, banking and finance, medicine and manufacturing. One of the most commonly used application areas of data mining is recognizing customer churn. Data mining is used to obtain behavior of churned customers by analyzing their previous transactions. In the same manner using with obtained tendency, other active customers are held in the system. It is possible to make by various marketing and customer retention activities. In this paper, it is aimed to recognize the churned customers of a bank who closed their saving accounts and determine common socio-demographic characteristics of these customers.
A Survey on Learning System Applications in Energy System Modeling and Prediction Turhal, Ümit Çiğdem; Demirci, Türker
International Journal of Intelligent Systems and Applications in Engineering 2016: Special Issue
Publisher : Prof. Dr. Ismail SARITAS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.2016Special Issue-146969

Abstract

Learning Systems (LS) such as machine learning, statistical pattern recognition and neural networks are computer programs that can learn from sample data and develop a prediction model that makes prediction for new cases. The most important think related with a prediction model is to achieve results as closer as to real situation while making predictions. This is important because being closer to real results help to reduce the costs of feasibility studies in system installation. The performance of Learning Systems has been raised in latest years such as it sometimes exceeds the performance of humans. That’s why the applications of Learning Systems have been increased in many areas. This paper reviews the present applications of Learning Systems in energy system modeling and prediction especially in renewable energy systems such as wind and solar. The aim of this paper is to create a vision for researchers by gathering the present applications and outline their merits and limits and the prediction of their future performance on specific applications. 
An Integrated Approach for Sustainable Supplier Selection in Fuzzy Environment ŞENOCAK, Ahmet Alp; GÖREN, Hacer GÜNER
International Journal of Intelligent Systems and Applications in Engineering 2016: Special Issue
Publisher : Prof. Dr. Ismail SARITAS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.270080

Abstract

The term sustainability, which means maintaining a balance or acting responsibly for the future, has come into prominence in many fields. One of the most crucial practice is cooperating with convenient collaborators and composing effective supply chains in terms of social, economic and environmental considerations. Therefore, sustainable supplier selection is getting more and more important to compete in rapidly changing environment. To deal with sustainable supplier selection problem, this study aims to determine the selection of appropriate suppliers and allocation of orders to them. The proposed approach operates in three stages. In the first stage, Fuzzy Decision Making Trial and Evaluation Laboratory is used to obtain the weights of the criteria from sustainability perspective. In the second stage, by using Fuzzy Grey Relational Analysis, a set of suppliers are ranked and their suitability scores are calculated. In the last stage, optimal order quantities to be procured by the suppliers are obtained via fuzzy linear programming including imprecise data of demand, error rate and capacity.
Estimation of Credit Card Customers Payment Status by Using kNN and MLP KOKLU, Murat; SABANCI, Kadir
International Journal of Intelligent Systems and Applications in Engineering 2016: Special Issue
Publisher : Prof. Dr. Ismail SARITAS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.2016Special Issue-146983

Abstract

The Default of Credit Card Clients dataset in the UCI machine learning repository was used in this study.  The credit card customers were classified if they would do payment or not (yes=1 no=0) for next month by using 23 information about them. Totally 30000 data in the dataset’s 66% was used for training and rest of them as 33% was used for tests. The Weka (Waikato Environment for Knowledge Analysis) software was used for estimation. In estimation Multilayer Perceptron (MLP) and k Nearest Neighbors (kNN) machine learning algorithms was used and success rates and error rates were calculated. With kNN estimation success rates for various number of neighborhood value was calculated one by one. The highest success rate was achieved as 80.6569% when the number of neighbor is 10. With MLP neural network model the estimation success rates was calculated when there are different number of neurons in the hidden layer of MLP. The best estimation success rate was achieved as 81.049% when there was only one neuron in the hidden layer.  MAE and RMSE values were obtained for this estimation success rate as 0.3237 and 0.388 respectively. 
FACE VERIFICATION SYSTEM IN MOBILE DEVICES BY USING COGNITIVE SERVICES Altun, Adem Alpaslan; Kolus, Cagatay
International Journal of Intelligent Systems and Applications in Engineering Vol 6, No 4 (2018)
Publisher : Prof. Dr. Ismail SARITAS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.2018448456

Abstract

Biometric systems enable people to distinguish between physical and behavioral characteristics. Face recognition systems, a type of biometric systems, use peoples’ facial features to recognize them. The aim of this study is to perform face recognition and verification system that can run on mobile devices. The developed application is based on comparing the faces in two photographs. The user uploads two photos to the system, the system identifies the faces in these photos and performs authentication between the two faces. As a result, the system gives the output that the two faces in the photo belong to the same or different persons. It provides a security measure thanks to the face identification and verification feature included in this application. This application can be integrated into various applications and used in systems such as user login.
The Minimization of Torque Ripples of Segmental Type Switched Reluctance Motor by Particle Swarm Optimization Terzioğlu, Hakan; Herdem, Saadetdin; BAL, Güngör
International Journal of Intelligent Systems and Applications in Engineering 2016: Special Issue
Publisher : Prof. Dr. Ismail SARITAS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.2017Special Issue-146974

Abstract

In this study, we realized a controller design which can reduce torque ripple of 10/8 Switched Reluctance Motor (SRM). To perform the study, a Switched Reluctance Motor with 5 phase, U type segmental rotor was used. The control of the SRM was actualized by bipolar converter used H-bridge topology. The control signals of converter are obtained by control circuit designed by using dsPIC33EP512MU810. One of the reasons of the current ripples in the SRM is ON-OFF times in a period of the control signals. When the ripples of the current reduced, the ripples of torque of the SRM also reduced. Therefore, in this study, the ON-OFF times in a period of phase control signals were determined by an algorithm used particle swarm optimization. When SRM was controlled by this algorithm developed, the decreasing of its torque ripples was determined.
A Hybrid Algorithm for Automated Guided Vehicle Routing Problem Söyleyici, Cansu; Keser, Sinem Bozkurt
International Journal of Intelligent Systems and Applications in Engineering 2016: Special Issue
Publisher : Prof. Dr. Ismail SARITAS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.2016Special Issue-146965

Abstract

Nowadays, automatic systems become crucial in many factories to achieve some tasks such as minimizing cost, maximizing efficiency, quality, and reliability. The planning is important for manufacturing systems to adopt changing conditions. Also, manufacturers want to obtain fast, reliable, qualified and economic products. Flexible Manufacturing Systems (FMSs) are used to meet this need. FMSs make production fast, qualified, reliable and economic by using computer-controlled structure that includes robots and transportation systems. Automated Guided Vehicles (AGVs) and FMS are thought to be integrated because FMSs use AGVs as a part of transportation in the factory. AGVs are used to carry loads, in other words products, in production areas, warehouses, factories that use magnets, landmarks, laser sensors, lines to know where they are. AGV scheduling and routing is NP-hard and open-ended problems. In the literature, there are many algorithms and methods are proposed to solve these problems. In this study, we present a hybrid algorithm that is composed of simulated annealing (SA) and Dijkstra’s algorithm to solve the routing problem. The hybrid algorithm is compared with SA algorithm in terms of distance cost using benchmark problems in the literature.
Network Traffic Classification via Kernel Based Extreme Learning Machine Ertam, Fatih; Avcı, Engin
International Journal of Intelligent Systems and Applications in Engineering 2016: Special Issue
Publisher : Prof. Dr. Ismail SARITAS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.267522

Abstract

The classification of data on the internet in order to make internet use more efficient has an important place especially for network administrators managing corporate networks. Studies for the classification of internet traffic have increased recently. By these studies, it is aimed to increase the quality of service on the network, use the network efficiently, create the service packages and offer them to the users. The first classification method used for the classification of the internet traffic was the classification for the use of port numbers. This classification method has already lost its validity although it was an effective and quick method of classification for the first usage times of the internet. Another classification method used for the classification of network traffic is called as load-based classification or deep packet analysis. This approach is based on the principle of classification by identifying signatures on packets flowing on the network. Another method of classification of the internet traffic which is commonly used in our day and has been also selected for this study is the kernel based on extreme learning machine based approaches. In this study, over 95% was achieved accuracies using different activation functions.
A Modified Artificial Algae Algorithm For Large Scale Global Optimization Problems Kocer, Havva Gul; Uymaz, Sait Ali
International Journal of Intelligent Systems and Applications in Engineering Vol 6, No 4 (2018)
Publisher : Prof. Dr. Ismail SARITAS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.2018448458

Abstract

Optimization technology is used to accelerate decision-making processes and to increase the quality of decision making in management and engineering problems. The development technology has made real-world problems large and complex. Many optimization methods that proposed for solving LSGO problems suffer from the “curse of dimensionality”, which implies that their performance deteriorates quickly as the dimensionality of the search space increases. Therefore, more efficient and robust algorithms are needed. When literature on large-scale optimization problems is examined, it is seen that algorithms with effective global search ability have better results. For the purpose, in this paper, Modified Artificial Algae Algorithm (MAAA) is proposed by modifying the original version of Artificial Algae Algorithm (AAA) inspiring by Differential Evolution Algorithm’s mutation strategies. AAA and MAAA are compared with each other by operating with the first 10 benchmark functions of CEC2010 Special Session on Large Scale Global Optimization. The results show that the hybridization process that applied by updating an additional fourth dimension with mutation strategies of DE after the helical motion of the AAA algorithm, contributes exploration phase and improves the AAA performance on LSGO.

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