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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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Stemming Implementation in Preprocessing Phase for Evaluating of Exams Using Data Mining Approach BALCI, Mehmet; TASDEMIR, Sakir; SARACOGLU, Ridvan
International Journal of Intelligent Systems and Applications in Engineering Vol 5, No 2 (2017)
Publisher : Advanced Technology and Science (ATScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.2017529086

Abstract

In educational activities, examinations are sometimes carried out in the form of multiple-choice tests or sometimes as open-ended long texts. When multiple-choice tests are performed, evaluating process is carried out either manual or computer-assisted. Exam questions prepared in the form of multiple choice tests are not suitable for every course. It may be necessary to use open-ended questionnaires in order for pupils to accurately measure their achievement in relation to the course. It can take a long time to evaluate examinations made with such questions. However, this process can create problems in terms of objective evaluation. Data mining, defined as the extraction of useful information from large quantities of data, can be used to process all kinds of data. The data mining method used in the processing of textual data is called text mining. In text processing studies, data is subject to preprocessing in order to obtain a high quality data set. The most important stage of preprocessing is stemming. In this study, stemming process is implemented to questions and correct answers taken from students. The results obtained in 2 different samples and 4 sentences are 71%, 69%, 86% and 78% correct. In order to be able to distinguish what the textual data written in the natural language really is, it is necessary to use the states of the words which are made up of construction and free from the suffixes. Therefore, in the pre-processing phase, stemming process is applied to the textual data in accordance with the grammar rules of the language they are written on, and stems of every word are found. Text processing is used in many areas of the natural language. Computer-aided solutions will be inevitable so that problems can be eliminated and open-ended questions can be quickly assessed. Despite the desirability of a computer aided solution for this measurement technique, studies of this solution are not included in the literature very much.
Long Term and Remote Health Monitoring with Smart Phones Kirci, Pinar; Kurt, Gokhan
International Journal of Intelligent Systems and Applications in Engineering Vol 4, No 4 (2016)
Publisher : Advanced Technology and Science (ATScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.2016426358

Abstract

The basic  aim of our work  is to provide  solutions with monitoring the heart beat rates  of disabled or old people. And also we expect to help the people who have specific heart diseases  like potential cardiac arrests and cardiac pacemaker carriers. Besides in case of emergency situations, our system will produce an immediate alarm to provide urgent help for the patients. In the system, emergency situations depend on the heart beat rates. If the heart beat rates of a person decreases at lower rates compared with normal heart beat rates or if the heart beat rates of a person increases at higher rates compared with normal heart beat rates or if  big heart beat rate changes occur during the predetermined time period then these situations will be evaluated as emergency situations and these situations should be announced to considered people and places like hospitals, patient’s doctor and patient’s family members. The proposed  system colloborates with smartphones and includes sensors to collect data from the patient. Also the system is used to process and compare data with pre defined normal heart beat rates by patient’s doctor and to notice if there is an emergency situation. Besides, in case of an emergengy situation, to inform considered people. But if there is not an emergengy  situation exists, then the system stores the collected data and sends them as  daily and weekly graphics to the patient’s doctor. These graphics are collected as a result of definite daily  activities like sleeping, sitting, standing, walking and jogging. The results are compared with the patient’s doctor’s stated normal heart rate intervals for every activity period. Furthermore, our proposed system structure includes heart pulse sensor, a smartphone screen, bluetooth interface and memory.
Sleep Stage Classification via Ensemble and Conventional Machine Learning Methods using Single Channel EEG Signals Ilhan, Hamza Osman; Bilgin, Gokhan
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.2017533859

Abstract

Sleep-stages play important roles in the diagnosis of the sleep disorders and the sleep-related illnesses. In this sense, accurate identification of the sleep-stages is a necessity for more robust and e client diagnosis systems. Several traditional machine-learning and pattern recognition algorithms are deployed on modern computer aided diagnosis systems. However, current results are not as satisfactory as expected. In the last two decade, a new concept has emerged with ‘ensemble learning’ title. It has attracted the attention of many researchers from various disciplines. In this study, several ensemble-learning methods are utilized and inspected on EEG signals for sleep-stage classification. Conventional machine-learning methods are also performed in same testing phase to report comparative results. Additionally, methods are evaluated in two different scenarios; subject specific and independent. Study proves that combination of DTs and SVMs in Bagging theorem surpasses all of the conventional methods used in the experiments. Moreover, test trials reveal that both conventional and ensemble models need to be improved for subject independent scenario which is more essential case in the development of independent computer based diagnosis systems.
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.
GA Based Selective Harmonic Elimination for Five-Level Inverter Using Cascaded H-bridge Modules Bektas, Enes; Karaca, Hulusi
International Journal of Intelligent Systems and Applications in Engineering Vol 4, No 2 (2016)
Publisher : Advanced Technology and Science (ATScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.97681

Abstract

Multilevel inverters (MLI) have been commonly used in industry especially to get quality output voltage in terms of total harmonic distortion (THD). In addition, development in semiconductor technology and advanced modulation techniques make MLI implementation more attractive. Selective Harmonic Elimination (SHE) that can be applied MLI at desired switching frequency offers elimination of harmonics in the output voltage. Also, by using SHE technique with cascaded multilevel inverters, the necessity of using filter in the output can be minimized. In this paper, SHE equations have been solved by using of Genetic Algorithm (GA) Toobox&Matlab and it has been aimed to eliminate desired harmonic orders at fundamental output voltage. Simulation results have clearly demonstrated that GA based SHE techniques can eliminate the demanded harmonic orders.
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.
Comparison among Feature Encoding Techniques for HIV-1 Protease Cleavage Specificity Turhal, Uğur; Gök, Murat; Durgut, Aykut
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.21005

Abstract

HIV-1 protease which is responsible for the generation of infectious viral particles by cleaving the virus polypeptides, play an indispensable role in the life cycle of HIV-1. Knowledge of the substrate specificity of HIV-1 protease will pave the way of development of efficacious HIV-1 protease inhibitors. In the prediction of HIV-1 protease cleavage site techniques, many efforts have been devoted. Last decade, several works have approached the prediction of HIV-1 protease cleavage site problem by applying a number of methods from the field of machine learning. However, it is still difficult for researchers to choose the best method due to the lack of an effective and up-to-date comparison. Here, we have made an extensive study on feature encoding techniques for the problem of HIV-1 protease specificity on diverse machine learning algorithms. Also, for the first time, we applied OEDICHO technique, which is a combination of orthonormal encoding and the binary representation of selected 10 best physicochemical properties of amino acids derived from Amino Acid index database, to predict HIV-1 protease cleavage sites.
An Artificial Neural Network Model for Wastewater Treatment Plant of Konya Tumer, Abdullah Erdal; Edebali, Serpil
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.65358

Abstract

In this study, modelling of Konya wastewater treatment plant was studied by using artificial neural network with different architectures in Matlab software. All data were obtained from wastewater treatment plant of Konya during daily records over four month. Treatment efficiency of the plant was determined by taking into account of input values of pH, temperature, COD, TSS and BOD with output values TSS. Performance of the model was compared via the parameters of Mean Squared Error (MSE), and correlation coefficient (R). The suitable architecture of the neural network model is determined after several trial and error steps. According to the modelling study, the ANN can predict the plant performance with correlation coefficient (R) between the observed and predicted output variable reached up to 0.96.
A Novel Hybrid Multi Criteria Decision Making Model: Application to Turning Operations Sofuoglu, Mehmet Alper; Orak, Sezan
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.2017531427

Abstract

Multi criteria decision making models (MCDM) are extensively used in material and process selection in engineering. In this study, a novel hybrid decision making model is developed. Best-Worst method (BWM) is hybridized with TOPSIS, Grey Relational Analysis (GRA) and Weighted Sum Approach (WSA). Developed hybrid models produce similar results in different weight value of decision makers so they are combined. The model is tested in a turning operation and an optimization study is conducted by using Taguchi experimental design. The developed model can be used by engineers and operators in manufacturing environment.
Artificial Bee Colony Algorithm Based Linear Quadratic Optimal Controller Design for a Nonlinear Inverted Pendulum Ata, Baris; Coban, Ramazan
International Journal of Intelligent Systems and Applications in Engineering Vol 3, No 1 (2015)
Publisher : Advanced Technology and Science (ATScience)

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

Abstract

This paper presents a linear quadratic optimal controller design for a nonlinear inverted pendulum. Linear Quadratic Regulator (LQR), an optimal control method, is usually used for control of the dynamical systems. Main design parameters in LQR are the weighting matrices; however there is no relevant systematic techniques presented to choose these matrices. Generally, selecting weighting matrices is performed by trial and error method since there is no direct relation between weighting matrices and time domain specifications like overshoot percentage, settling time, and steady state error. Also it is time consuming and highly depends on designer’s experience. In this paper LQR is used to control an inverted pendulum as a nonlinear dynamical system and the Artificial Bee Colony (ABC) algorithm is used for selecting weighting matrices to overcome LQR design difficulties. The ABC algorithm is a swarm intelligence based optimization algorithm and it can be used for multivariable function optimization efficiently.  The simulation results justify that the ABC algorithm is a very efficient way to determine LQR weighting matrices in comparison with trial and error method.

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