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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 12 Documents
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Search results for , issue " Vol 6, No 4 (2018)" : 12 Documents clear
Improved Nelder-Mead Optimization Method in Learning Phase of Artificial Neural Network Merdan, Mustafa Adnan; Kocer, Hasan Erdinc; Ibrahim, Mohammed Hussein
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.2018448461

Abstract

It is difficult to find the optimum weight values of artificial neural networks for optimization problem. In this study, Nelder-Mead optimization method [17] has been improved and used for determining the optimal values of weights. The results of the proposed improved Nelder-Mead method are compared with results of the standard Nelder-Mead method which is used in ANNs learning algorithm.  The most common data sets are taken from UCI machine learning repository.  According to the experimental results, in this study better results are achieved in terms of speed and performance.
Psychological Stress Detection from Social Media Data using a Novel Hybrid Model Ali, Mohammed Mahmood; Hajera, Shaikha
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.2018448457

Abstract

Psychological stress is considered as the biggest threat to individual’s health. Hence, it is vital to detect and manage stress before it turns into severe problem. However, conventional stress detection strategies rely on psychological scales and physiological devices, which require active individual participation making it labor-consuming, complex and expensive. With the rapid growth of social networks, people are willing to share moods via social media platforms making it practicable to leverage online social interaction data for stress detection. The developed novel hybrid model Psychological Stress Detection (PSD), automatically detect the individual’s psychological stress from social media. It comprises of three modules Probabilistic Naïve Bayes Classifier, Visual (Hue, Saturation, Value) and Social, to leverage text, image post and social interaction information we have defined the set of stress-related textual ‘F = {f1, f2, f3, f4}’, visual ‘vF = {vf1, vf2}’, social ‘sf’ to detect and predict stress from social media content. Experimental results show that the proposed PSD model improves the detection performance when compared to TensiStrength and Teenchat framework, PSD achieves 95% of Precision rate. PSD model would be useful in developing stress detection tools for mental health agencies and individuals.
A fuzzy-genetic based design of permanent magnet synchronous motor Mutluer, Mümtaz
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.2018648453

Abstract

This paper presents a fuzzy-genetic based design of permanent magnet synchronous motor. The selected motor structure with surface magnet and double layer winding is for high torque and low speed applications. The design approach involves combining fuzzy logic and genetic algorithm in a powerful combination. While the genetic algorithm is used in scanning of the solution space, the fuzzy logic approach has been utilized in selecting the most appropriate solutions. While choosing geometric parameters as input for optimization, design equations are obtained by using geometrical, electrical and magnetic properties of the motor. The output results are evaluated with motor efficiency, motor weight and weight of magnets as the objective function. Furthermore, the multiobjective design optimization results are compared with the results obtained for each single objective and tested with finite element method. The results are finally remarkable and quite compatible with the finite element method results.
A Review of Smart Parking System based on Internet of Things. Kaur, Harkiran; Malhotra, Jyoteesh
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.2018448450

Abstract

: The Internet of Things expands the wireless paradigm to the edge of the network, which enables to develop a different kind of applications or services. By using IoT the application becomes more reliable, flexible or portable. It has a large amount of nodes on the various geographic positions, to increase the awareness of location or reduce the latency. There are numerous IoT applications like smart grid, smart hospital, smart industry, smart traffic management etc. In this paper, we describe the smart traffic management and parking system using the internet to control the chaos and also discuss the various researches on this concept.
An Investigation of the Effect of Meteorological Parameters on Wind Speed Estimation Using Bagging Algorithm Emeksiz, Cem; Demir, Gülden
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.2018448459

Abstract

Wind speed is the most important parameter of the wind energy conversion system. Therefore temperature, humiditiy and pressure data, which has significant effect on the wind speed, have become extremely important. In the literature, various models have been used to realize the wind speed estimation. In this study; Six different data mining algorithms were used to determine the effect of meteorological parameters on wind speed estimation. The data were collected from the measurement station established on the campus of Gaziosmanpaşa University. We focused on the bagging algorithm to determine the appropriate combination of wind speed estimates.  The bagging algorithm was used for the first time in estimation of wind speed by taking into account meteorological parameters. To find the most efficiency method on such problem 10-fold cross validation technique was used for comparision. From results, It is concluded that bagging algorithm and temperature-humiditiy-pressure combination showed the best performance. Additionaly, temperature and pressure data are more effective in the wind speed estimation.
Surface Roughness Estimation for Turning Operation Based on Different Regression Models Using Vibration Signals Neseli, Suleyman; Yalcin, Gokhan; Yaldiz, suleyman
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.2018648454

Abstract

On machined parts, major indication of surface quality is surface roughness and also surface quality is one of the most specified customer requirements. In the turning process, the importance of machining parameter choice is enhancing, as it controls the required surface quality. To obtain the better surface quality, the most essential control parameters are tool overhang and tool geometry in turning operations. The goal of this study was to develop an empirical multiple regression models for prediction of surface roughness (Ra) from the input variables in finishing turning of 42CrMo4 steel. The main input parameters of this model are tool overhang and tool geometry such as tool nose radius, approaching angle, and rake angle in negative direction. Regression analysis with linear, quadratic and exponential data transformation is applied so as to find the best suitable model. The best results according to comparison of models considering determination coefficient (R2) are achieved with quadratic regression model. In addition, tool nose radius was determined as the most effective parameter on turning by variance analysis (ANOVA). Cutting experiments and statistical analysis demonstrate that the model developed in this work produces smaller errors than those from some of the existing models and have a satisfactory goodness in all three models construction and verification.
Text line Segmentation in Compressed Representation of Handwritten Document using Tunneling Algorithm R, Amarnath; P, Nagabhushan
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.2018448451

Abstract

Operating directly on the compressed document images without decompression would be an additional advantage for storage and transmission. In this research work, we perform text line segmentation directly in compressed representation of an unconstraint handwritten document image using tunneling algorithm. In this relation, we make use of text line terminal point which is the current state-of-the-art that enables text line segmentation. The terminal points spotted along both margins (left and right) of a document image for every text line are considered as source and target respectively. The effort in spotting the terminal positions is performed directly in the compressed domain. The tunneling algorithm uses a single agent to identify the coordinate positions in the compressed representation to perform text-line segmentation of the document. The agent starts at a source point and progressively tunnels a path routing in between two adjacent text lines and reaches the probable target. The agent’s navigation path from source to the target bypassing obstacles, if any, results in segregating the two adjacent text lines. However, the target point would be known only when the agent reaches destination; this is applicable for all source points and henceforth we could analyze the correspondence between source and target nodes. In compressed representation of a document image, the continuous pixel values in a spatial domain are available in the form of batches known as white-runs (background) and black-runs (foreground). These batches are considered as features of a document image represented in a Grid map. Performing text-line segmentation using these features makes the system inexpensive compared to spatial domain processing. Artificial Intelligence in Expert systems with dynamic programming and greedy strategies is employed for every search space for tunneling. An exhaustive experimentation is carried out on various benchmark datasets including ICDAR13 and the performances are reported.
Lupsix: A Cascade Framework for Lung Parenchyma Segmentation in Axial CT Images Koyuncu, Hasan
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.2018448460

Abstract

Lung imaging and computer aided diagnosis (CAD) play a critical role in detection of lung diseases. The most significant part of a lung based CAD is to fulfil the parenchyma segmentation, since disease information is kept in the parenchyma texture. For this purpose, parenchyma segmentation should be accurately performed to find the necessary diagnosis to be used in the treatment. Besides, lung parenchyma segmentation remains as a challenging task in computed tomography (CT) owing to the handicaps oriented with the imaging and nature of parenchyma. In this paper, a cascade framework involving histogram analysis, morphological operations, mean shift segmentation (MSS) and region growing (RG) is proposed to perform an accurate segmentation in thorax CT images. In training data, 20 axial CT images are utilized to define the optimum parameter values, and 150 images are considered as test data to objectively evaluate the performance of system. Five statistical metrics are handled to carry out the performance assessment, and a literature comparison is realized with the state-of-the-art techniques. As a result, parenchyma tissues are segmented with success rates as 98.07% (sensitivity), 99.72% (specificity), 99.3% (accuracy), 98.59% (Dice similarity coefficient) and 97.23% (Jaccard) on test dataset.
Breast Cancer Diagnosis by Different Machine Learning Methods Using Blood Analysis Data Aslan, Muhammet Fatih; Celik, Yunus; Sabanci, Kadir; Durdu, Akif
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.2018648455

Abstract

Today, one of the most common types of cancer is breast cancer. It is crucial to prevent the propagation of malign cells to reduce the rate of cancer induced mortality. Cancer detection must be done as early as possible for this purpose. Machine Learning techniques are used to diagnose or predict the success of treatment in medicine. In this study, four different machine learning algorithms were used to early detection of breast cancer. The aim of this study is to process the results of routine blood analysis with different ML methods and to understand how effective this method is for detection. Methods used can be listed as Artificial Neural Network (ANN), standard Extreme Learning Machine (ELM), Support Vector Machine (SVM) and K-Nearest Neighbor (k-NN). Dataset used were taken from UCI library. In this dataset age, body mass index (BMI), glucose, insulin, homeostasis model assessment (HOMA), leptin, adiponectin, resistin and chemokine monocyte chemoattractant protein 1 (MCP1)   attributes were used. Parameters that have the best accuracy values were found by using four different Machine Learning techniques. For this purpose, hyperparameter optimization method was used. In the end, the results were compared and discussed.
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.

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