International Journal of Intelligent Systems and Applications in Engineering
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.
Articles
200 Documents
<![CDATA[A Novel Multi-Swarm Approach for Numeric Optimization ]]>
<![CDATA[Babalik, Ahmet]]>
<![CDATA[ International Journal of Intelligent Systems and Applications in Engineering Vol 6, No 3 (2018) ]]>
Publisher : <![CDATA[Prof. Dr. Ismail SARITAS ]]>
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DOI: 10.18201/ijisae.2018644781
<![CDATA[In order to solve the numeric optimization problems, swarm-based meta-heuristic algorithms can be used as an alternative to solve optimization problems. Meta-heuristic algorithms do not guarantee finding the optimal solution but they produce acceptable solutions in a reasonable computation time. By depending on the nature of the problems and the structure of the meta-heuristic algorithms, different results are obtained by different algorithms, and none of the meta-heuristic algorithm could guarantee to find the optimal solution. Particle swarm optimization (PSO) and artificial bee colony (ABC) algorithms are well known meta-heuristic algorithms often used for solving numeric optimization problems. In this study, a novel multi-swarm approach based on PSO and ABC algorithms is suggested. The proposed multi-swarm approach includes PSO and ABC algorithms together and replacing the swarm which achieves better solutions than the other algorithm in a pre-defined migration period. By this migration, swarm always include better solutions concerned to the algorithm which achieves better results. While running PSO and ABC algorithms competitively, this migration ensures to utilize better solutions of both the solutions of PSO or ABC algorithms, and the convergence characteristic of each algorithm provides different approximation to the solution space. Thus, it is expected to obtain successful solutions and increasing the success rate at each migration cycle. The suggested approach has been tested on 14 well-known benchmark functions, and the results of the study are compared with the results in literature. The experimental results and comparisons show that the proposed approach is better than the other algorithms.]]>
<![CDATA[Speed Control of DC Motor Using Interval Type-2 Fuzzy Logic Controller ]]>
<![CDATA[Acikgoz, Hakan]]>
<![CDATA[ International Journal of Intelligent Systems and Applications in Engineering Vol 6, No 3 (2018) ]]>
Publisher : <![CDATA[Prof. Dr. Ismail SARITAS ]]>
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DOI: 10.18201/ijisae.2018644777
<![CDATA[Direct current (DC) motors are widely used for speed control and position control in industry. The simplicity of DC motor speed control is also the main reason for its widespread use. Recently, in parallel with rapid developments in power electronics, microprocessors and semiconductor materials, many control structures are designed for DC motors. In this study, the speed control of the DC motor is carried out using the Matlab/Simulink package program. Type-2 Fuzzy Logic controller (T2FLC) which has efficient performance in modelling uncertainties is proposed for the speed control of DC motor. The classical PI controller and Type-2 Fuzzy Logic controller (T2FLC) are applied to the speed control unit of the DC motor. Simulation studies have also been realized for the designed DC motor model under the same conditions. The results obtained from the classical PI controller and T2FLC have been examined and compared to disturbances such as tracking reference speeds and load changes. According to the obtained simulation results, it has been observed that T2FLC has better results than the classical PI controller in whole conditions. ]]>
<![CDATA[Development of a Prototype Using the Internet of Things for Kinetic Gait Analysis ]]>
<![CDATA[Caliskan, Muhammet]]>;
<![CDATA[Tumer, Abdullah Erdal]]>;
<![CDATA[Sengul, Sumeyra Busra]]>
<![CDATA[ International Journal of Intelligent Systems and Applications in Engineering Vol 6, No 3 (2018) ]]>
Publisher : <![CDATA[Prof. Dr. Ismail SARITAS ]]>
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DOI: 10.18201/ijisae.2018644783
<![CDATA[The proliferation of mobile devices and the gradual development of technology have led to the emergence of the concept of Internet of Things. The Internet of Things has led to an increase in the work done especially on the medical field. At the beginning of the reasons for using the Internet of Things in medical studies is to be able to detect and display instantaneous changes that physicians can not even observe. Another important cause is that it allows the patient to be observed in the natural world. This is especially true in gait analysis, where a natural gait must be measured for accurate diagnosis. This study is concerned with the use of Internet of Things technology in gait analysis. To follow the foot pressure distribution in the study, a prototype was installed in shoes using the Internet of Things. The prototype consists of a thin, flexible base that collects analog data from the 32 sensors and transmits it wirelessly to mobile or PC computers via Bluetooth technology. The software developed by the prototype shows the pressure on every small sensor in the room and draws a walking graph. The accuracy and reliability of the prototype were evaluated by preliminary experimental measures. The results show that the system has the capacity to reliably measure pressure distributions of the sole of the foot.]]>
<![CDATA[Novel approach to locate region of interest in mammograms for Breast cancer ]]>
<![CDATA[Divyashree, B V]]>;
<![CDATA[Amarnath, R]]>;
<![CDATA[Naveen, M]]>;
<![CDATA[Hemantha Kumar, G]]>
<![CDATA[ International Journal of Intelligent Systems and Applications in Engineering Vol 6, No 3 (2018) ]]>
Publisher : <![CDATA[Prof. Dr. Ismail SARITAS ]]>
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DOI: 10.18201/ijisae.2018644775
<![CDATA[Locating region of interest for breast cancer in the mammographic image is a challenging problem in medical image processing. In this research work, we perform breast region segmentation followed by identification of the region of interest. Initially red, green and blue bands of the image are sliced into multiple segments based on intensity values and threshold. Masking operation is performed on each segment for understanding background and foreground(breast). Intersection of these segments would provide the breast segmentation. Next, we calculate the entropy of the segmented breast region. Here, quad division is performed based on the entropy. More entropy would result in additional quad division in the region of interest. This approach is tested on a DDSM datasets comprising of positive and negative mammographic images for breast location. Illustration is provided for the model. However, breast cancer detection needs radiologistâs attention in classifying normal and malignant cases. This leads to reduce false positive and negative rates when post processing is required for the detection of breast cancer.]]>
<![CDATA[Human Gender Prediction on Facial Mobil Images using Convolutional Neural Networks ]]>
<![CDATA[Hacibeyoglu, Mehmet]]>;
<![CDATA[Ibrahim, Mohammed Hussein]]>
<![CDATA[ International Journal of Intelligent Systems and Applications in Engineering Vol 6, No 3 (2018) ]]>
Publisher : <![CDATA[Prof. Dr. Ismail SARITAS ]]>
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DOI: 10.18201/ijisae.2018644778
<![CDATA[The interest in automatic gender classification has increased rapidly, especially with the growth of online social networking platforms, social media applications, and commercial applications. Most of the images shared on these platforms are taken by mobile phone with different expressions, different angles and low resolution. In recent years, convolutional neural networks have become the most powerful method for image classification. Many researchers have shown that convolutional neural networks can achieve better performance by modifying different network layers of network architecture. Moreover, the selection of the appropriate activation function of neurons, optimizer and the loss function directly affects the performance of the convolutional neural networks. In this study, we propose a gender classification system from facial images taken by mobile phone using convolutional neural networks. The proposed convolutional neural networks have a simple network architecture with appropriate parameters can be used when rapid training is needed with the amount of limited training data. In the experimental study, the Adience benchmark dataset was used with 17492 different images with different gender and ages. The classification process was carried out by 10-fold cross validation. According the experimental results, the proposed convolutional neural networks predicted the gender of the images 98.8% correctly for training and 89.1% for testing.]]>
<![CDATA[Estimation of Turkey Electric Energy Demand until Year 2035 Using TLBO Algorithm ]]>
<![CDATA[TEFEK, Mehmet Fatih]]>;
<![CDATA[UGUZ, Harun]]>
<![CDATA[ International Journal of Intelligent Systems and Applications in Engineering 2016: Special Issue ]]>
Publisher : <![CDATA[Prof. Dr. Ismail SARITAS ]]>
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DOI: 10.18201/ijisae.266082
<![CDATA[In this study, the estimation of Turkey primary electric energy demand until 2035 is tried to estimate by using Teaching-Learning Based Optimization (TLBO) Algorithm. Two models are proposed which are based on economic indicators TLBO algorithm linear energy demand (TLBOEDL) and TLBO algorithm quadratic energy demand (TLBOEDQ). In both of these two models the indicators used are Gross Domestic Product (GDP), population, importation and exportation. After a comparison of these two models with real values between 1979 and 2005 years, it is applied to the estimation of Turkey electric energy demand until 2035 by three different scenario. The estimation results are suitable with the estimation of Turkey total primary energy supply of 2013 Energy Report of World Energy Council Turkish National Committee (WEC-TNC ).  ]]>
<![CDATA[RSSI and Flower Pollination Algorithm Based Location Estimation for Wireless Sensor Networks ]]>
<![CDATA[Sesli, Erhan]]>;
<![CDATA[Hacıoğlu, Gökçe]]>
<![CDATA[ International Journal of Intelligent Systems and Applications in Engineering 2016: Special Issue ]]>
Publisher : <![CDATA[Prof. Dr. Ismail SARITAS ]]>
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DOI: 10.18201/ijisae.265424
<![CDATA[Wireless Sensor Networks (WSN’s) have been finding to itself new applications continuously. Many of these applications need location information of nodes. The localization of nodes can be made by range based or range free localization methods conventionally. Angle-of-Arrival (AoA), Time-Difference-of-Arrival (TDoA), Received Signal Strength Indicator (RSSI), Time-of-Arrival (ToA) are well known range based methods. Therefore AoA, ToA and TDoA have some hardware and software difficulties for nodes which have limited processing and power sources. However RSSI based localization doesn’t cost high processing resources or complex hardware modifications. Most of the WSN nodes already have RSSI measurement capability. However RSSI measurements is vulnerable to noise and environmental effects. Therefore error of RSSI based localization can be over to an acceptable level. Centroid, APIT, DV-Hop and Amorphous are some of the range free localization methods. Range free methods can only give location information approximately but they don’t need any extra hardware or high processing capability. In this study WSN nodes are assumed randomly or regularly distributed on a certain area. Some of the nodes are beacon nodes. The beacon nodes are assumed as having higher power resources and GPS receivers. The locations of nodes are assumed as fixed. The beacon nodes send their location information sequentially. Localization of nodes are made through RSSI and location information of beacon nodes. The mean of RSSI is calculated to reduce effect of noise on it. A rough location estimation made by weighted centroid. A probabilistic based location estimation and flower pollination algorithm (FPA) are used separately to make final decision about the location. Rough estimates are used to limit search area of flower pollination algorithm in order to reduce convergence time.]]>
<![CDATA[Stiffness Analysis of Above Knee Prosthesis ]]>
<![CDATA[Ege, Mücahit]]>;
<![CDATA[Küçük, Serdar]]>
<![CDATA[ International Journal of Intelligent Systems and Applications in Engineering 2016: Special Issue ]]>
Publisher : <![CDATA[Prof. Dr. Ismail SARITAS ]]>
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DOI: 10.18201/ijisae.267047
<![CDATA[While a healthy human walks, his or her legs mutually perform good repeatability with high accuracy. This provides an esthetical movement and balance. People with above knee prosthesis want to perform walking as esthetical as a healthy human. Therefore, to achieve a healthy walking, the above knee prosthesis must provide a good stiffness performance. Especially stiffness values are required when adding a second axis movement to the ankle for eversion and inversion. In this paper, stiffness analysis of above-knee prosthesis is presented. The translational displacement of above knee prosthesis is obtained when the prosthesis is subjected to the external forces. Knowing stiffness values of the above knee prosthesis, designers can compute prosthesis parameters such as ergonomic structure, height, and weight and energy consumption.]]>
<![CDATA[The Diagnosis and Estimate of Chronic Kidney Disease Using the Machine Learning Methods ]]>
<![CDATA[ÇELİK, Enes]]>;
<![CDATA[Atalay, Muhammet]]>;
<![CDATA[Kondiloglu, Adil]]>
<![CDATA[ International Journal of Intelligent Systems and Applications in Engineering 2016: Special Issue ]]>
Publisher : <![CDATA[Prof. Dr. Ismail SARITAS ]]>
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DOI: 10.18201/ijisae.265967
<![CDATA[Chronic kidney disease is a prolonged disease that damages the kidneys and prevents the normal duties of the kidneys. This disease is diagnosed with an increase of urinary albumin excretion lasting more than three months or with significant reduction in a kidney functions. Chronic kidney disease can lead to complications such as high blood pressure, anemia, bone disease and cardiovascular disease. In this study we have been investigated to determine the factors that decisive for early detection of chronic kidney disease, launching early patients treatment processes, prevent complications resulting from the disease and predict of disease. The study aimed diagnosis and prediction of disease using the data set that composed of data of 250 patients with chronic kidney disease and 150 healthy people. First, the chronic kidney disease data was classified with machine learning algorithms and then training and test results were analysed. The estimation results of chronic kidney disease were compared with similar data and studies.]]>
<![CDATA[Biogeography-Based Optimization Algorithm for Designing of Planar Steel Frames ]]>
<![CDATA[TUNCA, OSMAN]]>;
<![CDATA[ÇARBAŞ, SERDAR]]>
<![CDATA[ International Journal of Intelligent Systems and Applications in Engineering 2016: Special Issue ]]>
Publisher : <![CDATA[Prof. Dr. Ismail SARITAS ]]>
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DOI: 10.18201/ijisae.266128
<![CDATA[The optimization can be defined as a solution of problem under specific conditions to achieve a specific purpose. Optimization strategies commonly used for solving of various problems and have gained great importance in recent years especially in engineering. Evolving optimization methods over the years has many varieties such as shape optimization, topology optimization, size optimization etc. The latest trend of optimization methods is metaheuristics which are more useful with easy applicable to complex problems regarding to traditional optimization methods. So that metaheuristics have supplanted the traditional methods particularly in engineering by the time. In this study, a planar steel frame which is designed according to the requirements comprised by AISC-LRFD (American Institute of Steel Construction-Load and Resistance Factor Design) has been optimized by aid of biogeography-based optimization (BBO) algorithm.]]>
