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Bilgi kazancı tabanlı yapay bağışıklık tanıma sistemi
(Selçuk Üniversitesi Fen Bilimleri Enstitüsü, 2007-12-28) Kodaz, Halife; Güneş, Salih
Tez çalışmasında bir yapay bağışıklık sınıflandırıcı sistemi olan Yapay Bağışıklık Tanıma Sistemi (YBTS) incelenmiş ve tespit edilen eksiklikleri gidermek amacıyla Bilgi Kazancı Tabanlı Yapay Bağışıklık Tanıma Sistemi (BK-YBTS) geliştirilmiştir. Bu eksiklikler arasında, YBTS'nin çalışma süresinin çok zaman alması ve sınıflandırma performansının düşük olması sayılabilir. Tüm bu eksikliklere, hücreler arası duyarlılık hesabında bütün özelliklerin eşit ağırlıkta kabul edilmesi, mutasyon mekanizmasının rasgele değer atama şeklinde gerçekleşmesi ve duyarlılık eşik değeri hesabı ile kaynak tahsisi mekanizmasında eğitim verilerinin sınıf değerlerinin kullanılmaması sebep olmaktadır. YBTS'den kaynaklanan bu eksiklikleri gidermek amacıyla geliştirilen sistemde; hücreler arası duyarlılık hesabında bilgi kazancı kavramı kullanılarak özelliklere ağırlık verilmiş, antikorlara mutasyon işlemi sırasında antikorun antijenle olan duyarlılığını temel alan bir mutasyon mekanizması kullanılmış ve duyarlılık eşiği hesabında ve kaynak tahsisi mekanizmasında eğitim verilerinin sınıf değerleri temel alınmıştır. BK-YBTS ile gerçekleştirilen tüm bu değişikliklerin etkinliğini göstermek amacıyla çeşitli veri kümeleri üzerinde BK-YBTS ve YBTS parametrelerinin değişik değerleri ile iki sistem karşılaştırılmıştır. Genel olarak BK-YBTS, YBTS'den daha yüksek sınıflandırma doğruluklarına ulaşmıştır. Özellikle mutasyon ve kaynak tahsisi mekanizmasının değiştirilmesiyle birlikte BK-YBTS, YBTS'den daha kısa sürede çalışmasını tamamlamıştır. Literatürdeki sınıflandırıcılarla karşılaştırıldığında BK-YBTS ile beraber kabul edilebilir sınıflandırma doğruluklarına ulaşılmıştır. Bu çalışmada sunulan yaklaşımların gelecekte farklı uygulamalara ve yeni Yapay Bağışıklık Sistemleri tasarlanmasına katkılar sağlayacağı değerlendirilmektedir.
Breast cancer and liver disorders classification using artificial immune recognition system (AIRS) with performance evaluation by fuzzy resource allocation mechanism
(PERGAMON-ELSEVIER SCIENCE LTD, 2007) Polat, Kemal; Sahan, Seral; Kodaz, Halife; Guenes, Salih
Artificial Immune Recognition System (AIRS) classification algorithm, which has an important place among classification algorithms in the field of Artificial Immune Systems, has showed an effective and intriguing performance on the problems it was applied. AIRS was previously applied to some medical classification problems including Breast Cancer, Cleveland Heart Disease, Diabetes and it obtained very satisfactory results. So, AIRS proved to be an efficient artificial intelligence technique in medical field. In this study, the resource allocation mechanism of AIRS was changed with a new one determined by Fuzzy-Logic. This system, named as Fuzzy-AIRS was used as a classifier in the diagnosis of Breast Cancer and Liver Disorders, which are of great importance in medicine. The classifications of Breast Cancer and BUPA Liver Disorders datasets taken from University of California at Irvine (UCI) Machine Learning Repository were done using 10-fold cross-validation method. Reached classification accuracies were evaluated by comparing them with reported classifiers in UCI web site in addition to other systems that are applied to the related problems. Also, the obtained classification performances were compared with AIRS with regard to the classification accuracy, number of resources and classification time. Fuzzy-AIRS, which reached to classification accuracy of 98.51% for breast cancer, classified the Liver Disorders dataset with 83.36% accuracy. For both datasets, Fuzzy-AIRS obtained the highest classification accuracy according to the UCI web site. Beside of this success, Fuzzy-AIRS gained an important advantage over the AIRS by means of classification time. In the experiments, it was seen that the classification time in Fuzzy-AIRS was reduced about 70% of AIRS for both datasets. By reducing classification time as well as obtaining high classification accuracies in the applied datasets, Fuzzy-AIRS classifier proved that it could be used as an effective classifier for medical problems. (C) 2005 Elsevier Ltd. All rights reserved.
Classification of internal carotid artery Doppler signals using fuzzy discrete hidden Markov model
(PERGAMON-ELSEVIER SCIENCE LTD, 2011) Uguz, Harun; Kodaz, Halife
We developed a biomedical system based on Discrete Hidden Markov Model (DHMM). The aim of our system is to classify the internal carotid artery Doppler signals. We applied a fuzzy approach to DHMM. Thus we decreased information loss and increased the classification performance. Our system reached 97.38% of classification accuracy with 5 fold cross validation. These results showed that the Fuzzy Discrete Hidden Markov Model (FDHMM) method is effective for classification of internal carotid artery Doppler signals. (C) 2010 Elsevier Ltd. All rights reserved.
Classification of Internal Carotid Artery Doppler Signals Using Hidden Markov Model and Wavelet Transform with Entropy
(Springer-Verlag Berlin, 2010) Uğuz, Harun; Kodaz, Halife
Doppler ultrasound has been usually preferred for investigation of the artery conditions in the last two decade, since it is a non-invasive method which is not risky. In this study, a biomedical system based on Discrete Hidden Markov Model (DHMM) has been developed in order to classify the internal carotid artery Doppler signals recorded from 191 subjects (136 of them had suffered from internal carotid artery stenosis and rest of them had been healthy subjects). Developed system comprises of three stages. In the first stage, for feature extraction, obtained Doppler signals were separated to its sub-bands using Discrete Wavelet Transform (DWT). In the second stage, entropy of each sub-band was calculated using Shannon entropy algorithm to reduce the dimensionality of the feature vectors via DWT. In the third stage, the reduced features of carotid artery Doppler signals were used as input patterns of the DHMM classifier. Our proposed method reached 97.38% classification accuracy with 5 fold cross validation (CV) technique. The classification results showed that purposed method is effective for classification of internal carotid artery Doppler signals.
Classification of Linked Data Sources Using Semantic Scoring
(IEICE-INST ELECTRONICS INFORMATION COMMUNICATIONS ENG, 2018) Yumusak, Semih; Dogdu, Erdogan; Kodaz, Halife
Linked data sets are created using semantic Web technologies and they are usually big and the number of such datasets is growing. The query execution is therefore costly, and knowing the content of data in such datasets should help in targeted querying. Our aim in this paper is to classify linked data sets by their knowledge content. Earlier projects such as LOD Cloud, LODStats, and SPARQLES analyze linked data sources in terms of content, availability and infrastructure. In these projects, linked data sets are classified and tagged principally using VoID vocabulary and analyzed according to their content, availability and infrastructure. Although all linked data sources listed in these projects appear to be classified or tagged, there are a limited number of studies on automated tagging and classification of newly arriving linked data sets. Here, we focus on automated classification of linked data sets using semantic scoring methods. We have collected the SPARQL endpoints of 1,328 unique linked datasets from Datahub, LOD Cloud, LODStats, SPARQLES, and SpEnD projects. We have then queried textual descriptions of resources in these data sets using their rdfs: comment and rdfs: label property values. We analyzed these texts in a similar manner with document analysis techniques by assuming every SPARQL endpoint as a separate document. In this regard, we have used WordNet semantic relations library combined with an adapted term frequency-inverted document frequency (tfidf) analysis on the words and their semantic neighbours. In WordNet database, we have extracted information about comment/label objects in linked data sources by using hypernym, hyponym, homonym, meronym, region, topic and usage semantic relations. We obtained some significant results on hypernym and topic semantic relations; we can find words that identify data sets and this can be used in automatic classification and tagging of linked data sources. By using these words, we experimented different classifiers with different scoring methods, which results in better classification accuracy results.
Community detection from biological and social networks: A comparative analysis of metaheuristic algorithms
(ELSEVIER, 2017) Atay, Yilmaz; Koc, Ismail; Babaoglu, Ismail; Kodaz, Halife
In order to analyze complex networks to find significant communities, several methods have been proposed in the literature. Modularity optimization is an interesting and valuable approach for detection of network communities in complex networks. Due to characteristics of the problem dealt with in this study, the exact solution methods consume much more time. Therefore, we propose six metaheuristic optimization algorithms, which each contain a modularity optimization approach. These algorithms are the original Bat Algorithm (BA), Gravitational Search Algorithm (GSA), modified Big BangBig Crunch algorithm (BB-BC), improved Bat Algorithm based on the Differential Evolutionary algorithm (BADE), effective Hyperheuristic Differential Search Algorithm (HDSA) and Scatter Search algorithm based on the Genetic Algorithm (SSGA). Four of these algorithms (HDSA, BADE, SSGA, BB-BC) contain new methods, whereas the remaining two algorithms (BA and GSA) use original methods. To clearly demonstrate the performance of the proposed algorithms when solving the problems, experimental studies were conducted using nine real-world complex networks - five of which are social networks and the rest of which are biological networks. The algorithms were compared in terms of statistical significance. According to the obtained test results, the HDSA proposed in this study is more efficient and competitive than the other algorithms that were tested. (C) 2016 Elsevier B.V. All rights reserved.
Designing a Special Purpose E-Commerce Website
(2009) İşcan, Hazim; Fındık, Oğuz; Kodaz, Halife; Erdi, Ali
Electronic commerce can be defined as the process of conducting the production, advertisement, sale, insurance and payment of products and services over computer networks. Electronic commerce, which is realized through carrying out one or more of the business transactions in the electronic environment, is composed of three stages as advertisement and market research, order and payment, and delivery. The fast spread of the internet has made electronic commerce a new and highly effective tool for performing business transactions. Electronic commerce has emerged as a product of the technological developments experienced in the last decade which facilitate the communication of information, together with the trend towards the liberalization of trade all around the world. Electronic commerce models, principles of electronic commerce, steps of electronic shopping and security in electronic commerce were examined within the scope of the present study. The general structure, running and implementation of electronic commerce was realized by way of a practical application. ASP software and Microsoft Access database were used in this study.
The energy demand estimation for Turkey using differential evolution algorithm
(SPRINGER INDIA, 2017) Beskirli, Mehmet; Hakli, Huseyin; Kodaz, Halife
The energy demand estimation commands great importance for both developing and developed countries in terms of the economy and country resources. In this study, the differential evolution algorithm ( DE) was used to forecast the long-term energy demand in Turkey. In addition to being employed for solving regular optimization problems, DE is also a global, meta-heuristic algorithm that enables fast, reliable and operative stochastic searches based on population. Considering the correlation between the increase in certain economic indicators in Turkey and the increase of energy consumption, two equations were used-one applying the linear form and the other the quadratic form. Turkey's long-term energy demand from 2012 to 2031 was estimated through the DE method in three different scenarios and in terms of the gross domestic product, import, export and population. To prove the success of the DE method in addressing the energy demand problem, the DE method was compared to other methods found in the literature. Results showed that the proposed DE method was more successful than the other methods. Furthermore, the future projections of energy demand obtained using the proposed method were compared to the indicators of energy demand estimated and observed by the Ministry of Energy and Natural Resources.
The estimation of the electricity energy demand using particle swarm optimization algorithm: A case study of Turkey
(ELSEVIER SCIENCE BV, 2017) Gulcu, Saban; Kodaz, Halife
Energy is the most important factor in improving the quality of life and advancing the economic and social progress. Demographic changes directly affect the energy demand. At present the worlds population is growing quickly. As of 2015, it was estimated at 7.3 billion. The population and the export of Turkey have been increasing for two decades. Consequently, electricity energy demand of Turkey has been increasing rapidly. This study aims to predict the future electricity energy demand of Turkey. In this paper, the prediction of the electricity demand of Turkey is modeled by using particle swarm optimization algorithm. The data of the gross domestic product, population, import and export are used as input data of the proposed model in the experiments. The GDP, import and export data are taken from the annual reports of the Turkish Ministry of Finance. The population data are taken from the Turkish Statistical Institute. The electricity demand data are taken from the Turkish Electricity Transmission Company. The statistical method R-2 and adjusted-R-2 are used as the performance criteria. The experimental results show that the generated model is very efficient. (c) 2017 The Authors. Published by Elsevier B.V.
Galactic Swarm Optimization using Artificial Bee Colony Algorithm
(IEEE, 2017) Kaya, Ersin; Babaoglu, Ismail; Kodaz, Halife
Galactic swarm optimization (GSO) algorithm is a novel meta-heuristic algorithm inspired by the motion of stars, galaxies and superclusters of galaxies under the influence of gravity. The GSO algorithm utilizes multiple cycles of exploration and exploitation in two levels. The first level covers the exploration, and different subpopulations of the candidate solutions are used for exploring the search space. The second level covers the exploitation, and best solutions obtained from the subpopulations are considered as a superswarm and used for exploiting the search space. The first implementation of GSO algorithm was presented by using particle swarm optimization algorithm (PSO) algorithm on both first and second levels. This study presents the preliminary results of an implementation of GSO algorithm by using artificial bee colony (ABC) algorithm on the first level and PSO algorithm on the second level. Due to the better exploration characteristics of ABC algorithm over PSO algorithm, this suggestion covers the usage of ABC algorithm on the first level, and the usage of PSO algorithm on the second level. The proposed approach is tested on 20 well-known online available benchmark problems and preliminary results are presented. According to the experimental results, the proposed approach achieves more successful results than the basic GSO approach.
Medical application of artificial immune recognition system (AIRS): Diagnosis of atherosclerosis from carotid artery Doppler signals
(PERGAMON-ELSEVIER SCIENCE LTD, 2007) Latifoglu, Fatma; Kodaz, Halife; Kara, Sadik; Gunes, Salih
This study was conducted to distinguish between atherosclerosis and healthy subjects. Hence, we have employed the maximum envelope of the carotid artery Doppler sonogrants derived from Fast Fourier Transformation-Welch method and Artificial Immune Recognition System (AIRS). The fuzzy appearance of the carotid artery Doppler signals makes physicians suspicious about the existence of diseases and sometimes causes false diagnosis. Our technique gets around this problem using AIRS to decide and assist the physician to make the final judgment in confidence. AIRS has reached 99.29% classification accuracy using 10-fold cross validation. Results show that the proposed method classified Doppler signals successfully. (c) 2006 Elsevier Ltd. All rights reserved.
Medical application of information gain based artificial immune recognition system (AIRS): Diagnosis of thyroid disease
(PERGAMON-ELSEVIER SCIENCE LTD, 2009) Kodaz, Halife; Oezsen, Seral; Arslan, Ahmet; Guenes, Salih
In this paper, we have made medical application of a new artificial immune system named the information gain based artificial immune recognition system (IG-AIRS) which minimizes the negative effects of taking into account all attributes in calculating Euclidean distance in shape-space representation which is used in many artificial immune systems. For medical data, thyroid disease data set was applied in the performance analysis of our proposed system. Our proposed system reached 95.90% classification accuracy with 10-fold CV method. This result ensured that IG-AIRS would be helpful in diagnosing thyroid function based on laboratory tests, and would open the way to various ill diagnoses support by using the recent clinical examination data, and we are actually in progress. (C) 2008 Elsevier Ltd. All rights reserved.
Medical application of information gain-based artificial immune recognition system (IG-AIRS): Classification of microorganism species
(PERGAMON-ELSEVIER SCIENCE LTD, 2009) Kara, Sadik; Aksebzeci, Bekir Hakan; Kodaz, Halife; Gunes, Salih; Kaya, Esma; Ozbilge, Hatice
In this paper, we have made medical application of a new artificial immune system named the information gain-based artificial immune recognition system (IG-AIRS) which is minimized the negative effects of taking into account all attributes in calculating Euclidean distance in shape-space representation which is used in many artificial immune systems. For medical data, microorganism dataset was applied in the performance analysis of our proposed system. Microorganism dataset was obtained using Cyranose 320 electronic nose. Our proposed system reached 92.35% classification accuracy with five-fold cross validation method. This result ensured that IG-AIRS would be helpful in classification of microorganism species based on laboratory tests, and would open the way to various microorganism species determine support by using electronic nose. (C) 2008 Elsevier Ltd. All rights reserved.
Modularity-Based Graph Clustering using Harmony Search Algorithm
(IEEE, 2015) Atay, Yilmaz; Kodaz, Halife
Real-world networks contain variety of meaningful information inside them that can be revealed. These networks can be biological, social, ecological and technological networks. Each of these contains specific information about their field. This information cannot be obtained with simple techniques. Various techniques and algorithms have been developed to uncover useful information from complex relationships inside the network. In this paper, to divide graphs according to modularity measure to subgraphs harmony search algorithm is used which is inspired by music improvisation. This algorithm has been tested with 5 different real-world networks. The obtained quantitative values for each network have been given in the tables. In addition the proposed algorithm, has achieved the best known modularity measure of Zachary's Karate Club network which is commonly used in the literature and the latest subsets generated according to this modularity measure has been given at the end of section V. According to the results obtained from experiments it has been observed that HM algorithm gives faster results on solution of problem addressed in this study than most algorithms like genetic algorithm and bat algorithm. However, the proposed algorithm requires a larger size of harmony memory and more number of iterations for maximum modularity values.
A New Adaptive Genetic Algorithm for Community Structure Detection
(SPRINGER INTERNATIONAL PUBLISHING AG, 2016) Atay, Yilmaz; Kodaz, Halife
Community structures exist in networks which has complex biological, social, technological and so on structures and contain important information. Networks and community structures in computer systems are presented by graphs and subgraphs respectively. Community structure detection problem is NP-hard problem and especially final results of the best community structures for large-complex networks are unknown. In this paper, to solve community structure detection problem a genetic algorithm-based algorithm, AGA-net, which is one of evolutionary techniques has been proposed. This algorithm which has the property of fast convergence to global best value without being trapped to local optimum has been supported by new parameters. Real-world network which are frequently used in literature has been used as test data and obtained results have been compared with 10 different algorithms. After analyzing the test results it has been observed that the proposed algorithm gives successful results for determination of meaningful communities from complex networks.
A new approach based on particle swarm optimization algorithm for solving data allocation problem
(ELSEVIER, 2018) Mahi, Mostafa; Baykan, Omer Kaan; Kodaz, Halife
The effectiveness distributed database systems highly depends on the state of site that its task is to allocate fragments. This allocation purpose is performed for obtaining the minimum execute time and transaction cost of queries. There are some NP-hard problems that Data Allocation Problem (DAP) is one of them and solving this problem by means of enumeration method can be computationally expensive. Recently heuristic algorithms have been used to achieve desirable solutions. Due to fewer control parameters, robustness, speed convergence characteristics and easy adaptation to the problem, this paper propose a novel method based on Particle Swarm Optimization (PSO) algorithm which is suitable to minimize the total transmission cost for both the each site - fragment dependency and the each inter - fragment dependency. The core of the study is to solve DAP by utilizing and adaptation PSO algorithm, PSO-DAP for short. Allocation of fragments to the site has been done with PSO algorithm and its performance has been evaluated on 20 different test problems and compared with the state-of-art algorithms. Experimental results and comparisons demonstrate that proposed method generates better quality solutions in terms of execution time and total cost than compared state-of-art algorithms. (C) 2017 Elsevier B.V. All rights reserved.
A new hybrid method based on fuzzy-artificial immune system and k-nn algorithm for breast cancer diagnosis
(PERGAMON-ELSEVIER SCIENCE LTD, 2007) Sahan, Seral; Polat, Kemal; Kodaz, Halife; Gunes, Salih
The use of machine learning tools in medical diagnosis is increasing gradually. This is mainly because the effectiveness of classification and recognition systems has improved in a great deal to help medical experts in diagnosing diseases. Such a disease is breast cancer, which is a very common type of cancer among woman. As the incidence of this disease has increased significantly in the recent years, machine learning applications to this problem have also took a great attention as well as medical consideration. This study aims at diagnosing breast cancer with a new hybrid machine learning method. By hybridizing a fuzzy-artificial immune system with k-nearest neighbour algorithm, a method was obtained to solve this diagnosis problem via classifying Wisconsin Breast Cancer Dataset (WBCD). This data set is a very commonly used data set in the literature relating the use of classification systems for breast cancer diagnosis and it was used in this study to compare the classification performance of our proposed method with regard to other studies. We obtained a classification accuracy of 99.14%, which is the highest one reached so far. The classification accuracy was obtained via 10-fold cross validation. This result is for WBCD but it states that this method can be used confidently for other breast cancer diagnosis problems, too. (c) 2006 Elsevier Ltd. All rights reserved.
A new hybrid method based on Particle Swarm Optimization, Ant Colony Optimization and 3-Opt algorithms for Traveling Salesman Problem
(ELSEVIER SCIENCE BV, 2015) Mahi, Mostafa; Baykan, Omer Kaan; Kodaz, Halife
The Traveling Salesman Problem (TSP) is one of the standard test problems used in performance analysis of discrete optimization algorithms. The Ant Colony Optimization (ACO) algorithm appears among heuristic algorithms used for solving discrete optimization problems. In this study, a new hybrid method is proposed to optimize parameters that affect performance of the ACO algorithm using Particle Swarm Optimization (PSO). In addition, 3-Opt heuristic method is added to proposed method in order to improve local solutions. The PSO algorithm is used for detecting optimum values of parameters alpha and beta which are used for city selection operations in the ACO algorithm and determines significance of inter-city pheromone and distances. The 3-Opt algorithm is used for the purpose of improving city selection operations, which could not be improved due to falling in local minimums by the ACO algorithm. The performance of proposed hybrid method is investigated on ten different benchmark problems taken from literature and it is compared to the performance of some well-known algorithms. Experimental results show that the performance of proposed method by using fewer ants than the number of cities for the TSPs is better than the performance of compared methods in most cases in terms of solution quality and robustness. (C) 2015 Elsevier B.V. All rights reserved.
A new optimization algorithm for solving wind turbine placement problem: Binary artificial algae algorithm
(PERGAMON-ELSEVIER SCIENCE LTD, 2018) Beskirli, Mehmet; Koc, Ismail; Hakli, Huseyin; Kodaz, Halife
The wind turbine has grown out to be one of the most common renewable energy sources around the world in recent years. As wind energy becomes more important, the significance of wind turbine placement also increases. This study was intended to position the wind turbines on a wind farm to achieve the highest performance possible. The turbine placement operation was designed for a 2 km x 2 km area. The surface of the area was calculated by dividing it into a 10 x 10 grid and a 20 x 20 grid with the use of binary coding. The calculation revealed ten different new binary algorithms using ten different transfer functions of the Artificial Algae Algorithm (AAA) that has been successfully applied to solve continuous optimization problems. These algorithms were applied to the turbine placement problem, and the algorithm that obtained the best result was called the Binary Artificial Algorithm (BinAAA). The results of the proposed algorithm for the binary turbine placement optimization problem were compared with those of other well-known algorithms in the relevant literature. The algorithm that was proposed in the study is an efficient algorithm for the placement problem of wind turbines since it optimized the binary search space and achieved the most successful result (C) 2017 Elsevier Ltd. All rights reserved.
A novel parallel multi-swarm algorithm based on comprehensive learning particle swarm optimization
(PERGAMON-ELSEVIER SCIENCE LTD, 2015) Gulcu, Saban; Kodaz, Halife
This article presented a parallel metaheuristic algorithm based on the Particle Swarm Optimization (PSO) to solve global optimization problems. In recent years, many metaheuristic algorithms have been developed. The PSO is one of them is very effective to solve these problems. But PSO has some shortcomings such as premature convergence and getting stuck in local minima. To overcome these shortcomings, many variants of PSO have been proposed. The comprehensive learning particle swarm optimizer (CLPSO) is one of them. We proposed a better variation of CLPSO, called the parallel comprehensive learning particle swarm optimizer (PCLPSO) which has multiple swarms based on the master-slave paradigm and works cooperatively and concurrently. The PCLPSO algorithm was compared with nine PSO variants in the experiments. It showed a great performance over the other PSO variants in solving benchmark functions including their large scale versions. Besides, it solved extremely fast the large scale problems. (C) 2015 Elsevier Ltd. All rights reserved.

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