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Öğe A3-ARTIFICIAL ATOM ALGORITHM: A NEW META-HEURISTIC COMPUTATIONAL INTELLIGENCE ALGORITHM INSPIRED BY CHEMICAL PROCESSES(Ministry Communications & High Technologies Republic Azerbaijan, 2018) Karci, AliMost of the meta-heuristic algorithms are based on the natural processes. They were inspired by physical, biological, social, chemical, social-biological, biological-geography, music, and hybrid processes. In this paper, we propose a new meta-heuristic algorithm based on the chemical process of compound formation. In order to form compound, some electrons are removed from some elements, and some electrons are attached to some elements. The formed bond is called the ionic bond. In another case, at least two elements share at least one electron in common, and the constructed bond is called the covalent bond. Proposed algorithm based on compound formation process in this paper is a population based algorithm, and population consists of candidate solutions-subject to evolution for convergence to optimal/near optimal solutions. These solutions were called atoms, and the attributes of each solution were called electrons. The set of solutions constructs Atom Set where initially, atoms are constructed randomly. Then there are two operators in the proposed algorithm to make candidate solutions be better. So, this process is applied to solutions until desired solution is obtained.The formation of covalent bond in chemistry is mimicked as covalent bond where better attribute value is shared between at least two atoms. The ionic bond process is mimicked as ionic bond, and some attribute values of each atom reassigned randomly. The obtained algorithm was called as Artificial Atom Algorithm (A3). It is superior with respect to other natural-inspired metaheuristic algorithms, since it is applied once and global optimum or near-global optimum point was obtained. On contrary, other meta-heuristic algorithms are applied more than once, and the better obtained result is selected as a result of experiment. Another important point is that this algorithm does not have more algorithm parameters. There are just ionic rate (covalent rate) parameter (ionic rate +covalent rate = 1).Öğe Application of Three Bar Truss Problem among Engineering Design Optimization Problems using Artificial Atom Algorithm(Ieee, 2018) Erdogan Yildirim, Ayse; Karci, AliOptimization is all of the transactions made in order to search for the ideal. In the real world, it is needed to optimization in many field. For this reason, optimization algorithms are popular topics that are frequently studied. In this study, it is focused on three bar truss problem which is one of the optimization problem in the engineering field. Artificial Atom Algorithm which is a new chemistry-based meta-heuristic optimization algorithm was used to solve this problem. The obtained results were compared with a Swarm Optimization Approach, Cuckoo Search Algorithm, Bat Algorithm, Mine Blast Algorithm and Cricket Algorithm which were used to solving the same problem.Öğe Applications and Comparisons of Optimization Algorithms Used in Convolutional Neural Networks(Ieee, 2019) Seyyarer, Ebubekir; Uckan, Taner; Hark, Cengiz; Ayata, Faruk; Inan, Mevlut; Karci, AliNowadays, it is clear that the old mathematical models are incomplete because of the large size of image data set. For this reason, the Deep Learning models introduced in the field of image processing meet this need in the software field In this study, Convolutional Neural Network (CNN) model from the Deep Learning Algorithms and the Optimization Algorithms used in Deep Learning have been applied to international image data sets. Optimization algorithms were applied to both datasets respectively, the results were analyzed and compared The success rate was approximately 96.21% in the Caltech 101 data set, while it was observed to be approximately 10% in the Cifar-100 data set.Öğe Applications of artificial atom algorithm to small-scale traveling salesman problems(Springer, 2018) Yildirim, Ayse Erdogan; Karci, AliMost of the meta-heuristic algorithms are based on the natural processes. They were inspired by physical, biological, social, chemical, social-biological, biological-geography, music, and hybrid processes. In this paper, artificial atom algorithm which was inspired by one of natural processes was applied to traveling salesman problem. The obtained results have shown that for small-scale TSP, artificial atom algorithm is closer to optimum than the other compared heuristic algorithms such as tabu search, genetic algorithm, particle swarm optimization, ant colony optimization, and their different combinations.Öğe Applications of Assoiation Rules Hiding Heuristic Approaches(Ieee, 2015) Farea, Afrah; Karci, AliData Mining allows large database owners to extract useful knowledge that could not be deduced with traditional approaches like statistics. However, these sometimes reveal sensitive knowledge or preach individual privacies. The term sanitization is given to the process of changing original database into another one from which we can mine without exposing sensitive knowledge. In this paper, we give a detailed explanation of some heuristic approaches for this purpose. We applied them on a number of publically available datasets and examine the results.Öğe Artificial Immune System Optimization Based Duplex Kinect Skeleton Fusion(Ieee, 2017) Gunduz, Ali Fatih; Sen, Mehmed Oguz; Karci, Ali; Yeroglu, CelaleddinHuman motion tracking, which requires both motion sensing hardware and algorithms based on computer vision, is an enjoyable and active research area with diverse applications. As a depth sensor device Kinect is a famous hardware component for this task. In this work, we studied using more than one Kinect camera to obtain better motion tracking which is applicable for motion capture. We synthetically created two camera data from one and then focused on de-noising and fusing these data in order to obtain more realistic skeleton joint coordinates. Artificial Immune System (AIS) optimization algorithm is suggested and used for this task. As a result we obtained 30% better fusion results from noisy synthetic data. Our results showed that AIS is a promising algorithm for obtaining optimal joint coordinates in the fusion of multiple Kinect skeleton data.Öğe Automatic Thresholding Method Developed With Entropy For Fabric Defect Detection(Ieee, 2019) Uzen, Huseyin; Firat, Huseyin; Karci, Ali; Hanbay, DavutFabric defect detection is one of the most important areas for quality control of products in the textile industry. Many different studies have developed methods for this problem. In this study, an automatic thresholding method developed with entropy has been proposed. Due to the low cost of calculation, the proposed automatic thresholding method will be very suitable for real-time applications. In this study, automatic thresholding method which is supported by 4 different entropy method was compared with otsu method which is one of automatic thresholding methods. Various tests have been made on different fabric types for comparisons. As a result of experimental studies, successful results of automatic thresholding methods supported with entropy were obtained for fabric defect detection. Renyi entropy method was the most successful result among the proposed methods.Öğe Centrality of Nodes with Karci Entropy(Ieee, 2018) Tugal, Ihsan; Karci, AliA measure of centrality can be used to identify important assets that affect a system. In this study, the most used centrality measures degree, closeness, betweenness, eigenvector centrality and entropy centrality were used to identify the most effective nodes. The central/influential nodes can be detected more accurately by the Karci entropy which has just begun to be used new in social networks. Karci entropy contain Shannon when a equal 1. The more accurate results were obtained when the a coefficient in Karci entropy was correctly selected. The effect of node degree and edge weights to the network were measured together. The applicability of the entropy-based method for the detection of the most effective nodes in weighted networks has been demonstrated. The success of proposed method has been offered by comparison with traditional methods.Öğe Collaboration Graph as a New Graph Definition Approach(Ieee, 2017) Ince, Kenan; Karci, AliModelling is a crucial step for analyzing the data. Graph is an important modelling technique for some areas especially if the data has some kind of relation between each other like complex networks. There are plenty of study in complex network area which uses graphs as a modelling tool. Collaboration networks are a kind of complex evolving networks. Also community detection and evaluation is an important topic in graph mining. Especially in recent years, the importance of social networks is increased and mining of these networks became more vital. However, there is no specific topic about collaboration graph which focus on how to evaluate the how strong a bond is and meaning of it. This study aims to propose a definition which named collaboration graph as a graph type for understanding structure of the network more clearly and less noisy.Öğe Collaboration Network Analysis of Turkey in Regional Basis(Ieee, 2017) Ince, Kenan; Karci, AliModeling of data is an important step in process of interpreting the data and to understand the desired situation more clearly. The topic of social network structures is one of the highly studied subject and modeling is very important for social network mining. One of the modeling tools for such structures is Graphs. Graphs have been used for modeling and visualization tool of many structures such that computer networks, social networks, data structures and parallel programming topologies, etc. In this study, our data is scientific publications of Turkey and the goal is to find the cliques of academic collaboration network of Turkey at regional base via BK (Bron-Kerbosch) algorithm. By finding cliques, we aim to explore how relations are established between universities of Turkey and to reveal prominent university in each region.Öğe Comparisons of Karci and Shannon entropies and their effects on centrality of social networks(Elsevier, 2019) Tugal, Ihsan; Karci, AliIn order to measure the amount of different information in a system, entropy concept can be used. Graph entropy measures nodes' contribution to the entropy of the graph. By this way, the influential actors can be identified. Due to this case, a new entropy-based method was proposed to identify the influential actors. Karci entropy was applied to the social networks first time. The alpha parameter allowed us to combine many different conditions together when measuring in the network. The other important contribution of this paper is to predict the value of alpha parameter of Karci entropy by using fuzzy logic. After that Karci and Shannon entropies were compared based on experimental results. Moreover, Kara entropy was compared to traditional centrality measures. If Karci entropy definition is considered as a set of entropies, Shannon entropy can be regarded as an element of this set. Accordingly, it can be concluded that Karci entropy is superior to Shannon entropy. (C) 2019 Elsevier B.V. All rights reserved.Öğe Connected Cubic Network Graph(Elsevier - Division Reed Elsevier India Pvt Ltd, 2017) Selcuk, Burhan; Karci, AliHypercube is a popular interconnection network. Due to the popularity of hypercube, more researchers pay a great effort to develop the different variants of hypercube. In this paper, we have proposed a variant of hypercube which is called as Connected Cubic Network Graphs, and have investigated the Hamilton-like properties of Connected Cubic Network Graphs (CCNG). Firstly, we defined CCNG and showed the characteristic analyses of CCNG. Then, we showed that the CCNG has the properties of Hamilton graph, and can be labeled using a Gray coding based recursive algorithm. Finally, we gave the comparison results, a routing algorithm and a bitonic sort algorithm for CCNG. In case of sparsity and cost, CCNG is better than Hypercube. (C) 2017 Karabuk University. Publishing services by Elsevier B.V.Öğe Continuous rotation invariant features for gradient-based texture classification(Academic Press Inc Elsevier Science, 2015) Hanbay, Kazim; Alpaslan, Nuh; Talu, Muhammed Fatih; Hanbay, Davut; Karci, Ali; Kocamaz, Adnan FatihExtracting rotation invariant features is a valuable technique for the effective classification of rotation invariant texture. The Histograms of Oriented Gradients (HOG) algorithm has been proved to be theoretically simple, and has been applied in many areas. Also, the co-occurrence HOG (CoHOG) algorithm provides a unified description including both statistical and differential properties of a texture patch. However, HOG and CoHOG have some shortcomings: they discard some important texture information and are not invariant to rotation. In this paper, based on the original HOG and CoHOG algorithms, four novel feature extraction methods are proposed. The first method uses Gaussian derivative filters named GDF-HOG. The second and the third methods use eigenvalues of the Hessian matrix named Eig(Hess)-HOG and Eig(Hess)-CoHOG, respectively. The fourth method exploits the Gaussian and means curvatures to calculate curvatures of the image surface named GM-CoHOG. We have empirically shown that the proposed novel extended HOG and CoHOG methods provide useful information for rotation invariance. The classification results are compared with original HOG and CoHOG algorithms methods on the CUReT, KTH-TIPS, KTH-TIPS2-a and UIUC datasets show that proposed four methods achieve best classification result on all datasets. In addition, we make a comparison with several well-known descriptors. The experiments of rotation invariant analysis are carried out on the Brodatz dataset, and promising results are obtained from those experiments. (C) 2014 Elsevier Inc. All rights reserved.Öğe Cricket behaviour-based evolutionary computation technique in solving engineering optimization problems(Springer, 2016) Canayaz, Murat; Karci, AliMeta-heuristicalgorithms are widely used in various areas such as engineering, statistics, industrial, image processing, artificial intelligence etc. In this study, the Cricket algorithm which is a novel nature-inspired meta-heuristic algorithm approach which can be used for the solution of some global engineering optimization problems was introduced. This novel approach is a meta-heuristic method that arose from the inspiration of the behaviour of crickets in the nature. It has a structure for the use in the solution of various problems. In the development stage of the algorithm, the good aspects of the Bat, Particle Swarm Optimization and Firefly were experimented for being applied to this algorithm. In addition to this, because of the fact that these insects intercommunicate through sound, the physical principles of sound propagation in the nature were practiced in the algorithm. Thanks to this, the compliance of the algorithm to real life tried to be provided. This new developed approach was applied on the familiar global engineering problems and the obtained results were compared with the results of the algorithm applied to these problems.Öğe Data Clustering on Breast Cancer Data Using Firefly Algorithm with Golden Ratio Method(Univ Suceava, Fac Electrical Eng, 2015) Demir, Murat; Karci, AliHeuristic methods are problem solving methods. In general, they obtain near-optimal solutions, and they do not take the care of provability of this case. The heuristic methods do not guarantee to obtain the optimal results; however, they guarantee to obtain near-optimal solutions in considerable time. In this paper, an application was performed by using firefly algorithm - one of the heuristic methods. The golden ratio was applied to different steps of firefly algorithm and different parameters of firefly algorithm to develop a new algorithm - called Firefly Algorithm with Golden Ratio (FAGR). It was shown that the golden ratio made firefly algorithm be superior to the firefly algorithm without golden ratio. At this aim, the developed algorithm was applied to WBCD database (breast cancer database) to cluster data obtained from breast cancer patients. The highest obtained success rate among all executions is 96% and the highest obtained average success rate in all executions is 94.5%.Öğe Deep and Statistical Features Classification Model for Electroencephalography Signals(Int Information & Engineering Technology Assoc, 2022) Karaduman, Mucahit; Karci, AliPeople strive to make sense of the complex electroencephalography (EEG) data generated by the brain. This study uses a prepared dataset to examine how easily people with alcohol use disorder (AUD) could be distinguished from healthy people. The signals from each electrode are connected to one another and are first represented as a single signal. The signal is then denoised through variation mode decomposition (VMD) during the preprocessing stage. The statistical and deep feature extraction phases are the two subsequent phases. The crucial step in the suggested strategy is to classify data using a combination of these two unique qualities. Deep and statistical feature performance was evaluated independently. Then, using the eigenvectors created by merging all of the collected features, classification was carried out using our DSFC (Deep - Statistical Features Classification) model. Although the classification accuracy rate using only statistical features was 81.2 percent and the classification accuracy rate using only deep learning was 95.71 percent, the classification accuracy rate utilizing hybrid features created using the suggested DSFC technique was 99.2%. Therefore, it can be proven that combining statistical and deep features can produce beneficial results.Öğe Detection of Covert Timing Channels with Machine Learning Methods Using Different Window Sizes(Ieee, 2019) Karadogan, Ismail; Karci, AliIn this study, delays between data packets were read by using different window sizes to detect data transmitted from covert timing channel in computer networks, and feature vectors were extracted from them and detection of hidden data by some classification algorithms was achieved with high performance rate.Öğe Determination of Efficient Green Wave Corridors in The Transportation Network with The Community Detection Method(Gazi Univ, 2024) Oztemiz, Furkan; Karci, AliSignaling systems play an important role in managing urban transport networks. Optimizing signaling systems significantly reduces traffic density in transport networks. One of the popular methods applied to increase the efficiency of the signaling system is the green wave application, which means the coordinated operation of the signaling systems. The green wave system prevents vehicles from being repeatedly caught in red light, reducing travel time, waiting time on the roads and carbon emissions of vehicles. The problem that will arise at this point is on which intersection points the green wave system will be applied. In this study, vehicle counting and signaling data of the city of Malatya were used and the transportation network data was converted into a weighted graph. By applying the walktrap community detection algorithm to the transportation network, the intersection points are grouped according to the vehicle transition similarities on them. It was applied to the green wave system physically for the intersection points in 2 different groups determined. The results show that there are significant increases in the number of vehicles passing per unit time in the regions where green waves are applied. This situation has resulted in a decrease in the number of vehicles waiting at red lights and significant reductions in carbon emissions from stationary vehicles into the atmosphere.Öğe Effects of the stochastic and deterministic movements in the optimization processes(Gazi Univ, Fac Engineering Architecture, 2022) Seyyarer, Ebubekir; Karci, Ali; Ates, AbdullahIn this study, a linear function representing the iris data set is obtained by making use of the MLR model. SGD, Momentum, Adagrad, RMSProp, Adadelta and Adam optimization algorithms are used to find the optimum values of coefficients of this function. An initialization method with initial population is recommended for these coefficients, which are generally initialized with a fixed or random value in MLRs. IAE, ITAE, MSE and ISE error functions are used as objective functions in the MLR model used. Initial populations of the methods are developed by using a proposed deterministic and classical stochastic initialization methods between upper and lower bounds. The method that are initialized stochasticaly is run several times as seen in literature and the mean values are calculated. On the other hand, the application that is initialized deterministic is only run once. According to the results of deterministic and stochastic initialization methods, it is observed that the coefficients and iteration numbers obtained in both applications are close to each other. Despite very high temporal gain is achieved from the application that is initialized deterministic. As a result of the comparisons, the linear model obtained with Adadelta and MSE reaches the result in the shortest time.Öğe Extractive multi-document text summarization based on graph independent sets(Cairo Univ, Fac Computers & Information, 2020) Uckan, Taner; Karci, AliWe propose a novel methodology for extractive, generic summarization of text documents. The Maximum Independent Set, which has not been used previously in any summarization study, has been utilized within the context of this study. In addition, a text processing tool, which we named KUSH, is suggested in order to preserve the semantic cohesion between sentences in the representation stage of introductory texts. Our anticipation was that the set of sentences corresponding to the nodes in the independent set should be excluded from the summary. Based on this anticipation, the nodes forming the Independent Set on the graphs are identified and removed from the graph. Thus, prior to quantification of the effect of the nodes on the global graph, a limitation is applied on the documents to be summarized. This limitation prevents repetition of word groups to be included in the summary. Performance of the proposed approach on the Document Understanding Conference (DUC-2002 and DUC-2004) datasets was calculated using ROUGE evaluation metrics. The developed model achieved a 0.38072 ROUGE performance value for 100-word summaries, 0.51954 for 200-word summaries, and 0.59208 for 400-word summaries. The values reported throughout the experimental processes of the study reveal the contribution of this innovative method. (C) 2019 Production and hosting by Elsevier B.V. on behalf of Faculty of Computers and Artificial Intelligence, Cairo University.
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