01. Araştırma Çıktıları | WoS | Scopus | TR-Dizin | PubMed

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Citation - WoS: 7
Citation - Scopus: 12
Analyzing the Effect of Data Preprocessing Techniques Using Machine Learning Algorithms on the Diagnosis of Covid-19
(Wiley, 2022) Erol, Gizemnur; Uzbaş, Betül; Yücelbaş, Cüneyt; Yücelbaş, Sule
Real-time polymerase chain reaction (RT-PCR) known as the swab test is a diagnostic test that can diagnose COVID-19 disease through respiratory samples in the laboratory. Due to the rapid spread of the coronavirus around the world, the RT-PCR test has become insufficient to get fast results. For this reason, the need for diagnostic methods to fill this gap has arisen and machine learning studies have started in this area. On the other hand, studying medical data is a challenging area because the data it contains is inconsistent, incomplete, difficult to scale, and very large. Additionally, some poor clinical decisions, irrelevant parameters, and limited medical data adversely affect the accuracy of studies performed. Therefore, considering the availability of datasets containing COVID-19 blood parameters, which are less in number than other medical datasets today, it is aimed to improve these existing datasets. In this direction, to obtain more consistent results in COVID-19 machine learning studies, the effect of data preprocessing techniques on the classification of COVID-19 data was investigated in this study. In this study primarily, encoding categorical feature and feature scaling processes were applied to the dataset with 15 features that contain blood data of 279 patients, including gender and age information. Then, the missingness of the dataset was eliminated by using both K-nearest neighbor algorithm (KNN) and chain equations multiple value assignment (MICE) methods. Data balancing has been done with synthetic minority oversampling technique (SMOTE), which is a data balancing method. The effect of data preprocessing techniques on ensemble learning algorithms bagging, AdaBoost, random forest and on popular classifier algorithms KNN classifier, support vector machine, logistic regression, artificial neural network, and decision tree classifiers have been analyzed. The highest accuracies obtained with the bagging classifier were 83.42% and 83.74% with KNN and MICE imputations by applying SMOTE, respectively. On the other hand, the highest accuracy ratio reached with the same classifier without SMOTE was 83.91% for the KNN imputation. In conclusion, certain data preprocessing techniques are examined comparatively and the effect of these data preprocessing techniques on success is presented and the importance of the right combination of data preprocessing to achieve success has been demonstrated by experimental studies.
Citation - WoS: 2
Apneic Events Detection Using Different Features of Airflow Signals
(MEHRAN UNIV ENGINEERING & TECHNOLOGY, 2019) Göğüş, Fatma Zehra; Tezel, Gülay
Apneic-event based sleep disorders are very common and affect greatly the daily life of people. However, diagnosis of these disorders by detecting apneic events are very difficult. Studies show that analyzes of airflow signals are effective in diagnosis of apneic-event based sleep disorders. According to these studies, diagnosis can be performed by detecting the apneic episodes of the airflow signals. This work deals with detection of apneic episodes on airflow signals belonging to Apnea-ECG (Electrocardiogram) and MIT (Massachusetts Institute of Technology) BIH (Bastons's Beth Isreal Hospital) databases. In order to accomplish this task, three representative feature sets namely classic feature set, amplitude feature set and descriptive model feature set were created. The performance of these feature sets were evaluated individually and in combination with the aid of the random forest classifier to detect apneic episodes. Moreover, effective features were selected by OneR Attribute Eval Feature Selection Algorithm to obtain higher performance. Selected 28 features for Apnea-ECG database and 31 features for MIT-BIH database from 54 features were applied to classifier to compare achievements. As a result, the highest classification accuracies were obtained with the usage of effective features as 96.21% for Apnea-ECG database and 92.23% for MIT-BIH database. Kappa values are also quite good (91.80 and 81.96%) and support the classification accuracies for both databases, too. The results of the study are quite promising for determining apneic events on a minute-by-minute basis.
An Application of Tree Seed Algorithm for Optimization of 50 and 100 Dimensional Numerical Functions
(Institute of Electrical and Electronics Engineers Inc., 2021) Güngör, İmral; Emiroğlu, Bülent Gürsel; Uymaz, S.A.; Kıran, Mustafa Servet
The Tree-Seed Algorithm is an optimization algorithm created by observing the process of growing and becoming a new tree, the seeds scattering around trees in natural life. In this study, TSA is applied to optimize high-dimensional functions. In previous studies, the performance of the tree seed algorithm applied for the optimization of low-dimensional functions has been proven. Thus, in addition to running the algorithm on 30-dimensional functions before, it has also been applied to solve 50-and 100-dimensional numerical functions. This improvement, called the tree seed algorithm, is based on the use of more solution update mechanisms instead of one mechanism. In the experiments, CEC2015 benchmarking functions are used and the developed tree seed algorithm is compared with the base state of TSA, artificial bee colony, particle swarm optimization and some variants of the differential evolution algorithm. Experimental results are obtained as mean, max, min solutions and standard deviation of 30 different runs. As a result, it is observed by the studies that the developed algorithm gives successful results. © 2021 IEEE.
Citation - WoS: 3
Citation - Scopus: 4
Approaches To Automated Land Subdivision Using Binary Search Algorithm in Zoning Applications
(Ice Publishing, 2022) Koç, İsmail; Çay, Tayfun; Babaoğlu, İsmail
The planned development of urban areas depends on zoning applications. Although zoning practices are performed using different techniques, the parcelling operations that shape the future view of the city are the same. Preparing the parcelling plans is an important step that has a direct impact on ownership structure and reallocation. Parcelling operations are traditionally handled manually by a technician. This is a serious problem in terms of time and cost. In this study, by taking the zoning legislation, the production of a pre-land subdivision plan has been automatically performed for a region of Konya, which is one of the major cities in Turkey. The parcelling processes have been performed in three different ways: the first parcelling technique is parcelling with edge values, the second is parcelling with area values and the third is parcelling using both edge and area values together. For the entire parcelling process, the area of the parcel has been calculated using the Gauss method. Moreover, to effectively determine the boundaries and to calculate the parcel area in the parcelling process, the binary search technique has been used in all the methods. The experimental results show that the parcelling operations were carried out very quickly and successfully.
Citation - WoS: 11
Citation - Scopus: 19
Artificial Intelligence in Healthcare Competition (teknofest-2021): Stroke Data Set
(AVES, 2022) Koç, U.; Sezer, E.A.; Özkaya, Y.A.; Yarbay, Y.; Taydaş, O.; Ayyıldız, V.A.; Bahadır, Murat
Objective: The artificial intelligence competition in healthcare was organized for the first time at the annual aviation, space, and technology festival (TEKNOFEST), Istanbul/Türkiye, in September 2021. In this article, the data set preparation and competition processes were explained in detail; the anonymized and annotated data set is also provided via official website for further research. Materials and Methods: Data set recorded over the period covering 2019 and 2020 were centrally screened from the e-Pulse and Teleradiology System of the Republic of Türkiye, Ministry of Health using various codes and filtering criteria. The data set was anonymized. The data set was prepared, pooled, curated, and annotated by 7 radiologists. The training data set was shared with the teams via a dedicated file transfer protocol server, which could be accessed using private usernames and passwords given to the teams under a non-disclosure agreement signed by the representative of each team. Results: The competition consisted of 2 stages. In the first stage, teams were given 192 digital imaging and communications in medicine images that belong to 1 of 3 possible categories namely, hemorrhage, ischemic, or non-stroke. Teams were asked to classify each image as either stroke present or absent. In the second stage of the competition, qualifying 36 teams were given 97 digital imaging and communications in medicine images that contained hemorrhage, ischemia, or both lesions. Among the employed methods, Unet and DeepLabv3 were the most frequently observed ones. Conclusion: Artificial intelligence competitions in healthcare offer good opportunities to collect data reflect-ing various cases and problems. Especially, annotated data set by domain experts is more valuable. © 2022, AVES. All rights reserved.
Atıksu Arıtma Tesislerinde Mikrokirleticilerinizlenmesi ve Kontrolü
(2019) Kara, Meryem; Nas, Bilgehan; Argun, Mehmet Emin; Yel, Esra; Dinç, Saliha; Koyuncu, Serdar
Yerüstü ve yeraltı sularında su kalitesinin belirlenmesi için AB Su Çerçeve Direktifindeki 45 öncelikli maddede yer alan MK?lerin izlenmesi yönünde araştırmalar hızlanmıştır. Su kütlelerinde MK?lerin önemli kaynaklarından biri atıksu arıtma tesisleri (AAT)?dir. Bu projede, küçük, orta ve büyük ölçekli 3 yerleşim yerinde 3 farklı arıtma prosesinde MK?ler 1 yıl süreyle izlenmiş ve proseslerin MK?leri giderme performansları ortaya konulmuştur. MK gruplarından; pestisit (Kloropirifos, Atrazin, Klorfenvinfos), fitalat ester (BBP, DEHP, DnOP), yüzey aktif madde (Oktil fenol, Nonil fenol), PAH (Benzo[b]fluoranten, Benzo[k]fluoranten, Benzo(a)piren, Indeno[1,2,3-cd]piren, Benzo[g,h,i]perilen, Fluoranten, Antrasen, Naftalin), VOC (Diklorometan, Benzen, 1.2-dikloroetan), ilaç etken madde (Diklofenak, Karbamazepin, 17-beta-estradiyol, 17-alfa-etinilestradiol) ve ağır metallerden (Cd, Pb, Hg, Ni) toplam 27 MK izlenmiştir. Konya (İleri biyolojik arıtma), Ereğli (Anaerobik ve fakültatif stabilizasyon), Zincirlikuyu (Yapay sulak alan) AAT?lerinden, atıksu geri kazanım tesisinden, pilot ölçekli ultrafiltrasyon (UF), nanofiltrasyon (NF) ve ters osmoz (RO) ünitelerinden oluşan membran tesisten her numune alma döneminde 17 su, 6 çamur numunesi alınarak GC/MS, LC/MSMS ve ICP/MS?de MK ölçülmüştür. Tesislerin ve arıtma proseslerinin MK verileri arasındaki ilişkilerin anlamlılığının istatistiksel olarak ortaya konulabilmesi için saçılma diyagramları, kutu diyagramlar, varyans analizi ve Korelasyon hesaplamaları yapılmıştır. Atıksularda en yüksek konsantrasyonda tespit edilen MK?ler; sırası ile Ni, DEHP, Nonil fenol, Naftalin, Pb ve Diklofenak?dır. VOC?ler en düşük konsantrasyondadır ve görülme sıklığı çok düşüktür. Her üç AAT?de arıtılmış atıksuda; Ni, Pb, Hg, DEHP, Nonil fenol, Naftalin, Diklofenak, Karbamazepin hariç olmak üzere diğer MK?ler 100 ng/L?den düşüktür. %80?in üzerinde verimle arıtılan MK?ler Konya AAT?de Oktil fenol (%93,4), Nonil fenol (%92,7), Atrazin (%92,1), Naftalin (%91,5) ve DEHP (%89,3); Ereğli AAT?de Atrazin (%83,8)?dir. Zincirlikuyu AAT?de hiçbir parametrede %80?den fazla giderim gerçekleşmemiştir. Konya AAT?de Karbamazepin ve Ni, Zincirlikuyu AAT?de Nonil fenol negatif kütle dengesi görülen ve çıkış suyunda konsantrasyonları artan MK?lerdir. Membran proseslerden, UF?in MK gideriminde etkin olmadığı, NF?in 17 MK?de %50?den fazla giderim sağladığı görülmüştür. RO, NF?den sonra bazı MK?lerde ilave giderim sağlamıştır.
Aydınlatma Özniteliği Kullanılarak Evrişimsel Sinir Ağı Modelleri İle Meyve Sınıflandırma
(2020) Büyükarıkan, Birkan; Ülker, Erkan
Aydınlatma, nesnelerin olduğu gibi görünmesini sağlayan doğal veya yapay kaynaklardır. Özellikle görüntü işleme uygulamalarında yakalanan görüntüdeki nesne bilgisinin eksiksiz ve doğru şekilde alınabilmesi için aydınlatmanın kullanılması bir gerekliliktir. Ancak aydınlatma kaynağının tür, parlaklık ve konumunun değişimi; nesnenin görüntüsü, rengi, gölgesi veya boyutunun da değişmesine ve nesnenin farklı olarak algılanmasına sebep olmaktadır. Bu sebeple görüntülerin ayırt edilmesinde güçlü bir yapay zeka tekniğinin kullanılması, sınıfların ayırt edilmesini kolaylaştıracaktır. Bir yapay zeka yöntemi olan Evrişimsel Sinir Ağları (ESA), otomatik olarak özellikleri çıkarabilen ve ağ eğitilirken öğrenme sağlandığı için bariz özellikleri kolaylıkla belirleyen bir algoritmadır. Çalışmada ALOI-COL veriseti kullanılmıştır. ALOI-COL, 12 farklı renk sıcaklığıyla elde edilmiş 1000 sınıftan oluşan bir verisetidir. ALOI-COL verisetindeki 29 sınıftan oluşan meyve görüntüleri, ESA mimarilerinden AlexNet, VGG16 ve VGG19 kullanılarak sınıflandırılmıştır. Verisetindeki görüntüler, görüntü işleme teknikleriyle zenginleştirilmiş ve her sınıftan 51 adet görüntü elde edilmiştir. Çalışma; %80-20 ve %60-40 eğitim-test olmak üzere iki yapıda incelenmiştir. 50 devir çalıştırılması sonucunda test verileri, AlexNet (%80-20) ve VGG16 (%60-40) mimarilerinde %100, VGG19 (%80-20) mimarisinde ise %86.49 doğrulukla sınıflandırılmıştır.
Citation - WoS: 2
B-Spline Curve Approximation by Utilizing Big Bang-Big Crunch Method
(TECH SCIENCE PRESS, 2020) İnik, Özkan; Ülker, Erkan; Koç, İsmail
The location of knot points and estimation of the number of knots are undoubtedly known as one of the most difficult problems in B-Spline curve approximation. In the literature, different researchers have been seen to use more than one optimization algorithm in order to solve this problem. In this paper, Big Bang-Big Crunch method (BB-BC) which is one of the evolutionary based optimization algorithms was introduced and then the approximation of B-Spline curve knots was conducted by this method. The technique of reverse engineering was implemented for the curve knot approximation. The detection of knot locations and the number of knots were randomly selected in the curve approximation which was performed by using BB-BC method. The experimental results were carried out by utilizing seven different test functions for the curve approximation. The performance of BB-BC algorithm was examined on these functions and their results were compared with the earlier studies performed by the researchers. In comparison with the other studies, it was observed that though the number of the knot in BB-BC algorithm was high, this algorithm approximated the B-Spline curves at the rate of minor error.
Citation - WoS: 44
Citation - Scopus: 47
Binary Artificial Algae Algorithm for Feature Selection
(Elsevier, 2022) Türkoğlu, Bahaeddin; Uymaz, Sait Ali; Kaya, Ersin
In this study, binary versions of the Artificial Algae Algorithm (AAA) are presented and employed to determine the ideal attribute subset for classification processes. AAA is a recently proposed algorithm inspired by microalgae's living behavior, which has not been consistently implemented to determine ideal attribute subset (feature selection) processes yet. AAA can effectively look into the feature space for ideal attributes combination minimizing a designed objective function. The proposed binary versions of AAA are employed to determine the ideal attribute combination that maximizes classification success while minimizing the count of attributes. The original AAA is utilized in these versions while its continuous spaces are restricted in a threshold using an appropriate threshold function after flattening them. In order to demonstrate the performance of the presented binary artificial algae algorithm model, an experimental study was conducted with the latest seven highperformance optimization algorithms. Several evaluation metrics are used to accurately evaluate and analyze the performance of these algorithms over twenty-five datasets with different difficulty levels from the UCI Machine Learning Repository. The experimental results and statistical tests verify the performance of the presented algorithms in increasing the classification accuracy compared to other state-of-the-art binary algorithms, which confirms the capability of the AAA algorithm in exploring the attribute space and deciding the most valuable features for classification problems. (C) 2022 Elsevier B.V. All rights reserved.
Citation - WoS: 31
Citation - Scopus: 32
A Binary Artificial Bee Colony Algorithm and Its Performance Assessment
(PERGAMON-ELSEVIER SCIENCE LTD, 2021) Kıran, Mustafa Servet
Artificial bee colony algorithm, ABC for short, is a swarm-based optimization algorithm proposed for solving continuous optimization problems. Due to its simple but effective structure, some binary versions of the algorithm have been developed. In this study, we focus on modification of its xor-based binary version, called as binABC. The solution update rule of basic ABC is replaced with a xor logic gate in binABC algorithm, and binABC works on discretely-structured solution space. The rest of components in binABC are the same as with the basic ABC algorithm. In order to improve local search capability and convergence characteristics of binABC, a stigmergic behavior-based update rule for onlooker bees of binABC and extended version of xor-based update rule are proposed in the present study. The developed version of binABC is applied to solve a modern benchmark problem set (CEC2015). To validate the performance of proposed algorithm, a series of comparisons are conducted on this problem set. The proposed algorithm is first compared with the basic ABC and binABC on CEC2015 set. After its performance validation, six binary versions of ABC algorithm are considered for comparison of the algorithms, and a comprehensive comparison among the state-of-art variants of swarm intelligence or evolutionary computation algorithms is conducted on this set of functions. Finally, an uncapacitated facility location problem set, a pure binary optimization problem, is considered for the comparison of the proposed algorithm and binary variants of ABC algorithm. The experimental results and comparisons show that the proposed algorithm is successful and effective in solving binary optimization problems as its basic version in solving continuous optimization problems.
Citation - WoS: 20
Citation - Scopus: 24
A Binary Social Spider Algorithm for Continuous Optimization Task
(SPRINGER, 2020) Baş, Emine; Ülker, Erkan
The social spider algorithm (SSA) is a new heuristic algorithm created on spider behaviors. The original study of this algorithm was proposed to solve continuous problems. In this paper, the binary version of SSA (binary SSA) is introduced to solve binary problems. Currently, there is insufficient focus on the binary version of SSA in the literature. The main part of the binary version is at the transfer function. The transfer function is responsible for mapping continuous search space to discrete search space. In this study, four of the transfer functions divided into two families, S-shaped and V-shaped, are evaluated. Thus, four different variations of binary SSA are formed as binary SSA-Tanh, binary SSA-Sigm, binary SSA-MSigm and binary SSA-Arctan. Two different techniques (SimSSA and LogicSSA) are developed at the candidate solution production schema in binary SSA. SimSSA is used to measure similarities between two binary solutions. With SimSSA, binary SSA's ability to discover new points in search space has been increased. Thus, binary SSA is able to find global optimum instead of local optimums. LogicSSA which is inspired by the logic gates and a popular method in recent years has been used to avoid local minima traps. By these two techniques, the exploration and exploitation capabilities of binary SSA in the binary search space are improved. Eighteen unimodal and multimodal standard benchmark optimization functions are employed to evaluate variations of binary SSA. To select the best variations of binary SSA, a comparative study is presented. The Wilcoxon signed-rank test has applied to the experimental results of variations of binary SSA. Compared to well-known evolutionary and recently developed methods in the literature, the variations of binary SSA performance is quite good. In particular, binary SSA-Tanh and binary SSA-Arctan variations of binary SSA showed superior performance.
Citation - WoS: 32
Citation - Scopus: 35
A Binary Social Spider Algorithm for Uncapacitated Facility Location Problem
(PERGAMON-ELSEVIER SCIENCE LTD, 2020) Baş, Emine; Ülker, Erkan
In order to find efficient solutions to real complex world problems, computer sciences and especially heuristic algorithms are often used. Heuristic algorithms can give optimal solutions for large scale optimization problems in an acceptable period. Social Spider Algorithm (SSA), which is a heuristic algorithm created on spider behaviors are studied. The original study of this algorithm was proposed to solve continuous problems. In this paper, the binary version of the Social Spider Algorithm called Binary Social Spider Algorithm (BinSSA) is proposed for binary optimization problems. BinSSA is obtained from SSA, by transforming constant search space to binary search space with four transfer functions. Thus, BinSSA variations are created as BinSSA1, BinSSA2, BinSSA3, and BinSSA4. The study steps of the original SSA are re-updated for BinSSA. A random walking schema in SSA is replaced by a candidate solution schema in BinSSA. Two new methods (similarity measure and logic gate) are used in candidate solution production schema for increasing the exploration and exploitation capacity of BinSSA. The performance of both techniques on BinSSA is examined. BinSSA is named as BinSSA(Sim&Logic). Local search and global search performance of BinSSA is increased by these two methods. Three different studies are performed with BinSSA. In the first study, the performance of BinSSA is tested on the classic eighteen unimodal and multimodal benchmark functions. Thus, the best variation of BinSSA and BinSSA (Sim&Logic) is determined as BinSSA4(Sim&Logic). BinSSA4(Sim&Logic) has been compared with other heuristic algorithms on CEC2005 and CEC2015 functions. In the second study, the uncapacitated facility location problems (UFLPs) are solved with BinSSA(Sim&Logic). UFL problems are one of the pure binary optimization problems. BinSSA is tested on low-scaled, middle-scaled, and large-scaled fifteen UFLP samples and obtained results are compared with eighteen state-of-art algorithms. In the third study, we solved UFL problems on a different dataset named M* with BinSSA(Sim&Logic). The results of BinSSA (Sim&Logic) are compared with the Local Search (LS), Tabu Search (TS), and Improved Scatter Search (ISS) algorithms. Obtained results have shown that BinSSA offers quality and stable solutions. (c) 2020 Elsevier Ltd. All rights reserved.
Citation - WoS: 11
Citation - Scopus: 13
Bingso: Galactic Swarm Optimization Powered by Binary Artificial Algae Algorithm for Solving Uncapacitated Facility Location Problems
(Springer London Ltd, 2022) Kaya, Ersin
Population-based optimization methods are frequently used in solving real-world problems because they can solve complex problems in a reasonable time and at an acceptable level of accuracy. Many optimization methods in the literature are either directly used or their binary versions are adapted to solve binary optimization problems. One of the biggest challenges faced by both binary and continuous optimization methods is the balance of exploration and exploitation. This balance should be well established to reach the optimum solution. At this point, the galactic swarm optimization (GSO) framework, which uses traditional optimization methods, stands out. In this study, the binary galactic swarm optimization (BinGSO) approach using binary artificial algae algorithm as the main search algorithm in GSO is proposed. The performance of the proposed binary approach has been performed on uncapacitated facility location problems (UFLPs), which is a complex problem due to its NP-hard structure. The parameter analysis of the BinGSO method was performed using the 15 Cap problems. Then, the BinGSO method was compared with both traditional binary optimization methods and the state-of-the-art methods which are used on Cap problems. Finally, the performance of the BinGSO method on the M* problems was examined. The results of the proposed approach on the M* problem set were compared with the results of the state-of-the-art methods. The results of the evaluation process showed that the BinGSO method is more successful than other methods through its ability to establish the balance between exploration and exploitation in UFLPs.
Citation - WoS: 11
Citation - Scopus: 13
Boosting Galactic Swarm Optimization With Abc
(SPRINGER HEIDELBERG, 2019) Kaya, Ersin; Uymaz, Sait Ali; Koçer, Barış
Galactic swarm optimization (GSO) is a new global metaheuristic optimization algorithm. It manages multiple sub-populations to explore search space efficiently. Then superswarm is recruited from the best-found solutions. Actually, GSO is a framework. In this framework, search method in both sub-population and superswarm can be selected differently. In the original work, particle swarm optimization is used as the search method in both phases. In this work, performance of the state of the art and well known methods are tested under GSO framework. Experiments show that performance of artificial bee colony algorithm under the GSO framework is the best among the other algorithms both under GSO framework and original algorithms.
Classification of Invoice Images by Using Convolutional Neural Networks [article]
(2022) Arslan, Ömer; Uymaz, Sait Ali
Abstract ? Today, as the companies grow, the number of personnel working within the company and the number of supplier companies that the company works with are also increasing. In parallel with this increase, the amount of expenditure made on behalf of the company increases, and more invoi- ces are created. Since the in-voices must be kept for legal reasons, physical invoices are transferred to the digital environment. Since large companies have large numbers of invoices, labor demand is higher in digitalizing invoices. In addition, as the number of invoices to be transferred to digital media increases, the number of possible errors during entry becomes more. This paper aims to automate the transfer of invoices to the digital environment. In this study, invoices be-longing to four different templates were used. Invoice images taken from a bank system were used for the first time in this study, and the original invoice dataset was prepared. Furthermore, two more datasets were obtained by applying preprocessing methods (Zero-Padding, Brightness Augmentation) on the original dataset. The Invoice classification system developed using Convolutional Neural Networks (CNN) archite- ctures named LeNet-5, VGG-19, and MobileNetV2 was trained on three different data sets. Data preprocessing techniques such as correcting the curvature and aspect ratio of the invoices and image augmentation with variable brightness ratio were applied to create the data sets. The datasets created with preprocessing techniques have increased the classification success of the proposed models. With this proposed model, invoice images were automatically classified according to their templates using CNN architectures. In experimental studies, a classification success rate of 99.83% was achieved in training performed on the data set produced by the data augmentation method.
Citation - WoS: 24
Citation - Scopus: 30
Classification of Physiological Disorders in Apples Fruit Using a Hybrid Model Based on Convolutional Neural Network and Machine Learning Methods
(Springer London Ltd, 2022) Büyükarıkan, Birkan; Ülker, Erkan
Physiological disorders in apples are due to post-harvest conditions. For this reason, automatic identification of physiological disorders is important in obtaining agricultural information. Image processing is one of the techniques that can help achieve the features of physiological disorders. Physiological disorders during image acquisition can be affected by the changes in brightness values created by different lighting conditions. This changes the results of the classification. In recent years, the convolutional neural network (CNN) has been a successful approach in automatically obtaining deep features from raw images in image classification problems. The study aims to classify physiological disorders using machine learning (ML) methods according to extracted deep features of the images under different lighting conditions. The data sets were created by acquired images (1080 images) and augmentation images (4320 images). Deep features were extracted using five popular pre-trained CNN models in these data sets, and these features were classified using five ML methods. The highest average accuracy was obtained with the VGG19(fc6) + SVM method in the data set-1 and data set-2 and were 96.11 and 96.09%, respectively. With this study, physiological disorders can be determined early, and needed precautions can be taken before and after harvest, not too late.
Citation - WoS: 25
Citation - Scopus: 31
Clustering Analysis Through Artificial Algae Algorithm
(Springer Heidelberg, 2022) Türkoğlu, Bahaeddin; Uymaz, Sait Ali; Kaya, Ersin
Clustering analysis is widely used in many areas such as document grouping, image recognition, web search, business intelligence, bio information, and medicine. Many algorithms with different clustering approaches have been proposed in the literature. As they are easy and straightforward, partitioning methods such as K-means and K-medoids are the most commonly used algorithms. These are greedy methods that gradually improve clustering quality, highly dependent on initial parameters, and stuck a local optima. For this reason, in recent years, heuristic optimization methods have also been used in clustering. These heuristic methods can provide successful results because they have some mechanism to escape local optimums. In this study, for the first time, Artificial Algae Algorithm was used for clustering and compared with ten well-known bio-inspired metaheuristic clustering approaches. The proposed AAA clustering efficiency is evaluated using statistical analysis, convergence rate analysis, Wilcoxon's test, and different cluster evaluating measures ranking on 25 well-known public datasets with different difficulty levels (features and instances). The results demonstrate that the AAA clustering method provides more accurate solutions with a high convergence rate than other existing heuristic clustering techniques.
Çok Amaçlı Mühendislik Tasarımı ve Kısıtlı Problemler için Hibrit Birçok Amaçlı Optimizasyon Algoritması
(2021) Karakoyun, Murat; Kodaz, Halife
Gerçek dünya problemlerine bakıldığında çoğunun birden fazla hedefi gerçekleştirmeye yönelik olduğu görülmektedir. Bu problemlerin çözümü için kullanılan birçok klasik yöntem mevcuttur. Klasik yöntemlerin çözüm geliştirme noktasında farklı sebeplerden dolayı eksik kalması araştırmacıları farklı yaklaşımlar geliştirmeye yöneltmiştir. Genellikle doğada sürü halinde yaşayan hayvanların veya farklı yaşam alanlarına sahip bitkilerin davranışlarından esinlenilerek geliştirilen doğa esinli algoritmalar bu yaklaşımlardan bir tanesi olmuştur. Bu çalışmada, tek amaçlı problemlerin çözümü için geliştirilmiş olan kurbağa sıçrama (SFLA) ve gri kurt optimizasyonu (GWO) algoritmaları hibrit bir şekilde kullanılarak çok amaçlı optimizasyon problemlerine uygulanmıştır. Önerilen algoritma bazı çok amaçlı mühendislik tasarımı ve çok amaçlı kısıtlı problemlerin üzerinde uygulanmıştır. Önerilen algoritmanın performansı NSGA-II, IBEA, MOCell ve PAES algoritmalarının performansı ile kıyaslanmıştır. Performans karşılaştırma metriği olarak HV, IGD, Spread ve Epsilon metrikleri kullanılmıştır. Performans analizi; elde edilen ortalama sonuçlar, Friedman sıralama testi ve Wilcoxon anlamlılık testi ile yapılmıştır. Deneysel sonuçlar, önerilen algoritmanın diğer algoritmalardan daha başarılı sonuçlar ürettiğini göstermiştir.
Citation - WoS: 8
Citation - Scopus: 9
A Comparative Study of Swarm Intelligence and Evolutionary Algorithms on Urban Land Readjustment Problem
(ELSEVIER, 2021) Koç, İsmail; Babaoğlu, İsmail
Land Readjustment and redistribution (LR) is a land management tool that helps regular urban development with the contribution of landowners. The main purpose of LR is to transform irregularly developed land parcels into suitable forms. Since it is necessary to handle many criteria simultaneously to solve LR problems, classical mathematical methods can be insufficient due to time limitation. Since LR problems are similar to traveling salesman problems and typical scheduling problems in terms of structure, they are kinds of NP-hard problems in combinatorial optimization. Therefore, metaheuristic algorithms are used in order to solve NP-hard problems instead of classical methods. At first, in this study, an effective problem-specific objective function is proposed to address the main criteria of the problem. In addition, a map-based crossover operator and three different mutation operators are proposed for the LR, and then a hybrid approach is implemented by utilizing those operators together. Furthermore, since the optimal value of the problem handled in real world cannot be exactly estimated, a synthetic dataset is proposed as a benchmarking set in LR which makes the success of algorithms can be objectively evaluated. This dataset consists of 5 different problems according to number of parcel which are 20, 40, 60, 80 and 100. Each problem set consists of 4 sub-problems in terms of number of landowners per-parcel which are 1, 2, 3 and 4. Therefore, the dataset consists of 20 kinds of problems. In this study, artificial bee colony, particle swarm optimization, differential evolution, genetic and tree seed algorithm are used. In the experimental studies, five algorithms are set to run under equal conditions using the proposed synthetic dataset. When the acquired experimental results are examined, genetic algorithm seems to be the most effective algorithm in terms of both speed and performance. Although artificial bee colony has better results from genetic algorithm in a few problems, artificial bee colony is the second most successful algorithm after genetic algorithm in terms of performance. However, in terms of time, artificial bee colony is an algorithm nearly as successful as genetic algorithm. On the other hand, the results of differential evolution, particle swarm optimization and tree seed algorithms are similar to each other in terms of solution quality. In conclusion, the statistical tests clearly show that genetic algorithm is the most effective technique in solving LR problems in terms of speed, performance and robustness. (C) 2020 Elsevier B.V. All rights reserved.
Citation - WoS: 3
Citation - Scopus: 4
Comparing the Performances of Six Nature-Inspired Algorithms on a Real-World Discrete Optimization Problem
(Springer, 2022) Haklı, Hüseyin; Uguz, Harun; Ortacay, Zeynep
Many new, nature-inspired optimization algorithms are proposed these days, and these algorithms are gaining popularity day by day. These algorithms are frequently preferred for these real-world problems as they need less information, are reliable and robust, and have a structure that can easily be applied to discrete problems. Too many algorithms result in difficulty choosing the correct technique for the problem, and selecting an unwise method affects the solution quality. In addition, some algorithms cannot be reliable for some specific real-world problems but very successful for others. In order to guide and give insight into the practitioners and researchers about this problem, studies involving the comparison and evaluation of the performance of algorithms are needed. In this study, the performances of six nature-inspired methods, which included five new implementations of differential evolutionary algorithms (DE), scatter search (SS), equilibrium optimizer (EO), marine predators algorithm (MPA), and honey badger algorithm (HBA) applied to land redistribution problem and genetic algorithms (GA), were compared. In order to compare the algorithms in detail, various performance indicators were used as problem based and algorithm based. Experimental results showed that DE and SS algorithms have a more successful performance than the other methods by solution quality, robustness, and many problem-based indicators.

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