Bilgisayar ve Bilişim Fakültesi Koleksiyonu
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Article Optimizing the Exploitation of Ornamental Rock Quarries by Analysing the Fracture Network(2024) Asimopolos Laurentiu; Asimopolos Natalia-silvia; Turanboy Alparslan; Ülker, Erkan; Asimopolos Adrian-aristideWithin the ERA-MIN project: Artificial Intelligence and Combined Survey Techniques for Stone Quarries Optimization (AI-COSTSQO acronym), a decision support system (DSS) was developed based on computerized expert systems, artificial intelligence (AI), as well as other algorithms, which could include multi-criteria methods and analytic hierarchy process (AHP). This system (DSS) included: generating block size distribution curves (BSDC), determining minimum and maximum limits of cuboids and trading volumes as well as establishing a set of rules for trading decisions, finding maximum cuboids from irregular polyhedral (MaxQ), 3D Graphics Representation of Blocks and Mapping/Contouring, Digital Elevation Models (DEM) from Point Clouds and Discrete Fracture Networks (DFNs) and In-situ Field Investigation, which represented the basis for the realization of the components of (DSS).Article Citation - WoS: 20Citation - Scopus: 23A Tree Seed Algorithm With Multi-Strategy for Parameter Estimation of Solar Photovoltaic Models(Elsevier, 2024) Beskirli, Ayse; Dag, Idiris; Kiran, Mustafa ServetTree seed algorithm, which is one of the metaheuristics algorithms recently proposed for the solution of continuous optimization problems, has an effective algorithmic structure inspired by the relation between trees and seeds. At the same time, the use of two different solution generation mechanisms by depending on the control parameter in TSA aims to balance the exploration and exploitation capabilities of the algorithm. However, when the structure of the algorithm is examined in detail, it is seen that there are some disadvantages such as loss of population diversity and getting stuck in local minimums. To overcome these disadvantages in the basic algorithm, three different approaches (self-adaptive weighting mechanism, chaotic elite learning approach and experience-based learning method) were proposed to TSA under the name of multi-strategies in this study. The algorithm improved with these approaches is named as the multi-strategy-based tree seed algorithm (MS-TSA). MS-TSA was first tested on CEC2017 functions. Then MS-TSA was applied to the problems in the CEC2020 competition and compared with the results of the best performing algorithms in this competition. As a result of the comparisons, MS-TSA was found to be a competitive method on solving benchmark functions. Then, parameter estimation of single diode, double diode and photovoltaic module models using the input data of various solar panels was carried out by the MS-TSA. The results obtained with MS-TSA were compared with both the results of the basic TSA and the results of well-known algorithms in the literature. The results obtained are 9.8642E-04, 9.8356E-04, 2.4251E-03, 1.7534E-03 respectively. As a result of the comparative analysis, the lowest RMSE value was obtained by MS-TSA. In addition, comprehensive performance analyzes of the algorithms were made with the convergence curve, boxplots, current (I)- voltage (V) and power (P)- voltage (V) charac- teristic curves obtained according to the experimental results. As a result of the experiments and analyses, MS- TSA was found to be a more successful method than the compared algorithms in parameter estimation of PV models.Book Part Performance Analysis of Metaheuristic Optimization Algorithms for a Real Water Distribution Network(Eğitim Yayınevi, 2024) Yılmaz, Volkan; Büyükyıldız, Meral; Baykan, Ömer Kaan; Kamanlı, MehmetItem A Review on Measurement of Particle Sizes by Image Processing Techniques(2023) Tongur, Vahit; Batıbay Ahmet Burçin; Karakoyun MuratThis review is based on how to measure particle sizes with different image processing techniques. In addition, particle size significantly affects the material's mechanical properties. In material science, the material's structure is analyzed to understand that a material can provide specific standards, such as toughness and durability. Therefore, it is essential to make this measurement carefully and accurately. The segmentation approach, frequently used in image processing, aims to isolate objects in an image from the background. In this sense, separating particles from the background is a problem of image processing. In image processing applications, there are different approaches used in segmentation, such as histogram-based, clustering-based, region amplification, separation, and merging. In this review, a comparative analysis was examined by recent studies on particle size measurement.Article Öksürük Sesi Kayıtlarından Spektral Özellikler ile Otomatik Covıd-19 Tespiti(2022) Demircan, SemiyeCOVID-19 pandemisi son iki yıldır dünyada hızla yayılmış ve bu alanda yapılan çalışmalar da artmıştır. COVID-19 olan hastaların, hasta olmayanlardan ayırt edilmesi de pandemideki en önemli sorunlardan bir tanesidir. Gerek hastalığın erken teşhisi gerekse hasta olmayanlara bulaşma riski açısından COVID-19’un otomatik tespiti oldukça önem arz etmektedir. Hastalığın teşhisinde farklı semptomların görülebilmesi ve hatta hiç semptom görülmeden bile oluşabilmesi teşhisi çok daha zor hale getirmiştir. Bu durum hastalığın teşhisi konusunda yapılan çalışmaları arttırmıştır. Öksürük ses kayıtları gibi solunum kayıtlarında var olan önemli özellikler kullanılarak teşhis yapılabilmesi de bu uygulamalardan bir tanesidir. Bu çalışmada öksürük ses kayıtları kullanılarak otomatik COVID-19 hastalık tespiti yapılmıştır. “COVID-19 Positive and Negative Patients' Cough Recordings” (HIMANSHU) veri seti kullanılarak gerçekleştirilen çalışmada ilk olarak ses dosyalarından Mel-Frekansı Kepstrum Katsayıları (MFCC) çıkarılmıştır. Farklı sayıda olan MFCC öznitelikleri istatistiksel değerler kullanılarak eşit boyutlu hale getirilmiştir. MFCC yöntemi ile elde edilen spektral özellikler 8, 16, 32, 64 tane olacak şekilde dört farklı uzunlukta katsayılar çıkarılmıştır. Son olarak makine öğrenmesi algoritmalarından Yapay Sinir Ağları (YSA), Naive Bayes (NB), K-en Yakın Komşu Algoritması (kNN), Rastgele Orman (RO) algoritmaları kullanılarak hastalık teşhisi yapılmıştır. Yapılan çalışmada COVID veya COVID-DEGİL şeklinde 2 sınıf kullanılmıştır. Uygulama on çapraz doğrulama yöntemi ile çalıştırılmıştır. Çalışma sonunda en yüksek sınıflandırma başarası kNN algoritması ile % 99.39 olarak gerçekleştirilmiştir.Article Privacy Preserving Multi-Proxy Based Encrypted Keyword Search(2022) Öksüz, ÖzgürThis paper presents a multi-proxy (2 proxies) based encrypted keyword search scheme that enjoys the following properties: This scheme provides data confidentially that encrypted data does not leak any keywords and documents to the attackers (data server and a proxy). Moreover, the proposed scheme provides trapdoor privacy. In other words, the attackers do not learn any information about searched keywords. Furthermore, even if a proxy is controlled by an attacker, the attacker does not learn any information about the queries (keywords that the user searches over the database) and database results. Different from other studies, this scheme provides lightweight user-side query and data processing. In other words, most of the job (query processing) is done by the proxies on behalf of the user. Finally, the proposed scheme relaxes the trust assumption that eliminates a single point of failure by introducing multi-proxy architecture.Article Identification of Covid-19 From Cough Sounds Using Non-Linear Analysis and Machine Learning(2021) Solak, Fatma ZehraAutomatic diagnosis of COVID-19 has an active role in reducing the spread of the disease by minimizing interaction with people. Machine learning models using various signals and images form the basis of automatic diagnosis. This study presents the machine learning based models for detecting COVID-19 infection using ‘Virufy’ dataset containing cough sound signals labeled as COVID-19 and Non-COVID-19. Since the number of COVID positive coughs in the set is less than those of COVID negative, firstly, data balancing was performed with the ADASYN oversampling technique in the study. Then, features were extracted by non-linear analysis of cough sounds using Multifractal Detrended Fluctuation Analysis (MDFA), Lempel–Ziv Complexity (LZC) and entropy measures. Later, the most effective features were selected by ReliefF method. Finally, five machine learning algorithms, namely Support Vector Machine with Radial Basis Function (SVM-RBF), Random Forest (RF), Adaboost, Artificial Neural Network (ANN), k Nearest Neighbor (kNN) were used to identify cough sounds as COVID-19 or Non-COVID19. As a result of the study, the cough sounds of COVID-19 patients and Non-COVID19 subjects were identified with 95.8% classification accuracy thanks to the RBF kernel function of SVM and the selected effective features. With this classifier, 93.1% sensitivity, 98.6% specificity, 98.6% precision, 0.92 kappa statistical values and 93.2% area under the ROC curve were obtained.Article Automatic Localization of Cephalometric Landmarks Using Convolutional Neural Networks(2021) Nourdine Mogham Njikam Mohamed; Uzbaş, BetülExperts have brought forward interesting and effective methods to address critical medical analysis problems. One of these fields of research is cephalometric analysis. During the analysis of tooth and the skeletal relationships of the human skull, cephalometric analysis plays an important role as it facilitates the interpretation of bone, tooth, and soft tissue structures of the patient. It is used during oral, craniofacial, and maxillofacial surgery and during treatments in orthodontic and orthopedic departments. The automatic localization of cephalometric landmarks reduces possible human errors and is time saving. To performed automatic localization of cephalometric landmarks, a deep learning model has been proposed inspired by the U-Net model. 19 cephalometric landmarks that are generally manually determined by experts are automatically obtained using this model. The cephalometric X-ray image dataset created under the context of IEEE 2015 International Symposium on Biomedical Imaging (ISBI 2015) is used and data augmentation is applied to it for this experiment. A Success Detection Rate SDR of 74% was achieved in the range of 2 mm, 81.4% in the range of 2.5mm, 86.3% in the range of 3mm, and 92.2% in the range of 4mm.Article Yüksek Güvenlikli Ağlar İçin DDS Kullanılarak Tek Yönlü Güvenli Veri Aktarımı(2021) Kılıç, AlperBilgi güvenliğinin oldukça hassas olduğu kritik bilgiler içeren siber sistemlerin ve ağların yetkisiz erişim ve dış müdahalelerden korunması oldukça önemlidir. Ağ güvenliğinin sağlanması ve fiziksel olarak tek yönlü güvenli veri aktarımının yapılması için son yıllarda veri diyotları olarak isimlendirilen sistemler kullanılmaktadır. Tek yönlü veri aktarımı için veri merkezli bir ara katman mimarisi olan Data Distribution Service (DDS) gerek güvenli veri aktarımı özelliği gerekse barındırdığı yönlendirme, filtreleme ve izleme özellikleri ile oldukça uygun bir teknolojidir. Bu çalışmada DDS ara katman mimarisini kullanan tek yönlü güvenli veri aktarım sistemi önerilmiş ve performansı incelenmiştir. Buna göre kabul edilebilir performans kaybı olsa dahi kritik bilgiler içeren ağ sistemleri için DDS mimarisindeki tek yönlü iletim sisteminin uygun bir çözüm olabileceği, siber güvenlik sistemleri için birçok avantajı barındıran bir seçenek olacağı değerlendirilmiştir.Article Dissimilarity Weighting for Graph-Based Point Cloud Segmentation Using Local Surface Gradients(Plusbase Academy, 2020) Sağlam Ali; Makineci Hasan Bilgehan; Baykan Ömer Kaan; Baykan NurdanProcessing of 3D point cloud data is seen as a problem due to the difficulties of processing millions of unstructured points. The point cloud segmentation process is a crucial pre-classification stage such that it reduces the high processing time required to extract meaningful information from raw data and produces some distinctive features for the classification stage. Local surface inclinations of objects are the most effective features of 3D point clouds to provide meaningful information about the objects. Sampling the points into sub-volumes (voxels) is a technique commonly used in the literature to obtain the required neighboring point groups to calculate local surface directions (with normal vectors). The graph-based segmentation approaches are widely used for the surface segmentation using the attributes of the local surface orientations and continuities. In this study, only two geometrical primitives which are normal vectors and barycenters of point groups are used to weight the connections between the adjacent voxels (vertices). The defined 14 possible dissimilarity calculations of three angular values getting from the primitives are experimented and evaluated on five sample datasets that have reference data for segmentation. Finally, the results of the measures are compared in terms of accuracy and F1 score. According to the results, the weight measure W7 (seventh calculation) gives 0.8026 accuracy and 0.7305 F1 score with higher standard deviations, while the original weight measure (W8) of the segmentation method gives 0.7890 accuracy and 0.6774 F1 score with lower standard deviations.Article Comparison of Meta-Heuristic Algorithms on Benchmark Functions(2019) Arıcı Ferda Nur; Kaya, ErsinOptimization is a process to search the most suitable solution for a problem within an acceptable time interval. The algorithms that solve the optimization problems are called as optimization algorithms. In the literature, there are many optimization algorithms with different characteristics. The optimization algorithms can exhibit different behaviors depending on the size, characteristics and complexity of the optimization problem. In this study, six well-known population based optimization algorithms (artificial algae algorithm - AAA, artificial bee colony algorithm - ABC, differential evolution algorithm - DE, genetic algorithm - GA, gravitational search algorithm - GSA and particle swarm optimization - PSO) were used. These six algorithms were performed on the CEC’17 test functions. According to the experimental results, the algorithms were compared and performances of the algorithms were evaluated.Article Optimal Coverage of Wireless Sensor Networks Based on Artificial Algae Algorithm (aaa)(2019) Jaber Wakass Saad; Kaya, ErsinIn the past few years, the demand for wireless sensor networks has increased significantly due to its small size, low cost and high efficiency. It has been used in many applications and in multiple fields. Owing to the everincreasing number of applications using the wireless sensor network, it was necessary to find solutions to the problems and challenges faced by the wireless sensor network. One of the important challenges faced by Wireless Network Sensor is coverage. The nodes bear the actual liability to cover the pre-defined region. That's means the sensor nodes is placed in such a way as to achieve the maximal coverage of the area. Artificial alga algorithm (AAA), which is a very effective optimization method, has been used to find the suitable solutions for the coverage problem. The results were compared with the results of three algorithms (Artificial bee colony algorithm (ABC), particle swarm optimization algorithm (PSO) & Differential evolution Algorithm (DE)) to address the coverage problem. AAA proved to be more effective in solving the coverage problem. The simulation of the algorithms is performed by MATLAB and the results are analyzed to show the effectiveness of the proposed algorithm.Article Ease-Of of Cobots(2020) Irım Fatih; Kaya, Ersin; Alkan Sami SafaCollaborative robots are relatively new in industry and in academy. While usage of cobots are rapidly increasing in industry as a result of the rise of 4th Industrial Revolution, they are also becoming more popular in non-industrial usages like home-use, office-use etc. Reason behind rapid spread of cobots to many industrial and non-industrial fields is their collaborative nature which allows intuitive interaction with human. Human has complex physical, intellectual, and physiological structure. Hence, cobots are intuitive for human to the extent that they provide as close interaction experience as possible to human abilities and human way-of-acting. To achieve this goal, one important aspect of cobots which need to be carefully designed is ease-of-use. There are a number of alternative methods for operating and programming cobots nowadays. This paper introduces the current status of research in this area and makes some comments on the importance of more improved methods for easier use of cobots. Moreover, it is concluded that -parallel to the fact that human is a complex creature- design of new methods for operating and programming cobots is a multidisciplinary field where disciplines like linguistics, psychology, mathematics, computer science, industrial design etc. need to contribute for better results.Article Optimization of Parameters of Cnn Based Method by Particle Swarm Optimization(The Ijacen Journal, 2020) İnik Özkan; Ülker ErkanCNN based models are being developed for the analysis of medical images. These models are varying according to the structure of the tissue to be analyzed and the image acquisition technique. In our previous study, we developed a CNN-based model to perform automatic counting of follicles in the ovary. In the developed model, there are 3 basic parameters that affect segmentation success. These are General Stride (GS), Neighbor Distance (ND) and Patch Accuracy (PA), respectively. It is almost impossible to find the optimum values of these parameters manually. For this reason, in this study, parameter optimization of CNN based model was performed with Particle Swarm Optimization (PSO).As a result of the experimental studies, it was observed that the optimization of these 3 parameters increased the segmentation success of the model by 4.27%.Article Çok Amaçlı Dağınık Arama Algoritmasının Zdt-dtlz Test Problemleri Üzerinde Uygulanması(2024) Haber, Zeynep; Uğuz, HarunDağınık arama algoritması, tek amaçlı optimizasyon problemlerinin çözümünde sıkça kullanılan bir yöntemdir. Ancak, çok amaçlı problemlerle başa çıkmak oldukça zorlu bir süreçtir. Bu makale, çok amaçlı optimizasyon problemleriyle başa çıkabilmek için \"Dağınık Arama Algoritması\" (DA) olarak adlandırılan yöntemin genişletilmesine yönelik bir öneri sunmaktadır. Önerilen yaklaşım, DA algoritmasına çok amaçlı optimizasyon algoritması olan Baskın Olmayan Sıralama Genetik Algoritması II (NSGA-II) yöntemindeki Yoğunluk Mesafesi (CD) ve Hızlı Bastırılmamış Sıralama kavramlarını ekleyerek hibrit çok amaçlı optimizasyon algoritması önermektedir. Bu önerilen algoritma, ZDT ve DTLZ test problemleri kullanılarak değerlendirilmiştir. Yapılan deneysel sonuçlar, önerilen Çok Amaçlı Dağınık Arama(ÇADA) algoritmasının 19 farklı çok amaçlı optimizasyon yöntemi ile karşılaştırıldığında, ZDT problemi için 2.40 IGD ortalama ile birinci sırada, DTLZ probleminde ise 0.0035 IGD ortalama değeri ile altıncı sırada yer aldığını göstermektedir. Bu sonuçlar, önerilen algoritmanın karşılaştırılabilir düzeyde başarılı bir performansa sahip olduğunu ortaya koymaktadır.Article Citation - WoS: 3Citation - Scopus: 1The Role of Magma Recharge and Mixing in Producing Compositional Modality in Post-Collisional Volcanic Rocks, Konya Volcanic Field, Central Anatolia (türkiye)(Elsevier Ltd, 2024) Asan, K.; Gündüz, M.; Korkmaz, G.G.; Kurt, H.The Neogene Erenlerdağ-Alacadağ (ErAVC) and Sulutas (SVC) volcanic complexes in the Konya Volcanic Field, Türkiye have distinctly different unimodal and bimodal compositional variations, respectively. They occurred in graben-like extensional basins behind the retreating Cyprus subduction zone between the African and Eurasian plates. We here investigate their compositional modality by using new and published whole-rock major and trace element and Sr-Nd-Pb isotope data. Both complexes are characterized by basaltic to rhyodacitic high-K calc-alkaline rocks with the geochemical signatures of orogenic volcanism, except for minor alkaline rocks in the SVC. Mass-balance models suggest that major element variations can be largely explained by the fractional crystallization of amphibole, plagioclase, and Fe-Ti oxides. However, Sr-Nd-Pb isotopes show correlations with SiO2 indicating that open-system processes played a role in their differentiation. Modeling of AFC (Assimilation and Fractional Crystallization) involving a recharge situation shows that low degrees of crustal assimilation (rate of assimilation/rate of fractional crystallization, r < 0.2 and crust/magma ratio, ρ: 15–16 %) of lower and upper crust-like rocks was involved in the differentiation of the ErAVC and SVC, respectively. However, the modeling suggests that magma recharge (β: rate of magma recharge/rate of assimilation) was more efficient in the ErAVC (β: 3.45, % ∼52.5 rate of recharge) relative to that of the SVC (β: 2.15, % ∼36.55 rate of recharge). We conclude that for the ErAVC and SVC, different parental magmas derived from the subduction-modified mantle source followed distinct differentiation paths in the crust, and their compositional modality was mainly controlled by the magma recharge and mixing process. © 2024 Elsevier LtdArticle A Novel Crossover Based Discrete Artificial Algae Algorithm for Solving Traveling Salesman Problem(ZARKA PRIVATE UNIV, 2024) Nureddin, Refik; Koç, İsmail; Uymaz, Sait AliThe Artificial Algae Algorithm (AAA) is a newly proposed metaheuristic algorithm that is inspired by microalgae behaviors. This algorithm has been proposed for solving continuous optimization problems and achieved good results for the continuous problems. In addition, binary versions of AAA are proposed in the literature. This paper presents a discrete version of AAA, which is named Discrete Artificial Algae Algorithm (DAAA). For discretization of AAA, Crossover operators (one-point and uniform) are used in the processes (helical movement, evolutionary process, and adaptation). In this study, in addition to crossover operators, transformation operators such as swapping, insertion, symmetry, and reversion are also used. DAAA's ' s performance was analyzed on a well-known discrete optimization problem called the Traveling Salesman Problem (TSP). DAAA was tested on thirty-two Benchmark instances of the TSP. These instances were small-sized, medium-sized, and large-sized. Firstly, the AAA processes (evolutionary process, adaptation, and helical movement) with the combination of nearest neighbor and transformation operators were tested for selected benchmark instances and this testing was called Process Analysis. After this process Analysis the best processes with which to continue were selected, and after this decision comparisons with other algorithms were started. The main comparison is between discrete Social Spider Algorithm (DSSA) and DAAA, and DAAA outperformed DSSA on most of the problems. Further, DAAA's ' s performance on some of the benchmark instances was compared with some of the well-known algorithms for TSP. In this comparison, DAAA has achieved better results than many other algorithms. Experimental results show that DAAA has the capability of solving discrete optimization problems and outperforming other algorithms. .Article Yeni Bir İkili Sürüş Eğitim Tabanlı Algoritma Üzerinde Transfer Fonksiyonlarının İncelenmesi(2023) Koç, İsmailKapasitesiz Tesis Yerleşim Problemi (UFLP), tesislerin optimal yerleşimini belirleyen NP-zor bir problemdir. UFLP, NP-Zor problem grubundan olduğu için, bu problemlerin büyük örneklerini çözmek için kesin yöntemlerin kullanılması, optimal çözümü elde etmek için gereken yüksek hesaplama süreleri nedeniyle ciddi şekilde sorun teşkil edebilir. Bu çalışmada, problemin karmaşıklığından dolayı sürü zekası algoritması tercih edilmiştir. Son yıllarda sürüş eğitimi ilkelerine dayalı olarak geliştirilen popülasyon tabanlı bir algoritma olan Sürüş eğitim tabanlı (DTBO) algoritması UFLP probleminin çözümünde kullanılmıştır. DTBO’nun temel versiyonu sürekli problemlerin çözümünü ele aldığından söz konusu algoritmanın ikili problemlerin çözümüne uyarlanması gerekmektedir. Bunun için literatürde kullanılan dokuz farklı transfer fonksiyonu yardımıyla DTBO algoritması ikili problemlerin çözümüne uygun olarak tasarlanmıştır. Deneysel çalışmalar transfer fonksiyonlarının adil kıyaslanabilmesi için eşit koşullarda altında gerçekleştirilmiştir. Gerçekleştirilen deneysel çalışmalarda dokuz transfer fonksiyonu içerisinden ikili Mode-DTBO algoritmasının en başarılı algoritma olduğu görülmektedir. Bu sonuçlara göre Mode tabanlı DTBO algoritmasının küçük, orta ve büyük ölçekli tüm problem setlerinde hem çözüm kalitesi açısından hem de zaman açısından çok başarılı olduğu görülmektedir. Ayrıca DTBO algoritması IWO (Yabani Ot Algoritması – Invasive Weed Optimization) algoritmasına ait 3 farklı transfer fonksiyonuyla (Mode, Sigmoid ve Tanh) da kıyaslanmıştır. Karşılaştırmalı sonuçlar incelendiğinde 12 problemin 8’inde (orta ve büyük ölçekli problem) Mode-DTBO yaklaşımının IWO’ya ait 3 farklı yaklaşımın hepsinden çok daha başarılı olduğu görülmüştür. Bununla beraber, küçük boyutlu 4 problem üzerinde ise Mode fonksiyonunu kullanan her iki algoritmanın da optimal değeri yakaladığı görülmüştür. Sonuç olarak, Mode-DTBO yönteminin ikili problemlerin çözümünde çok etkili bir alternatif sunacağı söylenebilir.Conference Object Sosyal Ağlarda Topluluk Tespiti İçin Yeni Bir Algoritma: Ayrık Denge Optimizasyonu(Konya Teknik Üniversitesi, 2022) Koç, İsmailModern ağ bilimi, karmaşık sistemleri anlamlandırmada önemli ilerlemeler sunmaktadır. Gerçek sistemleri temsil eden grafların en alakalı özelliklerinden biri topluluk yapısıdır. Bu tür topluluklar, örneğin insan vücudundaki dokular veya organlar gibi benzer bir rol oynayan, bir grafın oldukça bağımsız bölümleri olarak düşünülebilir. Topluluk tespiti (CD), sistemlerin genellikle graflarla temsil edildiği sosyoloji, biyoloji ve bilgisayar bilimlerinde büyük önem taşımaktadır. CD probleminin araştırılması birçok farklı algoritmayı motive etmesine rağmen, çoğu hesaplama maliyeti nedeniyle büyük ölçekli sosyal ağlar için uygun değildir. Ayrıca, olası topluluk yapılarını tanımlamanın yanı sıra, birçok pratik senaryoda keşfedilen toplulukların nasıl tanımlanacağı ve açıklanacağı da önemlidir. Bu tür gerekçelerde dolayı bu çalışmada klasik yöntemler yerine optimizasyon algoritmasının kullanılması tercih edilmiştir. Optimizasyon algoritması olarak ise son yıllarda geliştirilmiş olan Denge Optimizasyon (EO) Algoritması CD problemine uyarlanmıştır. EO temel versiyonu sürekli problemlerin çözümü üzerine önerildiğinden, ayrık bir problem olan CD problemi için EO yöntemi ayrık hale getirilmiştir. Deneysel çalışmalarda beş farklı sosyal ağ kullanılmıştır. Tüm çalışmalar adil bir kıyaslama yapabilmek için eşit koşullarda gerçekleştirilmiştir. EO algoritması iki farklı makalede yer alan önemli algoritmalarla çözüm kalitesi ve zaman açısından kıyaslanmıştır. Bu sonuçlara göre EO algoritmasının sosyal ağlarda CD probleminin çözümünde çok başarılı olduğu görülmüştür.Conference Object Category Prediction of Turkish Poems Using Artificial Intelligence and Natural Language Processing Methods With Mlp and Svm Algorithms(2023) Korkmaz, Sedat; Yönet, EmrePeople are able to communicate with each other through language. The languages that people use are called natural languages. Natural languages such as English, Turkish, French, etc. are used for communication. Similarly, people can communicate with machines, and for this purpose, natural languages can be made understandable by machines by subjecting them to a series of processes. For this purpose, it is necessary to analyze the canonical structures of natural languages and make them understandable. This process is basically carried out on four levels of analysis: Lexical Analysis, Syntactic Analysis, Semantic Analysis, and Discourse Analysis. Natural Language Processing (NLP) is a branch of artificial intelligence that deals with the processing of natural language input in the form of speech and text. The use of NLP is prevalent in a variety of fields, such as intelligent virtual assistants, search engines, social media monitoring platforms, automatic translation systems, text summarization systems, and text categorization systems. This study presents a model for predicting the categories of Turkish poems using natural language processing and machine learning methods. The project code was written in Python using the Anaconda development environment. The Zemberek library was used to perform various operations on Turkish texts. The dataset used consisted of 4198 poems taken from a website and categorized into 21 categories. During the data preprocessing stage, the texts were converted to lower case, punctuation marks, spaces, and stop-words were removed and root extraction was performed. The Term Frequency-Inverse Document Frequency (TF-IDF) method was used for text representation and evaluated the success rates of models created using Support Vector Machine (SVM) and Multilayer Perceptron (MLP) classifiers. The findings indicated that the SVM classifier outperformed the MLP classifier.
