Bilgisayar ve Bilişim Fakültesi Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.13091/10834

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Citation - WoS: 6
Citation - Scopus: 9
Ağaç-tohum Algoritmasının Cuda Destekli Grafik İşlem Birimi Üzerinde Paralel Uygulaması
(2018) Çınar, Ahmet Cevahir; Kıran, Mustafa Servet
Son yıllarda toplanan verinin artmasıyla birlikte verimli hesaplama yöntemlerinin de geliştirilmesi ihtiyacı artmaktadır. Çoğunlukla gerçek dünya problemlerinin zor olması sebebiyle optimal çözümü garanti etmese dahi makul zamanda yakın optimal çözümü garanti edebilen sürü zekâsı veya evrimsel hesaplama yöntemlerine olan ilgi de artmaktadır. Diğer bir açıdan seri hesaplama yöntemlerinde verinin veya işlemin paralelleştirilebileceği durumlarda paralel algoritmaların da geliştirilmesi ihtiyacı ortaya çıkmıştır. Bu çalışmada literatüre son yıllarda kazandırılmış olan popülasyon tabanlı ağaç-tohum algoritması ele alınmış ve CUDA platformu içerisinde paralel versiyonu geliştirilmiştir. Algoritmanın paralel versiyonunun performansı kıyas fonksiyonları üzerinde analiz edilmiş ve seri versiyonunun performansı ile karşılaştırılmıştır. Kıyas fonksiyonlarında problem boyutluluğu 10 olarak alınmış ve farklı popülasyon ve blok sayıları altında performans analizi yapılmıştır. Deneysel çalışmalar algoritmanın paralel versiyonunun algoritmanın seri sürümüne göre bazı problemler için 184,65 kata performans artışı sağladığı görülmüştür.
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.
Comparison of Textual Data Augmentation Methods on Sst-2 Dataset
(Springer Science and Business Media Deutschland GmbH, 2024) Çataltaş, M.; Baykan, N.A.; Cicekli, I.
Since the arrival of advanced deep learning models, more successful techniques have been proposed, significantly enhancing the performance of nearly all natural language processing tasks. While these deep learning models achieve the best results, large datasets are needed to get these results. However, data collection in large amounts is a challenging task and cannot be done successfully for every task. Therefore, data augmentation might be required to satisfy the need for large datasets by generating synthetic data samples using original data samples. This study aims to give an idea to those who will work in this field by comparing the successes of using a large dataset as a whole and data augmentation in smaller pieces at different rates. For this aim, this study presents a comparison of three textual data augmentation techniques, examining their efficacy based on the augmentation mechanism. Through empirical evaluations on the Stanford Sentiment Treebank dataset, the sampling-based method LAMBADA showed superior performance in low-data regime scenarios and moreover showcased better results than other methods when the augmentation ratio is increased, offering significant improvements in model robustness and accuracy. These findings offer insights for researchers on augmentation strategies, thereby enhancing generalization in future works. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
Citation - WoS: 2
Citation - Scopus: 4
FUZZY ADAPTIVE WHALE OPTIMIZATION ALGORITHM FOR NUMERIC OPTIMIZATION
(UNIV MALAYA, FAC COMPUTER SCIENCE & INFORMATION TECH, 2021) Kaya, Ersin; Kılıç, Alper; Babaoğlu, İsmail; Babalık, Ahmet
Meta-heuristic approaches are used as a powerful tool for solving numeric optimization problems. Since these problems are deeply concerned with their diversified characteristics, investigation of the utilization of algorithms is significant for the researchers. Whale optimization algorithm (WOA) is one of the novel meta-heuristic algorithms employed for solving numeric optimization problems. WOA deals with exploitation and exploration of the search space in three stages, and in every stage, all dimensions of the candidate solutions are updated. The drawback of this update scheme is to lead the convergence of the algorithm to stack. Some known meta-heuristic approaches treat this issue by updating one or a predetermined number of dimensions in their update scheme. To improve the exploitation behavior of WOA, a fuzzy logic controller (FLC) based adaptive WOA (FAWOA) is suggested in this study. An FLC realizes the update scheme of WOA, and the proposed FLC determines the rate of the change in terms of dimension. The suggested FAWOA is evaluated using 23 well-known benchmark problems and compared with some other meta-heuristic approaches. Considering the benchmark problems, FAWOA achieves best results on 11 problem and only differential evaluation algorithm achieve best results on 10 problems. The rest of the algorithms couldn't achieve the best results on not more than 5 problems. Besides, according to the Friedman and average ranking tests, FAWOA is the first ranked algorithm for solving the benchmark problems. Evaluation results show that the suggested FAWOA approach outperforms the other algorithms as well as the WOA in most of the benchmark problems.
Citation - WoS: 3
Citation - Scopus: 6
A Hierarchical Approach Based on Aco and Pso by Neighborhood Operators for Tsps Solution
(WORLD SCIENTIFIC PUBL CO PTE LTD, 2020) Eldem, Hüseyin; Ülker, Erkan
It is known that some of the algorithms in optimization field have originated from inspiration from animal behaviors in nature. Natural phenomena such as searching behavior of ants for food in a collective way, movements of birds and fish groups as swarms provided the inspiration for solutions of optimization problems. Traveling Salesman Problem (TSP), a classical problem of combinatorial optimization, has implementations in planning, scheduling and various scientific and engineering fields. Ant colony optimization (ACO) and Particle swarm optimization (PSO) techniques have been commonly used for TSP solutions. The aim of this paper is to propose a new hierarchical ACO- and PSO-based method for TSP solutions. Enhancing neighboring operators were used to achieve better results by hierarchical method. The performance of the proposed system was tested in experiments for selected TSPLIB benchmarks. It was shown that usage of ACO and PSO methods in hierarchical structure with neighboring operators resulted in better results than standard algorithms of ACO and PSO and hierarchical methods in literature.
Citation - Scopus: 1
A K-Elm Approach To the Prediction of Number of Students Taking Make-Up Exams
(Gazi Univ, Fac Engineering Architecture, 2022) Kıran, Mustafa Servet; Sıramkaya, Eyup; Esme, Engin
Purpose: The main objective of this study is to present a novel problem, and novel methodology to solve this problem. The problem is to predict the number of students who fail the course and will join the make-up exams. Theory and Methods: The number of students who fail the course should take a make-up exam, but some of them do not join these exams due to internal or external motivations, and this causes waste of resources. Majority of voting-based extreme learning machines have been proposed to solve the problem, and the ELM parameters have been optimized by artificial bee colony algorithm. Results: The proposed approach shows better performance than the extreme learning machines in terms of classification accuracy. Conclusion: Before the scheduling make-up exams, the number of students who will join the exams should be predicted by the proposed or similar approaches in order to use resources efficiently.
Citation - WoS: 4
Citation - Scopus: 4
Modifiye Hibrit Optimizasyon Yöntemi ile Rüzgâr-termal Güç Sistemleri için Ekonomik Dağıtım Probleminin Çözümü
(2019) Tefek, Mehmet Fatih; Uğuz, Harun
Ekonomik dağıtım problemi (EDP) karmaşık, sınırlamalı ve doğrusal olmayan bir optimizasyon problemidir. EDP’de talep edilen güç için, aktif güç baralarının minimum ve maksimum sınırları arasında sistemin yakıt maliyetini minimum yapmak amaçlanmaktadır. Bu çalışmada, Türkiye 19 baralı rüzgâr-termal güç sisteminin EDP çözümü amacıyla yerçekimsel arama algoritması (YAA) ile öğretme-öğrenme temelli optimizasyon (ÖÖTO) algoritmasının birleştirilmesi ile hızlı, etkili ve güvenilir bir hibrit optimizasyon algoritması olan modifiye hibrit yerçekimi arama-öğretme-öğrenme temelli optimizasyon yöntemi (MHYÖ) tasarlanmıştır. MHYÖ yöntemi, sınırlamalı optimizasyon problemi çözümü için YAA’nın güçlü global arama ve TLBO’nun yerel arama özelliği modifiye edilerek geliştirilmiştir. MHYÖ, literatürde iyi bilinen ve sık kullanılan on adet benchmark fonksiyonlarıyla deneysel amaçlı test edilmiştir. Geliştirilen MHYÖ yöntemi, EDP çözümü için ilk olarak 6-baralı rüzgâr-termal güç sisteminde talep edilen sırasıyla 400 MW, 450 MW ve 500 MW güç için uygulanmıştır. Daha sonra geliştirilen MHYÖ yöntemi, Türkiye 19 baralı rüzgâr-termal güç sisteminin EDP çözümü amacıyla sistemdeki toplam planlanan gücün %25, %27,5 ve %30 talep edilen güç oranına göre üç farklı durumda uygulanmıştır. Elde edilen sonuçlar diğer çalışmaların sonuçları ile kıyaslanmıştır. Bu sonuçlara göre, MHYÖ yönteminin hem yakıt maliyeti hem de hesaplama zamanı ikilisi açısından, kısa çalışma zamanında, güvenilir, etkili ve minimum yakıt maliyeti ile sonuçları bulduğunu göstermektedir.
Citation - WoS: 4
Citation - Scopus: 6
A New Approach Based on Collective Intelligence To Solve Traveling Salesman Problems
(MDPI, 2024) Kıran, Mustafa Servet; Beşkirli, Mehmet
This paper presents a novel approach based on the ant system algorithm for solving discrete optimization problems. The proposed method is based on path construction, path improvement techniques, and the footprint mechanism. Some information about the optimization problem and collective intelligence is used in order to create solutions in the path construction phase. In the path improvement phase, neighborhood operations are applied to the solution, which is the best of the population and is obtained from the path construction phase. The collective intelligence in the path construction phase is based on a footprint mechanism, and more footprints on the arc improve the selection chance of this arc. A selection probability is also balanced by using information about the problem (e.g., the distance between nodes for a traveling salesman problem). The performance of the proposed method has been investigated on 25 traveling salesman problems and compared with state-of-the-art algorithms. The experimental comparisons show that the proposed method produced comparable results for the problems dealt with in this study.
Citation - WoS: 1
Citation - Scopus: 3
A New Stability Approach Using Probabilistic Profile Along Direction of Excavation
(SHAHROOD UNIV TECHNOLOGY, 2020) Turanboy, A.; Ülker, E.; Küçüksütçü, C. B.
Estimation of the possible instability that may be encountered in the excavation slope(s) during the planning and application steps of the rock excavation processes is an important issue in geoengineering. In this paper, a modelling method is presented for assessing the probability of wedge failure involving new permanent or temporary slope(s) along the planned excavation direction. The geostructural rock slopes including wedge blocks are determined geometrically in the first step. Here, a structural data analysis system that includes a series of filterings, sortings, and linear equations used to reveal the necessary geometric conditions for the wedge form is developed and used. The second step involves the 3D visualization and Factor of Safety (FS) using the limit equilibrium analysis of wedges on both the actual and planned new excavation surfaces. The last step is the Monte Carlo simulation, which is used in assessing the instabilities on the actual and planned new excavation surfaces. These new slope surfaces that have not yet been excavated are called the virtual structures. As a result of this work, the mean and probabilistic FS variations in the planned excavation direction are obtained as profiles. We suggest the preliminary guidelines for the mean and probability of the wedge failure in the excavation direction. The model is tested on a motorway cut slope. The FS results obtained from the Monte Carlo simulation calculations are compared with the mean results and the changes are revealed with the reasons.
Citation - WoS: 14
Citation - Scopus: 15
Optimization of Butterworth and Bessel Filter Parameters With Improved Tree-Seed Algorithm
(Mdpi, 2023) Beşkirli, Mehmet; Kıran, Mustafa Servet
Filters are electrical circuits or networks that filter out unwanted signals. In these circuits, signals are permeable in a certain frequency range. Attenuation occurs in signals outside this frequency range. There are two types of filters: passive and active. Active filters consist of passive and active components, including transistors and operational amplifiers, but also require a power supply. In contrast, passive filters only consist of resistors and capacitors. Therefore, active filters are capable of generating signal gain and possess the benefit of high-input and low-output impedance. In order for active filters to be more functional, the parameters of the resistors and capacitors in the circuit must be at optimum values. Therefore, the active filter is discussed in this study. In this study, the tree seed algorithm (TSA), a plant-based optimization algorithm, is used to optimize the parameters of filters with tenth-order Butterworth and Bessel topology. In order to improve the performance of the TSA for filter parameter optimization, opposition-based learning (OBL) is added to TSA to form an improved TSA (I-TSA). The results obtained are compared with both basic TSA and some algorithms. The experimental results show that the I-TSA method is applicable to this problem by performing a successful prediction process.
Citation - Scopus: 2
Price Rank Prediction of a Company by Utilizing Data Mining Methods on Financial Disclosures
(IEICE-INST ELECTRONICS INFORMATION COMMUNICATION ENGINEERS, 2023) Kaçar, Mustafa Sami; Yumuşak, Semih; Kodaz, Halife
The use of reports in action has grown significantly in recent decades as data has become digitized. However, traditional statistical methods no longer work due to the uncontrollable expansion and complexity of raw data. Therefore, it is crucial to clean and analyze financial data using modern machine learning methods. In this study, the quarterly reports (i.e. 10Q filings) of publicly traded companies in the United States were analyzed by utilizing data mining methods. The study used 8905 quarterly reports of companies from 2019 to 2022. The proposed approach consists of two phases with a combination of three different machine learning methods. The first two methods were used to generate a dataset from the 10Q filings with extracting new features, and the last method was used for the classification problem. Doc2Vec method in Gensim framework was used to generate vectors from textual tags in 10Q filings. The generated vectors were clustered using the K-means algorithm to combine the tags according to their semantics. By this way, 94000 tags representing different financial items were reduced to 20000 clusters consisting of these tags, making the analysis more efficient and manageable. The dataset was created with the values corresponding to the tags in the clusters. In addition, PriceRank metric was added to the dataset as a class label indicating the price strength of the companies for the next financial quarter. Thus, it is aimed to determine the effect of a company's quarterly reports on the market price of the company for the next period. Finally, a Convolutional Neural Network model was utilized for the classification problem. To evaluate the results, all stages of the proposed hybrid method were compared with other machine learning techniques. This novel approach could assist investors in examining companies collectively and inferring new, significant insights. The proposed method was compared with different approaches for creating datasets by extracting new features and classification tasks, then eventually tested with different metrics. The proposed approach performed comparatively better than the other machine learning methods to predict future price strength based on past reports with an accuracy of 84% on the created 10Q filings dataset.
Citation - Scopus: 1
Water–rock Interaction in the Geothermal Systems Related To Post-Collision Zone Volcanism: a Case Study Based on Multivariate Statistical Analysis From the Kavak Geothermal Field (konya, Turkey)
(Academie des sciences, 2023) Gündüz, M.; Bozdag, A.; Bayram, A.F.; Bozdag, A.; Asan, K.; Sardini, P.
Water–rock interaction is the focus of geothermal energy studies and can be documented by traditional geochemical methods such as ion ratio method and hydrogeochemical modelling etc. Statistical approaches are also vital for the quantitative models, and mainly combined with the traditional methods. In this study, we re-evaluate the published data, including water chemistry and volcanic and metamorphic whole-rock geochemistry from the Kavak geothermal field (Konya, Turkey) by using multivariate statistical analysis (e.g. factor analysis and clustering analysis) to research possible interaction between the thermal waters and surrounding rocks. The Kavak geothermal field (KGF) overlies a metamorphic basement composed of the Paleozoic metacarbonates and metaclastic rocks, yet is located near the Erenlerdağ–Alacadağ volcanic complex (ErAVC). An example of unimodal orogenic volcanism in an extensional geodynamic setting, the Neogene ErAVC is composed of widespread high-K calcalkaline andesite to rhyodacite lavas with enclaves and their pyroclastic counterparts. The Kavak geothermal field covers a small area (∼7.5 km2) and lies along the Seydişehir fault zone in the southeast of the ErAVC. The Kavak thermal waters are meteoric in origin and peripheral waters (Ca–Na–HCO3) in the geothermal system related to the orogenic volcanism. The Kavak thermal waters are characterised by high K+ and Na+ cations, and low pH (between 6.4–6.9 pH) values relative to the cold waters around the KGF. Two types of thermal waters were identified in the KGF based on the slight difference in their hydrochemistry and discharge temperature. The first type thermal water (∼22 °C) has higher TDS and Cl/Br ratio and lower dissolved silica and Br content relative to the second type of water (up to 45 °C). The chemical relationship between the KGF and high-K ErAVC is clearly seen in the cation-based diagrams. Multivariate statistical analysis confirms that first type and second type thermal waters identified based on their hydrochemistry formed two separate statistical groups, and suggests that the chemistry of the KGF waters was mainly controlled by the composition of the ErAVC rather than those of the basement metamorphic rocks as a result of water–rock interaction. © 2023 Academie des sciences. All rights reserved.

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