Yazar "Arslan, Sibel" seçeneğine göre listele
Listeleniyor 1 - 20 / 23
Sayfa Başına Sonuç
Sıralama seçenekleri
Öğe A comprehensive review of automatic programming methods(Elsevier, 2023) Arslan, Sibel; Ozturk, CelalAutomatic programming (AP) is one of the most attractive branches of artificial intelligence because it provides effective solutions to problems with limited knowledge in many different application areas. AP methods can be used to determine the effects of a system's inputs on its outputs. Although there is increasing interest in solving many problems using these methods for a variety of applications, there is a lack of reviews that address the methods. Therefore, the goal of this paper is to provide a comprehensive literature review of AP methods. At the same time, we mention the main characteristics of the methods by grouping them according to how they represent solutions. We also try to give an outlook on the future of the field by highlighting possible bottlenecks and perspectives for the benefit of the researchers involved.& COPY; 2023 Elsevier B.V. All rights reserved.Öğe Advancing nanomaterials research: A comprehensive review of artificial intelligence applications in geotechnical properties(Techno-Press, 2024) Cemiloglu, Ahmed; Zhu, Licai; Arslan, Sibel; Nanehkaran, Yaser A.; Azarafza, Mohammad; Derakhshani, RezaThis article explores the role of artificial intelligence (AI) in predicting nanomaterial properties, particularly its significance within geotechnical engineering. By analyzing multiple AI-based studies, the review concentrates on the forecasting of nanomaterial-altered soil characteristics and behaviors. Encouraging findings from these studies underscore AI's ability to accurately predict the geotechnical properties of nanomaterials, though challenges remain, particularly in quantifying nanomaterial percentages and their implications across various applications. Future research should address these challenges to enhance the accuracy of AI-based prediction models in geotechnical engineering. Nonetheless, the growing adoption of AI for predicting nanomaterial properties demonstrates its potential to revolutionize geotechnical engineering. AI's capacity to uncover intricate patterns and relationships beyond human capabilities enables more precise soil behavior predictions, fostering innovative solutions to geotechnical challenges. Its ability to process vast datasets, adapt to various scenarios, and continuously learn from new information makes AI an indispensable tool for understanding nanomaterial properties and their impact on soil behavior. In summary, the integration of AI and geotechnical engineering represents a pivotal advancement in comprehending nanomaterial properties and their practical applications. As research advances and AI technologies evolve, transformative progress in geotechnical engineering is expected. By harnessing AI's capabilities, researchers can unlock groundbreaking insights, drive innovation, and shape a more resilient and sustainable future for the geotechnical engineering industry.Öğe Afrika Akbabaları Optimizasyon Algoritmasının Güncel Metasezgisellerle Karşılaştırmalı Analizi(Osmaniye Korkut Ata Üniversitesi, 2025) Arslan, Sibel; Zoralioğlu, Yıldız; Gul, Muhammed FurkanOptimizasyon problemlerinin karmaşıklığının artmasıyla birlikte yeni metasezgisel algoritmalar geliştirilmektedir. Bu algoritmalar farklı problemler üzerinde üstün performanslar sergileyerek başarılarını göstermektedir. Bu çalışmada, son zamanlarda önerilen 4 metasezgisel algoritma olan Yapay Sinekkuşu Algoritması (Artificial Hummingbird Algorithm, AHA), Afrika Akbabaları Optimizasyon Algoritması (African Vultures Optimization Algorithm, AVOA), Kerevit Optimizasyon Algoritması (Crayfish Optimization Algorithm, COA) ve Deniz Yırtıcıları Optimizasyon Algoritması’nın (Marine Predators Optimization Algorithm, MPA) 26 test fonksiyonu üzerindeki performansları karşılaştırılmıştır. Karşılaştırmalar sonucunda algoritmaların farklı fonksiyonlar üzerinde çok küçük farklarla birbirlerinden daha iyi performans gösterdiği gözlemlenmiştir. Aynı zamanda karşılaştırma sonuçları t-test istatistiksel testi ile değerlendirilmiştir. AVOA, çeşitli test fonksiyonları için çözümlerin kalitesini değerlendirmede diğer yeni metasezgisellere göre daha iyi veya karşılaştırılabilir performans göstermiştir. Gelecek araştırmalarda AVOA’nın farklı problemler üzerinde kullanılması hedeflenmektedir.Öğe Approximation of the Colebrook Equation for Flow Friction with Immune Plasma Programming(Institute of Electrical and Electronics Engineers Inc., 2022) Yetiskin, Begum; Arslan, SibelThe Colebrook equation, which calculates the flow friction, is used to calculate pressure loss in ventilation ducts with turbulent flow, pipes with water or oil. The computational complexity of the equation increases when the friction factor occurs on both sides of the equation. In this study, a new Colebrook approach to compute flow friction with lower cost is proposed based on the Immune Plasma Programming (IPP) automatic programming method based on the stages of immune plasma therapy. The success of IPP was compared with Artificial Bee Colony Programming (ABCP), quick ABCP, semantic ABCP, quick semantic ABCP. The simulation results show that IPP can be used to effectively solve real-world problems. © 2022 IEEE.Öğe Biosorption of methyl orange from aqueous solution with hemp waste, investigation of isotherm, kinetic and thermodynamic studies and modeling using multigene genetic programming(SPRINGER INT PUBL AGGEWERBESTRASSE 11, CHAM CH-6330, SWITZERLAND, 16.08.2022) Kütük, Nurşah; Arslan, SibelWater resources around the world are getting polluted day by day due to the rapidly developing industry. Industrial wastes have caused serious damage to the environment in recent years. Especially, dyes are waste products that mix with waters such as lakes, rivers and seas and have toxic and carcinogenic effects. In this study, the removal of methyl orange (MO) dye, which was chosen as a model dye compound, from aqueous solution by biosorption using hemp waste was investigated. The biosorption process was optimized by the parameters of pH, initial dye concentration and amount of biosorbent. Biosorption of MO to hemp waste was investigated by isotherms, kinetics and thermodynamic studies. It was determined that the biosorption equilibrium fitted to the Langmuir isotherm (R2 =0.9739). As a result of the experimental studies, 83% biosorption value and 1428 mg/g maximum biosorption capacity were reached with 250 mg/L dye concentration and 0.5 g/L biosorbent amount at pH = 2. It was determined that the reaction kinetics were in accordance with the pseudo-second-order kinetics (R2 =0.9911). In addition to, the study aims to evaluate to what extent the modeling of the biosorption process is successful. For this purpose, we used multigene genetic programming (MGGP), which has been renewed with the latest developments in the field of model extraction. The results show that MGGP is efficient for modeling the biosorption process in real environments. The analysis of MGGP models also showed that pH is the most important parameter affecting the biosorption process.Öğe Classification by Feature Selection of Autism Spectrum Disorder with Automatic Programming Methods(Institute of Electrical and Electronics Engineers Inc., 2023) Dikbas, Sedat; Arslan, SibelAutism Spectrum Disorder (Autism Spectrum Disorder, OSB) is a neuro-developmental disorder that negatively affects the communication of individuals due to the cells in the brain. With the early examination of OSB, children can receive more effective treatment and support. Artificial intelligence methods are used in the field of health by examining and analyzing the medical data of patients, and they are used in the diagnosis phase and achieve successful results. In this study, models for the classification of OSB with ABCP and GP from Automatic Programming (AP) methods were produced and used for the analysis of attributes affecting OSB. According to the experimental results, both methods achieved a classification rate of about %90. In addition, the most necessary attributes for OSB classification were determined as gender, Q-Chat score, and facial expression inference. © 2023 IEEE.Öğe Comparison of Black Widow Optimization and Aquila Optimizer with Current Metaheuristics(SET Teknoloji, 2024) Kalyon, Metin; Arslan, SibelMetaheuristic optimization algorithms are an optimization approach that produces acceptable solutions in situations where it is difficult to create a mathematical model in an optimization problem or in large-scale, multivariate optimization problems. Metaheuristics play a significant role in solving optimization problems. In this study, five current meta- heuristics (Aquila Optimizer (AO), Artificial Rabbits Optimization (ARO), Black Widow Optimization (BWO), Harris Hawk Optimization (HHO) and Sooty Tern Optimization Algorithm (STOA), which are inspired by swarm intelligence and foraging behavior of creatures in nature) are compared. These algorithms are discussed in detail and information is given about their working principles. As far as is known, this is the first time that the performances of these five algorithms have been compared. The algorithms were evaluated with unimodal and multimodal test functions. The simulation results demonstrate that AO and BWO are more successful than the other algorithms. It is also evaluated that the metaheuristics used in the study can be applied to many engineering problems.Öğe Comparison of Current Metaheuristic Algorithms with Different Performance Criteria(Duzce University, 2023) Arslan, SibelNowadays, metaheuristics play a very important role in solving optimization problems. In this study, Particle Swarm Optimization Algorithm (PSO), one of the most commonly used metaheuristics, was compared in three new metaheuristic (African Vulture Optimization Algorithm-AVOA, Improved Gray Wolf Optimization Algorithm- I-GWO and Marine Predators Algorithm-MPA) comparisons inspired by swarm intelligence and foraging behavior of creatures in nature. According to the experimental studies, AVOA and MPA achieve more successful results than other algorithms. The statistical significance of the results was evaluated using the Friedman Wilcoxon signed-rank test, and the significant superiority of these two algorithms was proven.Öğe Comparison of Current Metaheuristics for Solving Engineering Problems(Institute of Electrical and Electronics Engineers Inc., 2024) Tufan, Elif; Arslan, SibelNowadays, with the increasing number of metaheuristic, different strategies for solving engineering problems have been introduced. In this study, the Crayfish Optimizer Algorithm (COA), Spider Wasp Optimizer (SWO) and Goose Algorithm (Goose) are compared among the current metaheuristics inspired by the unique behaviors and survival functions of different animals in nature. To the best of our knowledge, this is the first study to compare the performance of these algorithms on three popular engineering problems (pressure vessel design, welded beam design and tension-compression design problems). When the experimental results and convergence rates are evaluated, it is observed that all metaheuristics exhibit very high performance. SWO, inspired by the hunting and nesting behavior strategies of spider wasps, is the most successful metaheuristic. SWO is followed by COA, modeled from the natural behavior of crayfish, and Goose, developed by imitating the flocking behavior of goose. The statistical significance of the results was assessed using t-tests, which demonstrate the superiority of SWO. In future studies, it is aimed that other metaheuristics, especially SWO, can be used in solving different engineering problems. © 2024 IEEE.Öğe COMPARISON OF METAHEURISTIC ALGORITHMS WITH DIFFERENT PERFORMANCE CRITERIA(2023) Zoralioğlu, Yıldız; Arslan, SibelNature-inspired metaheuristic algorithms are widely used because they achieve successful results in difficult optimization problems. Their popularity has led to the development of new metaheuristics for solving different engineering problems. New metaheuristics lead scientific research by providing faster and more efficient results. In this study, Artificial Rabbit Algorithm (ARO), Dwarf Mongoose Algorithm (DMO) and Genetic Algorithm (GA), which are recently developed metaheuristics, are compared. According to the literature review, the performances of these three algorithms are compared for the first time. Single and multi-modal standard quality test functions were used to evaluate the algorithms. The results of the algorithms were checked by t-test to see if there is a significant difference in terms of the functions used. According to the results obtained, it was observed that ARO produced more successful results than the other algorithms compared. This shows that the newly developed metaheuristics can be used in many engineering problems.Öğe EEG gürültü minimizasyonu için kafes temelli yeni bir yapay arı koloni algoritması(2023) Arslan, Sibel; Aslan, SelçukGeçtiğimiz yıllar büyük veri olarak adlandırılan yeni bir kavramla başlayan değişimlere tanıklık etmiştir. Bu yeni kavram ve özellikleri gerçek hayat en iyileme problemlerinin tanımlarını değiştirmiş ve daha önce önerilen çözüm yöntemlerinin performanslarının incelenmesi ve büyük veri kavramının özelliklerini dikkate alarak yeni yöntemlerin geliştirilmesi kritik hale gelmiştir. Arıların yiyecek arama davranışlarından ilham alan Yapay Arı Koloni (ABC) algoritması sürü zekâsı temelli yöntemlerinin en başarıları arasındadır. Bu çalışmada, ABC algoritmasının işçi ve gözcü arı fazları elektroensefalografi sinyallerinde gürültü minimizasyonunu gerektiren büyük veri en iyileme probleminin çözümü için düzenlenmiş ve kafes temelli ABC algoritması (LBABC) tanıtılmıştır. Önerilen yöntemin çözüm kapasitesinin analizi için farklı problem örneklerini içeren bir dizi uygulama gerçekleştirilmiştir. Elde edilen sonuçlar yaygın kullanılan yöntemler tarafından elde edilen sonuçlar ile de kıyaslanmıştır. Karşılaştırma sonuçlarından, yeni yönteminin test problemlerinin tamamına yakınında diğer yöntemlerden daha iyi ya da oldukça yakın çözümlere ulaşabildiği anlaşılmıştır.Öğe Feature Selection and Detection of COPD Using Automatic Programming Methods(Institute of Electrical and Electronics Engineers Inc., 2024) Karaca, Huseyin; Arslan, SibelChronic obstructive pulmonary disease (COPD) is a serious lung disease that severely limits patients' quality of life and can lead to further health complications if it is not diagnosed and treated in time. In this study, various Automatic Programming (AP) methods, including Genetic Programming (GP) and Multi-Gene Genetic Programming (MGGP), are used to achieve highly accurate predictions for diagnosis. Among the methods, MGGP stands out with a prediction accuracy of 100%. The results highlight the potential of AP methods in modeling complex nonlinear relationships in COPD data and identifying key features that influence the diagnosis of the disease. In addition, the effectiveness and efficiency of AP methods suggest that they can contribute to the development of early diagnosis and treatment strategies. © 2024 IEEE.Öğe Feature Selection Using Automatic Programming Methods in Hypertension Risk Prediction(Institute of Electrical and Electronics Engineers Inc., 2024) Yagmurcu, Merve; Arslan, SibelHypertension is a condition where the pressure in the blood vessels is higher than normal. It can lead to serious problems such as heart attack, stroke, heart failure, kidney disease and vision problems. Therefore, early diagnosis and treatment is important to find appropriate treatment strategies for the disease. In this study, automatic programming (AP) methods, were used and compared to analyze the risk of hypertension. These methods are Artificial Bee Colony Programming developed from the behavior of honeybees, Genetic Programming (GP) inspired by genetic selection and Immune Plasma Programming (IPP) based on immune plasma therapy. According to the performance evaluations obtained from the methods, GP and IPP were the most successful methods with test success rates of 0.91% and 0.89% respectively. In future research, Due to the success of the AP methods, we aim to develop different versions for health problems. © 2024 IEEE.Öğe Immune Plasma Programming: A new evolutionary computation-based automatic programming method(Elsevier, 2024) Arslan, SibelImmune plasma therapy, one of the treatment modalities, has proven effective in combating the now rapidly spreading COVID-19 and many other pandemics. The immune plasma algorithm (IPA), inspired by the application phases of this treatment modality, is a recently proposed metaheuristic algorithm. Since its introduction, it has achieved promising results in engineering applications. In this paper, we propose for the first time immune plasma programming (IPP) based on the structure of IPA as a new evolutionary computation -based automatic programming (AP) method. It is compared with well-known AP methods such as artificial bee colony programming, genetic programming, and cartesian ant programming using symbolic regression test problems. It is also compared with baseline methods, many of which are based on recurrent neural networks and a real-word problem is solved. The control parameters of IPP are also tuned separately. The results of the experiments and statistical tests have shown that the prediction accuracy and convergence speed of the models produced by IPP are high. Therefore, IPP has been proposed as a method that can be used to solve various problems.Öğe Investigating the best automatic programming method in predicting the aerodynamic characteristics of wind turbine blade(Pergamon-Elsevier Science Ltd, 2023) Arslan, Sibel; Koca, KemalAutomatic programming (AP) is a subfield of artificial intelligence (AI) that can automatically generate computer programs and solve complex engineering problems. This paper presents the accuracy of four different AP methods in predicting the aerodynamic coefficients and power efficiency of the AH 93-W-145 wind turbine blade at different Reynolds numbers and angles of attack. For the first time in the literature, Genetic Programming (GP) and Artificial Bee Colony Programming (ABCP) methods are used for such predictions. In addition, Airfoil Tools and JavaFoil are utilized for airfoil selection and dataset generation. The Reynolds number and angle of attack of the wind turbine airfoil are input parameters, while the coefficients CL, CD and power efficiency are output parameters. The results show that while all four methods tested in the study accurately predict the aerodynamic coefficients, Multi Gene GP (MGGP) method achieves the highest accuracy for R2Train and R2Test (R2 values in CD Train: 0.997-Test: 0.994, in CL Train: 0.991-Test: 0.990, in PE Train: 0.990-Test: 0.970). By providing the most precise model for properly predicting the aerodynamic performance of higher cambered wind turbine airfoils, this innovative and comprehensive study will close a research gap. This will make a significant contribution to the field of AI and aerodynamics research without experimental cost, labor, and additional time.Öğe Investigation and Optimization of Biosorbent Capacities of Some Plants Used in Daily Life(2024) Arslan, Sibel; Kütük, NurşahIn this study, sage, chamomile, and tarragon leaves, which are used as spices and consumed as beverages in daily life, were considered as different biosorbents that can be used in water purification by biosorption. At the same time, the effects of the parameters of initial dye concentration (10-200 mg/L), temperature (20-50 ⁰C) and contact time (0-120 min) on biosorption capacity were investigated. The biosorption processes were found to follow Freundlich isotherm and pseudo-second order (PSO) reaction kinetics. In the study, the process was also modeled using multi-tree evolutionary computation based automatic programming (AP) methods. The methods used initial dye concentration, temperature, and contact time as variables. According to the simulation results, these methods obtained nonlinear mathematical models of the processes with R^2 values as high as 0.99 for each biosorbent. By providing the most accurate models to accurately predict biosorption capacity, this study will make a significant contribution to the field of water treatment using experimental and AP methods.Öğe Mackey-Glass Time Series Prediction with Immune Plasma Programming(IEEE, 2023) Gul, Muhammed Furkan; Arslan, SibelAutomatic Programming (AP) is one of the subfields of artificial intelligence that enables efficient modeling of systems. Immune Plasma Programming (IPP), one of the newly proposed AP methods, is developed taking inspiration from plasma treatment. In this study, mathematical models using IPP for time series prediction are proposed. It is also compared with wellknown AP methods such as Genetic Programming and Artificial Bee Colony Programming. According to the simulation results, IPP has proven that it can be applied to real-world problems by showing superior performance on various performance criteria compared to other methods.Öğe A modified artificial bee colony algorithm for classification optimisation(INDERSCIENCE ENTERPRISES LTDWORLD TRADE CENTER BLDG, 29 ROUTE DE PRE-BOIS, CASE POSTALE 856, CH-1215 GENEVA, SWITZERLAND, 18.10.2022) Aslan, Selçuk; Arslan, SibelThe promising capabilities, easily implementable and customisable structures of the meta-heuristic algorithms have increased the researchers’ attentions to the well-known problems and their new approximations that are suitable to be solved with the meta-heuristics directly. In this study, an attempt was made to solve with an artificial bee colony (ABC)-based technique called classifierABC algorithm, a new approximation that defines the classification problem by using a set of linear equations. The performance of the classifierABC was investigated in detail by using various datasets and assigning different values to the algorithm specific control parameters. The results obtained by the classifierABC algorithm were also compared with the results of the other meta-heuristics including particle swarm optimisation (PSO), differential evaluation (DE), fireworks algorithm (FWA) and different variants of the FWA. Comparative studies showed that the classifierABC solves the new problem approximation more robustly and its solutions determine the classes of instances in sets with high accuracies.Öğe Solving Channel Assignment Problem in Cognitive Radio Networks with Immune Plasma Algorithm(Institute of Electrical and Electronics Engineers Inc., 2021) Kisa, Murat; Demirci, Sercan; Arslan, Sibel; Asian, SelçukThe new Coronavirus or COVID-19 pandemic has focused researchers from various disciplines including computer sciences on existing diagnosis and treatment methods. As a result of this increasing interest, Immune Plasma algorithm (IP algorithm or IPA) that is a new meta-heuristic referencing a treatment method called immune or convalescent plasma has been introduced recently. In this study, IP algorithm was modified by considering the channel assignment problem on cognitive networks and its performance was investigated on solving mentioned problem. Moreover, the results of the IPA based technique were compared with the results of the Brute force search. Comparative studies showed that IP algorithm is capable of obtaining better solutions than the Brute force search. © 2021 IEEEÖğe Stress Analysis of 2D-FG Rectangular Plates with Multi-Gene Genetic Programming(MDPIST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND, 16.08.2022) Arslan, Sibel; Demirbas, Munise Didem; Çakır, Didem; Ozturk,CelalFunctionally Graded Materials (FGMs) are designed for use in high-temperature applications. Since the mass production of FGM has not yet been made, the determination of its thermomechanical limits depends on the compositional gradient exponent value. In this study, an efficient working model is created for the thermal stress problem of the 2D-FG plate using Multi-gene Genetic Programming (MGGP). In our MGGP model in this study, data sets obtained from the numerical analysis results of the thermal stress problem are used, and formulas that give equivalent stress levels as output data, with the input data being the compositional gradient exponent, are obtained. For the current problem, efficient models that reduce CPU processing time are obtained by using the MGGP method.
