Bilgisayar ve Bilişim Fakültesi Koleksiyonu

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

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Citation - WoS: 3
Citation - Scopus: 6
Analysis of Machine Learning Classification Approaches for Predicting Students' Programming Aptitude
(MDPI, 2023) Çetinkaya, Ali; Baykan, Ömer Kaan; Kırgız, Havva
With the increasing prevalence and significance of computer programming, a crucial challenge that lies ahead of teachers and parents is to identify students adept at computer programming and direct them to relevant programming fields. As most studies on students' coding abilities focus on elementary, high school, and university students in developed countries, we aimed to determine the coding abilities of middle school students in Turkey. We first administered a three-part spatial test to 600 secondary school students, of whom 400 completed the survey and the 20-level Classic Maze course on Code.org. We then employed four machine learning (ML) algorithms, namely, support vector machine (SVM), decision tree, k-nearest neighbor, and quadratic discriminant to classify the coding abilities of these students using spatial test and Code.org platform data. SVM yielded the most accurate results and can thus be considered a suitable ML technique to determine the coding abilities of participants. This article promotes quality education and coding skills for workforce development and sustainable industrialization, aligned with the United Nations Sustainable Development Goals.
Citation - WoS: 7
Citation - Scopus: 9
Bindmo: a New Binary Dwarf Mongoose Optimization Algorithm on Based Z-Shaped, U-Shaped, and Taper-Shaped Transfer Functions for Cec-2017 Benchmarks
(Springer Science and Business Media Deutschland GmbH, 2024) Baş, Emine
Intelligent swarm optimization algorithms have become increasingly common due to their success in solving real-world problems. Dwarf Mongoose Optimization (DMO) algorithm is a newly proposed intelligent swarm optimization algorithm in recent years. It was developed for continuous optimization problem solutions in its original paper. But real-world problems are not always problems that take continuously variable values. Real-world problems are often problems with discrete variables. Therefore, heuristic algorithms proposed for continuous optimization problems need to be updated to solve discrete optimization problems. In this study, DMO has been updated for binary optimization problems and the Binary DMO (BinDMO) algorithm has been proposed. In binary optimization, the search space consists of binary variable values. Transfer functions are often used in the conversion of continuous variable values to binary variable values. In this study, twelve different transfer functions were used (four Z-shaped, four U-shaped, and four Taper-shaped). Thus, twelve different BinDMO variations were obtained (BinDMO1, BinDMO2, …, BinDMO12). The achievements of BinDMO variations were tested on thirteen different unimodal and multimodal classical benchmark functions. The effectiveness of population sizes on the effectiveness of BinDMO was also investigated. When the results were examined, it was determined that the most successful BinDMO variation was BinDMO1 (with Z1-shaped transfer function). The most successful BinDMO variation was compared with three different binary heuristic algorithms selected from the literature (SO, PDO, and AFT) on CEC-2017 benchmark functions. According to the average results, BinDMO was the most successful binary heuristic algorithm. This has proven that BinDMO can be chosen as an alternative algorithm for binary optimization problems. © The Author(s) 2024.
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: 12
Chaotic Artificial Algae Algorithm for Solving Global Optimization With Real-World Space Trajectory Design Problems
(Springer Heidelberg, 2025) Turkoğlu, Bahaeddin; Uymaz, Sait Ali; Kaya, Ersin
The artificial algae algorithm (AAA) is a recently introduced metaheuristic algorithm inspired by the behavior and characteristics of microalgae. Like other metaheuristic algorithms, AAA faces challenges such as local optima and premature convergence. Various strategies to address these issues and enhance the performance of the algorithm have been proposed in the literature. These include levy flight, local search, variable search, intelligent search, multi-agent systems, and quantum behaviors. This paper introduces chaos theory as a strategy to improve AAA's performance. Chaotic maps are utilized to effectively balance exploration and exploitation, prevent premature convergence, and avoid local minima. Ten popular chaotic maps are employed to enhance AAA's performance, resulting in the chaotic artificial algae algorithm (CAAA). CAAA's performance is evaluated on thirty benchmark test functions, including unimodal, multimodal, and fixed dimension problems. The algorithm is also tested on three classical engineering problems and eight space trajectory design problems at the European Space Agency. A statistical analysis using the Friedman and Wilcoxon tests confirms that CAA demonstrates successful performance in optimization problems.
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: 3
Citation - Scopus: 3
Classification of Physiological Disorders in Apples Using Deep Convolutional Neural Network Under Different Lighting Conditions
(Springer, 2023) Büyükarıkan, Birkan; Ülker, Erkan
Non-destructive testing of apple fruit, an important product in the world fresh fruit trade, according to physiological disorders, can be done with a computer vision system. However, in the vision system, images may be affected by the brightness values created by different lighting conditions. For this reason, it is a necessity to use algorithms that accurately and quickly detect physiological disorders. By using a convolutional neural network (CNN), an algorithm that enables easy extraction of features from images, determining physiological disorders becomes easier. This study aims to classify the images of apples with physiological disorders obtained under different lighting conditions with CNN models. This study created a dataset (images of different light colors, angles, and distances) with some physiological disorder images. A 5-fold cross-validation method was applied to improve the generalization ability of the models, and CNN models were trained end-to-end. In addition, the Friedman hypothesis test and post-hoc Nemenyi test were performed to compare the evaluation indicators of different CNN models. The average accuracy, precision, recall, and F1-score of the Xception model were 0.996, 0.994, 0.996, and 0.998, respectively. The classification accuracy of this model is followed by the ResNet101, MobileNet, ResNet152, ResNet18, ResNet34, ResNet50, EfficientNetB0, AlexNet, VGG16, and VGG19. Finally, Xception performed well, according to Friedman/Nemenyi test results.
Citation - WoS: 5
Citation - Scopus: 7
Convolutional Neural Network-Based Apple Images Classification and Image Quality Measurement by Light Colors Using the Color-Balancing Approach
(Springer, 2023) Büyükarıkan, Birkan; Ülker, Erkan
The appearance of an object is affected by the color and quality of the light on the surface and the location of the lighting source. Color-balancing methods can solve the problems caused by light changes. Color-balancing models increase the visibility of the image by changing color and clarity. The study aims to examine the images of physiological disorders in apples' classification performances of images in different light colors with color-balancing models with pre-trained CNN models. Physiological disorders were classified with 0.949 accuracies in the ResNet50V2 model and sharpness data set in the green light color. With the proposed approaches, there was an increase in performance compared to the original data set. The best success in all light colors is in the sharpness data set type. In addition, the quality of the images was measured using MSE, PSNR, and SSIM. PSNR increased in the warm and cold white sharpness data set type and green light CLAHE data set type. Finally, experimental studies have shown that color balancing significantly affects classification success.
Citation - Scopus: 4
Enhanced Pyramidal Residual Networks for Single Image Super-Resolution
(Springer Science and Business Media Deutschland GmbH, 2024) Babaoğlu, İ.; Kahveci, S.; Kılıç, A.
Several super-resolution (SR) techniques are introduced in the literature, including traditional and machine learning-based algorithms. Especially, deep learning-based SR approaches emerge with demands for better quality images providing deeper subpixel enhancement. Dealing with the image enhancement task in the satellite images domain, a new SR method for single image SR, namely Enhanced Deep Pyramidal Residual Networks, is introduced in this study. The proposed method overcomes the potential instability problem of Enhanced Deep Residual Networks for Single Image Super-Resolution (EDSR) approach by gradually increasing the feature maps depending upon Pyramidal Residual Networks architecture. The EDSR itself is a good algorithm in the SR domain. However, it has a strict structure for increasing the block size. To overcome this problem with the aim of increasing the algorithm’s performance, the pyramidal residual networks gradually increasing hypothesis is utilized in the proposed approach, which is the main contribution and novelty of this study. Besides, by using the pyramidal residual networks gradually increasing hypothesis in the proposed approach, the parameter size of the models is also reduced, which affects the computational time. Two different models are proposed by considering addition and multiplication manners, and the proposed models are evaluated using well-known remote sensing datasets NWPU-RESISC45 and UC Merced. The results obtained by the proposed model are compared with the results of traditional image enhancement algorithms together with the EDSR itself, EDSR with deeper structure, Super-Resolution Generative Adversarial Networks approach, and Residual Local Feature Networks approach in terms of peak signal to noise ratio (PSNR) and structural similarity index measure (SSIM) metrics and showed that the proposed models present better quality images. Moreover, considering the computational time and complexity, it is shown that some proposed models achieve approximately 27% less output parameter having similar PSNR and SSIM values and computational time for EDSR itself and 65% less output parameter having better PSNR and SSIM values and 16% lower computational time for EDSR with deeper structure. © The Author(s) 2024.
Citation - WoS: 1
Citation - Scopus: 1
Enhancing Classification Accuracy Through Feature Extraction: a Comparative Study of Discretization and Clustering Approaches on Sensor-Based Datasets
(Springer London Ltd, 2023) Esme, Engin
Accuracy in a classification problem is directly related to the ability of features to adequately represent the differences between classes. In sensor-based datasets, measurements taken from the sensor form feature vectors. Measuring a given physical signal with different sensors enables it to be expressed with various feature vectors. For this reason, using sensor fusion is preferred in data acquisition. However, each new sensor added to the system brings problems such as complex sensory and supply circuit structures, extra energy consumption, signal sampling complexity, and time-consumption. On the other hand, in cases where sensor fusion cannot be applied, the ability of data from one sensor to represent classes may be insufficient. To avoid these problems, discretization and clustering approaches are suitable to derive more features from fewer sensors. The aim is to improve the accuracy of classifiers by deriving new feature vectors that can represent sensor data. This research reveals the contributions of clustering and discretization approaches as feature extraction methods to improve classification accuracy. In this study, three widely used machine learning techniques are investigated on Perfume, Wine, Seeds, and Gas datasets from the UCI repository. This comprehensive empirical study indicates that the accuracy of classifiers improves by up to 20% on datasets obtained from some sensors by using both discretization and clustering as feature-extracting methods.
Citation - WoS: 4
Citation - Scopus: 6
Enhancing Signer-Independent Recognition of Isolated Sign Language Through Advanced Deep Learning Techniques and Feature Fusion
(MDPI, 2024) Akdağ, Ali; Baykan, Ömer Kaan
Sign Language Recognition (SLR) systems are crucial bridges facilitating communication between deaf or hard-of-hearing individuals and the hearing world. Existing SLR technologies, while advancing, often grapple with challenges such as accurately capturing the dynamic and complex nature of sign language, which includes both manual and non-manual elements like facial expressions and body movements. These systems sometimes fall short in environments with different backgrounds or lighting conditions, hindering their practical applicability and robustness. This study introduces an innovative approach to isolated sign language word recognition using a novel deep learning model that combines the strengths of both residual three-dimensional (R3D) and temporally separated (R(2+1)D) convolutional blocks. The R3(2+1)D-SLR network model demonstrates a superior ability to capture the intricate spatial and temporal features crucial for accurate sign recognition. Our system combines data from the signer's body, hands, and face, extracted using the R3(2+1)D-SLR model, and employs a Support Vector Machine (SVM) for classification. It demonstrates remarkable improvements in accuracy and robustness across various backgrounds by utilizing pose data over RGB data. With this pose-based approach, our proposed system achieved 94.52% and 98.53% test accuracy in signer-independent evaluations on the BosphorusSign22k-general and LSA64 datasets.
Citation - WoS: 14
Citation - Scopus: 15
Experimental and Numerical Investigation of Rc Column Strengthening With Cfrp Strips Subjected To Low-Velocity Impact Load
(TECHNO-PRESS, 2021) Mercimek, Ömer; Anıl, Özgür; Ghoroubi, Rahim; Sakin, Shaimaa; Yılmaz, Tolga
Reinforced concrete (RC) square columns are vulnerable to sudden dynamic impact loadings such as the vehicle crash to the bridges of highway or seaway, rock fall, the collision of masses with the effect of flood and landslide. In this experimental study RC square columns strengthened with and without CFRP strip subjected to sudden low velocity lateral impact loading were investigated. Drop-hammer testing machine was used to apply the impact loading to RC square columns. The test specimens were manufactured with square cross sections with 1/3 geometric scale. In scope of the study, 6 test specimens were manufactured and tested. The main variables considered in the study were the application point of impact loading, and CFRP strip spacing. A 9.0 kg mass was allowed to fall freely from a height of 1.0 m to apply the impact loading on the columns. During the impact tests, accelerations, impact force, column mid-point displacements and CFRP strip strains measurements were taken. The general behavior of test specimens, collapse mechanisms, acceleration, displacement, impact load and strain time relationships were interpreted, and the load displacement relationships were obtained. The data from the experimental study was used to investigate the effect of variables on the impact performances of RC columns. It has been observed that the strengthening method applied to reinforced concrete columns, which are designed with insufficient shear strength, insufficient shear reinforcement and produced with low strength concrete, using CFRP strips significantly improves the behavior of the columns under the effect of sudden dynamic impact loading and increases their performance. As a result of the increase in the hardness and rigidity of the specimens strengthened by wrapping with CFRP strips, the accelerations due to the impact loading increased, the displacements decreased and the number of shear cracks formed decreased and the damage was limited. Moreover, the finite element analyses of tested specimens were performed using ABAQUS software to further investigate the impact behavior.
Citation - WoS: 4
Citation - Scopus: 5
Identification of Full-Night Sleep Parameters Using Morphological Features of Ecg Signals: a Practical Alternative To Eeg and Eog Signals
(Elsevier Sci Ltd, 2024) Yücelbaş, Şule; Yücelbaş, Cüneyt; Tezel, Gülay; Özsen, Seral; Yosunkaya, Şebnem
Electroencephalogram (EEG) signals, which are among the most important recordings used in Polysomnography for sleep staging, are more challenging and demanding than electrocardiography (ECG) signals, both in terms of acquisition and interpretation. When examining the studies of other researchers on sleep parameters in the literature, it is evident that EEG signals are predominantly used for determining arousal (AR), K-complex (Kc), and sleep spindle (Ss) parameters. Furthermore, it is understood that electrooculography (EOG) signals are employed for detecting slow eye movements (SEM) and rapid eye movements (REM) parameters.This study is a continuation of our previous research, where we used only EEG signals for Kc and Ss detection. In this study, an approach that includes ECG signals in the determination of sleep parameters to bring practicality to sleep staging studies was adopted. For this purpose, firstly, 16 morphological features were extracted from ECG recordings taken from a total of 24 subjects after various preprocessing steps. Subsequently, these data were used to work on the detection of five different sleep parameters: AR, Kc, Ss, SEM, and REM, using the Random Subspace (RaSE) ensemble learning algorithm. The results were calculated according to various statistical criteria and a classification accuracy of over 78 % was obtained in all parameters. As a result, the sleep parameters that could be determined most successfully using the ECG signal were SEM and arousal, respectively. In addition, feature elimination was performed for these datasets using Symmetric Uncertainty (SU) ranking. As a result of the reclassification process using 9 and 12 features, the effectiveness of which was determined for both datasets, respectively, significant increases were observed in the performance outputs. Experimental results have shown that ECG signals can be used as an alternative to EEG and EOG signals in the determination of full-night sleep parameters.
Citation - WoS: 6
Citation - Scopus: 7
Improving Efficiency in Convolutional Neural Networks With 3d Image Filters
(Elsevier Sci Ltd, 2022) Uyar, Kübra; Taşdemir, Şakir; Ülker, Erkan; Ünlükal, Nejat; Solmaz, Merve
Background and objective: The effective performance of deep networks has provided the solution to various stateof-the-art problems. Convolutional Neural Network (CNN) is accepted as an accurate, effective, and reliable practice in image-based applications. However, there is a need to use pre-trained models in case of insufficient data in CNN. This study aims to present an alternative solution to this problem with the proposed 3D image based filter generation approach with simpler CNNs for the classification of small datasets. Methods: In this study, a novel 3D image filters-based CNN (Hist3DCNN) is proposed. The proposed filter generation approach is based on 3D object images taken from different perspectives. The efficiency of Hist3DCNN is shown on a novel histological dataset that contains blood, connective, epithelium, muscle, and nerve tissue images. Various case studies are carried out with generated filters assigned as the initial value to AlexNet and the designed Hist3DCNN model that is simpler than AlexNet. Results: Based on results, the classification accuracy of AlexNet with proposed filters used in convolution layers were 84.65% and 85.34%. The accuracy was increased to 85.47% by Hist3DCNN on the histological image classification. Moreover, four different benchmark datasets were tested to demonstrate the robustness of Hist3DCNN on various datasets. Conclusions: This study provides a new aspect to literature due to 3D image-based filter generation approach to initialize convolution filters. Experimental results validate that Hist3DCNN can be used as a filter value initialization method with simple CNN models that contain less learnable parameters for the classification task of small datasets.
Citation - WoS: 2
Citation - Scopus: 2
Isolated Sign Language Recognition Through Integrating Pose Data and Motion History Images
(Peerj Inc, 2024) Akdağ, Ali; Baykan, Ömer Kaan
This article presents an innovative approach for the task of isolated sign language recognition (SLR); this approach centers on the integration of pose data with motion history images (MHIs) derived from these data. Our research combines spatial information obtained from body, hand, and face poses with the comprehensive details provided by three-channel MHI data concerning the temporal dynamics of the sign. Particularly, our developed finger pose-based MHI (FP-MHI) feature significantly enhances the recognition success, capturing the nuances of finger movements and gestures, unlike existing approaches in SLR. This feature improves the accuracy and reliability of SLR systems by more accurately capturing the fine details and richness of sign language. Additionally, we enhance the overall model accuracy by predicting missing pose data through linear interpolation. Our study, based on the randomized leaky rectified linear unit (RReLU) enhanced ResNet-18 model, successfully handles the interaction between manual and non-manual features through the fusion of extracted features and classification with a support vector machine (SVM). This innovative integration demonstrates competitive and superior results compared to current methodologies in the field of SLR across various datasets, including BosphorusSign22k-general, BosphorusSign22k, LSA64, and GSL, in our experiments.
Citation - WoS: 30
Citation - Scopus: 33
Lstm and Filter Based Comparison Analysis for Indoor Global Localization in Uavs
(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2021) Yusefi, Abdullah; Durdu, Akif; Aslan, Muhammet Fatih; Sungur, Cemil
Deep learning (DL) based localization and Simultaneous Localization and Mapping (SLAM) has recently gained considerable attention demonstrating remarkable results. Instead of constructing hand-crafted algorithms through geometric theories, DL based solutions provide a data-driven solution to the problem. Taking advantage of large amounts of training data and computing capacity, these approaches are increasingly developing into a new field that offers accurate and robust localization systems. In this work, the problem of global localization for unmanned aerial vehicles (UAVs) is analyzed by proposing a sequential, end-to-end, and multimodal deep neural network based monocular visual-inertial localization framework. More specifically, the proposed neural network architecture is three-fold; a visual feature extractor convNet network, a small IMU integrator bi-directional long short-term memory (LSTM), and a global pose regressor bi-directional LSTM network for pose estimation. In addition, by fusing the traditional IMU filtering methods instead of LSTM with the convNet, a more time-efficient deep pose estimation framework is presented. It is worth pointing out that the focus in this study is to evaluate the precision and efficiency of visual-inertial (VI) based localization approaches concerning indoor scenarios. The proposed deep global localization is compared with the various state-of-the-art algorithms on indoor UAV datasets, simulation environments and real-world drone experiments in terms of accuracy and time-efficiency. In addition, the comparison of IMU-LSTM and IMU-Filter based pose estimators is also provided by a detailed analysis. Experimental results show that the proposed filter-based approach combined with a DL approach has promising performance in terms of accuracy and time efficiency in indoor localization of UAVs.
Moaaa/D: a Decomposition-Based Novel Algorithm and a Structural Design Application
(Springer Science and Business Media Deutschland GmbH, 2024) Altiok, M.; Gündüz, M.
When real-world engineering challenges are examined adequately, it becomes clear that multi-objective need to be optimized. Many engineering problems have been handled utilizing the decomposition-based optimization approach according to the literature. The performance of multi-objective evolutionary algorithms is highly dependent on the balance of convergence and diversity. Diversity and convergence are not appropriately balanced in the decomposition technique, as they are in many approaches, for real-world problems. A novel Multi-Objective Artificial Algae Algorithm based on Decomposition (MOAAA/D) is proposed in the paper to solve multi-objective structural problems. MOAAA/D is the first multi-objective algorithm that uses the decomposition-based method with the artificial algae algorithm. MOAAA/D, which successfully draws a graph on 24 benchmark functions within the area of two common metrics, also produced promising results in the structural design problem to which it was applied. To facilitate the design of the "rectangular reinforced concrete column" using MOAAA/D, a solution space was derived by optimizing the rebar ratio and the concrete quantity to be employed. © The Author(s) 2024.
Citation - WoS: 4
Citation - Scopus: 6
Modified Coot Bird Optimization Algorithm for Solving Community Detection Problem in Social Networks
(Springer London Ltd, 2024) Aslan, Murat; Koç, İsmail
Community detection (CD) is a powerful way to extract meaningful information from networks such as political election networks, biological networks, social networks, technological networks. This study proposes a modified discrete version of Coot bird natural life model (COOT) optimization algorithm to solve CD problem in the networks. The basic COOT method is based on the different collective behaviors of the birds of the coot family. These collective actions of coots are regular and irregular movements on the water surface. The position update rule of the basic COOT method does not provide a balance between exploitation and exploration ability for the problem addressed in this study. Therefore, a new update mechanism is integrated into the basic COOT method to extend the local and global search tendencies of the basic COOT method. In the proposed COOT method (for short MCOOT), in order to create a new position for the current coot individual, first the original update mechanism of COOT method is carried out; then, the proposed update mechanism is executed. Three important modifications have been made in the new update mechanism: (1) Some dimensions of the current coot individual are randomly selected in the range of 1 to the dimension size of the problem; (2) the selected dimensions of the coot individual are updated according to the proposed update rule; (3) a genetic mutation operator is executed on the current coot position according to a mutation probability to improve the exploration ability. Furthermore, in the proposed MCOOT method, the continuous values of the current coot positions are converted to discrete values, because the CD problem is a discrete problem. Based on these modifications, in order to analyze and validate the effectiveness of the proposed MCOOT, it is applied on ten different small-sized or large-sized network problems. Finally, the experimental results of MCOOT method are compared with those of some state-of-the-art optimization methods in terms of solution quality and time evaluation. According to the experiments of our study, the proposed algorithm is obtained the best results for all community detection problems used in this study when compared with 22 other algorithms. As a result, the proposed method achieves superior or comparable performance in terms of solution quality and robustness according to the general results. Therefore, the proposed method can be much more competitive, especially for discrete problems.
Citation - WoS: 7
Citation - Scopus: 11
A Multi-Objective Genetic Algorithm for the Hot Mix Asphalt Problem
(Springer London Ltd, 2022) Altıok, Mustafa; Alakara, Erdinç Halis; Gündüz, Mesut; Ağaoğlu, Melih Naci
It is desirable for the work done in any construction process to be both cost-effective and durable. A thorough consideration of the matter reveals that the optimization of real-world problems involves multiple objectives. Bituminous hot mixtures, which are widely used in motorway construction, consist of aggregate and bitumen. The ratio between the different types of aggregate and bitumen forms the input to the real-world problem defined in this article, and the results of a test of the obtained asphalt in three different fields form the output. Our aim is to optimize these three outputs simultaneously to obtain a solution space with the most appropriate inputs. To optimize this problem, a new multi-objective optimization approach is proposed and tested in various ways and is finally adapted to the hot mix asphalt problem. Since the mathematical model of the objective function for this problem is fairly difficult, a fuzzy logic expert system is developed to act as the objective function. We believe that our approach to solving complex problems such as these forms a significant contribution to the literature.
Citation - WoS: 5
Citation - Scopus: 14
Multi-Stream Isolated Sign Language Recognition Based on Finger Features Derived From Pose Data
(MDPI, 2024) Akdag, Ali; Baykan, Ömer Kaan
This study introduces an innovative multichannel approach that focuses on the features and configurations of fingers in isolated sign language recognition. The foundation of this approach is based on three different types of data, derived from finger pose data obtained using MediaPipe and processed in separate channels. Using these multichannel data, we trained the proposed MultiChannel-MobileNetV2 model to provide a detailed analysis of finger movements. In our study, we first subject the features extracted from all trained models to dimensionality reduction using Principal Component Analysis. Subsequently, we combine these processed features for classification using a Support Vector Machine. Furthermore, our proposed method includes processing body and facial information using MobileNetV2. Our final proposed sign language recognition method has achieved remarkable accuracy rates of 97.15%, 95.13%, 99.78%, and 95.37% on the BosphorusSign22k-general, BosphorusSign22k, LSA64, and GSL datasets, respectively. These results underscore the generalizability and adaptability of the proposed method, proving its competitive edge over existing studies in the literature.
Citation - WoS: 2
Citation - Scopus: 6
A New Binary Arithmetic Optimization Algorithm for Uncapacitated Facility Location Problem
(Springer Science and Business Media Deutschland GmbH, 2023) Baş, Emine; Yildizdan, G.
Arithmetic Optimization Algorithm (AOA) is a heuristic method developed in recent years. The original version was developed for continuous optimization problems. Its success in binary optimization problems has not yet been sufficiently tested. In this paper, the binary form of AOA (BinAOA) has been proposed. In addition, the candidate solution production scene of BinAOA is developed with the xor logic gate and the BinAOAX method was proposed. Both methods have been tested for success on well-known uncapacitated facility location problems (UFLPs) in the literature. The UFL problem is a binary optimization problem whose optimum results are known. In this study, the success of BinAOA and BinAOAX on UFLP was demonstrated for the first time. The results of BinAOA and BinAOAX methods were compared and discussed according to best, worst, mean, standard deviation, and gap values. The results of BinAOA and BinAOAX on UFLP are compared with binary heuristic methods used in the literature (TSA, JayaX, ISS, BinSSA, etc.). As a second application, the performances of BinAOA and BinAOAX algorithms are also tested on classical benchmark functions. The binary forms of AOA, AOAX, Jaya, Tree Seed Algorithm (TSA), and Gray Wolf Optimization (GWO) algorithms were compared in different candidate generation scenarios. The results showed that the binary form of AOA is successful and can be preferred as an alternative binary heuristic method. © 2023, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.

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