24 research outputs found
Novel Hybrid Scaffolds for the Cultivation of Osteoblast Cells
No full textTurkoglu Sasmazel, Hilal/0000-0002-0254-4541In this study, natural biodegradable polysaccharide, chitosan, and synthetic biodegradable polymer, poly(epsilon-caprolactone) (PCL) were used to prepare 3D, hybrid polymeric tissue scaffolds (PCL/chitosan blend and PCL/chitosan/PCL layer by layer scaffolds) by using the electrospinning technique. The hybrid scaffolds were developed through HA addition to accelerate osteoblast cell growth. Characteristic examinations of the scaffolds were performed by micrometer, SEM, contact angle measurement system, ATR-FTIR, tensile machine and swelling experiments. The thickness of all electrospun scaffolds was determined in the range of 0.010 +/- 0.001-0.012 +/- 0.002 mm. In order to optimize electrospinning processes, suitable bead-free and uniform scaffolds were selected by using SEM images. Blending of PCL with chitosan resulted in better hydrophilicity for the PCL/chitosan scaffolds. The characteristic peaks of PCL and chitosan in the blend and layer by layer nanofibers were observed. The PCL/chitosan/PCL layer by layer structure had higher elastic modulus and tensile strength values than both individual PCL and chitosan structures. The layer by layer scaffolds exhibited the PBS absorption values of 184.2; 197.2% which were higher than those of PCL scaffolds but lower than those of PCL/chitosan blend scaffolds. SaOs-2 osteosarcoma cell culture studies showed that the highest ALP activities belonged to novel PCL/chitosan/PCL layer by layer scaffolds meaning better cell differentiation on the surfaces. (C) 2011 Elsevier B.V. All rights reserved.Turkish Academy of Science (TUBA) L'Oreal; L'OrealThe author is greatly thankful to Turkish Academy of Science (TUBA) & L'Oreal for honoring this study with the award "Young Women in Science" in Materials Science in 2009. Her special thanks also go to L'Oreal for the precious financial support. The author also appreciates the invaluable contribution of AWAC (Academic Writing Advisory Center) to this study in linguistic terms
Turkish truffles I: 18 new records for Turkey
No full textWOS: 000352486200014We report the first records of 18 truffle species in Turkey. Three belong to the Ascomycota: Elaphomyces leucocarpus, E. muricatus, and Genea sphaerica; and 15 to the Basidiomycota: Alpova corsicus, Gautieria otthii, G. retirugosa, G. trabutii, Hymenogaster citrinus, H. hessei, H. luteus, H. lycoperdineus, Hysterangium clathroides, H. epiroticum, H. fragile, H. nephriticum, Leucogaster tozzianus, Octaviania asterosperma, and Protoglossum aromaticum. We also report new localities within Turkey for Picoa juniperi, P. lefebvrei, Geopora cooperi, Terfezia arenaria, T. claveryi, Tuber aestivum, and T. nitidum in the Ascomycota; and Leucogaster nudus, Hymenogaster thwaitesii, H. vulgaris, and Melanogaster broomeanus in the Basidiomycota.Scientific and Technological Research Council of Turkey projectTurkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK) [T-BAG-111T530, BIDEB-2221]The first author received funding from the Scientific and Technological Research Council of Turkey project number T-BAG-111T530 and BIDEB-2221. We appreciate the help from Abdulkadir Simsek, Ahmet Oksuzoglu, Cemhan Bucak, Coskun Bilgi, Duran Celik, Ekrem Toprak, Esra Er, Fatih Kaya, Gulsum Turkoglu, Idris Sener, Kadir Bazlica, Kadir Ceryan, Mehmet Halil Solak, Mehmet Metin, Mehmet Yucel, Murat Kilic, Mustafa Demir, Mustafa Turuncoglu, Niyazi Ulucoban, Okan Kursun, Osman Coban, Serkan Sevinc, Seyit Ahmet Akay, Tolga Keser, Ugur Demirbilek, Veysel Kodalak, and Yavuzalp Turkoglu in the collection of some of the specimens
HENOS: A Henon Map-Based Chaotic Oversampling Strategy for Imbalanced Data Classification
No full textThe effectiveness of machine learning models is profoundly influenced by the quality and distribution of training data. However, real-world datasets are often highly imbalanced, where conventional classification algorithms tend to favor the majority class, resulting in poor recognition of minority instances. To address this challenge, we propose HENOS, a novel oversampling method that leverages the nonlinear dynamics of the Henon map to synthesize minority class samples in a more structurally diverse and boundary-aware manner. Unlike traditional interpolation-based techniques such as SMOTE, which often fail to capture complex data distributions, HENOS introduces a deterministic yet chaotic mechanism to generate synthetic instances that preserve local data characteristics while enhancing class separability. We conduct comprehensive experiments on 37 benchmark datasets, evaluating HENOS across three distinct classifiers AdaBoost, Naive Bayes, and Artificial Neural Networks (ANN) using Area Under the Curve (AUC) and F1-score metrics. The results, validated by the Friedman statistical test, demonstrate that HENOS consistently outperforms existing oversampling methods with statistically significant gains. These findings highlight the potential of chaos-theoretic principles in tackling data imbalance and open avenues for their integration into advanced learning systems, including deep architectures
A cluster-assisted differential evolution-based hybrid oversampling method for imbalanced datasets
No full textClass imbalance remains a significant challenge in machine learning, leading to biased models that favor the majority class while failing to accurately classify minority instances. Traditional oversampling methods, such as Synthetic Minority Over-sampling Technique (SMOTE) and its variants, often struggle with class overlap, poor decision boundary representation, and noise accumulation. To address these limitations, this study introduces ClusterDEBO, a novel hybrid oversampling method that integrates K-Means clustering with differential evolution (DE) to generate synthetic samples in a more structured and adaptive manner. The proposed method first partitions the minority class into clusters using the silhouette score to determine the optimal number of clusters. Within each cluster, DE-based mutation and crossover operations are applied to generate diverse and well-distributed synthetic samples while preserving the underlying data distribution. Additionally, a selective sampling and noise reduction mechanism is employed to filter out low-impact synthetic samples based on their contribution to classification performance. The effectiveness of ClusterDEBO is evaluated on 44 benchmark datasets using k-Nearest Neighbors (kNN), decision tree (DT), and support vector machines (SVM) as classifiers. The results demonstrate that ClusterDEBO consistently outperforms existing oversampling techniques, leading to improved class separability and enhanced classifier robustness. Moreover, statistical validation using the Friedman test confirms the significance of the improvements, ensuring that the observed gains are not due to random variations. The findings highlight the potential of cluster-assisted differential evolution as a powerful strategy for handling imbalanced datasets.Peer reviewe
A Novel Diversity Guided Galactic Swarm Optimization With Feedback Mechanism
No full textGalactic Swarm Optimization (GSO) is an optimization method inspired by the movements of stars and star clusters in the galaxy. This method aims to find the best solution in two phases using other known optimization methods. The first phase explores the search space, while the second phase tries to refine the best solution. In GSO, the population of the best individuals obtained from the first phase is used as the initial population for the second phase. This process is repeated until the stopping criterion is met. Although the knowledge obtained from the first phase is transferred to the second phase in GSO, there is no knowledge transfer from the second phase to the first phase. In this study, we propose a modification where the knowledge obtained in the second phase is transferred back to the first phase. Additionally, the Particle Swarm Optimization (PSO) method, used as the search method in the original study, has a fast convergence problem, which hinders an effective discovery process in the first phase of GSO. To address this, the proposed diversity-guided modification regulates population diversity and enhances exploration. To demonstrate the performance of the proposed method, twenty-six traditional benchmark functions and three engineering design problems were used. The proposed method was compared with the original GSO and six current optimization methods. The results of the experimental study were analyzed using statistical tests. The experimental results and analyses show that the proposed method performs successfully.</p
Voice Analysis in Dogs with Deep Learning: Development of a Fully Automatic Voice Analysis System for Bioacoustics Studies
No full textExtracting behavioral information from animal sounds has long been a focus of research in bioacoustics, as sound-derived data are crucial for understanding animal behavior and environmental interactions. Traditional methods, which involve manual review of extensive recordings, pose significant challenges. This study proposes an automated system for detecting and classifying animal vocalizations, enhancing efficiency in behavior analysis. The system uses a preprocessing step to segment relevant sound regions from audio recordings, followed by feature extraction using Short-Time Fourier Transform (STFT), Mel-frequency cepstral coefficients (MFCCs), and linear-frequency cepstral coefficients (LFCCs). These features are input into convolutional neural network (CNN) classifiers to evaluate performance. Experimental results demonstrate the effectiveness of different CNN models and feature extraction methods, with AlexNet, DenseNet, EfficientNet, ResNet50, and ResNet152 being evaluated. The system achieves high accuracy in classifying vocal behaviors, such as barking and howling in dogs, providing a robust tool for behavioral analysis. The study highlights the importance of automated systems in bioacoustics research and suggests future improvements using deep learning-based methods for enhanced classification performance
Chaotic golden ratio guided local search for big data optimization
Get PDFBiological systems where order arises from disorder inspires for many metaheuristic optimization techniques. Self-organization and evolution are the common behaviour of chaos and optimization algorithms. Chaos can be defined as an ordered state of disorder that is hypersensitive to initial conditions. Therefore, chaos can help create order out of disorder. In the scope of this work, Golden Ratio Guided Local Search method was improved with inspiration by chaos and named as Chaotic Golden Ratio Guided Local Search (CGRGLS). Chaos is used as a random number generator in the proposed method. The coefficient in the equation for determining adaptive step size was derived from the Singer Chaotic Map. Performance evaluation of the proposed method was done by using CGRGLS in the local search part of MLSHADE-SPA algorithm. The experimental studies carried out with the electroencephalographic signal decomposition-based optimization problems, named as Big Data optimization problem (Big-Opt), introduced at the Congress on Evolutionary Computing Big Data Competition (CEC’2015). Experimental results have shown that the local search method developed using chaotic maps has an effect that increases the performance of the algorithm
HENOS: A Henon Map-Based Chaotic Oversampling Strategy for Imbalanced Data Classification
No full textThe effectiveness of machine learning models is profoundly influenced by the quality and distribution of training data. However, real-world datasets are often highly imbalanced, where conventional classification algorithms tend to favor the majority class, resulting in poor recognition of minority instances. To address this challenge, we propose HENOS, a novel oversampling method that leverages the nonlinear dynamics of the Henon map to synthesize minority class samples in a more structurally diverse and boundary-aware manner. Unlike traditional interpolation-based techniques such as SMOTE, which often fail to capture complex data distributions, HENOS introduces a deterministic yet chaotic mechanism to generate synthetic instances that preserve local data characteristics while enhancing class separability. We conduct comprehensive experiments on 37 benchmark datasets, evaluating HENOS across three distinct classifiers AdaBoost, Naive Bayes, and Artificial Neural Networks (ANN) using Area Under the Curve (AUC) and F1-score metrics. The results, validated by the Friedman statistical test, demonstrate that HENOS consistently outperforms existing oversampling methods with statistically significant gains. These findings highlight the potential of chaos-theoretic principles in tackling data imbalance and open avenues for their integration into advanced learning systems, including deep architectures
A cluster-assisted differential evolution-based hybrid oversampling method for imbalanced datasets
No full textClass imbalance remains a significant challenge in machine learning, leading to biased models that favor the majority class while failing to accurately classify minority instances. Traditional oversampling methods, such as Synthetic Minority Over-sampling Technique (SMOTE) and its variants, often struggle with class overlap, poor decision boundary representation, and noise accumulation. To address these limitations, this study introduces ClusterDEBO, a novel hybrid oversampling method that integrates K-Means clustering with differential evolution (DE) to generate synthetic samples in a more structured and adaptive manner. The proposed method first partitions the minority class into clusters using the silhouette score to determine the optimal number of clusters. Within each cluster, DE-based mutation and crossover operations are applied to generate diverse and well-distributed synthetic samples while preserving the underlying data distribution. Additionally, a selective sampling and noise reduction mechanism is employed to filter out low-impact synthetic samples based on their contribution to classification performance. The effectiveness of ClusterDEBO is evaluated on 44 benchmark datasets using k-Nearest Neighbors (kNN), decision tree (DT), and support vector machines (SVM) as classifiers. The results demonstrate that ClusterDEBO consistently outperforms existing oversampling techniques, leading to improved class separability and enhanced classifier robustness. Moreover, statistical validation using the Friedman test confirms the significance of the improvements, ensuring that the observed gains are not due to random variations. The findings highlight the potential of cluster-assisted differential evolution as a powerful strategy for handling imbalanced datasets.</p
Optimizing Artificial Neural Networks Using Mountain Gazelle Optimizer
Get PDFThe performance of artificial neural networks heavily depends on the optimization of network parameters, specifically weights and biases, during the training process. Effectively adjusting these parameters is essential to minimize the error between predicted and actual outputs. While traditional training algorithms, such as gradient-based methods, have been widely used, they often face challenges like premature convergence and stagnation in local optima. Training an ANN can, therefore, be viewed as an optimization problem, where the goal is to fine-tune parameters to achieve accurate and efficient performance. In this study, we introduce a novel approach to optimizing neural network parameters using the Mountain Gazelle Optimizer (MGO), a nature-inspired metaheuristic algorithm that mimics the social hierarchy and behavioral patterns of wild mountain gazelles. The MGO algorithm leverages its unique features, including hierarchical social structure and adaptive movement strategies, to effectively navigate the complex parameter space of neural networks. The algorithm’s search mechanism integrates four key behavioral strategies: Territorial Solitary Males (TSM) for refining optimal solutions, Maternity Herds (MH) for balancing exploration and exploitation, Bachelor Male Herds (BMH) for global exploration, and Migration to Search for Food (MSF) for introducing randomness to prevent stagnation in local optima. These mechanisms work collaboratively, ensuring a dynamic and balanced optimization process throughout the training phase. To evaluate the effectiveness of the proposed approach, we conducted comprehensive experiments using various classification datasets from the UCI repository. The performance of the MGO-based neural network optimizer was compared with traditional backpropagation and several state-of-the-art optimization algorithms. Experimental results demonstrate that the proposed method exhibits superior performance in terms of convergence speed and avoiding local optima, suggesting that MGO is a promising alternative for optimizing artificial neural networks during training
