14 research outputs found
Bozkırlı Muhammed Bahaeddin efendi ve “Baisü’l-Mağfire Fi Beyani Ekvali’l-Vahde” isimli eseri (metin ve inceleme
No full textBahâeddîn Efendi (1831-1906) IXX. Yüzyılda Konya’da yetişmiş önemli bir simadır. İlmi eğitiminin yanında tasavvufî eğitimini de babası Memiş Efendiden alan Bahâeddîn Efendi Nakşibendî tarikatı şeyhi olmuş ve babasının birinci halifesi olmuştur. Pek çok talebe yetiştirmiş olan Bahâeddîn Efendinin etkileri talebeleri ve oğulları vasıtasıyla hala Konya ve civarında hissedilmektedir. Onun yazma halinde bulunan Bâisü’l-Mağfire fî beyâni ekvâli’l-vahde isimli eseri bizim tezimizin konusunu oluşturmuştur. Biz bu çalışmamızda Bahâeddîn Efendinin bu eserini, üç nüshasını ele alarak önce tahkikini sonra da tercümesini yaptık. Bahâeddîn Efendi bu eserinde İslam düşünce tarihinde en çok tartışılan meselelerden birisi olan vahdet-i vücûd konusunu esas almıştır. Özetle o bu eserinde vahdet-i vücûd görüşünün fenâ mertebesinden kaynaklanan zevkli bir hal olduğunu, meselenin şeriata tatbik edilmesinin ise mümkün olamayacağını delilleriyle anlatır.Bahaeddin Effendi (1831-1906) is an important Sufi figure who lived in Konya. He learned Islamic disciplines and Sufism from his father Memiş Effendi. Afterwards, he became his father’s first caliph as a Naqshbandi sheikh. A prolific teacher with many students, effects of Bahaeddin Effendi is still felt around Konya region. The focus of this study is his work titled as “Baisu’l Magfire fi Beyani Ekvali’l Vahde.” The thesis evaluates three copies of the book. The thesis includes an analysis of the text and its translation. This book of Bahaeddin Effendi is written on one of the much disputed subjects in the history of Islamic thought: Wahdat al-Wujud (unity of being). In sum, the author asserts that the view of Wahdat al-Wujud is a state of spiritual ecstasy, which is experienced at the level of fana, or “annihilation in God.” He explains that the feelings in this special state cannot be generalized to be considered acceptable within general Islamic rules
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
Hulasat Al-Hisap adlı eserin geometri öğretimi açısından incelenmesi ve yeni müfredat ile karşılaştırılması
No full textHULASAT AL-HİSAP ADLI ESERİN GEOMETRİ ÖĞRETİMİ AÇISINDAN İNCELENMESİ VE YENİ MÜFREDAT İLE KARŞILAŞTIRILMASI Bu araştırmada Kuyucaklızade Mehmet Atıf Efendi’nin 1826 yılında tamamlamış ve hazırlamış olduğu, Bahaeddin Amili’ye ait Hulasati’l Hisâb isimli eserin bir Osmanlıca tercümesi olan, “Nihâyetü’l- Elbâb Fî Tercemeti Hulâsati’l- Hisâb” isimli eserin geometri ve cebir anlatan kısmı, transkripsiyonu yapılarak günümüz Türkçe’sine çevirilmiştir. Araştırmanın bu kısmında döküman analizi modeli kullanılmıştır. Bu eser yeni matematik müfredatına göre geometri ve cebir öğretimi açısından incelenerek eserde günümüzden farklı olarak kullanılan kavramlar, yöntemler ve teknikler doküman analizi ile araştırılmıştır. Eserde klasik matematiksel sembollere yer verildiği için yapılan işlemler modern matematik sembolleri kullanılarak yeniden ifade edilmiştir. Eserdeki başlıca kullanılan kelimeler ve matematik terimlerinin anlamları ile ilgili bir sözlük oluşturulmuştur. Hesap, Geometri, Matematik, Problem Çözme, Bahaeddin Amili,THE EXAMINATION OF THE BOOK NAMED HULASAT AL-HİSAP IN TERMS OF TEACHING GEOMETRY AND COMPARING IT WITH THE NEW CURRICULUM In this research, the “Nihâyetü’l- Elbâb Fî Tercemeti Hulâsati’l- Hisâb”, which is an Ottoman Turkish translation -made by Kuyucaklızade Mehmet Efendi in 1826- of Hulasati’l Hisâb by the author Bahaeddin Amili, was translated into current Turkish. The geometry and algebra parts of the book were translated. In that section of the research, document analysis model was used. The book was studied in terms of geometry and algebra with respect to new mathematics curriculum, in addition, concepts, methods and techniques, which are different from new ones, were searched by document analysis. Operations were represented again with the modern mathematical symbols due to the reason that classical mathematical symbols had used in the book. A dictionary, which includes frequently used words in the book and meanings of mathematical terms, was prepared. Key Words: Calculation, Geometry, Mathematic, Problem Solving, Bahaeddin Amil
Chaotic golden ratio guided local search for big data optimization
No full textBiological 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
No full textThe 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
