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Erişime Açık

A Brain MRI Segmentation Method Using Feature Weighting and a Combination of Efficient Visual Features

Taymaz Akan

Determining the area of brain tumors is an essential and fundamental step in automatic diagnosis and treatment systems. The authors present a method based on a combination of efficient visual features and fuzzy c-means clustering to detect brain tumors. For this purpose, first, the background area of the images is removed by the new thresholding method, then the useful and efficient features are extracted. The authors use this new feature space for clustering-based segmentation. The proposed clustering algorithm gives a different importance to the extracted features in the segmentation process ...Daha fazlası

Kitap Bölümü 2023 Applied Computer Vision and Soft Computing with Interpretable AI , pp. 1 - 34 7 Görüntülenme

Süresiz Ambargo

Wave Net-TSRS Model for Time Series Prediction in Finance

Aref Yelghı

In economies, it can be used as a very important money market instrument in terms of ensuring economic stability with central bank reserves and maintaining adequate liquidity. In recent years, researchers have focused on forecasting reverse fluctuation without influencing reserve factors. Interest, inflation, and exchange rate are used as finance economic variables in the estimation of economy reserves because those are influencing factors of reserves held in central banks that can cause their reserves to rise and fall. In the research, the monthly base data of China, Japan, South Korea, India ...Daha fazlası

Kitap Bölümü 2024 Springer Science and Business Media Deutschland GmbH 10 Görüntülenme Süresiz Ambargo

Erişime Açık

Harris Hawks Optimization Method based on Convolutional Neural Network for Face Recognition Systems

Cevat Rahebi

This paper discusses the momentum gradient dependent on the convolutional neural organization’s strong point. It is a new methodology introduced to detect evenness in the data set of faces. The proposed face recognition framework was created for various purposes. Through Gabor wavelet change, facial evenness was extracted from the face-preparing information. After that, we applied a profound learning process to carry out verification. After applying the proposed method to YALE and ORL data sets, we simulated them using MATLAB 2021a. Before this, similar trials were directly applied through Har ...Daha fazlası

Kitap Bölümü 2022 HORA 2022 - 4th International Congress on Human-Computer Interaction, Optimization and Robotic Applications, Proceedings 93 Görüntülenme

Erişime Açık

A cyber defense system against phishing attacks with deep learning game theory and LSTM-CNN with African vulture optimization algorithm (AVOA)

Cevat Rahebi

Phishing attacks pose a significant threat to online security, utilizing fake websites to steal sensitive user information. Deep learning techniques, particularly convolutional neural networks (CNNs), have emerged as promising tools for detecting phishing attacks. However, traditional CNN-based image classification methods face limitations in effectively identifying fake pages. To address this challenge, we propose an image-based coding approach for detecting phishing attacks using a CNN-LSTM hybrid model. This approach combines SMOTE, an enhanced GAN based on the Autoencoder network, and swar ...Daha fazlası

Makale 2024 International Journal of Information Security 21 Görüntülenme

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Comparative Study for Sentiment Analysis of Financial Tweets with Deep Learning Methods

Cevat Rahebi

Nowadays, Twitter is one of the most popular social networking services. People post messages called “tweets”, which may contain photos, videos, links and text. With the vast amount of interaction on Twitter, due to its popularity, analyzing Twitter data is of increasing importance. Tweets related to finance can be important indicators for decision makers if analyzed and interpreted in relation to stock market. Financial tweets containing keywords from the BIST100 index were collected and the tweets were tagged as “POSITIVE”, “NEGATIVE” and “NEUTRAL”. Binary and multi-class datasets were creat ...Daha fazlası

Makale 2024 Applied Sciences14 ( 2 ) 34 Görüntülenme

Erişime Açık

Richards’s curve induced Banach space valued multivariate neural network approximation

Seda Karateke

Here, we present multivariate quantitative approximations of Banach space valued continuous multivariate functions on a box or RN , N ∈ N, by the multivariate normalized, quasi-interpolation, Kantorovichtype and quadrature-type neural network operators. We examine also the case of approximation by iterated operators of the last four types. These approximations are achieved by establishing multidimensional Jackson type inequalities involving the multivariate modulus of continuity of the engaged function or its high-order Fréchet derivatives. Our multivariate operators are defined using a multid ...Daha fazlası

Makale 2022 Arabian Journal of Mathematics12 ( 1 ) , pp. 1 - 33 116 Görüntülenme

Süresiz Ambargo

A long short-term memory (LSTM)-based distributed denial of service (DDoS) detection and defense system design in public cloud network environment

Hakan Aydın

The fact that cloud systems are under the increasing risks of cyber attacks has made the phenomenon of information security first a need and then a necessity for these systems. Distributed Denial of Service (DDoS) attacks can exploit, disrupt, change, prevent or damage cloud services. Accurate and timely detection and prevention of these attacks are very important in terms of ensuring information security. During the COVID-19 period, the increase in the use of information technologies and especially the internet has made cyber attacks a real concern. Deep learning (DL) has become widely used f ...Daha fazlası

Makale 2022 Computers & Security118 214 Görüntülenme Süresiz Ambargo

Erişime Açık

Comparison of Classification Success Rates of Different Machine Learning Algorithms in the Diagnosis of Breast Cancer

Hakan Aydın

Objective: To identify which Machine Learning (ML) algorithms are the most successful in predicting and diagnosing breast cancer according to accuracy rates. Methods: The “College of Wisconsin Breast Cancer Dataset”, which consists of 569 data and 30 features, was classified using Support Vector Machine (SVM), Naive Bayes (NB), Random Forest (RF), Decision Tree (DT), K-Nearest Neighbor (KNN), Logistic Regression (LR), Multilayer Perceptron (MLP), Linear Discriminant Analysis (LDA), XgBoost (XGB), Ada-Boost (ABC) and Gradient Boosting (GBC) ML algorithms. Before the classification process, the ...Daha fazlası

Makale 2022 Asian Pacific Journal of Cancer Prevention ( 10 ) , pp. 3 - 3297 83 Görüntülenme

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An Approach Using in Communication Network Apply in Healthcare System Based on the Deep Learning Autoencoder Classifcation Optimization Metaheuristic Method

Cevat Rahebi

Parkinson’s disease is a neurodegenerative disorder and afects the nerve cells that produce dopamine in the brain. In this paper, we investigated comparative studies on the diferent scenarios such as AutoEncoder and Ant Colony Optimization feature selection algorithms for the efective features in diagnosis of Parkinson’s disease. These algorithms are implemented to the voice dataset obtained from online repository. Then selected features are presented to the Decision tree, SVM, K-NN, Ensemble, Naive Bayes and Discriminant classifers for each of the binary classifcation problems. The proposed ...Daha fazlası

Makale 2023 Wireless Personal Communications 13 Görüntülenme

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A multimodal butterfly optimization using fitness-distance balance

Taymaz Akan

Due to the multimodal nature of real-world optimization problems, in recent years, there has been a great interest in multi-modal optimization algorithms. Multimodal optimization problems involve identifying multiple local/global optima. Niching techniques have been widely used to tackle multi-modal optimization problems. Most of the existing niching methods either require predefined niching parameters or extra information about the problem space. This paper presents a novel multimodal algorithm based on Butterfly Optimization Algorithm, which is constructed using the Fitness-Distance Balance ...Daha fazlası

Makale 2023 SOFT COMPUTING27 ( 23 ) , pp. 1 - 17922 22 Görüntülenme Süresiz Ambargo

Süresiz Ambargo

2D Vector Representation of Binomial Hierarchical Tree Items

Metin Zontul | Seda Karateke

Today Artificial Intelligence (AI) algorithms need to represent different kinds of input items in numeric or vector format. Some input data can easily be transformed to numeric or vector format but the structure of some special data prevents direct and easy transformation. For instance, we can represent air condition using humidity, pressure, and temperature values with a vector that has three features and we can understand the similarity of two different air measurements using cosine-similarity of two vectors. But if we are dealing with a general ontology tree, which has elements "entity"as t ...Daha fazlası

Makale 2022 2022 International Conference On Theoretical And Applied Computer Science And Engineering (Ictasce) , pp. 1 - 126 136 Görüntülenme Süresiz Ambargo

Erişime Açık

Performance Comparison of Deep and Machine Learning Approaches Toward COVID-19 Detection

Buket İşler

The coronavirus (COVID-19) is a disease declared a global pan-demic that threatens the whole world. Since then, research has accelerated and varied to find practical solutions for the early detection and correct identification of this disease. Several researchers have focused on using the potential of Artificial Intelligence (AI) techniques in disease diagnosis to diagnose and detect the coronavirus. This paper developed deep learning (DL) and machine learning (ML)-based models using laboratory findings to diagnose COVID-19. Six different methods are used in this study: K -nearest neighbor (KN ...Daha fazlası

Makale 2023 Intelligent Automation And Soft Computing37 ( 2 ) , pp. 2 - 2261 24 Görüntülenme