Tolga Ensari

• Dr.
• Professor (Assistant) at Istanbul University

In this study, a two-stage approach for developing a Semantic-Based Image Retrieval system supported by Ontology is proposed. In the initial stage, the Object Detection process is employed to identify objects within the image. Subsequently, a predicate describing the relationship between these two objects is determined using the developed Bi-direct...

Mikrodizi teknolojisi gen ifadesindeki farklılıkların tespit edilmesinde kullanılır. Bu teknoloji ilaç geliştirme süreçlerinden tedavi süreçlerinin iyileştirilmesine birçok alanda katkı sağlamaktadır. Bu çalışmada, kronik hipoksi tedavisinin fare beyni üzerindeki etkisi ve oksidatif strese maruz kalan fare nöronlarının gen üzerindeki etkisi ile ilg...

Semantic-Based Image Retrieval (SBIR) is the process of searching for images in a database that have similar relationships between the objects detected in an image and the defined objects. The relationships in the subject-predicate-object structure are transformed into a structure that computers can make sense out of with Ontologies and are used to...

This study suggests that by implementing machine learning methods on a sociodemographic data set can be helpful in preventing domestic violence. This approach is important in predicting high-risk factors that an offender may cause and it offers treatment, and financial or mental health aids in order to prevent domestic violence. In this sense, this...

Biyoenformatik, biyolojik bilgilerin bilgisayar teknolojileri yardımıyla incelenmesini ve değerlendirilmesini sağlayan bir araştırma alanıdır. Çok disiplinli bu alan sayesinde tıbbi veriler üzerinde yapılan çalışmalarda hızla yol alınabilmekte, gerek hastalıkların teşhis-tedavi süreçlerinde gerek önlenmesi süreçlerinde başarılı çözümler bulunabilme...

COVID-19 has become the world’s worst pandemic and has claimed over six million lives as of March 2022. The virus is now in alongside cancer as one of the most common causes of death. Likewise, there is no definitive or unique treatment for COVID-19 outside of a selected few drugs approved by the Food and Drug Administration (FDA). While Artificial...

The Graphical User Interfaces (GUIs) of web applications include visuals and designs that allow users to interact with machines. Once the GUI design is done, it is necessary to generate its GUI code. However, the GUI code generation process is highly time consuming as well as highly dependent on software developers. Therefore, the development of au...

Machine learning is one of the most popular research areas, and it is commonly used in wireless communications and networks. Security and fast communication are among of the key requirements for next generation wireless networks. Machine learning techniques are getting more important day-by-day since the types, amount, and structure of data is cont...

Post-traumatic stress disorder (PTSD) is defined as a traumatic injury developed after facing or witnessing a life-threatening experience or event such as a natural disaster, a pandemic, a serious accident, a terrorist act, war/combat, rape or other violent personal assault. Machine Learning (ML) has been widening its scope on psychological and phy...

Plants are one of the most important components of the environment. Millions of people are undernourished because of global warming whose adverse effects such as drought has made it difficult for sustainable crop breeding programs. This paper is aimed to propose and test computer vision and machine learning image-based methods precisely convolution...

Intrusion detection systems are one of the most important tools used against the threats to network security in ever-evolving network structures. Along with evolving technology, it has become a necessity to design powerful intrusion detection systems and integrate them into network systems. The main purpose of this research is to develop a new meth...

Abstract. Nowadays, one of the most challenging problems in software development has long been the realization of high-quality software products and solutions under time pressure. In order to meet these needs, capturing metrics from within big data which is generated through software development life cycle and analyzing the data using machine learn...

This paper proposes a method named Population-based Algorithm(PBA) to decide the best hyperparameters for a neural network (NN). The study focuses on which type of hyperparameters achieve better results in neural network problems. Population-based algorithm inspired from evolutionary algorithms and uses basic steps of genetic algorithms. The distin...

Machine learning is one of the most popular research areas, and it is commonly used in wireless communications and networks. Security and fast communication are among of the key requirements for next generation wireless networks. Machine learning techniques are getting more important day-by-day since the types, amount, and structure of data is cont...

In this study, the performance of the prominent feature extraction and modeling methods in speaker recognition systems are evaluated on the specifically created database. The main feature of the database is that subjects are siblings or relatives. After giving the basic information about speaker recognition systems, outstanding properties of the me...

Digital transformation of the world goes very fast during last two decades. Today, data is power and very important. Firstly, magnetic tapes and then digital data storages have been used to collect all data. After this process, big data and its tool machine learning became very popular in both literature and industry. People use machine learning in...

Automatic modulation recognition (AMR) is becoming more important because it is usable in advanced general-purpose communication such as, cognitive radio, as well as, specific applications. Therefore, developments should be made for widely used modulation types; machine learning techniques should be employed for this problem. In this study, we have...

Automatic modulation recognition (AMR) becomes more important because of usable in advanced general-purpose communication such as cognitive radio as well as specific applications. Therefore, developments should be made for widely used modulation types; machine learning techniques should be tried for this problem. In this study, we evaluate performa...

Hadoop is Java based programming framework for distributed storage and processing of large data sets on commodity hardware. It is developed by Apache Software Foundation as open source framework. Hadoop basically has two main components. First one is Hadoop Distributed File System (HDFS) for distributed storage and second part is MapReduce for dist...

Nonnegative Matrix Factorization (NMF) is one of the most promising techniques to reduce the dimensionality of the data. This presentation compares the method with other popular matrix decomposition approaches for various pattern analysis tasks. Among others, NMF has been also widely applied for clustering and latent feature extraction. Several typ...

Occluded face recognition is one the most interesting problems of applied computer vision. Among many face recognition approaches, the Nonnegative Matrix Factorization (NMF) turns out to be one of the popular techniques especially for part-based learning. It aims to factorize a nonnegative data matrix into two nonnegative matrices and obtains a wel...

Nonnegative Matrix Factorization (NMF) is one of the popular techniques to reduce the number of attributes of the data. It has been also widely used for clustering. Several types of the objective functions have been used for NMF in the literature. In this paper, we propose to maximize the correntropy similarity measure to produce the factorization...

This paper deals with the global robust asymptotic stability of the equilibrium point of class of delayed neural networks having uncertain parameters whose values are unknown but bounded. By introducing a new upper bound norm for the interconnection matrix of the neural system and employing suitable Lyapunov functionals, we obtain new delay indepen...

In this note, we study the equilibrium and stability properties of neural networks with time varying delays. Our main results give sufficient conditions for the existence, uniqueness and global asymptotic stability of the equilibrium point. The proposed conditions establish the relationships between network parameters of the neural systems and the...

This paper presents a new sufficient condition for the uniqueness and global asymptotic stability of the equilibrium point for a class of neural networks with time-varying delays. The result is obtained by the use of a more general type of Lyapunov-Krasovskii functional, establishing a relation between the network parameters of the neural system an...

This work presents a new sufficient condition for the uniqueness and global asymptotic stability (GAS) of the equilibrium point for a larger class of neural networks with time varying delays. It is shown that the use of a more general type of Lyapunov-Krasovskii functional leads to establish global asymptotic stability of a larger class of delayed...

In this paper, Cellular Neural Networks (CNNs) have been applied to noisy Synthetic Aperture Radar (SAR) image to improve its performance and appearance. The image has been obtained from Erzurum, Turkey. Because of the importance of imaging quality and appearance for remote sensing applications, CNN has been applied to data for image processing app...