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Different methods are used for a mobile robot to go to a specific target location. These methods work in different ways for online and offline scenarios. In the offline scenario, an environment map is created once, and fixed path planning is made on this map to reach the target. Path planning algorithms such as A* and RRT (Rapidly-Exploring Random Tree) are the examples of offline methods. The most obvious situation here is the need to re-plan the path for changing conditions of the loaded map. On the other hand, in the online scenario, the robot moves dynamically to a given target without using a map by using the perceived data coming from the sensors. Approaches such as SFM (Social Force Model) are used in online systems. However, these methods suffer from the requirement of a lot of dynamic sensing data. Thus, it can be said that the need for re-planning and mapping in offline systems and various system design requirements in online systems are the subjects that focus on autonomous mobile robot research. Recently, deep neural network powered Q-Learning methods are used as an emerging solution to the aforementioned problems in mobile robot navigation. In this study, machine learning algorithms with deep Q-Learning (DQN) and Deep DQN architectures, are evaluated for the solution of the problems presented above to realize path planning of an autonomous mobile robot to avoid obstacles., Comment: 5 Pages, 10 figures, Presented in; Interdisciplinary Conference on Mechanics, Computers and Electrics ANKARA/TURKEY 27-28 November 2021

Natural language is one of the most fundamental features that distinguish people from other living things and enable people to communicate each other. Language is a tool that enables people to express their feelings and thoughts and to transfers cultures through generations. Texts and audio are examples of natural language in daily life. In the natural language, many words disappear in time, on the other hand new words are derived. Therefore, while the process of natural language processing (NLP) is complex even for human, it is difficult to process in computer system. The area of linguistics examines how people use language. NLP, which requires the collaboration of linguists and computer scientists, plays an important role in human computer interaction. Studies in NLP have increased with the use of artificial intelligence technologies in the field of linguistics. With the deep learning methods which are one of the artificial intelligence study areas, platforms close to natural language are being developed. Developed platforms for language comprehension, machine translation and part of speech (POS) tagging benefit from deep learning methods. Recurrent Neural Network (RNN), one of the deep learning architectures, is preferred for processing sequential data such as text or audio data. In this study, Turkish POS tagging model has been proposed by using Bidirectional Long-Short Term Memory (BLSTM) which is an RNN type. The proposed POS tagging model is provided to natural language researchers with a platform that allows them to perform and use their own analysis. In the development phase of the platform developed by using BLSTM, the error rate of the POS tagger has been reduced by taking feedback with expert opinion., Comment: 8 pages, in Turkish, 5 figures, ICITS 2019 02-04 May 2019 K{\i}r\c{s}ehir T\"urkiye

Knowledge networks can be defined as social networks that enable the transfer of the knowledge, which is defined as the intellectual product formed as a result of the work of human intelligence, to be transferred to any other means of communication. A knowledge network represents a large number of people, resources and relationships between them, to create the highest value, primarily to accumulate and use knowledge through the process of generating and transmitting knowledge. General structure of knowledge networks; it consists of three basic stages: gathering, organizing and disseminating knowledge. The first step, knowledge collection, institutions and organizations to enter the network structure of the knowledge that is present. The organizing phase is the structuring of irregular and unstructured knowledge in the network structure according to certain standards and recording them regularly in the structure. Knowledge dissemination can be expressed as the transfer of organized knowledge in accordance with user knowledge and needs. The purpose of the training knowledge network is to ensure communication between the student, the teacher and the guide, and to store the knowledge that is formed in a course, to enable the system to be recorded in a systematic way, to disseminate it according to the needs of the users and to update the knowledge. A dynamic website and content management system have been developed for the training knowledge network. The training knowledge network has a flexible structure to support web technologies. By making production, management and publication of knowledge within the framework of specific needs, it provides knowledge network for different problems, producing different and tailored solutions. With the example of education knowledge network, a knowledge network that can be developed according to the innovative knowledge network structure is designed., Comment: 6 Pages, in Turkish language, 4 figures, Conference Proceeding

It is known that internet have been widespread in many areas of life and enabled machines to communicate each other. The terms of Internet of Things (IoT) has emerged as a result of networks consist of machines and equipment that work independent of operator and communicate each other. Sensors, embedded systems, communication technologies and data storage systems like clouds create wide area networks that can communicate and share data. In this study, the application of the IoT on a low speed mechanical benchmark driven by a direct current motor has been designed. It is aimed to keep the motor speed fixed at desired value according to the changings occurs depending on varying the pressure acting on the actuators. In presented study, a speed control is performed by establishing a modeling and simulation mechanism that will give the closest results to the real system., Comment: in Turkish language. International Symposium on Advanced Engineering Technologies 2019