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    A family of circulant megastable chaotic oscillators, its application for the detection of a feeble signal and PID controller for time-delay systems by using chaotic SCA algorithm
    (Pergamon-Elsevier Science Ltd, 2021) Rajagopal, Karthikeyan; Cimen, Murat Erhan; Jafari, Sajad; Singh, Jay Prakash; Roy, Binoy Krishna; Akmese, Omer Faruk; Akgul, Akif
    Chaotic systems with cyclic symmetry are very rare and have been less discussed in the literature. Similarly, megastable oscillators, which can have a finite or infinite number of coexisting attractors, have also attracted researchers. We propose a class of cyclic symmetry oscillators with the megastable property with infinite coexisting attractors for the first time in the literature. Various dynamical properties of the proposed oscillators are discussed in detail. An application for the detection of a feeble signal by using the proposed circulant megastable oscillator is presented. Since chaotic oscillators are highly sensitive to a tiny change in the parameters or an external input to the oscillator, this property of the proposed oscillator is used for the detection of a feeble signal. Simulated results validate the effectiveness of the proposed application. After that, a new chaotic Sine-Cosine Algorithm (SCA) is developed using the randomness of megastable oscillators. Subsequently, this new chaotic sine-cosine algorithm is used to determine the PID controller parameters of time-delay systems concerning the objective function. As a result, the proposed chaotic sine-cosine algorithm presents better performance for time-delay systems when compared with the available algorithms in the literature. (c) 2021 Elsevier Ltd. All rights reserved.
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    Study on Professional Guidance on Teaching Settings
    (Elsevier Science Bv, 2014) Aksoy, Hamit; Kor, Hakan; Akmese, Omer Faruk; Erbay, Hasan
    Vocational guidance activities have an important role during the learning process. The happiness of individuals in their career is directly related with their love of the job they do. As a consequence, the importance should be paid to the vocational preference in the early stages of educational life. In this study, a research has been carried out about the secondary school students in Turkey. In the first step of the study students are asked which profession they will prefer. In the second step Personality Inventory Holland test is applied to students in order to see which profession field they tend to. In the last part of the research, the findings that are gathered by comparing the profession that each student in the sample would like to choose and the profession or professions that the student is apt to as a result of the test will be interpreted and suggestions will be offered to student him/herself, his family and to the institutions that is thought to be suitable. (C) 2014 Published by Elsevier Ltd.
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    USE OF MACHINE LEARNING TECHNIQUES FOR THE FORECAST OF STUDENT ACHIEVEMENT IN HIGHER EDUCATION
    (Natl Acad Pedagogical Sciences Ukraine, Inst Info Technol & Learning Tools, 2021) Akmese, Omer Faruk; Kor, Hakan; Erbay, Hasan
    The machine learning method, which is a sub-branch of artificial intelligence and which makes predictions with mathematical and statistical operations, is used frequently in education as in every field of life. Nowadays, it is seen that millions of data are recorded continuously, and a large amount of data accumulation has occurred. Although data accumulation increases exponentially, the number of analysts and their capabilities to process these data are insufficient. Although we live in the information age, it is more accurate to say that we live in the data age. By using stored and accumulated data, it is becoming increasingly essential to reveal meaningful relationships and trends and to make predictions for the future. It is important to analyze the data obtained from the education process and to evaluate the success of the students and the factors affecting success. These analyses may also contribute to future training activities. In this study, a data set, including socio-demographic variables of students enrolled in distance education at Hitit University, was used. The authors estimated the success of the students with demographic and social variables such as age, gender, city, family income, family education level. The primary purpose is to provide students with information about their estimated academic achievement at the beginning of the process. Thus, at the beginning of the education process, students' success can be increased by informing the students who are predicted to be unsuccessful. Diversification and enhancement of this data may also support other decision-making mechanisms in the training process. Additionally, the factors affecting students' academic success were researched, and the students' educational outcomes were evaluated. Prediction success was compared using various machine learning algorithms. As a result of the analysis, it was determined that the Random Forest algorithm was more predictive of student achievement than others.