Browsing by Author "Meraliyev M."

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    3D MODELING OF THE OFFSHORE WIND TURBINES INTEGRATED STRUCTURE FOR KAZAKHSTAN REGION
    (SDU University, 2017) Amirgaliyev Y. ; Khassanov D. ; Meraliyev M.
    Abstract. In this scientific paper we examine meteorological characteristics of the field “Kashagan” located in the north Caspian Sea, the types of offshore oil and gas constructers, offshore wind turbine structure integrated for the middle depths of the Caspian shelf, and the load acting on them during operation on a shelf. Dimensional solid 3D model and 3D animation presentation of offshore wind turbine is built in the program Autodesk Maya 2012. Created the design scheme of supporting jacket for offshore wind turbine. Jacket designed for the field in the middle depths of the Caspian Sea. Carried out joint account of supporting columns and turbine structure on the static load—own weight of construction and the wind turbine weight of the structure, and the wind load by finite-elements. Also the calculations studied the stress-strain state of the structure. The finite element method are mastered and calculated by using a software package Autodesk Inventor Professional 2014. The calculation results can be used in the design of offshore wind turbine structures for oil and gas platforms. Also, the thesis presents the basic calculation of the estimated cost of construction and installation works supporting truss design and offshore wind turbine, its payback period. Sections health and the environment are considered potential risks to personnel and possible threats to the environment, and provide measures for their prevention and reduction.
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    ItemOpen Access
    Detection of diseases using machine learning algorithms
    (2017) Meraliyev M.
    The recent advancements in computer technologies and storage capabilities have produced an incredible amount of data and information from many sources such as social networks, online databases, and health information systems. Nowadays, many countries around the world are changing the way of implementing health care to the patients and the people by utilising the benefits of advancements in computer technologies and communications through electronic health. This huge amount of data can be tuned into knowledge and more useful form of data by using computing and machine learning tools. It is believed that engineering this amount of data can aid in developing expert systems for decision support that can assist physicians in diagnosing and predicting some debilitating life threatening diseases such as breast cancer. Expert systems for decision support can reduce cost, the waiting time and free human experts (physicians) for more research, as well as reduce the errors and mistakes that can be made by humans due to fatigue and tiredness. However, the process of utilising health data effectively, involves many challenges such as the problem of missing features values, the curse of dimensionality due to a large number of features (attributes), and the course of actions to determine the features, that can lead to more accurate results (more accurate diagnosis). Effective machine learning tools can assist in early detection of diseases such as breast cancer, and the current work in this thesis focuses on investigating novel approaches to diagnose breast cancer based on machine learning tools, and involves development of new techniques to construct and process missing features values, investigate different feature selection methods, and how to employ them into diagnosis process.