Classification of Different Recycled Rubber-Epoxy Composite Based on Their Hardness Using Laser-Induced Breakdown Spectroscopy (LIBS) with Comparison Machine Learning Algorithms

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2023

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Mdpi

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Electrical-Electronics Engineering
The Department of Electrical and Electronics Engineering covers communications, signal processing, high voltage, electrical machines, power distribution systems, radar and electronic warfare, RF, electromagnetic and photonics topics. Most of the theoretical courses in our department are supported by qualified laboratory facilities. Our department has been accredited by MÜDEK since 2013. Within the scope of joint training (COOP), in-company training opportunities are offered to our students. 9 different companies train our students for one semester within the scope of joint education and provide them with work experience. The number of students participating in joint education (COOP) is increasing every year. Our students successfully completed the joint education program that started in the 2019-2020 academic year and started work after graduation. Our department, which provides pre-graduation opportunities to its students with Erasmus, joint education (COOP) and undergraduate research projects, has made an agreement with Upper Austria University of Applied Sciences (Austria) starting from this year and offers its students undergraduate (Atılım University) and master's (Upper Austria) degrees with 3+2 education program. Our department, which has the only European Remote Radio Laboratory in Foundation Universities, has a pioneering position in research (publication, project, patent).
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Mechanical Engineering
(2009)
The Atılım University Department of Mechanical Engineering started education in 2009, and offers graduate and doctorate degree programs, in addition to its undergraduate program. Our main goal is to graduate Mechanical Engineers who have the skills to design, analyze and synthesize; who are able to convert advanced technology and innovations into products; and who have the culture of research and cooperation. While our graduates reach this goal, they adopt the principle of life-long learning, and develop a sense of entrepreneurship, paying importance to professional ethics. With a curriculum prepared in line with the criteria of MÜDEK, we help our students develop themselves professionally, and socially. Graduates of mechanical engineering may be employed in many sectors and in a wide array of positions. Able to work under any field that involves production and energy conversion, graduates of the department may also gain expertise in fields such as aviation, automotive, or material engineering.
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Department of Electrical & Electronics Engineering
Department of Electrical and Electronics Engineering (EE) offers solid graduate education and research program. Our Department is known for its student-centered and practice-oriented education. We are devoted to provide an exceptional educational experience to our students and prepare them for the highest personal and professional accomplishments. The advanced teaching and research laboratories are designed to educate the future workforce and meet the challenges of current technologies. The faculty's research activities are high voltage, electrical machinery, power systems, signal and image processing and photonics. Our students have exciting opportunities to participate in our department's research projects as well as in various activities sponsored by TUBİTAK, and other professional societies. European Remote Radio Laboratory project, which provides internet-access to our laboratories, has been accomplished under the leadership of our department with contributions from several European institutions.

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Abstract

This paper aims toward the successful detection of harmful materials in a substance by integrating machine learning (ML) into laser-induced breakdown spectroscopy (LIBS). LIBS is used to distinguish five different synthetic polymers where eight different heavy material contents are also detected by LIBS. Each material intensity-wavelength graph is obtained and the dataset is constructed for classification by a machine learning (ML) algorithm. Seven popular machine learning algorithms are applied to the dataset which include eight different substances with their wavelength-intensity value. Machine learning algorithms are used to train the dataset, results are discussed and which classification algorithm is appropriate for this dataset is determined.

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aslan, ozgur/0000-0002-1042-0805

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LIBS, rubber-polymers, hardness, machine learning, classification

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0

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Volume

8

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2

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https://doi.org/10.3390/inventions8020054
 https://hdl.handle.net/20.500.14411/2513

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