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Reading right books contributes to children's imagination and braindevelopment, enhances their language and emotional comprehension abilities, andstrengthens their relationships with others. Building upon the critical role ofreading books in individual development, this paper aims to develop analgorithm that determines the suitability of books for middle school studentsby analyzing their structural and semantic features. Using methods described,an algorithm will be created that can be utilized by institutions andindividuals responsible for children's education, such as the Ministry ofNational Education officials and schools. This algorithm will facilitate theselection of books to be taught at the middle school level. With the algorithm,the book selection process for the middle school curriculum can be expedited,and it will serve as a preliminary reference source for those who evaluatebooks by reading them. In this paper, the Python programming language wasemployed, utilizing natural language processing methods. Additionally, anartificial neural network (ANN) was trained using the data which had beenpreprocessed to construct an original dataset. To train this network, suitablebooks for middle school students were provided by the MEB, Oxford and Cambridgeand with content assessed based on the "R" criterion, and inappropriate booksfor middle school students in terms of content were included. This trainedneural network achieved a 90.06% consistency rate in determining theappropriateness of the test-provided books. Considering the obtained findings,it can be concluded that the developed software has achieved the desiredobjective.

Identification of Books That are Suitable for Middle School Students Using Artificial Neural Networks