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"This text serves well as a follow-on text to Python Programming Fundamentalsby Kent D. Lee and published by Springer, but does not require you to have readthat text. In this text the next steps are taken to teach you how to handle largeamounts of data efficiently. A number of algorithms are introduced and the need forthem is motivated through examples that bring meaning to the problems we face ascomputer programmers. An algorithm is a well-defined procedure for accomplishinga task. Algorithms are an important part of Computer Science and this textexplores many algorithms to give you the background you need when writingprograms of your own. The goal is that having seen some of the sorts of algorithms presented in this text, you will be able to apply these techniques to other programs you write in the future."

"This clearly structured and easy to read textbook explains the concepts and techniques required to write programs that can handle large amounts of data efficiently.Project-oriented and classroom-tested, the book presents a number of important algorithms supported by motivating examples that bring meaning to the problems faced by computer programmers. The idea of computational complexity is also introduced, demonstrating what can and cannot be computed efficiently so that the programmer can make informed judgements about the algorithms they use. The text assumes some basic experience in computer programming and familiarity in an object-oriented language, but not necessarily with Python.Topics and features:- Includes both introductory and advanced data structures and algorithms topics, with suggested chapter sequences for those respective courses provided in the preface- Provides learning goals, review questions and programming exercises in each chapter, as well as numerous illustrative examples- Offers downloadable programs and supplementary files at an associated website, with instructor materials available from the author- Presents a primer on Python for those coming from a different language background- Reviews the use of hashing in sets and maps, along with an examination of binary search trees and tree traversals, and material on depth first search of graphs- Discusses topics suitable for an advanced course, such as membership structures, heaps, balanced binary search trees, B-trees and heuristic search."

"Penggunaan Python dipilih karena bahasa pemrograman ini bersifat open source dengan banyak tersedianya berbagai sumber dan Python juga diklaim sebagai bahasa yang menggabungkan kapabilitas, dengan kode sintaks yang sangat jelas, dan dilengkapi dengan bahasa yang besar dan komprehensif. Library Open CV juga tersedia secara gratis dan menyediakan banyak fungsi pemrosesan gambar. Pengoreksian citra CBCT yang dilakukan pada penelitian ini bertujuan untuk meningkatkan kualitas citra CBCT dengan melihat meningkatnya nilai yang didapat pada citra CBCT terkoreksi. Phantom CIRS 002LFC di-scan pada CBCT menggunakan half bow tie filter sesuai dengan protokol yang digunakan untuk scanning organ thorax. Penelitian ini menggunakan data citra pasien dengan diagnosa kanker paru dan laring masing-masing berjumlah dua dan satu orang. Hasil kalibrasi CBCT terhadap CT diperoleh bahwa nilai HU citra CBCT linier terhadap citra CT. Evaluasi PSNR dan SSIM digunakan pada penelitian ini sebagai parameter keberhasilan dari citra yang dikoreksi.

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Python was chosen because this programming language is open source with many sources available and Python is also claimed to be a language that combines capabilities, with very clear syntax code, and is equipped with a large and complete language. CV Open Library is also available free of charge and provides many drawing functions. CBCT image correction carried out in this study aims to improve the quality of CBCT images by looking at the value obtained in the corrected CBCT image. Phantom CIRS 002LFC was scanned on CBCT using a half bow tie filter according to the protocol used for scanning the thorax organs. This study uses image data of patients diagnosed with lung and laryngeal cancer, respectively, two and one person. The CBCT calibration results against CT showed that the HU value of CBCT images was linear to CT images. PSNR and SSIM evaluations were used in this study as the confidence parameters of the corrected image."