Problem Solving Using Python And R
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Problem Solving Using Python And R
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Author : Dr.N.Thinaharan
language : en
Publisher: SK Research Group of Companies
Release Date : 2022-11-08
Problem Solving Using Python And R written by Dr.N.Thinaharan and has been published by SK Research Group of Companies this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-11-08 with Education categories.
Dr.N.Thinaharan, Assistant Professor, Department of Computer Science, Thanthai Hans Roever College Autonomous , Perambalur,Tamil Nadu, India. Mr.K.Sundaravadivelu, Assistant Professor, Department of Computer Science,School of Information Technology, Madurai Kamaraj University, Madurai, Tamil Nadu, India. Dr.K.Mahesh Babu, Assistant Professor, Department of Computer Science, Bishop Heber College Autonomous, Tiruchirappalli, Tamil Nadu, India. Dr.T.Arul Mozhidevan, Assistant Professor, Department of Computer science, Bishop Heber College Autonomous, Tiruchirappalli, Tamil Nadu, India. Mrs.K.Saraswathi,Assistant Professor, Department of Computer science, Thanthai Hans Roever College Autonomous, Perambalur,Tamil Nadu, India.
Programming And Problem Solving Using Python
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Author : Anuradha A. Puntambekar
language : en
Publisher: Technical Publications
Release Date : 2020-12-01
Programming And Problem Solving Using Python written by Anuradha A. Puntambekar and has been published by Technical Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-12-01 with Computers categories.
This textbook is designed to learn python programming from scratch. At the beginning of the book general problem solving concepts such as types of problems, difficulties in problem solving, and problem solving aspects are discussed. From this book, you will start learning the Python programming by knowing about the variables, constants, keywords, data types, indentation and various programming constructs. The most commonly used types such as Lists, Tuples, dictionaries are also discussed with necessary examples and illustrations. The book includes the concepts of functions, lambda functions, modules and strings. In the later part of this book the concept of object oriented programming using Python is discussed in detail. Finally how to handle files and directories using Python is discussed. At the end of book some sample programs in Python are given that are based on the programming constructs. Python will be most demanded language after Java in future. So learning Python is need for today’s software professionals. This book serves the purpose of teaching Python programming in the simplest and easiest manner.
Extending Power Bi With Python And R
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Author : Luca Zavarella
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-03-29
Extending Power Bi With Python And R written by Luca Zavarella and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-03-29 with Computers categories.
Ingest, transform, manipulate, and visualize your data beyond Power BI's capabilities. Purchase of the print or Kindle book includes a free eBook in PDF format. Key Features Discover best practices for using Python and R in Power BI by implementing non-trivial code Enrich your Power BI dashboards using external APIs and machine learning models Create any visualization, as complex as you want, using Python and R scripts Book DescriptionThe latest edition of this book delves deep into advanced analytics, focusing on enhancing Python and R proficiency within Power BI. New chapters cover optimizing Python and R settings, utilizing Intel's Math Kernel Library (MKL) for performance boosts, and addressing integration challenges. Techniques for managing large datasets beyond available RAM, employing the Parquet data format, and advanced fuzzy matching algorithms are explored. Additionally, it discusses leveraging SQL Server Language Extensions to overcome traditional Python and R limitations in Power BI. It also helps in crafting sophisticated visualizations using the Grammar of Graphics in both R and Python. This Power BI book will help you master data validation with regular expressions, import data from diverse sources, and apply advanced algorithms for transformation. You'll learn how to safeguard personal data in Power BI with techniques like pseudonymization, anonymization, and data masking. You'll also get to grips with the key statistical features of datasets by plotting multiple visual graphs in the process of building a machine learning model. The book will guide you on utilizing external APIs for enrichment, enhancing I/O performance, and leveraging Python and R for analysis. You'll reinforce your learning with questions at the end of each chapter.What you will learn Configure optimal integration of Python and R with Power BI Perform complex data manipulations not possible by default in Power BI Boost Power BI logging and loading large datasets Extract insights from your data using algorithms like linear optimization Calculate string distances and learn how to use them for probabilistic fuzzy matching Handle outliers and missing values for multivariate and time-series data Apply Exploratory Data Analysis in Power BI with R Learn to use Grammar of Graphics in Python Who this book is for This book is for business analysts, business intelligence professionals, and data scientists who already use Microsoft Power BI and want to add more value to their analysis using Python and R. Working knowledge of Power BI is required to make the most of this book. Basic knowledge of Python and R will also be helpful.
Data Science And Analytics With Python R And Spss Programming
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Author : V.K. Jain
language : en
Publisher: KHANNA PUBLISHING HOUSE
Release Date :
Data Science And Analytics With Python R And Spss Programming written by V.K. Jain and has been published by KHANNA PUBLISHING HOUSE this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.
The Book has been written completely as per AICTE recommended syllabus on "Data Sciences". SALIENT FEATURES OF THE BOOK: Explains how data is collected, managed and stored for data science. With complete courseware for understand the key concepts in data science including their real-world applications and the toolkit used by data scientists. Implement data collection and management. Provided with state of the arts subjectwise. With all required tutorials on R, Python and Bokeh, Anaconda, IBM SPSS-21 and Matplotlib.
Elementary Mechanics Using Python
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Author : Anders Malthe-Sørenssen
language : en
Publisher: Springer
Release Date : 2015-05-29
Elementary Mechanics Using Python written by Anders Malthe-Sørenssen and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-05-29 with Science categories.
This book – specifically developed as a novel textbook on elementary classical mechanics – shows how analytical and numerical methods can be seamlessly integrated to solve physics problems. This approach allows students to solve more advanced and applied problems at an earlier stage and equips them to deal with real-world examples well beyond the typical special cases treated in standard textbooks. Another advantage of this approach is that students are brought closer to the way physics is actually discovered and applied, as they are introduced right from the start to a more exploratory way of understanding phenomena and of developing their physical concepts. While not a requirement, it is advantageous for the reader to have some prior knowledge of scientific programming with a scripting-type language. This edition of the book uses Python, and a chapter devoted to the basics of scientific programming with Python is included. A parallel edition using Matlab instead of Python is also available. Last but not least, each chapter is accompanied by an extensive set of course-tested exercises and solutions.
Introduction To Programming And Problem Solving Using Scala
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Author : Mark C. Lewis
language : en
Publisher: CRC Press
Release Date : 2016-10-14
Introduction To Programming And Problem Solving Using Scala written by Mark C. Lewis and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-10-14 with Computers categories.
Praise for the first edition: "The well-written, comprehensive book...[is] aiming to become a de facto reference for the language and its features and capabilities. The pace is appropriate for beginners; programming concepts are introduced progressively through a range of examples and then used as tools for building applications in various domains, including sophisticated data structures and algorithms...Highly recommended. Students of all levels, faculty, and professionals/practitioners.—D. Papamichail, University of Miami in CHOICE Magazine Mark Lewis’ Introduction to the Art of Programming Using Scala was the first textbook to use Scala for introductory CS courses. Fully revised and expanded, the new edition of this popular text has been divided into two books. Introduction to Programming and Problem-Solving Using Scala is designed to be used in first semester college classrooms to teach students beginning programming with Scala. The book focuses on the key topics students need to know in an introductory course, while also highlighting the features that make Scala a great programming language to learn. The book is filled with end-of-chapter projects and exercises, and the authors have also posted a number of different supplements on the book website. Video lectures for each chapter in the book are also available on YouTube. The videos show construction of code from the ground up and this type of "live coding" is invaluable for learning to program, as it allows students into the mind of a more experienced programmer, where they can see the thought processes associated with the development of the code. About the Authors Mark Lewis is a Professor at Trinity University. He teaches a number of different courses, spanning from first semester introductory courses to advanced seminars. His research interests included simulations and modeling, programming languages, and numerical modeling of rings around planets with nearby moons. Lisa Lacher is an Assistant Professor at the University of Houston, Clear Lake with over 25 years of professional software development experience. She teaches a number of different courses spanning from first semester introductory courses to graduate level courses. Her research interests include Computer Science Education, Agile Software Development, Human Computer Interaction and Usability Engineering, as well as Measurement and Empirical Software Engineering.
Solving Differential Equations In R
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Author : Karline Soetaert
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-06-06
Solving Differential Equations In R written by Karline Soetaert and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-06-06 with Computers categories.
Mathematics plays an important role in many scientific and engineering disciplines. This book deals with the numerical solution of differential equations, a very important branch of mathematics. Our aim is to give a practical and theoretical account of how to solve a large variety of differential equations, comprising ordinary differential equations, initial value problems and boundary value problems, differential algebraic equations, partial differential equations and delay differential equations. The solution of differential equations using R is the main focus of this book. It is therefore intended for the practitioner, the student and the scientist, who wants to know how to use R for solving differential equations. However, it has been our goal that non-mathematicians should at least understand the basics of the methods, while obtaining entrance into the relevant literature that provides more mathematical background. Therefore, each chapter that deals with R examples is preceded by a chapter where the theory behind the numerical methods being used is introduced. In the sections that deal with the use of R for solving differential equations, we have taken examples from a variety of disciplines, including biology, chemistry, physics, pharmacokinetics. Many examples are well-known test examples, used frequently in the field of numerical analysis.
Frame Analysis And Processing In Digital Video Using Python And Tkinter
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Author : Vivian Siahaan
language : en
Publisher: BALIGE PUBLISHING
Release Date : 2024-03-27
Frame Analysis And Processing In Digital Video Using Python And Tkinter written by Vivian Siahaan and has been published by BALIGE PUBLISHING this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-03-27 with Computers categories.
The first project in chapter one which is Canny Edge Detector presented here is a graphical user interface (GUI) application built using Tkinter in Python. This application allows users to open video files (of formats like mp4, avi, or mkv) and view them along with their corresponding Canny edge detection frames. The application provides functionalities such as playing, pausing, stopping, navigating through frames, and jumping to specific times within the video. Upon opening the application, users are greeted with a clean interface comprising two main sections: the video display panel and the control panel. The video display panel consists of two canvas widgets, one for displaying the original video and another for displaying the Canny edge detection result. These canvases allow users to visualize the video and its corresponding edge detection in real-time. The control panel houses various buttons and widgets for controlling the video playback and interaction. Users can open video files using the "Open Video" button, select a zoom scale for viewing convenience, jump to specific times within the video, play/pause the video, stop the video, navigate through frames, and even open another instance of the application for simultaneous use. The core functionality lies in the methods responsible for displaying frames and performing Canny edge detection. The show_frame() method retrieves frames from the video, resizes them based on the selected zoom scale, and displays them on the original video canvas. Similarly, the show_canny_frame() method applies the Canny edge detection algorithm to the frames, enhances the edges using dilation, and displays the resulting edge detection frames on the corresponding canvas. The application also supports mouse interactions such as dragging to pan the video frames within the canvas and scrolling to navigate through frames. These interactions are facilitated by event handling methods like on_press(), on_drag(), and on_scroll(), ensuring smooth user experience and intuitive control over video playback and exploration. Overall, this project provides a user-friendly platform for visualizing video content and exploring Canny edge detection results, making it valuable for educational purposes, research, or practical applications involving image processing and computer vision. This second project in chapter one implements a graphical user interface (GUI) application for performing edge detection using the Prewitt operator on videos. The purpose of the code is to provide users with a tool to visualize videos, apply the Prewitt edge detection algorithm, and interactively control playback and visualization parameters. The third project in chapter one which is "Sobel Edge Detector" is implemented in Python using Tkinter and OpenCV serves as a graphical user interface (GUI) for viewing and analyzing videos with real-time Sobel edge detection capabilities. The "Frei-Chen Edge Detection" project as fourth project in chapter one is a graphical user interface (GUI) application built using Python and the Tkinter library. The application is designed to process and visualize video files by detecting edges using the Frei-Chen edge detection algorithm. The core functionality of the application lies in the implementation of the Frei-Chen edge detection algorithm. This algorithm involves convolving the video frames with predefined kernels to compute the gradient magnitude, which represents the strength of edges in the image. The resulting edge-detected frames are thresholded to convert grayscale values to binary values, enhancing the visibility of edges. The application also includes features for user interaction, such as mouse wheel scrolling to zoom in and out, click-and-drag functionality to pan across the video frames, and input fields for jumping to specific times within the video. Additionally, users have the option to open multiple instances of the application simultaneously to analyze different videos concurrently, providing flexibility and convenience in video processing tasks. Overall, the "Frei-Chen Edge Detection" project offers a user-friendly interface for edge detection in videos, empowering users to explore and analyze visual data effectively. The "KIRSCH EDGE DETECTOR" project as the fifth project in chapter one is a Python application built using Tkinter, OpenCV, and NumPy libraries for performing edge detection on video files. It handles the visualization of the edge-detected frames in real-time. It retrieves the current frame from the video, applies Gaussian blur for noise reduction, performs Kirsch edge detection, and applies thresholding to obtain the binary edge image. The processed frame is then displayed on the canvas alongside the original video. This "SCHARR EDGE DETECTOR" as the sixth project in chapter one is creating a graphical user interface (GUI) to visualize edge detection in videos using the Scharr algorithm. It allows users to open video files, play/pause video playback, navigate frame by frame, and apply Scharr edge detection in real-time. The GUI consists of multiple components organized into panels. The main panel displays the original video on the left side and the edge-detected video using the Scharr algorithm on the right side. Both panels utilize Tkinter Canvas widgets for efficient rendering and manipulation of video frames. Users can interact with the application using control buttons located in the control panel. These buttons include options to open a video file, adjust the zoom scale, jump to a specific time in the video, play/pause video playback, stop the video, navigate to the previous or next frame, and open another instance of the application for parallel video analysis. The core functionality of the application lies in the VideoScharr class, which encapsulates methods for video loading, playback control, frame processing, and edge detection using the Scharr algorithm. The apply_scharr method implements the Scharr edge detection algorithm, applying a pair of 3x3 convolution kernels to compute horizontal and vertical derivatives of the image and then combining them to calculate the edge magnitude. Overall, the "SCHARR EDGE DETECTOR" project provides users with an intuitive interface to explore edge detection techniques in videos using the Scharr algorithm. It combines the power of image processing libraries like OpenCV and the flexibility of Tkinter for creating interactive and responsive GUI applications in Python. The first project in chapter two is designed to provide a user-friendly interface for processing video frames using Gaussian filtering techniques. It encompasses various components and functionalities tailored towards efficient video analysis and processing. The GaussianFilter Class serves as the backbone of the application, managing GUI initialization and video processing functionalities. The GUI layout is constructed with Tkinter widgets, comprising two main panels for video display and control buttons. Key functionalities include opening video files, controlling playback, adjusting zoom levels, navigating frames, and interacting with video frames via mouse events. Additionally, users can process frames using OpenCV for Gaussian filtering to enhance video quality and reduce noise. Time navigation functionality allows users to jump to specific time points in the video. Moreover, the application supports multiple instances for simultaneous video analysis in independent windows. Overall, this project offers a comprehensive toolset for video analysis and processing, empowering users with an intuitive interface and diverse functionalities. The second project in chapter two presents a Tkinter application tailored for video frame filtering utilizing a mean filter. It offers comprehensive functionalities including opening, playing/pausing, and stopping video playback, alongside options to navigate to previous and next frames, jump to specified times, and adjust zoom scale. Displayed on separate canvases, the original and filtered video frames are showcased distinctly. Upon video file opening, the application utilizes imageio.get_reader() for video reading, while play_video() and play_filtered_video() methods handle frame display. Individual frame rendering is managed by show_frame() and show_mean_frame(), incorporating noise addition through the add_noise() method. Mouse wheel scrolling, canvas dragging, and scrollbar scrolling are facilitated through event handlers, enhancing user interaction. Supplementary functionalities include time navigation, frame navigation, and the ability to open multiple instances using open_another_player(). The main() function initializes the Tkinter application and executes the event loop for GUI display. The third project in chapter two aims to develop a user-friendly graphical interface application for filtering video frames with a median filter. Supporting various video formats like MP4, AVI, and MKV, users can seamlessly open, play, pause, stop, and navigate through video frames. The key feature lies in real-time application of the median filter to enhance frame quality by noise reduction. Upon video file opening, the original frames are displayed alongside filtered frames, with users empowered to control zoom levels and frame navigation. Leveraging libraries such as tkinter, imageio, PIL, and OpenCV, the application facilitates efficient video analysis and processing, catering to diverse domains like surveillance, medical imaging, and scientific research. The fourth project in chapter two exemplifies the utilization of a bilateral filter within a Tkinter-based graphical user interface (GUI) for real-time video frame filtering. The script showcases the application of bilateral filtering, renowned for its ability to smooth images while preserving edges, to enhance video frames. The GUI integrates two main components: canvas panels for displaying original and filtered frames, facilitating interactive viewing and manipulation. Upon video file opening, original frames are displayed on the left panel, while bilateral-filtered frames appear on the right. Adjustable parameters within the bilateral filter method enable fine-tuning for noise reduction and edge preservation based on specific video characteristics. Control functionalities for playback, frame navigation, zoom scaling, and time jumping enhance user interaction, providing flexibility in exploring diverse video filtering techniques. Overall, the script offers a practical demonstration of bilateral filtering in real-time video processing within a Tkinter GUI, enabling efficient exploration of filtering methodologies. The fifth project in chapter two integrates a video player application with non-local means denoising functionality, utilizing tkinter for GUI design, PIL for image processing, imageio for video file reading, and OpenCV for denoising. The GUI, set up by the NonLocalMeansDenoising class, includes controls for playback, zoom, time navigation, and frame browsing, alongside features like mouse wheel scrolling and dragging for user interaction. Video loading and display are managed through methods like open_video and play_video(), which iterate through frames, resize them, and add noise for display on the canvas. Non-local means denoising is applied using the apply_non_local_denoising() method, enhancing frames before display on the filter canvas via show_non_local_frame(). The GUI fosters user interaction, offering controls for playback, zoom, time navigation, and frame browsing, while also ensuring error handling for seamless operation during video loading, processing, and denoising. The sixth project in chapter two provides a platform for filtering video frames using anisotropic diffusion. Users can load various video formats and control playback (play, pause, stop) while adjusting zoom levels and jumping to specific timestamps. Original video frames are displayed alongside filtered versions achieved through anisotropic diffusion, aiming to denoise images while preserving critical edges and structures. Leveraging OpenCV and imageio for image processing and PIL for manipulation tasks, the application offers a user-friendly interface with intuitive control buttons and multi-video instance support, facilitating efficient analysis and enhancement of video content through anisotropic diffusion-based filtering. The seventh project in chapter two is built with Tkinter and OpenCV for filtering video frames using the Wiener filter. It offers a user-friendly interface for opening video files, controlling playback, adjusting zoom levels, and applying the Wiener filter for noise reduction. With separate panels for displaying original and filtered video frames, users can interact with the frames via zooming, scrolling, and dragging functionalities. The application handles video processing internally by adding random noise to frames and applying the Wiener filter, ensuring enhanced visual quality. Overall, it provides a convenient tool for visualizing and analyzing videos while showcasing the effectiveness of the Wiener filter in image processing tasks. The first project in chapter three showcases optical flow observation using the Lucas-Kanade method. Users can open video files, play, pause, and stop them, adjust zoom levels, and jump to specific frames. The interface comprises two panels for original video display and optical flow results. With functionalities like frame navigation, zoom adjustment, and time-based jumping, users can efficiently analyze optical flow patterns. The Lucas-Kanade algorithm computes optical flow between consecutive frames, visualized as arrows and points, allowing users to observe directional changes and flow strength. Mouse wheel scrolling facilitates zoom adjustments for detailed inspection or broader perspective viewing. Overall, the application provides intuitive navigation and robust optical flow analysis tools for effective video observation. The second project in chapter three is designed to visualize optical flow with Kalman filtering. It features controls for video file manipulation, frame navigation, zoom adjustment, and parameter specification. The application provides side-by-side canvases for displaying original video frames and optical flow results, allowing users to interact with the frames and explore flow patterns. Internally, it employs OpenCV and NumPy for optical flow computation using the Farneback method, enhancing stability and accuracy with Kalman filtering. Overall, it offers a user-friendly interface for analyzing video data, benefiting fields like computer vision and motion tracking. The third project in chapter three is for optical flow analysis in videos using Gaussian pyramid techniques. Users can open video files and visualize optical flow between consecutive frames. The interface presents two panels: one for original video frames and the other for computed optical flow. Users can adjust zoom levels and specify optical flow parameters. Control buttons enable common video playback actions, and multiple instances can be opened for simultaneous analysis. Internally, OpenCV, Tkinter, and imageio libraries are used for video processing, GUI development, and image manipulation, respectively. Optical flow computation relies on the Farneback method, with resulting vectors visualized on the frames to reveal motion patterns.
Sustainable Development Through Machine Learning Ai And Iot
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Author : Pawan Whig
language : en
Publisher: Springer Nature
Release Date : 2023-11-18
Sustainable Development Through Machine Learning Ai And Iot written by Pawan Whig and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-11-18 with Computers categories.
This book constitutes the revised selected papers of the First International Conference, ICSD 2023, virtually held in Delhi, India, during July 15–16, 2023. The book comprises 31 full papers that were selected from a total of 129 submissions. It provides insights into the latest research and advancements in sustainable development through the integration of machine learning, artificial intelligence, and IoT technologies. It serves as a valuable resource for researchers, practitioners, and policymakers working in the field of sustainable development.
Springer Handbook Of Geographic Information
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Author : Wolfgang Kresse
language : en
Publisher: Springer Science & Business Media
Release Date : 2011-10-31
Springer Handbook Of Geographic Information written by Wolfgang Kresse and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-10-31 with Computers categories.
Computer science provides a powerful tool that was virtually unknown three generations ago. Some of the classical fields of knowledge are geodesy (surveying), cartography, and geography. Electronics have revolutionized geodetic methods. Cartography has faced the dominance of the computer that results in simplified cartographic products. All three fields make use of basic components such as the Internet and databases. The Springer Handbook of Geographic Information is organized in three parts, Basics, Geographic Information and Applications. Some parts of the basics belong to the larger field of computer science. However, the reader gets a comprehensive view on geographic information because the topics selected from computer science have a close relation to geographic information. The Springer Handbook of Geographic Information is written for scientists at universities and industry as well as advanced and PhD students.